@article{
 title = {The global methane budget 2000-2017},
 type = {article},
 year = {2020},
 pages = {1561-1623},
 volume = {12},
 id = {f711e10a-25a2-38ca-8ca7-a6dbb1d7b27d},
 created = {2019-09-25T09:11:00.364Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
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 abstract = {Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008-2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr-1 (range 550-594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr-1 or ĝ1/4 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336-376 Tg CH4 yr-1 or 50 %-65 %). The mean annual total emission for the new decade (2008-2017) is 29 Tg CH4 yr-1 larger than our estimate for the previous decade (2000-2009), and 24 Tg CH4 yr-1 larger than the one reported in the previous budget for 2003-2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr-1, range 594-881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (ĝ1/4 65 % of the global budget, < 30ĝ N) compared to mid-latitudes (ĝ1/4 30 %, 30-60ĝ N) and high northern latitudes (ĝ1/4 4 %, 60-90ĝ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr-1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr-1 by 8 Tg CH4 yr-1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.},
 bibtype = {article},
 author = {Saunois, Marielle and R. Stavert, Ann and Poulter, Ben and Bousquet, Philippe and G. Canadell, Josep and B. Jackson, Robert and A. Raymond, Peter and J. Dlugokencky, Edward and Houweling, Sander and K. Patra, Prabir and Ciais, Philippe and K. Arora, Vivek and Bastviken, David and Bergamaschi, Peter and R. Blake, Donald and Brailsford, Gordon and Bruhwiler, Lori and M. Carlson, Kimberly and Carrol, Mark and Castaldi, Simona and Chandra, Naveen and Crevoisier, Cyril and M. Crill, Patrick and Covey, Kristofer and L. Curry, Charles and Etiope, Giuseppe and Frankenberg, Christian and Gedney, Nicola and I. Hegglin, Michaela and Höglund-Isaksson, Lena and Hugelius, Gustaf and Ishizawa, Misa and Ito, Akihiko and Janssens-Maenhout, Greet and M. Jensen, Katherine and Joos, Fortunat and Kleinen, Thomas and B. Krummel, Paul and L. Langenfelds, Ray and G. Laruelle, Goulven and Liu, Licheng and MacHida, Toshinobu and Maksyutov, Shamil and C. McDonald, Kyle and McNorton, Joe and A. Miller, Paul and R. Melton, Joe and Morino, Isamu and Müller, Jurek and Murguia-Flores, Fabiola and Naik, Vaishali and Niwa, Yosuke and Noce, Sergio and O'Doherty, Simon and J. Parker, Robert and Peng, Changhui and Peng, Shushi and P. Peters, Glen and Prigent, Catherine and Prinn, Ronald and Ramonet, Michel and Regnier, Pierre and J. Riley, William and A. Rosentreter, Judith and Segers, Arjo and J. Simpson, Isobel and Shi, Hao and J. Smith, Steven and Paul Steele, L. and F. Thornton, Brett and Tian, Hanqin and Tohjima, Yasunori and N. Tubiello, Francesco and Tsuruta, Aki and Viovy, Nicolas and Voulgarakis, Apostolos and S. Weber, Thomas and Van Weele, Michiel and R. Van Der Werf, Guido and F. Weiss, Ray and Worthy, Doug and Wunch, Debra and Yin, Yi and Yoshida, Yukio and Zhang, Wenxin and Zhang, Zhen and Zhao, Yuanhong and Zheng, Bo and Zhu, Qing and Zhu, Qiuan and Zhuang, Qianlai},
 doi = {10.5194/essd-12-1561-2020},
 journal = {Earth System Science Data},
 number = {3}
}

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008-2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr-1 (range 550-594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr-1 or ĝ1/4 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336-376 Tg CH4 yr-1 or 50 %-65 %). The mean annual total emission for the new decade (2008-2017) is 29 Tg CH4 yr-1 larger than our estimate for the previous decade (2000-2009), and 24 Tg CH4 yr-1 larger than the one reported in the previous budget for 2003-2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr-1, range 594-881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (ĝ1/4 65 % of the global budget, < 30ĝ N) compared to mid-latitudes (ĝ1/4 30 %, 30-60ĝ N) and high northern latitudes (ĝ1/4 4 %, 60-90ĝ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr-1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr-1 by 8 Tg CH4 yr-1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.

@article{
 title = {Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications},
 type = {article},
 year = {2020},
 pages = {789-819},
 volume = {13},
 id = {5442cf4e-a73e-32cc-8f73-c266d4c7907d},
 created = {2020-05-07T09:37:21.726Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
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 read = {false},
 starred = {false},
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 hidden = {false},
 citation_key = {Reuter2020},
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 abstract = {Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5 × 5data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets)-single-observation random error (1s): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1s): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are singleobservation random error (1s): 17.4 ppb (monthly: 8.7 ppb); global bias:-2:0 ppb (-2:9 ppb); and spatiotemporal bias (1s): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).},
 bibtype = {article},
 author = {Reuter, Maximilian and Buchwitz, Michael and Schneising, Oliver and Noël, Stefan and Bovensmann, Heinrich and Burrows, John P. and Boesch, Hartmut and Di Noia, Antonio and Anand, Jasdeep and Parker, Robert J. and Somkuti, Peter and Wu, Lianghai and Hasekamp, Otto P. and Aben, Ilse and Kuze, Akihiko and Suto, Hiroshi and Shiomi, Kei and Yoshida, Yukio and Morino, Isamu and Crisp, David and O'Dell, Christopher W. and Notholt, Justus and Petri, Christof and Warneke, Thorsten and Velazco, Voltaire A. and Deutscher, Nicholas M. and Griffith, David W.T. and Kivi, Rigel and Pollard, David F. and Hase, Frank and Sussmann, Ralf and Té, Yao V. and Strong, Kimberly and Roche, Sébastien and Sha, Mahesh K. and De Mazière, Martine and Feist, Dietrich G. and Iraci, Laura T. and Roehl, Coleen M. and Retscher, Christian and Schepers, Dinand},
 doi = {10.5194/amt-13-789-2020},
 journal = {Atmospheric Measurement Techniques},
 number = {2}
}

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5 × 5data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets)-single-observation random error (1s): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1s): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are singleobservation random error (1s): 17.4 ppb (monthly: 8.7 ppb); global bias:-2:0 ppb (-2:9 ppb); and spatiotemporal bias (1s): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).

@article{
 title = {A decade of GOSAT Proxy satellite CH4 observations},
 type = {article},
 year = {2020},
 pages = {3383-3412},
 volume = {12},
 id = {1a915c55-730b-36f2-bb0b-6f8ee5853ce3},
 created = {2020-07-07T18:54:20.596Z},
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 starred = {false},
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 hidden = {false},
 citation_key = {Parker2020},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992},
 private_publication = {false},
 abstract = {This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4=XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of -0:84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).},
 bibtype = {article},
 author = {Parker, Robert J and Webb, Alex and Boesch, Hartmut and Somkuti, Peter and Barrio Guillo, Rocio and Di Noia, Antonio and Kalaitzi, Nikoleta and Anand, Jasdeep S. and Bergamaschi, Peter and Chevallier, Frederic and Palmer, Paul I and Feng, Liang and Deutscher, Nicholas M and Feist, Dietrich G and Griffith, David W.T. and Hase, Frank and Kivi, Rigel and Morino, Isamu and Notholt, Justus and Oh, Young Suk and Ohyama, Hirofumi and Petri, Christof and Pollard, David F. and Roehl, Coleen and Sha, Mahesh K. and Shiomi, Kei and Strong, Kimberly and Sussmann, Ralf and Té, Yao and Velazco, Voltaire A. and Warneke, Thorsten and Wennberg, Paul O. and Wunch, Debra},
 doi = {10.5194/essd-12-3383-2020},
 journal = {Earth System Science Data},
 number = {4}
}

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4=XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of -0:84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

@article{
 title = {A new space-borne perspective of crop productivity variations over the US Corn Belt},
 type = {article},
 year = {2020},
 keywords = {Crops,Fluorescence,GOSAT;,Yield},
 pages = {107826},
 volume = {281},
 websites = {https://doi.org/10.1016/j.agrformet.2019.107826},
 publisher = {Elsevier},
 id = {6abadd1c-f2c4-3c35-a77e-9eddf0091246},
 created = {2020-07-11T20:52:36.429Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.859Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Somkuti2020a},
 private_publication = {false},
 abstract = {Remotely-sensed solar-induced chlorophyll fluorescence (SIF) provides a means to assess vegetation productivity in a more direct way than via the greenness of leaves. SIF is produced by plants alongside photosynthesis so it is generally thought to provide a more direct probe of plant status. We analyze inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT's sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.},
 bibtype = {article},
 author = {Somkuti, Peter and Bösch, Hartmut and Feng, Liang and Palmer, Paul I. and Parker, Robert J. and Quaife, Tristan},
 doi = {10.1016/j.agrformet.2019.107826},
 journal = {Agricultural and Forest Meteorology},
 number = {November 2019}
}

Remotely-sensed solar-induced chlorophyll fluorescence (SIF) provides a means to assess vegetation productivity in a more direct way than via the greenness of leaves. SIF is produced by plants alongside photosynthesis so it is generally thought to provide a more direct probe of plant status. We analyze inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT's sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.

@article{
 title = {Quantifying sources of Brazil ’ s CH 4 emissions between 2010 and 2018 from satellite data},
 type = {article},
 year = {2020},
 pages = {1-40},
 id = {825dca01-92ec-3410-9847-364c221aad98},
 created = {2020-07-11T20:52:36.625Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:12:42.044Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tunnicliffe, Rachel L and Ganesan, Anita L and Parker, Robert J and Boesch, Hartmut and Gedney, Nicola and Poulter, Benjamin and Zhang, Zhen and Lavriˇ, Jošt V and Walter, David and Rigby, Matthew and Henne, Stephan and Young, Dickon and Doherty, Simon O},
 number = {June}
}

@article{
 title = {Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia},
 type = {article},
 year = {2020},
 keywords = {Anaerobic oxidation of methane,Atmospheric methane,Atmospheric sciences,Carbon monoxide,Environmental science,Growth rate,Methane,Surge,Tropics,Wetland},
 pages = {1-27},
 id = {a1b62473-5975-39fb-b3fb-a2185166e998},
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 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.642Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yin2020},
 private_publication = {false},
 abstract = {After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-tracer atmospheric inversions that have the capacity to assimilate the major tracers in the methane oxidation chain-namely methane, formaldehyde, and carbon monoxide-to simultaneously optimize both the methane sources and sinks at each model grid. 5 We show that the recent surge of the atmospheric growth rate between 2010-2013 and 2014-2017 is most likely explained by an increase of global CH 4 emissions by 17.5±1.5 Tg yr −1 (mean±1σ), while variations in CH 4 sinks remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropics wetlands (∼35%) and anthropogenic emissions in China (∼20%). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH 4 emissions.},
 bibtype = {article},
 author = {Yin, Yi and Chevallier, Frederic and Ciais, Philippe and Bousquet, Philippe and Saunois, Marielle and Zheng, Bo and Worden, John and Bloom, A Anthony and Parker, Robert and Jacob, Daniel and Dlugokencky, Edward and Frankenberg, Christian},
 doi = {10.5194/acp-2020-649},
 journal = {Atmospheric Chemistry and Physics Discussions},
 number = {July}
}

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-tracer atmospheric inversions that have the capacity to assimilate the major tracers in the methane oxidation chain-namely methane, formaldehyde, and carbon monoxide-to simultaneously optimize both the methane sources and sinks at each model grid. 5 We show that the recent surge of the atmospheric growth rate between 2010-2013 and 2014-2017 is most likely explained by an increase of global CH 4 emissions by 17.5±1.5 Tg yr −1 (mean±1σ), while variations in CH 4 sinks remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropics wetlands (∼35%) and anthropogenic emissions in China (∼20%). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH 4 emissions.

@article{
 title = {Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations},
 type = {article},
 year = {2020},
 pages = {5669-5691},
 volume = {17},
 id = {443d9d0a-6cd5-3bdf-9009-64ad0940c109},
 created = {2020-08-11T15:36:02.269Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T20:16:41.406Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Parker2020a},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992},
 private_publication = {false},
 abstract = {Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developedWetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by -7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (-1:9 ppb) than the Northern Hemisphere (-9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (-11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.},
 bibtype = {article},
 author = {Parker, Robert J and Wilson, Chris and Bloom, A Anthony and Comyn-Platt, Edward and Hayman, Garry and McNorton, Joe and Boesch, Hartmut and Chipperfield, Martyn P},
 doi = {10.5194/bg-17-5669-2020},
 journal = {Biogeosciences},
 number = {22}
}

Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developedWetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by -7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (-1:9 ppb) than the Northern Hemisphere (-9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (-11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.

@article{
 title = {Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model},
 type = {article},
 year = {2020},
 keywords = {Air mass,Atmospheric chemistry,Atmospheric sciences,Climatology,Computer science,Flux,Mass flux,Polar vortex,Stratosphere,Total Carbon Column Observing Network,Troposphere},
 pages = {3839-3862},
 volume = {13},
 websites = {https://gmd.copernicus.org/articles/13/3839/2020/},
 month = {8},
 day = {31},
 id = {98e9f916-92a0-3b2b-81cc-cf5b3ba0251f},
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 last_modified = {2021-03-31T19:34:12.204Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Stanevich, Ilya and Jones, Dylan B. A. and Strong, Kimberly and Parker, Robert J. and Boesch, Hartmut and Wunch, Debra and Notholt, Justus and Petri, Christof and Warneke, Thorsten and Sussmann, Ralf and Schneider, Matthias and Hase, Frank and Kivi, Rigel and Deutscher, Nicholas M. and Velazco, Voltaire A. and Walker, Kaley A. and Deng, Feng},
 doi = {10.5194/gmd-13-3839-2020},
 journal = {Geoscientific Model Development},
 number = {9}
}

@article{
 title = {Toward High Precision XCO<inf>2</inf> Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements},
 type = {article},
 year = {2020},
 volume = {125},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096482357&doi=10.1029%2F2020JD032794&partnerID=40&md5=ec6246cd6393276ac57cf2366ac0190b},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yang2020},
 source_type = {article},
 notes = {cited By 1},
 private_publication = {false},
 bibtype = {article},
 author = {Yang, D and Boesch, H and Liu, Y and Somkuti, P and Cai, Z and Chen, X and Di Noia, A and Lin, C and Lu, N and Lyu, D and Parker, R J and Tian, L and Wang, M and Webb, A and Yao, L and Yin, Z and Zheng, Y and Deutscher, N M and Griffith, D W T and Hase, F and Kivi, R and Morino, I and Notholt, J and Ohyama, H and Pollard, D F and Shiomi, K and Sussmann, R and Té, Y and Velazco, V A and Warneke, T and Wunch, D},
 doi = {10.1029/2020JD032794},
 journal = {Journal of Geophysical Research: Atmospheres},
 number = {22}
}

@article{
 title = {The significance of fast radiative transfer for hyperspectral SWIR XCO<inf>2</inf> retrievals},
 type = {article},
 year = {2020},
 volume = {11},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096545471&doi=10.3390%2Fatmos11111219&partnerID=40&md5=9a45450b3ee42766d14c5515ff01a82b},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Somkuti2020},
 source_type = {article},
 notes = {cited By 0},
 private_publication = {false},
 bibtype = {article},
 author = {Somkuti, P and Bösch, H and Parker, R J},
 doi = {10.3390/atmos11111219},
 journal = {Atmosphere},
 number = {11}
}

@article{
 title = {Quantifying sources of Brazil's CH4 emissions between 2010 and 2018 from satellite data},
 type = {article},
 year = {2020},
 pages = {13041-13067},
 volume = {20},
 id = {d9b8af48-b083-3ea9-827d-dae1ccc68845},
 created = {2021-03-31T19:11:05.115Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:34:14.198Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {L.Tunnicliffe2020},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992},
 private_publication = {false},
 abstract = {Brazil's CH4 emissions over the period 2010- 2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010- 2018 are 33:63:6Tgyr1, which are comprised of 19:0 2:6Tgyr1 from anthropogenic (primarily related to agriculture and waste), 13:01:9Tgyr1 from wetlands and 1:7 0:3Tgyr1 from biomass burning sources. In addition, between the 2011-2013 and 2014-2018 periods, Brazil's mean emissions rose by 6:95:3Tgyr1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015-2016 El Nino by 3:72:7Tgyr1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.},
 bibtype = {article},
 author = {L. Tunnicliffe, Rachel and L. Ganesan, Anita and J. Parker, Robert and Boesch, Hartmut and Gedney, Nicola and Poulter, Benjamin and Zhang, Zhen and Walter, David and Rigby, Matthew and Henne, Stephan and Young, Dickon and O'Doherty, Simon},
 doi = {10.5194/acp-20-13041-2020},
 journal = {Atmospheric Chemistry and Physics},
 number = {21}
}

Brazil's CH4 emissions over the period 2010- 2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010- 2018 are 33:63:6Tgyr1, which are comprised of 19:0 2:6Tgyr1 from anthropogenic (primarily related to agriculture and waste), 13:01:9Tgyr1 from wetlands and 1:7 0:3Tgyr1 from biomass burning sources. In addition, between the 2011-2013 and 2014-2018 periods, Brazil's mean emissions rose by 6:95:3Tgyr1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015-2016 El Nino by 3:72:7Tgyr1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.

@article{
 title = {Earth system music: music generated from the United Kingdom Earth System Model (UKESM1)},
 type = {article},
 year = {2020},
 pages = {263-278},
 volume = {3},
 id = {37e80091-8012-3e13-95de-ffbeb9bf8ac7},
 created = {2021-03-31T19:11:05.116Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:05.116Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {DeMora2020},
 private_publication = {false},
 abstract = {Abstract. Scientific data are almost always represented graphically in figures or in videos. With the ever-growing interest from the general public in understanding climate sciences, it is becoming increasingly important that scientists present this information in ways that are both accessible and engaging to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces. Each of these pieces presents a unique aspect of the ocean component of the UKESM1, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were created in the Musical Instrument Digital Interface (MIDI) format and then performed by a digital piano synthesiser. An associated video showing the time development of the data in time with the music was also created. The music and video were published on the lead author's YouTube channel. A brief description of the methodology was also posted alongside the video. We also discuss the limitations of this pilot study and describe several approaches to extend and expand upon this work.},
 bibtype = {article},
 author = {de Mora, Lee and Sellar, Alistair A. and Yool, Andrew and Palmieri, Julien and Smith, Robin S. and Kuhlbrodt, Till and Parker, Robert J. and Walton, Jeremy and Blackford, Jeremy C. and Jones, Colin G.},
 doi = {10.5194/gc-3-263-2020},
 journal = {Geoscience Communication},
 number = {2}
}

Abstract. Scientific data are almost always represented graphically in figures or in videos. With the ever-growing interest from the general public in understanding climate sciences, it is becoming increasingly important that scientists present this information in ways that are both accessible and engaging to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces. Each of these pieces presents a unique aspect of the ocean component of the UKESM1, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were created in the Musical Instrument Digital Interface (MIDI) format and then performed by a digital piano synthesiser. An associated video showing the time development of the data in time with the music was also created. The music and video were published on the lead author's YouTube channel. A brief description of the methodology was also posted alongside the video. We also discuss the limitations of this pilot study and describe several approaches to extend and expand upon this work.

@article{
 title = {Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model},
 type = {article},
 year = {2020},
 keywords = {Air mass,Atmospheric chemistry,Atmospheric sciences,Climatology,Computer science,Flux,Mass flux,Polar vortex,Stratosphere,Total Carbon Column Observing Network,Troposphere},
 pages = {1-42},
 volume = {13},
 websites = {https://gmd.copernicus.org/articles/13/3839/2020/},
 month = {8},
 day = {31},
 id = {7d1eff22-0446-3626-9fd2-d4b465963d74},
 created = {2021-03-31T19:11:05.309Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:05.309Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Stanevich2020},
 private_publication = {false},
 bibtype = {article},
 author = {Stanevich, Ilya and Jones, Dylan B. A. and Strong, Kimberly and Parker, Robert J. and Boesch, Hartmut and Wunch, Debra and Notholt, Justus and Petri, Christof and Warneke, Thorsten and Sussmann, Ralf and Schneider, Matthias and Hase, Frank and Kivi, Rigel and Deutscher, Nicholas M. and Velazco, Voltaire A. and Walker, Kaley A. and Deng, Feng},
 doi = {10.5194/gmd-13-3839-2020},
 journal = {Geoscientific Model Development},
 number = {9}
}

@article{
 title = {Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010-2015},
 type = {article},
 year = {2019},
 pages = {7859-7881},
 volume = {19},
 id = {c2d8620c-d05b-36d6-9017-94c0b36633c0},
 created = {2019-10-03T10:14:50.902Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-07-11T20:52:37.754Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Maasakkers2019},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p><strong>Abstract.</strong> We use 2010&amp;ndash;2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used even when prior error standard deviation distributions are log-normal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil/gas emissions in the EDGAR v4.3.2 inventory, but little error in the US where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil/gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC) and our inversion is generally more consistent with the UNFCCC data. The observed 2010&amp;ndash;2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546&amp;thinsp;Tg&amp;thinsp;a<sup>&amp;minus;1</sup>) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8&amp;thinsp;±&amp;thinsp;0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.</p>},
 bibtype = {article},
 author = {Maasakkers, Joannes D. and Jacob, Daniel J. and Sulprizio, Melissa P. and Scarpelli, Tia R. and Nesser, Hannah and Sheng, Jian Xiong and Zhang, Yuzhong and Hersher, Monica and Anthony Bloom, A. and Bowman, Kevin W. and Worden, John R. and Janssens-Maenhout, Greet and Parker, Robert J.},
 doi = {10.5194/acp-19-7859-2019},
 journal = {Atmospheric Chemistry and Physics},
 number = {11}
}

Abstract. We use 2010&ndash;2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used even when prior error standard deviation distributions are log-normal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil/gas emissions in the EDGAR v4.3.2 inventory, but little error in the US where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil/gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC) and our inversion is generally more consistent with the UNFCCC data. The observed 2010&ndash;2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546&thinsp;Tg&thinsp;a^&minus;1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8&thinsp;±&thinsp;0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

@article{
 title = {An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data},
 type = {article},
 year = {2019},
 pages = {1-30},
 id = {073bac06-c9cc-35bf-b5de-a8abdb2edffb},
 created = {2020-03-03T11:09:29.048Z},
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 last_modified = {2021-03-31T19:34:14.497Z},
 read = {false},
 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Lunt2019},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992,ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p><strong>Abstract.</strong> Emissions of methane (CH<sub>4</sub>) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH<sub>4</sub> budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH<sub>4</sub> columns can help to narrow down some of the uncertainties in the tropical CH<sub>4</sub> emission budget. We use proxy column retrievals of atmospheric CH<sub>4</sub> (XCH<sub>4</sub>) from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5&amp;thinsp;&amp;times;&amp;thinsp;0.625) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH<sub>4</sub> are less sensitive to scattering due to clouds and aerosol than full physics retrievals but the method assumes that the global distribution of carbon dioxide (CO<sub>2</sub>) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH<sub>4</sub> data products that are determined using different model CO<sub>2</sub> fields. We infer monthly emissions from GOSAT XCH<sub>4</sub> data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010&amp;ndash;2016 range from 75 (72&amp;ndash;78)&amp;thinsp;Tg&amp;thinsp;yr<sup>&amp;minus;1</sup> to 80 (78&amp;ndash;83)&amp;thinsp;Tg&amp;thinsp;yr<sup>&amp;minus;1</sup>, dependent on the proxy XCH<sub>4</sub> data used, with larger differences in northern hemisphere Africa than southern hemisphere Africa. We find a robust positive linear trend in tropical African CH<sub>4</sub> emissions for our seven-year study period, with values of 1.5 (1.1&amp;ndash;1.9)&amp;thinsp;Tg&amp;thinsp;yr<sup>&amp;minus;1</sup> or 2.1 (1.7&amp;ndash;2.5)&amp;thinsp;Tg&amp;thinsp;yr<sup>&amp;minus;1</sup>, dependent on the CO<sub>2</sub> data product used in the proxy retrieval. A substantial portion of this increase is due to a short-term increase in emissions of 3&amp;thinsp;Tg&amp;thinsp;yr<sup>&amp;minus;1</sup> between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data we find this increase in CH<sub>4</sub> emission is consistent with an increase in wetland extent due to increased inflow from the White Nile. We find a strong seasonality in emissions across northern hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH<sub>4</sub> emissions from the wetland area of the Congo basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.</p>},
 bibtype = {article},
 author = {Lunt, Mark F. and Palmer, Paul I. and Feng, Liang and Taylor, Christopher M. and Boesch, Hartmut and Parker, Robert J.},
 doi = {10.5194/acp-2019-477},
 journal = {Atmospheric Chemistry and Physics Discussions}
}

Abstract. Emissions of methane (CH₄) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH₄ budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH₄ columns can help to narrow down some of the uncertainties in the tropical CH₄ emission budget. We use proxy column retrievals of atmospheric CH₄ (XCH₄) from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5&thinsp;&times;&thinsp;0.625) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH₄ are less sensitive to scattering due to clouds and aerosol than full physics retrievals but the method assumes that the global distribution of carbon dioxide (CO₂) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH₄ data products that are determined using different model CO₂ fields. We infer monthly emissions from GOSAT XCH₄ data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010&ndash;2016 range from 75 (72&ndash;78)&thinsp;Tg&thinsp;yr^&minus;1 to 80 (78&ndash;83)&thinsp;Tg&thinsp;yr^&minus;1, dependent on the proxy XCH₄ data used, with larger differences in northern hemisphere Africa than southern hemisphere Africa. We find a robust positive linear trend in tropical African CH₄ emissions for our seven-year study period, with values of 1.5 (1.1&ndash;1.9)&thinsp;Tg&thinsp;yr^&minus;1 or 2.1 (1.7&ndash;2.5)&thinsp;Tg&thinsp;yr^&minus;1, dependent on the CO₂ data product used in the proxy retrieval. A substantial portion of this increase is due to a short-term increase in emissions of 3&thinsp;Tg&thinsp;yr^&minus;1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data we find this increase in CH₄ emission is consistent with an increase in wetland extent due to increased inflow from the White Nile. We find a strong seasonality in emissions across northern hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH₄ emissions from the wetland area of the Congo basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

@article{
 title = {Global atmospheric carbon monoxide budget 2000-2017 inferred from multi-species atmospheric inversions},
 type = {article},
 year = {2019},
 pages = {1-42},
 volume = {1},
 id = {68918d63-c96d-3bc9-acc6-095dba665d54},
 created = {2020-07-11T20:52:36.638Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.743Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zheng2019},
 private_publication = {false},
 abstract = {Abstract. Atmospheric carbon monoxide (CO) concentrations have been decreasing since 2000 as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories surprisingly estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO, formaldehyde (HCHO), and methane (CH4) that are all major components of the atmospheric CO cycle. Three inversions (i.e., 2000–2017, 2005–2017, and 2010–2017) are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent during overlapping periods), driven by reduced anthropogenic emissions in the U.S. and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in Equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions estimate that surface CO emissions contradict the global bottom-up inventories in the world's top two emitters for the sign of anthropogenic emission trends in China (e.g., here −0.8 ± 0.5 % yr−1 since 2000 while the prior gives 1.3 ± 0.4 % yr−1) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0 ± 0.6 % yr−1 since 2000 smaller than 3.5 ± 0.4 % yr−1 in the prior inventory). The posterior model CO concentrations and trends agree well with independent ground-based observations and correct the prior model bias. The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates CO, HCHO, and CH4 has a good representation of the global CO budget, therefore matches best with independent observations, while the inversion only assimilating CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production. The global CO budget data from all three inversions in this study can be accessed from https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019).},
 bibtype = {article},
 author = {Zheng, Bo and Chevallier, Frederic and Yin, Yi and Ciais, Philippe and Fortems-Cheiney, Audrey and Deeter, Merritt N. and Parker, Robert J. and Wang, Yilong and Worden, Helen M. and Zhao, Yuanhong},
 doi = {10.5194/essd-2019-61},
 journal = {Earth System Science Data Discussions}
}

Abstract. Atmospheric carbon monoxide (CO) concentrations have been decreasing since 2000 as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories surprisingly estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO, formaldehyde (HCHO), and methane (CH4) that are all major components of the atmospheric CO cycle. Three inversions (i.e., 2000–2017, 2005–2017, and 2010–2017) are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent during overlapping periods), driven by reduced anthropogenic emissions in the U.S. and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in Equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions estimate that surface CO emissions contradict the global bottom-up inventories in the world's top two emitters for the sign of anthropogenic emission trends in China (e.g., here −0.8 ± 0.5 % yr−1 since 2000 while the prior gives 1.3 ± 0.4 % yr−1) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0 ± 0.6 % yr−1 since 2000 smaller than 3.5 ± 0.4 % yr−1 in the prior inventory). The posterior model CO concentrations and trends agree well with independent ground-based observations and correct the prior model bias. The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates CO, HCHO, and CH4 has a good representation of the global CO budget, therefore matches best with independent observations, while the inversion only assimilating CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production. The global CO budget data from all three inversions in this study can be accessed from https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019).

@article{
 title = {UKESM1: Description and Evaluation of the U.K. Earth System Model},
 type = {article},
 year = {2019},
 pages = {4513-4558},
 volume = {11},
 id = {f6277872-098c-3321-9587-ce2aba585b67},
 created = {2020-07-11T20:52:37.476Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T20:16:36.833Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Sellar2019},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992},
 private_publication = {false},
 abstract = {We document the development of the first version of the U.K. Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2-ES, with enhancements to all component models and new feedback mechanisms. These include a new core physical model with a well-resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric-stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane, and nitrous oxide; two-moment, five-species, modal aerosol; and ocean biogeochemistry with two-way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land, and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall, the model performs well, with a stable pre-industrial state and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger-than-observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealized simulations show a high climate sensitivity relative to previous generations of models: Equilibrium climate sensitivity is 5.4 K, transient climate response ranges from 2.68 to 2.85 K, and transient climate response to cumulative emissions is 2.49 to 2.66 K TtC−1.},
 bibtype = {article},
 author = {Sellar, Alistair A. and Jones, Colin G. and Mulcahy, Jane P. and Tang, Yongming and Yool, Andrew and Wiltshire, Andy and O'Connor, Fiona M. and Stringer, Marc and Hill, Richard and Palmieri, Julien and Woodward, Stephanie and de Mora, Lee and Kuhlbrodt, Till and Rumbold, Steven T. and Kelley, Douglas I. and Ellis, Rich and Johnson, Colin E. and Walton, Jeremy and Abraham, Nathan Luke and Andrews, Martin B. and Andrews, Timothy and Archibald, Alex T. and Berthou, Ségolène and Burke, Eleanor and Blockley, Ed and Carslaw, Ken and Dalvi, Mohit and Edwards, John and Folberth, Gerd A. and Gedney, Nicola and Griffiths, Paul T. and Harper, Anna B. and Hendry, Maggie A. and Hewitt, Alan J. and Johnson, Ben and Jones, Andy and Jones, Chris D. and Keeble, James and Liddicoat, Spencer and Morgenstern, Olaf and Parker, Robert J. and Predoi, Valeriu and Robertson, Eddy and Siahaan, Antony and Smith, Robin S. and Swaminathan, Ranjini and Woodhouse, Matthew T. and Zeng, Guang and Zerroukat, Mohamed},
 doi = {10.1029/2019MS001739},
 journal = {Journal of Advances in Modeling Earth Systems},
 number = {12}
}

We document the development of the first version of the U.K. Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2-ES, with enhancements to all component models and new feedback mechanisms. These include a new core physical model with a well-resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric-stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane, and nitrous oxide; two-moment, five-species, modal aerosol; and ocean biogeochemistry with two-way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land, and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall, the model performs well, with a stable pre-industrial state and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger-than-observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealized simulations show a high climate sensitivity relative to previous generations of models: Equilibrium climate sensitivity is 5.4 K, transient climate response ranges from 2.68 to 2.85 K, and transient climate response to cumulative emissions is 2.49 to 2.66 K TtC−1.

@article{
 title = {Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016},
 type = {article},
 year = {2018},
 pages = {1-22},
 websites = {https://www.atmos-chem-phys-discuss.net/acp-2018-158/},
 month = {3},
 day = {14},
 id = {635772c3-6512-3a30-ae5f-3b532f3cc888},
 created = {2018-04-29T21:53:52.361Z},
 accessed = {2018-04-29},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.965Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Buchwitz2018a},
 private_publication = {false},
 abstract = {The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined globally and for selected latitude bands from satellite retrievals of column-average dry-air mole fractions of CO2, i.e., XCO2, for the years 2003 to 2016. The global XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0 ± 0.24 ppm/year; R: 0.87). This new and independent data set confirms record large growth rates around 3 ppm/year in 2015 and 2016, which are attributed to the 2015/2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil fuel burning related emissions in explaining the variance of the atmospheric CO2 growth rate.},
 bibtype = {article},
 author = {Buchwitz, Michael and Reuter, Maximilian and Schneising, Oliver and Noël, Stefan and Gier, Bettina and Bovensmann, Heinrich and Burrows, John P. and Boesch, Hartmut and Anand, Jasdeep and Parker, Robert J. and Somkuti, Peter and Detmers, Rob G. and Hasekamp, Otto P. and Aben, Ilse and Butz, André and Kuze, Akihiko and Suto, Hiroshi and Yoshida, Yukio and Crosp, David and O'Dell, Christopher},
 doi = {10.5194/acp-2018-158},
 journal = {Atmospheric Chemistry and Physics Discussions}
}

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined globally and for selected latitude bands from satellite retrievals of column-average dry-air mole fractions of CO2, i.e., XCO2, for the years 2003 to 2016. The global XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0 ± 0.24 ppm/year; R: 0.87). This new and independent data set confirms record large growth rates around 3 ppm/year in 2015 and 2016, which are attributed to the 2015/2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil fuel burning related emissions in explaining the variance of the atmospheric CO2 growth rate.

@article{
 title = {2010-2015 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors},
 type = {article},
 year = {2018},
 pages = {1-18},
 websites = {https://www.atmos-chem-phys-discuss.net/acp-2017-1110/},
 month = {1},
 day = {4},
 id = {e8b438b3-35ac-3ce5-84e6-0fd79df76c52},
 created = {2018-04-29T21:53:52.645Z},
 accessed = {2018-04-29},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:30.118Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Sheng2018},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {We use six years (2010–2015) of methane column observations from the Greenhouse Gases Observing Satellite (GOSAT) to examine trends in atmospheric methane concentrations over North America and infer trends in emissions. Local methane enhancements above background are diagnosed in the GOSAT data on a 0.5° × 0.5° grid by estimating the local background as the low (10th–25th) percentiles of the deseasonalized frequency distributions of the data for individual years. Trends in methane enhancements on the 0.5° × 0.5° grid are then aggregated nationally and for individual source sectors, using information from state-of-science bottom-up inventories, to increase statistical power. Our results suggest that US methane emissions increased by 2.1 ± 1.4 % a−1 (mean ± one standard deviation) over the six-year period, with contributions from both oil/gas systems (possibly unconventional oil/gas production) and from livestock in the Midwest (possibly swine manure management). Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show interannual variability driven by wetlands emissions and correlated with wetland areal extent. The US emission trends inferred from the GOSAT data account for about 20 % of the observed increase in global methane over the 2010–2014 period but may be too small to be detectable with surface observations from the North American Carbon Program (NACP) network.},
 bibtype = {article},
 author = {Sheng, Jian-Xiong and Jacob, Daniel J. and Turner, Alexander J. and Maasakkers, Joannes D. and Benmergui, Joshua and Bloom, Anthony A. and Arndt, Claudia and Gautam, Ritesh and Zavala-Araiza, Daniel and Boesch, Hartmut and Parker, Robert J.},
 doi = {10.5194/acp-2017-1110},
 journal = {Atmospheric Chemistry and Physics Discussions}
}

We use six years (2010–2015) of methane column observations from the Greenhouse Gases Observing Satellite (GOSAT) to examine trends in atmospheric methane concentrations over North America and infer trends in emissions. Local methane enhancements above background are diagnosed in the GOSAT data on a 0.5° × 0.5° grid by estimating the local background as the low (10th–25th) percentiles of the deseasonalized frequency distributions of the data for individual years. Trends in methane enhancements on the 0.5° × 0.5° grid are then aggregated nationally and for individual source sectors, using information from state-of-science bottom-up inventories, to increase statistical power. Our results suggest that US methane emissions increased by 2.1 ± 1.4 % a−1 (mean ± one standard deviation) over the six-year period, with contributions from both oil/gas systems (possibly unconventional oil/gas production) and from livestock in the Midwest (possibly swine manure management). Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show interannual variability driven by wetlands emissions and correlated with wetland areal extent. The US emission trends inferred from the GOSAT data account for about 20 % of the observed increase in global methane over the 2010–2014 period but may be too small to be detectable with surface observations from the North American Carbon Program (NACP) network.

@article{
 title = {Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH 4 observations},
 type = {article},
 year = {2018},
 keywords = {GOSAT,JULES,Land surface model,Methane,Wetlands},
 pages = {261-275},
 volume = {211},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0034425718300178},
 month = {6},
 id = {c1b44443-2ce1-3013-88be-37dabf9208ac},
 created = {2020-07-11T20:52:36.399Z},
 accessed = {2018-04-24},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:34:12.822Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Parker2018},
 folder_uuids = {b33ad02d-4bea-4e0e-a371-9e8282b55d36,336e7fe6-90e1-4682-8f50-8551a15fb992,fb67c5ee-e49e-4faf-b3ce-432b3e85f5a2,ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {Natural wetlands are the largest source of methane emissions, contributing 20–40% of global emissions and dominating the inter-annual variability. Large uncertainties remain on their variability and response to climate change. This study uses atmospheric methane observations from the GOSAT satellite to evaluate methane wetland emission estimates. We assess how well simulations reproduce the observed methane inter-annual variability by evaluating the detrended seasonal cycle. The latitudinal means agree well but maximum differences in the tropics of 28.1–34.8 ppb suggest that all simulations fail to capture the extent of the tropical wetland seasonal cycle. We focus further analysis on the major natural wetlands in South America: the seasonally flooded savannah of the Pantanal (Brazil) and Llanos de Moxos (Bolivia) regions; and the riverine wetlands formed by the Paraná River (Argentina). We see large discrepancies between simulation and observation over the Pantanal and Llanos de Moxos region in 2010, 2011 and 2014 and over the Paraná River region in 2010 and 2014. We find highly consistent behaviour between the time and location of these methane anomalies and the change in wetland extent, driven by precipitation related to El Niño Southern Oscillation activity. We conclude that the inability of land surface models to increase wetland extent through overbank inundation is the primary cause of these observed discrepancies and can lead to under-estimation of methane fluxes by as much as 50% (5.3–11.8 Tg yr −1 ) of the observed emissions for the combined Pantanal and Paraná regions. As the hydrology of these regions is heavily linked to ENSO variability, being able to reproduce changes in wetland behaviour is important for successfully predicting their methane emissions.},
 bibtype = {article},
 author = {Parker, Robert J. and Boesch, Hartmut and McNorton, Joe and Comyn-Platt, Edward and Gloor, Manuel and Wilson, Chris and Chipperfield, Martyn P. and Hayman, Garry D. and Bloom, A. Anthony},
 doi = {10.1016/j.rse.2018.02.011},
 journal = {Remote Sensing of Environment},
 number = {April}
}

Natural wetlands are the largest source of methane emissions, contributing 20–40% of global emissions and dominating the inter-annual variability. Large uncertainties remain on their variability and response to climate change. This study uses atmospheric methane observations from the GOSAT satellite to evaluate methane wetland emission estimates. We assess how well simulations reproduce the observed methane inter-annual variability by evaluating the detrended seasonal cycle. The latitudinal means agree well but maximum differences in the tropics of 28.1–34.8 ppb suggest that all simulations fail to capture the extent of the tropical wetland seasonal cycle. We focus further analysis on the major natural wetlands in South America: the seasonally flooded savannah of the Pantanal (Brazil) and Llanos de Moxos (Bolivia) regions; and the riverine wetlands formed by the Paraná River (Argentina). We see large discrepancies between simulation and observation over the Pantanal and Llanos de Moxos region in 2010, 2011 and 2014 and over the Paraná River region in 2010 and 2014. We find highly consistent behaviour between the time and location of these methane anomalies and the change in wetland extent, driven by precipitation related to El Niño Southern Oscillation activity. We conclude that the inability of land surface models to increase wetland extent through overbank inundation is the primary cause of these observed discrepancies and can lead to under-estimation of methane fluxes by as much as 50% (5.3–11.8 Tg yr −1 ) of the observed emissions for the combined Pantanal and Paraná regions. As the hydrology of these regions is heavily linked to ENSO variability, being able to reproduce changes in wetland behaviour is important for successfully predicting their methane emissions.

@article{
 title = {A measurement-based verification framework for UK greenhouse gas emissions: An overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project},
 type = {article},
 year = {2018},
 pages = {11753-11777},
 volume = {18},
 id = {ce3418ee-1143-38f2-9660-2060d1dd917a},
 created = {2020-07-11T20:52:36.518Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.908Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Palmer2018},
 private_publication = {false},
 abstract = {We describe the motivation, design, and execution of the Greenhouse gAs Uk and Global Emissions (GAUGE) project. The overarching scientific objective of GAUGE was to use atmospheric data to estimate the magnitude, distribution, and uncertainty of the UK greenhouse gas (GHG, defined here as CO2, CH4, and N2O) budget, 2013-2015. To address this objective, we established a multi-year and interlinked measurement and data analysis programme, building on an established tall-tower GHG measurement network. The calibrated measurement network comprises ground-based, airborne, ship-borne, balloon-borne, and space-borne GHG sensors. Our choice of measurement technologies and measurement locations reflects the heterogeneity of UK GHG sources, which range from small point sources such as landfills to large, diffuse sources such as agriculture. Atmospheric mole fraction data collected at the tall towers and on the ships provide information on sub-continental fluxes, representing the backbone to the GAUGE network. Additional spatial and temporal details of GHG fluxes over East Anglia were inferred from data collected by a regional network. Data collected during aircraft flights were used to study the transport of GHGs on local and regional scales. We purposely integrated new sensor and platform technologies into the GAUGE network, allowing us to lay the foundations of a strengthened UK capability to verify national GHG emissions beyond the project lifetime. For example, current satellites provide sparse and seasonally uneven sampling over the UK mainly because of its geographical size and cloud cover. This situation will improve with new and future satellite instruments, e.g. measurements of CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P. We use global, nested, and regional atmospheric transport models and inverse methods to infer geographically resolved CO2 and CH4 fluxes. This multi-model approach allows us to study model spread in a posteriori flux estimates. These models are used to determine the relative importance of different measurements to infer the UK GHG budget. Attributing observed GHG variations to specific sources is a major challenge. Within a UK-wide spatial context we used two approaches: (1) Δ14CO2 and other relevant isotopologues (e.g. δ13CCH4) from collected air samples to quantify the contribution from fossil fuel combustion and other sources, and (2) geographical separation of individual sources, e.g. agriculture, using a high-density measurement network. Neither of these represents a definitive approach, but they will provide invaluable information about GHG source attribution when they are adopted as part of a more comprehensive, long-term national GHG measurement programme. We also conducted a number of case studies, including an instrumented landfill experiment that provided a test bed for new technologies and flux estimation methods. We anticipate that results from the GAUGE project will help inform other countries on how to use atmospheric data to quantify their nationally determined contributions to the Paris Agreement.},
 bibtype = {article},
 author = {Palmer, Paul I. and O'Doherty, Simon and Allen, Grant and Bower, Keith and Bösch, Hartmut and Chipperfield, Martyn P. and Connors, Sarah and Dhomse, Sandip and Feng, Liang and Finch, Douglas P. and Gallagher, Martin W. and Gloor, Emanuel and Gonzi, Siegfried and Harris, Neil R.P. and Helfter, Carole and Humpage, Neil and Kerridge, Brian and Knappett, Diane and Jones, Roderic L. and Le Breton, Michael and Lunt, Mark F. and Manning, Alistair J. and Matthiesen, Stephan and Muller, Jennifer B.A. and Mullinger, Neil and Nemitz, Eiko and O'Shea, Sebastian and Parker, Robert J. and Percival, Carl J. and Pitt, Joseph and Riddick, Stuart N. and Rigby, Matthew and Sembhi, Harjinder and Siddans, Richard and Skelton, Robert L. and Smith, Paul and Sonderfeld, Hannah and Stanley, Kieran and Stavert, Ann R. and Wenger, Angelina and White, Emily and Wilson, Christopher and Young, DIckon},
 doi = {10.5194/acp-18-11753-2018},
 journal = {Atmospheric Chemistry and Physics},
 number = {16}
}

We describe the motivation, design, and execution of the Greenhouse gAs Uk and Global Emissions (GAUGE) project. The overarching scientific objective of GAUGE was to use atmospheric data to estimate the magnitude, distribution, and uncertainty of the UK greenhouse gas (GHG, defined here as CO2, CH4, and N2O) budget, 2013-2015. To address this objective, we established a multi-year and interlinked measurement and data analysis programme, building on an established tall-tower GHG measurement network. The calibrated measurement network comprises ground-based, airborne, ship-borne, balloon-borne, and space-borne GHG sensors. Our choice of measurement technologies and measurement locations reflects the heterogeneity of UK GHG sources, which range from small point sources such as landfills to large, diffuse sources such as agriculture. Atmospheric mole fraction data collected at the tall towers and on the ships provide information on sub-continental fluxes, representing the backbone to the GAUGE network. Additional spatial and temporal details of GHG fluxes over East Anglia were inferred from data collected by a regional network. Data collected during aircraft flights were used to study the transport of GHGs on local and regional scales. We purposely integrated new sensor and platform technologies into the GAUGE network, allowing us to lay the foundations of a strengthened UK capability to verify national GHG emissions beyond the project lifetime. For example, current satellites provide sparse and seasonally uneven sampling over the UK mainly because of its geographical size and cloud cover. This situation will improve with new and future satellite instruments, e.g. measurements of CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P. We use global, nested, and regional atmospheric transport models and inverse methods to infer geographically resolved CO2 and CH4 fluxes. This multi-model approach allows us to study model spread in a posteriori flux estimates. These models are used to determine the relative importance of different measurements to infer the UK GHG budget. Attributing observed GHG variations to specific sources is a major challenge. Within a UK-wide spatial context we used two approaches: (1) Δ14CO2 and other relevant isotopologues (e.g. δ13CCH4) from collected air samples to quantify the contribution from fossil fuel combustion and other sources, and (2) geographical separation of individual sources, e.g. agriculture, using a high-density measurement network. Neither of these represents a definitive approach, but they will provide invaluable information about GHG source attribution when they are adopted as part of a more comprehensive, long-term national GHG measurement programme. We also conducted a number of case studies, including an instrumented landfill experiment that provided a test bed for new technologies and flux estimation methods. We anticipate that results from the GAUGE project will help inform other countries on how to use atmospheric data to quantify their nationally determined contributions to the Paris Agreement.

@article{
 title = {Attribution of recent increases in atmospheric methane through 3-D inverse modelling},
 type = {article},
 year = {2018},
 pages = {18149-18168},
 volume = {18},
 id = {39e5dcb1-410f-346a-8583-a17cbe508425},
 created = {2020-07-11T20:52:36.538Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.755Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {McNorton2018},
 private_publication = {false},
 abstract = {The atmospheric methane (CH4) growth rate has varied considerably in recent decades. Unexplained renewed growth after 2006 followed 7 years of stagnation and coincided with an isotopic trend toward CH4 more depleted in 13C, suggesting changes in sources and/or sinks. Using surface observations of both CH4 and the relative change of isotopologue ratio (δ13 CH4) to constrain a global 3-D chemical transport model (CTM), we have performed a synthesis inversion for source and sink attribution. Our method extends on previous studies by providing monthly and regional attribution of emissions from six different sectors and changes in atmospheric sinks for the extended 2003-2015 period. Regional evaluation of the model CH4 tracer with independent column observations from the Greenhouse Gases Observing Satellite (GOSAT) shows improved performance when using posterior fluxes (R =0:94-0.96, RMSE= 8:3- 16.5 ppb), relative to prior fluxes (R =0:60-0.92, RMSE= 48:6-64.6 ppb). Further independent validation with data from the Total Carbon Column Observing Network (TCCON) shows a similar improvement in the posterior fluxes (R =0:87, RMSE= 18:8 ppb) compared to the prior fluxes (R =0:69, RMSE= 55:9 ppb). Based on these improved posterior fluxes, the inversion results suggest the most likely cause of the renewed methane growth is a post-2007 1:8± 0:4% decrease in mean OH, a 12:9±2:7% increase in energy sector emissions, mainly from Africa-Middle East and southern Asia-Oceania, and a 2:6±1:8%increase in wetland emissions, mainly from northern Eurasia. The posterior wetland flux increases are in general agreement with bottom-up estimates, but the energy sector growth is greater than estimated by bottom-up methods. The model results are consistent across a range of sensitivity analyses. When forced to assume a constant (annually repeating) OH distribution, the inversion requires a greater increase in energy sector (13:6±2:7 %) and wetland (3:6±1:8 %) emissions and an 11:5±3:8% decrease in biomass burning emissions. Assuming no prior trend in sources and sinks slightly reduces the posterior growth rate in energy sector and wetland emissions and further increases the magnitude of the negative OH trend. We find that possible tropospheric Cl variations do not influence δ13CH4 and CH4 trends, although we suggest further work on Cl variability is required to fully diagnose this contribution. While the study provides quantitative insight into possible emissions variations which may explain the observed trends, uncertainty in prior source and sink estimates and a paucity of δ13CH4 observations limit the robustness of the posterior estimates.},
 bibtype = {article},
 author = {McNorton, Joe and Wilson, Chris and Gloor, Manuel and Parker, Rob J. and Boesch, Hartmut and Feng, Wuhu and Hossaini, Ryan and Chipperfield, Martyn P.},
 doi = {10.5194/acp-18-18149-2018},
 journal = {Atmospheric Chemistry and Physics},
 number = {24}
}

The atmospheric methane (CH4) growth rate has varied considerably in recent decades. Unexplained renewed growth after 2006 followed 7 years of stagnation and coincided with an isotopic trend toward CH4 more depleted in 13C, suggesting changes in sources and/or sinks. Using surface observations of both CH4 and the relative change of isotopologue ratio (δ13 CH4) to constrain a global 3-D chemical transport model (CTM), we have performed a synthesis inversion for source and sink attribution. Our method extends on previous studies by providing monthly and regional attribution of emissions from six different sectors and changes in atmospheric sinks for the extended 2003-2015 period. Regional evaluation of the model CH4 tracer with independent column observations from the Greenhouse Gases Observing Satellite (GOSAT) shows improved performance when using posterior fluxes (R =0:94-0.96, RMSE= 8:3- 16.5 ppb), relative to prior fluxes (R =0:60-0.92, RMSE= 48:6-64.6 ppb). Further independent validation with data from the Total Carbon Column Observing Network (TCCON) shows a similar improvement in the posterior fluxes (R =0:87, RMSE= 18:8 ppb) compared to the prior fluxes (R =0:69, RMSE= 55:9 ppb). Based on these improved posterior fluxes, the inversion results suggest the most likely cause of the renewed methane growth is a post-2007 1:8± 0:4% decrease in mean OH, a 12:9±2:7% increase in energy sector emissions, mainly from Africa-Middle East and southern Asia-Oceania, and a 2:6±1:8%increase in wetland emissions, mainly from northern Eurasia. The posterior wetland flux increases are in general agreement with bottom-up estimates, but the energy sector growth is greater than estimated by bottom-up methods. The model results are consistent across a range of sensitivity analyses. When forced to assume a constant (annually repeating) OH distribution, the inversion requires a greater increase in energy sector (13:6±2:7 %) and wetland (3:6±1:8 %) emissions and an 11:5±3:8% decrease in biomass burning emissions. Assuming no prior trend in sources and sinks slightly reduces the posterior growth rate in energy sector and wetland emissions and further increases the magnitude of the negative OH trend. We find that possible tropospheric Cl variations do not influence δ13CH4 and CH4 trends, although we suggest further work on Cl variability is required to fully diagnose this contribution. While the study provides quantitative insight into possible emissions variations which may explain the observed trends, uncertainty in prior source and sink estimates and a paucity of δ13CH4 observations limit the robustness of the posterior estimates.

@article{
 title = {Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data},
 type = {article},
 year = {2018},
 keywords = {Carbon cycle,Fire,Global warming,Tropical forests},
 volume = {373},
 id = {59f6dc64-3b2d-313b-a488-d4c7cdb0b4eb},
 created = {2020-07-11T20:52:36.892Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.643Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gloor2018},
 private_publication = {false},
 abstract = {The outstanding tropical land climate characteristic over the past decades is rapid warming, with no significant large-scale precipitation trends. This warming is expected to continue but the effects on tropical vegetation are unknown. El Niño-related heat peaks may provide a test bed for a future hotter world. Here we analyse tropical land carbon cycle responses to the 2015/16 El Niño heat and drought anomalies using an atmospheric transport inversion. Based on the global atmospheric CO2 and fossil fuel emission records, we find no obvious signs of anomalously large carbon release compared with earlier El Niño events, suggesting resilience of tropical vegetation. We find roughly equal net carbon release anomalies from Amazonia and tropical Africa, approximately 0.5 PgC each, and smaller carbon release anomalies from tropical East Asia and southern Africa. Atmospheric CO anomalies reveal substantial fire carbon release from tropical East Asia peaking in October 2015 while fires contribute only a minor amount to the Amazonian carbon flux anomaly. Anomalously large Amazonian carbon flux release is consistent with downregulation of primary productivity during peak negative near-surface water anomaly (October 2015 to March 2016) as diagnosed by solar-induced fluorescence. Finally, we find an unexpected anomalous positive flux to the atmosphere from tropical Africa early in 2016, coincident with substantial CO release. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.},
 bibtype = {article},
 author = {Gloor, Emanuel and Wilson, Chris and Chipperfield, Martyn P. and Chevallier, Frederic and Buermann, Wolfgang and Boesch, Hartmut and Parker, Robert and Somkuti, Peter and Gatti, Luciana V. and Correia, Caio and Domingues, Lucas G. and Peters, Wouter and Miller, John and Deeter, Merritt N. and Sullivan, Martin J.P.},
 doi = {10.1098/rstb.2017.0302},
 journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
 number = {1760}
}

The outstanding tropical land climate characteristic over the past decades is rapid warming, with no significant large-scale precipitation trends. This warming is expected to continue but the effects on tropical vegetation are unknown. El Niño-related heat peaks may provide a test bed for a future hotter world. Here we analyse tropical land carbon cycle responses to the 2015/16 El Niño heat and drought anomalies using an atmospheric transport inversion. Based on the global atmospheric CO2 and fossil fuel emission records, we find no obvious signs of anomalously large carbon release compared with earlier El Niño events, suggesting resilience of tropical vegetation. We find roughly equal net carbon release anomalies from Amazonia and tropical Africa, approximately 0.5 PgC each, and smaller carbon release anomalies from tropical East Asia and southern Africa. Atmospheric CO anomalies reveal substantial fire carbon release from tropical East Asia peaking in October 2015 while fires contribute only a minor amount to the Amazonian carbon flux anomaly. Anomalously large Amazonian carbon flux release is consistent with downregulation of primary productivity during peak negative near-surface water anomaly (October 2015 to March 2016) as diagnosed by solar-induced fluorescence. Finally, we find an unexpected anomalous positive flux to the atmosphere from tropical Africa early in 2016, coincident with substantial CO release. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

@article{
 title = {Copernicus Climate Change Service (C3S) global satellite observations of atmospheric carbon dioxide and methane},
 type = {article},
 year = {2018},
 keywords = {Carbon dioxide,Climate change,Essential Climate Variables,Essential climate variables,Greenho,Greenhouse gases,Methane,Satellite,carbon dioxide,climate change,essential climate variables,greenhouse gases,methane,satellite},
 pages = {57-60},
 volume = {2018-Octob},
 websites = {https://doi.org/10.1007/s42423-018-0004-6},
 publisher = {Springer Singapore},
 id = {7ba5de53-ed00-3773-bd1d-0aab369a4038},
 created = {2020-07-11T20:52:37.456Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-07-11T21:12:27.045Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Buchwitz2018},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {Buchwitz, Michael and Reuter, Maximilian and Schneising, Oliver and Bovensmann, Heinrich and Burrows, John P. and Boesch, Hartmut and Anand, Jasdeep and Parker, Robert and Detmers, Rob G. and Aben, Ilse and Hasekamp, Otto P. and Crevoisier, Cyril and Armante, Raymond and Zehner, Claus and Schepers, Dinand},
 doi = {10.1007/s42423-018-0004-6},
 journal = {Proceedings of the International Astronautical Congress, IAC},
 number = {1}
}

@article{
 title = {Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4:XCO2 retrievals, 2010-2014},
 type = {article},
 year = {2017},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85017497633&partnerID=MN8TOARS},
 id = {a7d9056f-4919-3363-a298-81e1e3a78508},
 created = {2018-04-24T19:12:14.034Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:31.482Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Feng2017},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {Feng, L and Palmer, P I and Bösch, H and Parker, R J and Webb, A J and Correia, C S C and Deutscher, N M and Domingues, L G and Feist, D G and Gatti, L V and Gloor, E and Hase, F and Kivi, R and Liu, Y and Miller, J B and Morino, I and Sussmann, R and Strong, K and Uchino, O and Wang, J and Zahn, A},
 doi = {10.5194/acp-17-4781-2017},
 journal = {Atmospheric Chemistry and Physics}
}

@article{
 title = {Global height-resolved methane retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp},
 type = {article},
 year = {2017},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85032946559&partnerID=MN8TOARS},
 id = {2c68e93d-7ea6-3b7a-ae68-ec3c89e4fdda},
 created = {2018-04-24T19:12:14.132Z},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:34.457Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Siddans2017},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {Siddans, R and Knappett, D and Kerridge, B and Waterfall, A and Hurley, J and Latter, B and Boesch, H and Parker, R},
 doi = {10.5194/amt-10-4135-2017},
 journal = {Atmospheric Measurement Techniques}
}

@article{
 title = {Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models},
 type = {article},
 year = {2017},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85008602494&partnerID=MN8TOARS},
 id = {abd829f1-6ca2-35f5-950e-af9850a03abc},
 created = {2018-04-24T19:12:14.234Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.640Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Belikov2017},
 private_publication = {false},
 bibtype = {article},
 author = {Belikov, D A and Maksyutov, S and Ganshin, A and Zhuravlev, R and Deutscher, N M and Wunch, D and Feist, D G and Morino, I and Parker, R J and Strong, K and Yoshida, Y and Bril, A and Oshchepkov, S and Boesch, H and Dubey, M K and Griffith, D and Hewson, W and Kivi, R and Mendonca, J and Notholt, J and Schneider, M and Sussmann, R and Velazco, V A and Aoki, S},
 doi = {10.5194/acp-17-143-2017},
 journal = {Atmospheric Chemistry and Physics}
}

@article{
 title = {Atmospheric observations show accurate reporting and little growth in India's methane emissions},
 type = {article},
 year = {2017},
 keywords = {Atmospheric chemistry,Climate change},
 pages = {836},
 volume = {8},
 websites = {http://dx.doi.org/10.1038/s41467-017-00994-7,https://www.nature.com/articles/s41467-017-00994-7.pdf,http://www.nature.com/articles/s41467-017-00994-7,http://www.scopus.com/inward/record.url?eid=2-s2.0-85031013498&partnerID=MN8TOARS},
 month = {12},
 publisher = {Springer US},
 day = {10},
 id = {151b3530-d566-3de0-82ff-77b28f9a2271},
 created = {2018-04-29T21:05:09.298Z},
 accessed = {2018-04-24},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:34.195Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ganesan2017},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH4) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6–24.3) Tg yr−1, which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr−1) between 2010 and 2015, suggesting that major CH4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors.},
 bibtype = {article},
 author = {Ganesan, Anita L. and Rigby, Matt and Lunt, Mark F. and Parker, Robert J. and Boesch, Hartmut and Goulding, N. and Umezawa, Taku and Zahn, Andreas and Chatterjee, Abhijit and Prinn, Ronald G. and Tiwari, Yogesh K. and Van Der Schoot, Marcel and Krummel, Paul B.},
 doi = {10.1038/s41467-017-00994-7},
 journal = {Nature Communications},
 number = {1}
}

Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH4) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6–24.3) Tg yr−1, which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr−1) between 2010 and 2015, suggesting that major CH4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors.

@article{
 title = {Satellite-derived methane hotspot emission estimates using a fast data-driven method},
 type = {article},
 year = {2017},
 pages = {5751-5774},
 volume = {17},
 websites = {https://www.atmos-chem-phys.net/17/5751/2017/,http://www.scopus.com/inward/record.url?eid=2-s2.0-85018926310&partnerID=MN8TOARS},
 month = {5},
 day = {9},
 id = {0360a2f8-832c-320d-857f-4a57a419ec1f},
 created = {2018-05-19T18:46:39.024Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:32.783Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Buchwitz2017},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p>Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. <i>X</i>CH<sub>4</sub>, can be used to quantify methane emissions. Maps of time-averaged satellite-derived <i>X</i>CH<sub>4</sub> show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1<i>σ</i> uncertainties directly from maps of annually averaged satellite <i>X</i>CH<sub>4</sub>. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite <i>X</i>CH<sub>4</sub> data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5–8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.</p>},
 bibtype = {article},
 author = {Buchwitz, Michael and Schneising, Oliver and Reuter, Maximilian and Heymann, Jens and Krautwurst, Sven and Bovensmann, Heinrich and Burrows, John P. and Boesch, Hartmut and Parker, Robert J. and Somkuti, Peter and Detmers, Rob G. and Hasekamp, Otto P. and Aben, Ilse and Butz, André and Frankenberg, Christian and Turner, Alexander J.},
 doi = {10.5194/acp-17-5751-2017},
 journal = {Atmospheric Chemistry and Physics},
 number = {9}
}

Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH₄, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH₄ show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH₄. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH₄ data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5–8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

@article{
 title = {Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set},
 type = {article},
 year = {2017},
 keywords = {Climate change},
 pages = {276-295},
 volume = {203},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85017506678&partnerID=MN8TOARS,http://linkinghub.elsevier.com/retrieve/pii/S0034425716305065},
 month = {12},
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 citation_key = {Buchwitz:2017},
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 private_publication = {false},
 bibtype = {article},
 author = {Buchwitz, M. and Reuter, M. and Schneising, O. and Hewson, W. and Detmers, R.G. G and Boesch, H. and Hasekamp, O.P. P and Aben, I. and Bovensmann, H. and Burrows, J.P. P and Butz, A. and Chevallier, F. and Dils, B. and Frankenberg, C. and Heymann, J. and Lichtenberg, G. and De Mazière, M. and Notholt, J. and Parker, R. and Warneke, T. and Zehner, C. and Griffith, D.W.T. W T and Deutscher, N.M. M and Kuze, A. and Suto, H. and Wunch, D. and Maziere, M De and Notholt, J. and Parker, R. and Warneke, T. and Zehner, C. and Griffith, D.W.T. W T and Deutscher, N.M. M and Kuze, A. and Suto, H. and Wunch, D.},
 doi = {http://doi.org/10.1016/j.rse.2016.12.027},
 journal = {Remote Sensing of Environment}
}

@article{
 title = {Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes},
 type = {article},
 year = {2016},
 pages = {10111-10131},
 volume = {16},
 websites = {http://www.atmos-chem-phys.net/16/10111/2016/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84982105602&partnerID=MN8TOARS},
 month = {8},
 day = {11},
 id = {bc69e319-6025-331d-8ab8-f02916193efd},
 created = {2018-05-19T18:46:37.633Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:32.938Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Parker2016},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p>The 2015–2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015–2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH<sub>4</sub> (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon.<br><br>Here, for the first time, we use satellite observations of CH<sub>4</sub> and CO<sub>2</sub> from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions.<br><br>Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH<sub>4</sub> is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH<sub>4</sub> total column values typically exceeding 35 ppb above those of background “clean air” soundings. By examining the CH<sub>4</sub> and CO<sub>2</sub> excess concentrations in the fire-affected GOSAT observations, we determine the CH<sub>4</sub> to CO<sub>2</sub> (CH<sub>4</sub> ∕ CO<sub>2</sub>) fire emission ratio for the entire 2-month period of the most extreme burning (September–October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH<sub>4</sub> to CO<sub>2</sub> emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm<sup>−1</sup>. This is higher than that found over both the Amazon (5.1 ppb ppm<sup>−1</sup>) and southern Africa (4.4 ppb ppm<sup>−1</sup>), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellite-derived Indonesian ERs (6.18–13.6 ppb ppm<sup>−1</sup>) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53–19.67 ppb ppm<sup>−1</sup>), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation).<br><br>The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood.</p>},
 bibtype = {article},
 author = {Parker, Robert J. and Boesch, Hartmut and Wooster, Martin J. and Moore, David P. and Webb, Alex J. and Gaveau, David and Murdiyarso, Daniel},
 doi = {10.5194/acp-16-10111-2016},
 journal = {Atmospheric Chemistry and Physics},
 number = {15}
}

The 2015–2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015–2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH₄ (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon.

Here, for the first time, we use satellite observations of CH₄ and CO₂ from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions.

Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH₄ is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH₄ total column values typically exceeding 35 ppb above those of background “clean air” soundings. By examining the CH₄ and CO₂ excess concentrations in the fire-affected GOSAT observations, we determine the CH₄ to CO₂ (CH₄ ∕ CO₂) fire emission ratio for the entire 2-month period of the most extreme burning (September–October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH₄ to CO₂ emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm⁻¹. This is higher than that found over both the Amazon (5.1 ppb ppm⁻¹) and southern Africa (4.4 ppb ppm⁻¹), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellite-derived Indonesian ERs (6.18–13.6 ppb ppm⁻¹) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53–19.67 ppb ppm⁻¹), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation).

The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood.

@article{
 title = {Role of regional wetland emissions in atmospheric methane variability},
 type = {article},
 year = {2016},
 keywords = {Biogeochemical cycles,Land/atmosphere interactions,Remote sensing and disasters,Troposphere: composition and chemistry,Wetlands,and modeling,atmosphere,methane,processes,wetlands},
 pages = {11,411-433,444},
 volume = {43},
 websites = {http://doi.wiley.com/10.1002/2016GL070649,http://www.scopus.com/inward/record.url?eid=2-s2.0-84998772075&partnerID=MN8TOARS},
 month = {11},
 day = {16},
 id = {a211a559-462e-3a77-a4b9-f48068b9f420},
 created = {2018-05-19T18:46:38.772Z},
 accessed = {2018-04-29},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:34:13.699Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {McNorton:2016},
 source_type = {article},
 notes = {<b>From Duplicate 2 (<i>Role of regional wetland emissions in atmospheric methane variability</i> - McNorton, J; Gloor, E; Wilson, C; Hayman, G D; Gedney, N; Comyn-Platt, E; Marthews, T; Parker, R J; Boesch, H; Chipperfield, M P)<br/></b><br/><b>From Duplicate 1 (<i>Role of regional wetland emissions in atmospheric methane variability</i> - McNorton, J; Gloor, E; Wilson, C; Hayman, G D; Gedney, N; Comyn-Platt, E; Marthews, T; Parker, R J; Boesch, H; Chipperfield, M P)<br/></b><br/>2016GL070649},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992,fb67c5ee-e49e-4faf-b3ce-432b3e85f5a2,ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {McNorton, J. and Gloor, E. and Wilson, C. and Hayman, G. D. and Gedney, N. and Comyn-Platt, E. and Marthews, T. and Parker, R. J. and Boesch, H. and Chipperfield, M. P.},
 doi = {10.1002/2016GL070649},
 journal = {Geophysical Research Letters},
 number = {21}
}

@article{
 title = {CH4 concentrations over the Amazon from GOSAT consistent with in situ vertical profile data},
 type = {article},
 year = {2016},
 keywords = {CH4,Constituent sources and sinks,GOSAT,Remote sensing,South America,Wetlands,aircraft,amazon,methane,wetlands},
 pages = {6-11,11,20},
 volume = {121},
 websites = {http://doi.wiley.com/10.1002/2016JD025263,http://www.scopus.com/inward/record.url?eid=2-s2.0-84988039658&partnerID=MN8TOARS},
 month = {9},
 day = {27},
 id = {a3b3d4f9-7617-3e76-83d1-3dd5fd13b746},
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 last_modified = {2020-03-03T11:09:30.983Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Webb:2016},
 source_type = {article},
 notes = {<b>From Duplicate 2 (<i>CH4 concentrations over the Amazon from GOSAT consistent with in situ vertical profile data</i> - Webb, Alex J; Bösch, Hartmut; Parker, Robert J; Gatti, Luciana V; Gloor, Emanuel; Palmer, Paul I; Basso, Luana S; Chipperfield, Martyn P; Correia, Caio S C; Domingues, Lucas G; Feng, Liang; Gonzi, Siegfried; Miller, John B; Warneke, Thorsten; Wilson, Christopher)<br/></b><br/><b>From Duplicate 2 (<i>CH4 concentrations over the Amazon from GOSAT consistent with in situ vertical profile data</i> - Webb, Alex J; Bösch, Hartmut; Parker, Robert J; Gatti, Luciana V; Gloor, Emanuel; Palmer, Paul I; Basso, Luana S; Chipperfield, Martyn P; Correia, Caio S C; Domingues, Lucas G; Feng, Liang; Gonzi, Siegfried; Miller, John B; Warneke, Thorsten; Wilson, Christopher)<br/></b><br/>2016JD025263},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {Webb, Alex J. and Bösch, Hartmut and Parker, Robert J. and Gatti, Luciana V. and Gloor, Emanuel and Palmer, Paul I. and Basso, Luana S. and Chipperfield, Martyn P. and Correia, Caio S. C. and Domingues, Lucas G. and Feng, Liang and Gonzi, Siegfried and Miller, John B. and Warneke, Thorsten and Wilson, Christopher},
 doi = {10.1002/2016JD025263},
 journal = {Journal of Geophysical Research: Atmospheres},
 number = {18}
}

@article{
 title = {Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: Sensitivity to measurement bias inside and outside Europe},
 type = {article},
 year = {2016},
 pages = {1289-1302},
 volume = {16},
 websites = {https://www.atmos-chem-phys.net/16/1289/2016/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84957818451&partnerID=MN8TOARS},
 month = {2},
 day = {4},
 id = {bb2edd6a-6d39-34b6-9feb-815b89594f1d},
 created = {2018-07-07T20:20:45.587Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.973Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Feng2016},
 private_publication = {false},
 abstract = {Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.},
 bibtype = {article},
 author = {Feng, L. and Palmer, P. I. and Parker, R. J. and Deutscher, N. M. and Feist, D. G. and Kivi, R. and Morino, I. and Sussmann, R.},
 doi = {10.5194/acp-16-1289-2016},
 journal = {Atmospheric Chemistry and Physics},
 number = {3}
}

Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

@article{
 title = {Assessing 5 years of GOSAT Proxy XCH4 data and associated uncertainties},
 type = {article},
 year = {2015},
 pages = {4785-4801},
 volume = {8},
 websites = {http://www.atmos-meas-tech.net/8/4785/2015/,https://www.atmos-meas-tech.net/8/4785/2015/amt-8-4785-2015.pdf,http://www.scopus.com/inward/record.url?eid=2-s2.0-84947567904&partnerID=MN8TOARS},
 month = {11},
 day = {17},
 id = {f0aaa644-b0a7-35c2-9a44-d104db18d78e},
 created = {2018-05-19T18:46:37.568Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:34:12.355Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {parker:2015},
 source_type = {article},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992,fb67c5ee-e49e-4faf-b3ce-432b3e85f5a2},
 private_publication = {false},
 abstract = {We present 5 years of GOSAT XCH 4 retrieved us-ing the " proxy " approach. The Proxy XCH 4 data are val-idated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (a measure of the rela-tive accuracy) is found to be 4.2 ppb. For the first time the XCH 4 / XCO 2 ratio component of the Proxy retrieval is val-idated (bias of 0.014 ppb ppm −1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm −1 (∼ 0.72 %)). The uncertainty relating to the model XCO 2 component of the Proxy XCH 4 is assessed through the use of an ensem-ble of XCO 2 models. While each individual XCO 2 model is found to agree well with the TCCON validation data (r = 0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO 2 value of the ensemble has a smaller scatter against TCCON (a stan-dard deviation of 0.92 ppm) than any of the individual mod-els whilst maintaining a small bias (0.15 ppm). This model median XCO 2 is used to calculate the Proxy XCH 4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typ-ically that the model XCO 2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased sum-mertime reflected sunlight. We assess the significance of these model and retrieval un-certainties on flux inversion by comparing the GOSAT XCH 4 against modelled XCH 4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differ-ences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that al-ready provided by the assimilated surface measurements. Published by Copernicus Publications on behalf of the European Geosciences Union. 4786 R. J. Parker et al.: Assessing 5 years of GOSAT Proxy XCH 4 data and associated uncertainties},
 bibtype = {article},
 author = {Parker, R. J. and Boesch, H. and Byckling, K. and Webb, A. J. and Palmer, P. I. and Feng, L. and Bergamaschi, P. and Chevallier, F. and Notholt, J. and Deutscher, N. and Warneke, T. and Hase, F. and Sussmann, R. and Kawakami, S. and Kivi, R. and Griffith, D. W.T. T. and Velazco, V.},
 doi = {10.5194/amt-8-4785-2015},
 journal = {Atmospheric Measurement Techniques},
 number = {11}
}

We present 5 years of GOSAT XCH 4 retrieved us-ing the " proxy " approach. The Proxy XCH 4 data are val-idated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (a measure of the rela-tive accuracy) is found to be 4.2 ppb. For the first time the XCH 4 / XCO 2 ratio component of the Proxy retrieval is val-idated (bias of 0.014 ppb ppm −1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm −1 (∼ 0.72 %)). The uncertainty relating to the model XCO 2 component of the Proxy XCH 4 is assessed through the use of an ensem-ble of XCO 2 models. While each individual XCO 2 model is found to agree well with the TCCON validation data (r = 0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO 2 value of the ensemble has a smaller scatter against TCCON (a stan-dard deviation of 0.92 ppm) than any of the individual mod-els whilst maintaining a small bias (0.15 ppm). This model median XCO 2 is used to calculate the Proxy XCH 4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typ-ically that the model XCO 2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased sum-mertime reflected sunlight. We assess the significance of these model and retrieval un-certainties on flux inversion by comparing the GOSAT XCH 4 against modelled XCH 4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differ-ences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that al-ready provided by the assimilated surface measurements. Published by Copernicus Publications on behalf of the European Geosciences Union. 4786 R. J. Parker et al.: Assessing 5 years of GOSAT Proxy XCH 4 data and associated uncertainties

@article{
 title = {Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements},
 type = {article},
 year = {2015},
 pages = {3433-3445},
 volume = {8},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84940055759&partnerID=MN8TOARS,http://www.atmos-meas-tech.net/8/3433/2015/},
 month = {8},
 day = {25},
 id = {6f878ece-2937-3727-953b-27a339a70cbb},
 created = {2018-05-19T18:46:37.655Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:31.265Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Worden2015},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p>Evaluating surface fluxes of CH<sub>4</sub> using total column data requires models to accurately account for the transport and chemistry of methane in the free troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column, and quantifying free-tropospheric CH<sub>4</sub> concentrations helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH<sub>4</sub> concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH<sub>4</sub> (dry-mole air fraction of methane) from the Greenhouse gases Observing SATellite – Thermal And Near-infrared for carbon Observation (GOSAT TANSO, herein GOSAT for brevity). The calculated precision of these estimates ranges from 10 to 30 ppb for a monthly average on a 4&deg; &times; 5&deg; latitude/longitude grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. Comparisons between these data and the GEOS-Chem model demonstrate that these lower-tropospheric CH<sub>4</sub> estimates can resolve enhanced concentrations over flux regions that are challenging to resolve with total column measurements. We also use the GEOS-Chem model and surface measurements in background regions across a range of latitudes to determine that these lower-tropospheric estimates are biased low by approximately 65 ppb, with an accuracy of approximately 6 ppb (after removal of the bias) and an actual precision of approximately 30 ppb. This 6 ppb accuracy is consistent with the accuracy of TES and GOSAT methane retrievals.</p>},
 bibtype = {article},
 author = {Worden, J. R. and Turner, A. J. and Bloom, A. and Kulawik, S. S. and Liu, J. and Lee, M. and Weidner, R. and Bowman, K. and Frankenberg, C. and Parker, R. and Payne, V. H.},
 doi = {10.5194/amt-8-3433-2015},
 journal = {Atmospheric Measurement Techniques},
 number = {8}
}

Evaluating surface fluxes of CH₄ using total column data requires models to accurately account for the transport and chemistry of methane in the free troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column, and quantifying free-tropospheric CH₄ concentrations helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH₄ concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH₄ (dry-mole air fraction of methane) from the Greenhouse gases Observing SATellite – Thermal And Near-infrared for carbon Observation (GOSAT TANSO, herein GOSAT for brevity). The calculated precision of these estimates ranges from 10 to 30 ppb for a monthly average on a 4° × 5° latitude/longitude grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. Comparisons between these data and the GEOS-Chem model demonstrate that these lower-tropospheric CH₄ estimates can resolve enhanced concentrations over flux regions that are challenging to resolve with total column measurements. We also use the GEOS-Chem model and surface measurements in background regions across a range of latitudes to determine that these lower-tropospheric estimates are biased low by approximately 65 ppb, with an accuracy of approximately 6 ppb (after removal of the bias) and an actual precision of approximately 30 ppb. This 6 ppb accuracy is consistent with the accuracy of TES and GOSAT methane retrievals.

@article{
 title = {Does GOSAT capture the true seasonal cycle of carbon dioxide?},
 type = {article},
 year = {2015},
 pages = {13023-13040},
 volume = {15},
 websites = {http://www.atmos-chem-phys.net/15/13023/2015/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84948140495&partnerID=MN8TOARS},
 month = {11},
 day = {24},
 id = {2d750ca0-9dda-3283-b49b-10a537fc61fd},
 created = {2018-05-19T18:46:38.340Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.653Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lindqvist2015},
 private_publication = {false},
 abstract = {<p>The seasonal cycle accounts for a dominant mode of total column CO<sub>2</sub> (XCO<sub>2</sub>) annual variability and is connected to CO<sub>2</sub> uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO<sub>2</sub> seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO<sub>2</sub> Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO<sub>2</sub>, as well as the regional growth rates in XCO<sub>2</sub> through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO<sub>2</sub> between ACOS and TCCON, but ACOS generally infers a date of maximum XCO<sub>2</sub> 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO<sub>2</sub>. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO<sub>2</sub> depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO<sub>2</sub> doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO<sub>2</sub> seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.</p>},
 bibtype = {article},
 author = {Lindqvist, H. and O'Dell, C. W. and Basu, S. and Boesch, H. and Chevallier, F. and Deutscher, N. and Feng, L. and Fisher, B. and Hase, F. and Inoue, M. and Kivi, R. and Morino, I. and Palmer, P. I. and Parker, R. and Schneider, M. and Sussmann, R. and Yoshida, Y.},
 doi = {10.5194/acp-15-13023-2015},
 journal = {Atmospheric Chemistry and Physics},
 number = {22}
}

The seasonal cycle accounts for a dominant mode of total column CO₂ (XCO₂) annual variability and is connected to CO₂ uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO₂ seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO₂ Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO₂, as well as the regional growth rates in XCO₂ through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO₂ between ACOS and TCCON, but ACOS generally infers a date of maximum XCO₂ 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO₂. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO₂ depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO₂ doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO₂ seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

@article{
 title = {The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets},
 type = {article},
 year = {2015},
 keywords = {Climate change},
 pages = {344-362},
 volume = {162},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84929061664&partnerID=MN8TOARS,http://linkinghub.elsevier.com/retrieve/pii/S0034425713003520},
 month = {6},
 id = {46082df7-6cdc-35e1-8d69-0515066f8263},
 created = {2021-03-31T19:11:05.314Z},
 accessed = {2018-04-29},
 file_attached = {false},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:05.314Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Buchwitz:2015},
 source_type = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Buchwitz, M. and Reuter, M. and Schneising, O. and Boesch, H. and Guerlet, S. and Dils, B. and Aben, I. and Armante, R. and Bergamaschi, P. and Blumenstock, T. and Bovensmann, H. and Brunner, D. and Buchmann, B. and Burrows, J.P. P and Butz, A. and Chadin, A and Chevallier, F. and Crevoisier, C.D. D and Deutscher, N.M. M and Frankenberg, C. and Hase, F. and Hasekamp, O.P. P and Heymann, J. and Kaminski, T. and Laeng, A. and Lichtenberg, G. and Maziere, M De and Noel, S and Notholt, J. and Orphal, J. and Popp, C. and Parker, R. and Scholze, M. and Sussmann, R. and Stiller, G.P. P and Warneke, T. and Zehner, C. and Bril, A. and Crisp, D. and Griffith, D.W.T. W T and Kuze, A. and O'Dell, C. and Oshchepkov, S. and Sherlock, V. and Suto, H. and Wennberg, P. and Wunch, D. and Yokota, T. and Yoshida, Y. and Chedin, A and Chevallier, F. and Crevoisier, C.D. D and Deutscher, N.M. M and Frankenberg, C. and Hase, F. and Hasekamp, O.P. P and Heymann, J. and Kaminski, T. and Laeng, A. and Lichtenberg, G. and Maziere, M De and Noel, S and Notholt, J. and Orphal, J. and Popp, C. and Parker, R. and Scholze, M. and Sussmann, R. and Stiller, G.P. P and Warneke, T. and Zehner, C. and Bril, A. and Crisp, D. and Griffith, D.W.T. W T and Kuze, A. and O'Dell, C. and Oshchepkov, S. and Sherlock, V. and Suto, H. and Wennberg, P. and Wunch, D. and Yokota, T. and Yoshida, Y. and Chédin, A. and Chevallier, F. and Crevoisier, C.D. D and Deutscher, N.M. M and Frankenberg, C. and Hase, F. and Hasekamp, O.P. P and Heymann, J. and Kaminski, T. and Laeng, A. and Lichtenberg, G. and De Mazière, M. and Noël, S. and Notholt, J. and Orphal, J. and Popp, C. and Parker, R. and Scholze, M. and Sussmann, R. and Stiller, G.P. P and Warneke, T. and Zehner, C. and Bril, A. and Crisp, D. and Griffith, D.W.T. W T and Kuze, A. and O'Dell, C. and Oshchepkov, S. and Sherlock, V. and Suto, H. and Wennberg, P. and Wunch, D. and Yokota, T. and Yoshida, Y.},
 doi = {http://dx.doi.org/10.1016/j.rse.2013.04.024},
 journal = {Remote Sensing of Environment}
}

@article{
 title = {Inverse modelling of CH4 emissions for 2010-2011 using different satellite retrieval products from GOSAT and SCIAMACHY},
 type = {article},
 year = {2015},
 pages = {113-133},
 volume = {15},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84920843550&partnerID=MN8TOARS,http://www.atmos-chem-phys.net/15/113/2015/},
 month = {1},
 day = {9},
 id = {63c78c11-2afd-3c9e-b288-7da094139f62},
 created = {2021-03-31T19:11:05.339Z},
 accessed = {2018-04-24},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:15:30.010Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Alexe:2015},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {Beginning in 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument onboard the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent CH4 measurements were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument onboard ENVISAT. The GOSAT and SCIAMACHY XCH4 retrievals can be compared during their circa 32 month period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modeling system. Additionally, high-accuracy measurements from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) global air sampling network are used, providing strong constraints of the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. 2 year average emission maps show a~good overall agreement among all GOSAT-based inversions, and compared to the SCIAMACHY-based inversion, with consistent flux adjustment patterns, particularly across Equatorial Africa and North America. The inversions are validated against independent shipboard and aircraft observations, and XCH4 measurements available from the Total Carbon Column Observing Network (TCCON). All GOSAT and SCIAMACHY inversions show very similar validation performance.},
 bibtype = {article},
 author = {Alexe, M. and Bergamaschi, P. and Segers, A. and Detmers, R. and Butz, A. and Hasekamp, O. and Guerlet, S. and Parker, R. and Boesch, H. and Frankenberg, C. and Scheepmaker, R. A. and Dlugokencky, E. and Sweeney, C. and Wofsy, S. C. and Kort, E. A.},
 doi = {10.5194/acp-15-113-2015},
 journal = {Atmospheric Chemistry and Physics},
 number = {1}
}

Beginning in 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument onboard the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent CH4 measurements were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument onboard ENVISAT. The GOSAT and SCIAMACHY XCH4 retrievals can be compared during their circa 32 month period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modeling system. Additionally, high-accuracy measurements from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) global air sampling network are used, providing strong constraints of the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. 2 year average emission maps show a~good overall agreement among all GOSAT-based inversions, and compared to the SCIAMACHY-based inversion, with consistent flux adjustment patterns, particularly across Equatorial Africa and North America. The inversions are validated against independent shipboard and aircraft observations, and XCH4 measurements available from the Total Carbon Column Observing Network (TCCON). All GOSAT and SCIAMACHY inversions show very similar validation performance.

@article{
 title = {Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data},
 type = {article},
 year = {2015},
 pages = {7049-7069},
 volume = {15},
 websites = {http://www.atmos-chem-phys.net/15/7049/2015/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84933576228&partnerID=MN8TOARS},
 month = {6},
 day = {30},
 id = {869c4c2f-7fb2-394d-90a0-3e95a83f6515},
 created = {2021-03-31T19:11:05.451Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:15:24.355Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Turner:2015},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p>We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a<sup>&minus;1</sup> with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a<sup>&minus;1</sup>, as compared to 24.9–27.0 Tg a<sup>&minus;1</sup> in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a<sup>&minus;1</sup> in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a<sup>&minus;1</sup>.</p>},
 bibtype = {article},
 author = {Turner, A. J. and Jacob, D. J. and Wecht, K. J. and Maasakkers, J. D. and Lundgren, E. and Andrews, A. E. and Biraud, S. C. and Boesch, H. and Bowman, K. W. and Deutscher, N. M. and Dubey, M. K. and Griffith, D. W. T. and Hase, F. and Kuze, A. and Notholt, J. and Ohyama, H. and Parker, R. and Payne, V. H. and Sussmann, R. and Sweeney, C. and Velazco, V. A. and Warneke, T. and Wennberg, P. O. and Wunch, D.},
 doi = {10.5194/acp-15-7049-2015},
 journal = {Atmospheric Chemistry and Physics},
 number = {12}
}

We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a⁻¹ with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a⁻¹, as compared to 24.9–27.0 Tg a⁻¹ in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a⁻¹ in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a⁻¹.

@article{
 title = {Influence of differences in current GOSAT XCO2retrievals on surface flux estimation},
 type = {article},
 year = {2014},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84897175247&partnerID=MN8TOARS},
 id = {089cecf0-f4c7-3cc4-8102-30cf5df76347},
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 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
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 read = {false},
 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Takagi2014},
 private_publication = {false},
 bibtype = {article},
 author = {Takagi, H and Houweling, S and Andres, R J and Belikov, D and Bril, A and Boesch, H and Butz, A and Guerlet, S and Hasekamp, O and Maksyutov, S and Morino, I and Oda, T and O'Dell, C W and Oshchepkov, S and Parker, R and Saito, M and Uchino, O and Yokota, T and Yoshida, Y and Valsala, V},
 doi = {10.1002/2013GL059174},
 journal = {Geophysical Research Letters}
}

@article{
 title = {The greenhouse gas climate change initiative (GHG-CCI): Comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON},
 type = {article},
 year = {2014},
 pages = {1723-1744},
 volume = {7},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84902440911&partnerID=MN8TOARS},
 id = {acea6cb2-c9e3-3dac-b33c-c9f387037509},
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 private_publication = {false},
 bibtype = {article},
 author = {Dils, B and Buchwitz, M and Reuter, M and Schneising, O and Boesch, H and Parker, R and Guerlet, S and Aben, I and Blumenstock, T and Burrows, J P and Butz, A and Deutscher, N M and Frankenberg, C and Hase, F and Hasekamp, O P and Heymann, J and De Mazière, M and Notholt, J and Sussmann, R and Warneke, T and Griffith, D and Sherlock, V and Wunch, D},
 doi = {10.5194/amt-7-1723-2014},
 journal = {Atmospheric Measurement Techniques},
 number = {6}
}

@article{
 title = {Satellite-inferred European carbon sink larger than expected},
 type = {article},
 year = {2014},
 pages = {13739-13753},
 volume = {14},
 websites = {http://www.atmos-chem-phys.net/14/13739/2014/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84919820311&partnerID=MN8TOARS},
 month = {12},
 day = {22},
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 accessed = {2018-04-29},
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 hidden = {false},
 citation_key = {Reuter2014},
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 abstract = {<p>Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g. 0.27±0.16 GtC a<sup>&minus;1</sup> for 2000–2005 (Schulze et al., 2009), 0.17±0.44 GtC a<sup>&minus;1</sup> for 2001–2007 (Peters et al., 2010), 0.45±0.40 GtC a<sup>&minus;1</sup> for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC a<sup>&minus;1</sup> for 2001–2004 (Peylin et al., 2013)). Inverse models assimilate in situ CO<sub>2</sub> atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO<sub>2</sub> measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (1.0–1.3 GtC a<sup>&minus;1</sup> for 09/2009–08/2010 (Basu et al., 2013), 1.2–1.8 GtC a<sup>&minus;1</sup> in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short-range (days) particle dispersion modelling, rendering it insensitive to large-scale retrieval biases and less sensitive to long-range transport errors. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a<sup>&minus;1</sup> in 2010) than previously expected. This is qualitatively consistent among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003–2010, ACOS (O’Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY (Bovensmann et al., 1999) and GOSAT (Kuze et al., 2009)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a<sup>&minus;1</sup> for 2001–2004), is similar in magnitude to the reported uncertainty (0.42 GtC a<sup>&minus;1</sup>). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited.</p>},
 bibtype = {article},
 author = {Reuter, M. and Buchwitz, M. and Hilker, M. and Heymann, J. and Schneising, O. and Pillai, D. and Bovensmann, H. and Burrows, J. P. and Bösch, H. and Parker, R. and Butz, A. and Hasekamp, O. and O'Dell, C. W. and Yoshida, Y. and Gerbig, C. and Nehrkorn, T. and Deutscher, N. M. and Warneke, T. and Notholt, J. and Hase, F. and Kivi, R. and Sussmann, R. and Machida, T. and Matsueda, H. and Sawa, Y.},
 doi = {10.5194/acp-14-13739-2014},
 journal = {Atmospheric Chemistry and Physics},
 number = {24}
}

Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g. 0.27±0.16 GtC a⁻¹ for 2000–2005 (Schulze et al., 2009), 0.17±0.44 GtC a⁻¹ for 2001–2007 (Peters et al., 2010), 0.45±0.40 GtC a⁻¹ for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC a⁻¹ for 2001–2004 (Peylin et al., 2013)). Inverse models assimilate in situ CO₂ atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO₂ measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (1.0–1.3 GtC a⁻¹ for 09/2009–08/2010 (Basu et al., 2013), 1.2–1.8 GtC a⁻¹ in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short-range (days) particle dispersion modelling, rendering it insensitive to large-scale retrieval biases and less sensitive to long-range transport errors. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a⁻¹ in 2010) than previously expected. This is qualitatively consistent among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003–2010, ACOS (O’Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY (Bovensmann et al., 1999) and GOSAT (Kuze et al., 2009)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a⁻¹ for 2001–2004), is similar in magnitude to the reported uncertainty (0.42 GtC a⁻¹). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited.

@article{
 title = {Estimating regional fluxes of CO2 and CH4 using space-borne observations of XCH2: XCO2},
 type = {article},
 year = {2014},
 pages = {12883-12895},
 volume = {14},
 websites = {http://www.atmos-chem-phys.net/14/12883/2014/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84915819107&partnerID=MN8TOARS,https://www.atmos-chem-phys.net/14/12883/2014/acp-14-12883-2014.pdf},
 month = {12},
 publisher = {Copernicus GmbH},
 day = {8},
 id = {6e0495a9-9b35-3f86-9949-56d6f509601c},
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 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2018-07-07T20:22:47.968Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Fraser:2014a},
 source_type = {article},
 private_publication = {false},
 abstract = {We use the GEOS-Chem global 3-D atmospheric chemistry transport model to interpret XCH 4 : XCO 2 col-umn ratios retrieved from the Japanese Greenhouse Gases Observing Satellite (GOSAT). The advantage of these data over CO 2 and CH 4 columns retrieved independently using a full physics optimal estimation algorithm is that they are less prone to scattering-related regional biases. We show that the model is able to reproduce observed global and regional spatial (mean bias = 0.7 %) and temporal variations (global r 2 = 0.92) of this ratio with a model bias < 2.5 %. We also show that these variations are driven by emissions of CO 2 and CH 4 that are typically 6 months out of phase, which may reduce the sensitivity of the ratio to changes in either gas. To simultaneously estimate fluxes of CO 2 and CH 4 we use a maximum likelihood estimation approach. We use two ap-proaches to resolve independent flux estimates of these two gases using GOSAT observations of XCH 4 : XCO 2 : (1) the a priori error covariance between CO 2 and CH 4 describing common source from biomass burning; and (2) also fitting independent surface atmospheric measurements of CH 4 and CO 2 mole fraction that provide additional constraints, im-proving the effectiveness of the observed GOSAT ratio to constrain flux estimates. We demonstrate the impact of these two approaches using numerical experiments. A posteriori flux estimates inferred using only the GOSAT ratios and tak-ing advantage of the error covariance due to biomass burning are not consistent with the true fluxes in our experiments, as the inversion system cannot judge which species' fluxes to adjust. This reflects the weak dependence of XCH 4 : XCO 2 on biomass burning. We find that adding the surface data ef-fectively provides an " anchor " to the inversion that dramati-cally improves the ability of the GOSAT ratios to infer both CH 4 and CO 2 fluxes. We show that the regional flux esti-mates inferred from GOSAT XCH 4 : XCO 2 ratios together with the surface mole fraction data during 2010 are typi-cally consistent with or better than the corresponding values inferred from fitting XCH 4 or the full-physics XCO 2 data products, as judged by a posteriori uncertainties. We show that the fluxes inferred from the ratio measurements perform best over regions where there is a large seasonal cycle such as Tropical South America, for which we report a small but significant annual source of CO 2 compared to a small annual sink inferred from the XCO 2 data. We argue that given that the ratio measurements are less compromised by systematic error than the full physics data products, the resulting a poste-riori estimates and uncertainties provide a more faithful de-scription of the truth. Based on our analysis we also argue that by using the ratios we may be reaching the current limits on the precision of these observed space-based data. NAf SAf BEr TEr TrAs Aus Eur oceans Fig. 1. Distribution of the 13 geographical regions for which we estimate CO 2 and CH 4 fluxes, and the location of 57 co-operative flask sampling sites with data covering the study period, January–December 2010. The land regions, informed by previous work (Gurney et al., 2002) include: Boreal North America (BNA), Temperate North America (TNA), Tropical South America (TrSA), Temperate South America (TSA), Northern Africa (NAf), Southern Africa (SAf), Boreal Eurasia (BEr), Temperate Eurasia (TEr), Tropical Asia (TrAs), Australa-sia (Aus), and Europe (Eur). The ground-based measurement sites run by NOAA ESRL, CSIRO GASLAB, and EC are denoted by white circles, white diamonds, and white squares, respectively. 15 Figure 1. Distribution of the 13 geographical regions for which we estimate CO 2 and CH 4 fluxes, and the location of 57 co-operative flask sampling sites with data covering the study period, January–December 2010. The land regions, informed by previous work (Gurney et al., 2002), include Boreal North America (BNA), Temperate North America (TNA), Tropical South America (TrSA), Temperate South America (TSA), Northern Africa (NAf), Southern Africa (SAf), Boreal Eurasia (BEr), Temperate Eurasia (TEr), Tropical Asia (TrAs), Australasia (Aus), and Europe (Eur). The ground-based measurement sites run by NOAA ESRL, CSIRO GASLAB, and EC are denoted by white circles, white diamonds, and white squares, respectively.},
 bibtype = {article},
 author = {Fraser, A. and Palmer, P. I. and Feng, L. and Bösch, H. and Parker, R. and Dlugokencky, E. J. and Krummel, P. B. and Langenfelds, R. L.},
 doi = {10.5194/acp-14-12883-2014},
 journal = {Atmospheric Chemistry and Physics},
 number = {23}
}

We use the GEOS-Chem global 3-D atmospheric chemistry transport model to interpret XCH 4 : XCO 2 col-umn ratios retrieved from the Japanese Greenhouse Gases Observing Satellite (GOSAT). The advantage of these data over CO 2 and CH 4 columns retrieved independently using a full physics optimal estimation algorithm is that they are less prone to scattering-related regional biases. We show that the model is able to reproduce observed global and regional spatial (mean bias = 0.7 %) and temporal variations (global r 2 = 0.92) of this ratio with a model bias < 2.5 %. We also show that these variations are driven by emissions of CO 2 and CH 4 that are typically 6 months out of phase, which may reduce the sensitivity of the ratio to changes in either gas. To simultaneously estimate fluxes of CO 2 and CH 4 we use a maximum likelihood estimation approach. We use two ap-proaches to resolve independent flux estimates of these two gases using GOSAT observations of XCH 4 : XCO 2 : (1) the a priori error covariance between CO 2 and CH 4 describing common source from biomass burning; and (2) also fitting independent surface atmospheric measurements of CH 4 and CO 2 mole fraction that provide additional constraints, im-proving the effectiveness of the observed GOSAT ratio to constrain flux estimates. We demonstrate the impact of these two approaches using numerical experiments. A posteriori flux estimates inferred using only the GOSAT ratios and tak-ing advantage of the error covariance due to biomass burning are not consistent with the true fluxes in our experiments, as the inversion system cannot judge which species' fluxes to adjust. This reflects the weak dependence of XCH 4 : XCO 2 on biomass burning. We find that adding the surface data ef-fectively provides an " anchor " to the inversion that dramati-cally improves the ability of the GOSAT ratios to infer both CH 4 and CO 2 fluxes. We show that the regional flux esti-mates inferred from GOSAT XCH 4 : XCO 2 ratios together with the surface mole fraction data during 2010 are typi-cally consistent with or better than the corresponding values inferred from fitting XCH 4 or the full-physics XCO 2 data products, as judged by a posteriori uncertainties. We show that the fluxes inferred from the ratio measurements perform best over regions where there is a large seasonal cycle such as Tropical South America, for which we report a small but significant annual source of CO 2 compared to a small annual sink inferred from the XCO 2 data. We argue that given that the ratio measurements are less compromised by systematic error than the full physics data products, the resulting a poste-riori estimates and uncertainties provide a more faithful de-scription of the truth. Based on our analysis we also argue that by using the ratios we may be reaching the current limits on the precision of these observed space-based data. NAf SAf BEr TEr TrAs Aus Eur oceans Fig. 1. Distribution of the 13 geographical regions for which we estimate CO 2 and CH 4 fluxes, and the location of 57 co-operative flask sampling sites with data covering the study period, January–December 2010. The land regions, informed by previous work (Gurney et al., 2002) include: Boreal North America (BNA), Temperate North America (TNA), Tropical South America (TrSA), Temperate South America (TSA), Northern Africa (NAf), Southern Africa (SAf), Boreal Eurasia (BEr), Temperate Eurasia (TEr), Tropical Asia (TrAs), Australa-sia (Aus), and Europe (Eur). The ground-based measurement sites run by NOAA ESRL, CSIRO GASLAB, and EC are denoted by white circles, white diamonds, and white squares, respectively. 15 Figure 1. Distribution of the 13 geographical regions for which we estimate CO 2 and CH 4 fluxes, and the location of 57 co-operative flask sampling sites with data covering the study period, January–December 2010. The land regions, informed by previous work (Gurney et al., 2002), include Boreal North America (BNA), Temperate North America (TNA), Tropical South America (TrSA), Temperate South America (TSA), Northern Africa (NAf), Southern Africa (SAf), Boreal Eurasia (BEr), Temperate Eurasia (TEr), Tropical Asia (TrAs), Australasia (Aus), and Europe (Eur). The ground-based measurement sites run by NOAA ESRL, CSIRO GASLAB, and EC are denoted by white circles, white diamonds, and white squares, respectively.

@article{
 title = {On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements},
 type = {article},
 year = {2014},
 pages = {577-592},
 volume = {14},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892774161&partnerID=MN8TOARS},
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 bibtype = {article},
 author = {Cressot, C and Chevallier, F and Bousquet, P and Crevoisier, C and Dlugokencky, E J and Fortems-Cheiney, A and Frankenberg, C and Parker, R and Pison, I and Scheepmaker, R A and Montzka, S A and Krummel, P B and Steele, L P and Langenfelds, R L},
 doi = {10.5194/acp-14-577-2014},
 journal = {Atmospheric Chemistry and Physics},
 number = {2}
}

@article{
 title = {Spatially resolving methane emissions in California: Constraints from the CalNex aircraft campaign and from present (GOSAT, TES) and future (TROPOMI, geostationary) satellite observations},
 type = {article},
 year = {2014},
 pages = {4119-4148},
 volume = {14},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84908136706&partnerID=MN8TOARS,http://www.atmos-chem-phys.net/14/8173/2014/},
 month = {8},
 publisher = {Copernicus GmbH},
 day = {14},
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 accessed = {2018-04-29},
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 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:15:26.265Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wecht:2014},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p>We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2° × 2/3° horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May–June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg a<sup>−1</sup>, compared with 1.92 Tg a<sup>−1</sup> in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg a<sup>−1</sup> in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg a<sup>&minus;1</sup>), livestock (0.87 Tg a<sup>&minus;1</sup>), and gas/oil (0.64 Tg a<sup>&minus;1</sup>). EDGAR v4.2 underestimates emissions from livestock, while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08 Tg a<sup>−1</sup> that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.</p>},
 bibtype = {article},
 author = {Wecht, K. J. and Jacob, D. J. and Sulprizio, M. P. and Santoni, G. W. and Wofsy, S. C. and Parker, R. and Bösch, H. and Worden, J.},
 doi = {10.5194/acp-14-8173-2014},
 journal = {Atmospheric Chemistry and Physics},
 number = {3}
}

We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2° × 2/3° horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May–June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg a⁻¹, compared with 1.92 Tg a⁻¹ in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg a⁻¹ in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg a⁻¹), livestock (0.87 Tg a⁻¹), and gas/oil (0.64 Tg a⁻¹). EDGAR v4.2 underestimates emissions from livestock, while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08 Tg a⁻¹ that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.

@article{
 title = {Natural and anthropogenic methane fluxes in Eurasia: A mesoscale quantification by generalized atmospheric inversion},
 type = {article},
 year = {2014},
 pages = {5393-5414},
 volume = {12},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84908012110&partnerID=MN8TOARS,http://www.scopus.com/inward/record.url?eid=2-s2.0-84942155890&partnerID=MN8TOARS,https://www.biogeosciences.net/12/5393/2015/},
 month = {9},
 day = {18},
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 last_modified = {2021-03-31T19:11:08.391Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Berchet2014},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {<p><p><strong>Abstract.</strong> Eight surface observation sites providing quasi-continuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate 1 year of these in situ observations in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the mesoscale in the Siberian lowlands for the year 2010. To do so, we first inquire about the way the inversion uses the observations and the way the fluxes are constrained by the observation sites. As atmospheric inversions at the mesoscale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversion systems. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution quasi-continuous signal is representative of very local emission patterns difficult to analyse with a mesoscale system. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites than the ones currently in operation are then necessary to constrain the whole Siberian lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. Finally, the tolerance intervals for posterior methane fluxes are of roughly 20 % (resp. 50 %) of the fluxes for anthropogenic (resp. wetland) sources. About 50–70 % of Siberian lowlands emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian lowlands, we find a regional methane budget of 5–28 TgCH<sub>4</sub> for the year 2010, i.e. 1–5 % of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are tentatively used for the evaluation of the inversion results, but they exhibit only a marginal signal from the fluxes within the region of interest.</p></p>},
 bibtype = {article},
 author = {Berchet, A. and Pison, I. and Chevallier, F. and Paris, J.-D. and Bousquet, P. and Bonne, J.-L. and Arshinov, M.Yu. Y. and Belan, B. D. and Cressot, C. and Davydov, D. K. and Dlugokencky, E. J. and Fofonov, A. V. and Galanin, A. and Lavrič, J. and Machida, T. and Parker, R. and Sasakawa, M. and Spahni, R. and Stocker, B. D. and Winderlich, J. and Lavric, J and Machida, T. and Parker, R. and Sasakawa, M. and Spahni, R. and Stocker, B. D. and Winderlich, J.},
 doi = {10.5194/bgd-11-14587-2014},
 journal = {Biogeosciences},
 number = {18}
}

Abstract. Eight surface observation sites providing quasi-continuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate 1 year of these in situ observations in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the mesoscale in the Siberian lowlands for the year 2010. To do so, we first inquire about the way the inversion uses the observations and the way the fluxes are constrained by the observation sites. As atmospheric inversions at the mesoscale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversion systems. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution quasi-continuous signal is representative of very local emission patterns difficult to analyse with a mesoscale system. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites than the ones currently in operation are then necessary to constrain the whole Siberian lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. Finally, the tolerance intervals for posterior methane fluxes are of roughly 20 % (resp. 50 %) of the fluxes for anthropogenic (resp. wetland) sources. About 50–70 % of Siberian lowlands emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian lowlands, we find a regional methane budget of 5–28 TgCH₄ for the year 2010, i.e. 1–5 % of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are tentatively used for the evaluation of the inversion results, but they exhibit only a marginal signal from the fluxes within the region of interest.

@article{
 title = {A joint effort to deliver satellite retrieved atmospheric CO2 concentrations for surface flux inversions: The ensemble median algorithm EMMA},
 type = {article},
 year = {2013},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84874384013&partnerID=MN8TOARS},
 id = {73720e90-49bb-3f8e-abdf-83be9d3c4dcd},
 created = {2018-04-24T19:12:15.295Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.803Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Reuter2013},
 private_publication = {false},
 bibtype = {article},
 author = {Reuter, M and Bösch, H and Bovensmann, H and Bril, A and Buchwitz, M and Butz, A and Burrows, J P and O'Dell, C W and Guerlet, S and Hasekamp, O and Heymann, J and Kikuchi, N and Oshchepkov, S and Parker, R and Pfeifer, S and Schneising, O and Yokota, T and Yoshida, Y},
 doi = {10.5194/acp-13-1771-2013},
 journal = {Atmospheric Chemistry and Physics}
}

@article{
 title = {HDO/H2O ratio retrievals from GOSAT},
 type = {article},
 year = {2013},
 pages = {599-612},
 volume = {6},
 websites = {https://www.atmos-meas-tech.net/6/599/2013/amt-6-599-2013.pdf,http://www.scopus.com/inward/record.url?eid=2-s2.0-84882768790&partnerID=MN8TOARS},
 id = {213f66cf-0fb6-3ca3-a850-854b37fba5a4},
 created = {2018-04-25T09:32:23.937Z},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:07.035Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Boesch2013},
 private_publication = {false},
 abstract = {We report a new shortwave infrared (SWIR) retrieval of the column-averaged HDO/H2O ratio from the Japanese Greenhouse Gases Observing Satellite (GOSAT). From synthetic simulation studies, we have estimated that the inferred delta D values will typically have random errors between 20 parts per thousand (desert surface and 30 degrees solar zenith angle) and 120 parts per thousand (conifer surface and 60 degrees solar zenith angle). We find that the retrieval will have a small but significant sensitivity to the presence of cirrus clouds, the HDO a priori profile shape and atmospheric temperature, which has the potential of introducing some regional-scale biases in the retrieval. From comparisons to ground-based column observations from the Total Carbon Column Observing Network (TCCON), we find differences between delta D from GOSAT and TCCON of around -30 parts per thousand for northern hemispheric sites which increase up to -70 parts per thousand for Australian sites. The bias for the Australian sites significantly reduces when decreasing the spatial co-location criteria, which shows that spatial averaging contributes to the observed differences over Australia. The GOSAT retrievals allow mapping the global distribution of delta D and its variations with season, and we find in our global GOSAT retrievals the expected strong latitudinal gradients with significant enhancements over the tropics. The comparisons to the ground-based TCCON network and the results of the global retrieval are very encouraging, and they show that delta D retrieved from GOSAT should be a useful product that can be used to complement datasets from thermal-infrared sounder and ground-based networks and to extend the delta D dataset from SWIR retrievals established from the recently ended SCIAMACHY mission.},
 bibtype = {article},
 author = {Boesch, H. and Deutscher, N. M. and Warneke, T. and Byckling, K. and Cogan, A. J. and Griffith, D. W.T. T and Notholt, J. and Parker, R. J. and Wang, Z.},
 doi = {10.5194/amt-6-599-2013},
 journal = {Atmospheric Measurement Techniques},
 number = {3}
}

We report a new shortwave infrared (SWIR) retrieval of the column-averaged HDO/H2O ratio from the Japanese Greenhouse Gases Observing Satellite (GOSAT). From synthetic simulation studies, we have estimated that the inferred delta D values will typically have random errors between 20 parts per thousand (desert surface and 30 degrees solar zenith angle) and 120 parts per thousand (conifer surface and 60 degrees solar zenith angle). We find that the retrieval will have a small but significant sensitivity to the presence of cirrus clouds, the HDO a priori profile shape and atmospheric temperature, which has the potential of introducing some regional-scale biases in the retrieval. From comparisons to ground-based column observations from the Total Carbon Column Observing Network (TCCON), we find differences between delta D from GOSAT and TCCON of around -30 parts per thousand for northern hemispheric sites which increase up to -70 parts per thousand for Australian sites. The bias for the Australian sites significantly reduces when decreasing the spatial co-location criteria, which shows that spatial averaging contributes to the observed differences over Australia. The GOSAT retrievals allow mapping the global distribution of delta D and its variations with season, and we find in our global GOSAT retrievals the expected strong latitudinal gradients with significant enhancements over the tropics. The comparisons to the ground-based TCCON network and the results of the global retrieval are very encouraging, and they show that delta D retrieved from GOSAT should be a useful product that can be used to complement datasets from thermal-infrared sounder and ground-based networks and to extend the delta D dataset from SWIR retrievals established from the recently ended SCIAMACHY mission.

@article{
 title = {First satellite measurements of carbon dioxide and methane emission ratios in wildfire plumes},
 type = {article},
 year = {2013},
 keywords = {CH4,CO2,GOSAT,emission,ratio,wildfire},
 pages = {4098-4102},
 volume = {40},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880938592&partnerID=MN8TOARS,http://doi.wiley.com/10.1002/grl.50733},
 month = {8},
 publisher = {Wiley Online Library},
 day = {16},
 id = {b68a37fb-e92f-3cf8-9ed9-dd056bfc9aa1},
 created = {2018-05-19T18:46:37.765Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-07-11T20:52:37.911Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ross:2013},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {Using methane and carbon dioxide atmospheric mixing ratios retrieved using SWIR spectra from the Greenhouse Gases Observing SATellite (GOSAT), we report the first wildfire plume CH4 to CO2 emission ratios (ERCH4/CO2) determined from space. We demonstrate the approach's potential using forward modeling and identify a series of real GOSAT spectra containing wildfire plumes. These show significantly changed total-column CO2 and CH4 mixing ratios, and from these we calculate ERCH4/CO2 for boreal forest, tropical forest, and savanna fires as 0.00603, 0.00527, and 0.00395 mol mol−1, respectively. These ERs are statistically significantly different from each other and from the “normal” atmospheric CH4 to CO2 ratio and generally agree with past ground and airborne studies.},
 bibtype = {article},
 author = {Ross, Adrian N. and Wooster, Martin J. and Boesch, Hartmut and Parker, Robert},
 doi = {10.1002/grl.50733},
 journal = {Geophysical Research Letters},
 number = {15}
}

Using methane and carbon dioxide atmospheric mixing ratios retrieved using SWIR spectra from the Greenhouse Gases Observing SATellite (GOSAT), we report the first wildfire plume CH4 to CO2 emission ratios (ERCH4/CO2) determined from space. We demonstrate the approach's potential using forward modeling and identify a series of real GOSAT spectra containing wildfire plumes. These show significantly changed total-column CO2 and CH4 mixing ratios, and from these we calculate ERCH4/CO2 for boreal forest, tropical forest, and savanna fires as 0.00603, 0.00527, and 0.00395 mol mol−1, respectively. These ERs are statistically significantly different from each other and from the “normal” atmospheric CH4 to CO2 ratio and generally agree with past ground and airborne studies.

@article{
 title = {Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space. Part 2: Algorithm intercomparison in the GOSAT data processing for CO2 retrievals over TCCON sites},
 type = {article},
 year = {2013},
 keywords = {GOSAT algorithms},
 pages = {1493-1512},
 volume = {118},
 websites = {http://doi.wiley.com/10.1002/jgrd.50146,http://www.scopus.com/inward/record.url?eid=2-s2.0-84880299854&partnerID=MN8TOARS},
 month = {2},
 publisher = {Wiley-Blackwell},
 day = {16},
 id = {cc54e34b-12ad-3095-84f9-0e7203a306ab},
 created = {2018-12-14T09:41:29.195Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:11:06.915Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Oshchepkov2013},
 private_publication = {false},
 bibtype = {article},
 author = {Oshchepkov, Sergey and Bril, Andrey and Yokota, Tatsuya and Wennberg, Paul O. and Deutscher, Nicholas M. and Wunch, Debra and Toon, Geoffrey C. and Yoshida, Yukio and O'Dell, Christopher W. and Crisp, David and Miller, Charles E. and Frankenberg, Christian and Butz, André and Aben, Ilse and Guerlet, Sandrine and Hasekamp, Otto and Boesch, Hartmut and Cogan, Austin and Parker, Robert and Griffith, David and Macatangay, Ronald and Notholt, Justus and Sussmann, Ralf and Rettinger, Markus and Sherlock, Vanessa and Robinson, John and Kyrö, Esko and Heikkinen, Pauli and Feist, Dietrich G. and Morino, Isamu and Kadygrov, Nikolay and Belikov, Dmitry and Maksyutov, Shamil and Matsunaga, Tsuneo and Uchino, Osamu and Watanabe, Hiroshi},
 doi = {10.1002/jgrd.50146},
 journal = {Journal of Geophysical Research Atmospheres},
 number = {3}
}

@article{
 title = {Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements},
 type = {article},
 year = {2013},
 pages = {5697-5713},
 volume = {13},
 websites = {https://www.atmos-chem-phys.net/13/5697/2013/acp-13-5697-2013.pdf,http://www.atmos-chem-phys.net/13/5697/2013/,http://www.scopus.com/inward/record.url?eid=2-s2.0-84886749092&partnerID=MN8TOARS},
 month = {6},
 publisher = {Copernicus GmbH},
 day = {13},
 id = {1d1f70f5-c348-3fd4-bc92-f9ed1312ee31},
 created = {2020-07-11T20:52:36.347Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-07-11T21:12:04.947Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Fraser:2013},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {We use an ensemble Kalman filter (EnKF), together with the GEOS-Chem chemistry transport model, to estimate regional monthly methane (CH4) fluxes for the period June 2009–December 2010 using proxy dry-air column-averaged mole fractions of methane (XCH4) from GOSAT (Greenhouse gases Observing SATellite) and/or NOAA ESRL (Earth System Research Laboratory) and CSIRO GASLAB (Global Atmospheric Sampling Laboratory) CH4 surface mole fraction measurements. Global posterior estimates using GOSAT and/or surface measurements are between 510–516 Tg yr−1, which is less than, though within the uncertainty of, the prior global flux of 529 ± 25 Tg yr−1. We find larger differences between regional prior and posterior fluxes, with the largest changes in monthly emissions (75 Tg yr−1) occurring in Temperate Eurasia. In non-boreal regions the error reductions for inversions using the GOSAT data are at least three times larger (up to 45%) than if only surface data are assimilated, a reflection of the greater spatial coverage of GOSAT, with the two exceptions of latitudes >60° associated with a data filter and over Europe where the surface network adequately describes fluxes on our model spatial and temporal grid. We use CarbonTracker and GEOS-Chem XCO2 model output to investigate model error on quantifying proxy GOSAT XCH4 (involving model XCO2) and inferring methane flux estimates from surface mole fraction data and show similar resulting fluxes, with differences reflecting initial differences in the proxy value. Using a series of observing system simulation experiments (OSSEs) we characterize the posterior flux error introduced by non-uniform atmospheric sampling by GOSAT. We show that clear-sky measurements can theoretically reproduce fluxes within 10% of true values, with the exception of tropical regions where, due to a large seasonal cycle in the number of measurements because of clouds and aerosols, fluxes are within 15% of true fluxes. We evaluate our posterior methane fluxes by incorporating them into GEOS-Chem and sampling the model at the location and time of surface CH4 measurements from the AGAGE (Advanced Global Atmospheric Gases Experiment) network and column XCH4 measurements from TCCON (Total Carbon Column Observing Network). The posterior fluxes modestly improve the model agreement with AGAGE and TCCON data relative to prior fluxes, with the correlation coefficients (r2) increasing by a mean of 0.04 (range: −0.17 to 0.23) and the biases decreasing by a mean of 0.4 ppb (range: −8.9 to 8.4 ppb).},
 bibtype = {article},
 author = {Fraser, A. and Palmer, P. I. and Feng, L. and Boesch, H. and Cogan, A. and Parker, R. and Dlugokencky, E. J. and Fraser, P. J. and Krummel, P. B. and Langenfelds, R. L. and O'Doherty, S. and Prinn, R. G. and Steele, L. P. and van der Schoot, M. and Weiss, R. F.},
 doi = {10.5194/acp-13-5697-2013},
 journal = {Atmospheric Chemistry and Physics},
 number = {11}
}

We use an ensemble Kalman filter (EnKF), together with the GEOS-Chem chemistry transport model, to estimate regional monthly methane (CH4) fluxes for the period June 2009–December 2010 using proxy dry-air column-averaged mole fractions of methane (XCH4) from GOSAT (Greenhouse gases Observing SATellite) and/or NOAA ESRL (Earth System Research Laboratory) and CSIRO GASLAB (Global Atmospheric Sampling Laboratory) CH4 surface mole fraction measurements. Global posterior estimates using GOSAT and/or surface measurements are between 510–516 Tg yr−1, which is less than, though within the uncertainty of, the prior global flux of 529 ± 25 Tg yr−1. We find larger differences between regional prior and posterior fluxes, with the largest changes in monthly emissions (75 Tg yr−1) occurring in Temperate Eurasia. In non-boreal regions the error reductions for inversions using the GOSAT data are at least three times larger (up to 45%) than if only surface data are assimilated, a reflection of the greater spatial coverage of GOSAT, with the two exceptions of latitudes >60° associated with a data filter and over Europe where the surface network adequately describes fluxes on our model spatial and temporal grid. We use CarbonTracker and GEOS-Chem XCO2 model output to investigate model error on quantifying proxy GOSAT XCH4 (involving model XCO2) and inferring methane flux estimates from surface mole fraction data and show similar resulting fluxes, with differences reflecting initial differences in the proxy value. Using a series of observing system simulation experiments (OSSEs) we characterize the posterior flux error introduced by non-uniform atmospheric sampling by GOSAT. We show that clear-sky measurements can theoretically reproduce fluxes within 10% of true values, with the exception of tropical regions where, due to a large seasonal cycle in the number of measurements because of clouds and aerosols, fluxes are within 15% of true fluxes. We evaluate our posterior methane fluxes by incorporating them into GEOS-Chem and sampling the model at the location and time of surface CH4 measurements from the AGAGE (Advanced Global Atmospheric Gases Experiment) network and column XCH4 measurements from TCCON (Total Carbon Column Observing Network). The posterior fluxes modestly improve the model agreement with AGAGE and TCCON data relative to prior fluxes, with the correlation coefficients (r2) increasing by a mean of 0.04 (range: −0.17 to 0.23) and the biases decreasing by a mean of 0.4 ppb (range: −8.9 to 8.4 ppb).

@article{
 title = {Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): Comparison with ground-based TCCON observations and GEOS-Chem model calculations},
 type = {article},
 year = {2012},
 pages = {n/a-n/a},
 volume = {117},
 websites = {http://doi.wiley.com/10.1029/2012JD018087,http://www.scopus.com/inward/record.url?eid=2-s2.0-84868647602&partnerID=MN8TOARS},
 month = {11},
 publisher = {Wiley Online Library},
 day = {16},
 id = {344d2986-7479-3f4d-9db8-3e69088e98d3},
 created = {2018-05-19T18:46:38.882Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2020-03-03T11:09:30.740Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Cogan:2012},
 source_type = {article},
 folder_uuids = {ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 abstract = {We retrieved column-averaged dry air mole fractions of atmospheric carbon dioxide () from backscattered short-wave infrared (SWIR) sunlight measured by the Japanese Greenhouse gases Observing SATellite (GOSAT). Over two years of retrieved from GOSAT is compared with inferred from collocated SWIR measurements by seven ground-based Total Carbon Column Observing Network (TCCON) stations. The average difference between GOSAT and TCCON for individual TCCON sites ranges from −0.87 ppm to 0.77 ppm with a mean value of 0.1 ppm and standard deviation of 0.56 ppm. We find an average bias between all GOSAT and TCCON retrievals of −0.20 ppm with a standard deviation of 2.26 ppm and a correlation coefficient of 0.75. One year of was retrieved from GOSAT globally, which was compared to global 3-D GEOS-Chem chemistry transport model calculations. We find that the latitudinal gradient, seasonal cycles, and spatial variability of GOSAT and GEOS-Chem agree well in general with a correlation coefficient of 0.61. Regional differences between GEOS-Chem model calculations and GOSAT observations are typically less than 1 ppm except for the Sahara and central Asia where a mean difference between 2 to 3 ppm is observed, indicating regional biases in the GOSAT retrievals unobserved by the current TCCON network. Using a bias correction scheme based on linear regression these regional biases are significantly reduced, approaching the required accuracy for surface flux inversions.},
 bibtype = {article},
 author = {Cogan, A. J. and Boesch, H. and Parker, R. J. and Feng, L. and Palmer, P. I. and Blavier, J.-F.L. L. F.L. and Deutscher, N. M. and MacAtangay, R. and Notholt, J. and Roehl, C. and Warneke, T. and Wunch, D.},
 doi = {10.1029/2012JD018087},
 journal = {Journal of Geophysical Research Atmospheres},
 number = {21}
}

We retrieved column-averaged dry air mole fractions of atmospheric carbon dioxide () from backscattered short-wave infrared (SWIR) sunlight measured by the Japanese Greenhouse gases Observing SATellite (GOSAT). Over two years of retrieved from GOSAT is compared with inferred from collocated SWIR measurements by seven ground-based Total Carbon Column Observing Network (TCCON) stations. The average difference between GOSAT and TCCON for individual TCCON sites ranges from −0.87 ppm to 0.77 ppm with a mean value of 0.1 ppm and standard deviation of 0.56 ppm. We find an average bias between all GOSAT and TCCON retrievals of −0.20 ppm with a standard deviation of 2.26 ppm and a correlation coefficient of 0.75. One year of was retrieved from GOSAT globally, which was compared to global 3-D GEOS-Chem chemistry transport model calculations. We find that the latitudinal gradient, seasonal cycles, and spatial variability of GOSAT and GEOS-Chem agree well in general with a correlation coefficient of 0.61. Regional differences between GEOS-Chem model calculations and GOSAT observations are typically less than 1 ppm except for the Sahara and central Asia where a mean difference between 2 to 3 ppm is observed, indicating regional biases in the GOSAT retrievals unobserved by the current TCCON network. Using a bias correction scheme based on linear regression these regional biases are significantly reduced, approaching the required accuracy for surface flux inversions.

@article{
 title = {Acetylene C2H2 retrievals from MIPAS data and regions of enhanced upper tropospheric concentrations in August 2003},
 type = {article},
 year = {2011},
 pages = {10243-10257},
 volume = {11},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80054078942&partnerID=MN8TOARS,http://www.atmos-chem-phys.net/11/10243/2011/},
 month = {10},
 day = {13},
 id = {1e2a3238-818e-3c49-ab68-8e0ce17718cc},
 created = {2018-04-24T21:30:50.207Z},
 accessed = {2018-04-24},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2018-10-29T19:56:14.857Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Parker2011},
 private_publication = {false},
 bibtype = {article},
 author = {Parker, R. J. and Remedios, J. J. and Moore, D. P. and Kanawade, V. P.},
 doi = {10.5194/acp-11-10243-2011},
 journal = {Atmospheric Chemistry and Physics},
 number = {19}
}

@article{
 title = {Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground-based TCCON data and model calculations},
 type = {article},
 year = {2011},
 keywords = {GEOS‐Chem,GOSAT,TCCON,methane},
 pages = {n/a-n/a},
 volume = {38},
 websites = {http://doi.wiley.com/10.1029/2011GL047871,http://www.scopus.com/inward/record.url?eid=2-s2.0-80051479870&partnerID=MN8TOARS},
 month = {8},
 publisher = {Wiley Online Library},
 id = {50308209-42ca-313e-a20c-986430cebb91},
 created = {2018-12-14T09:41:28.702Z},
 accessed = {2018-04-29},
 file_attached = {true},
 profile_id = {d95b48cf-7d36-3cc0-8da5-00b63cdd3d88},
 last_modified = {2021-03-31T19:34:12.985Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Parker:2011},
 source_type = {article},
 folder_uuids = {336e7fe6-90e1-4682-8f50-8551a15fb992,ab3634b7-8958-4bc2-97a4-5154f4deb67e},
 private_publication = {false},
 bibtype = {article},
 author = {Parker, Robert and Boesch, Hartmut and Cogan, Austin and Fraser, Annemarie and Feng, Liang and Palmer, Paul I. and Messerschmidt, Janina and Deutscher, Nicholas and Griffith, David W. T. and Notholt, Justus and Wennberg, Paul O. and Wunch, Debra},
 doi = {10.1029/2011GL047871},
 journal = {Geophysical Research Letters},
 number = {15}
}

@article{
 title = {Intercomparison of integrated IASI and AATSR calibrated radiances at 11 and 12 μm},
 type = {article},
 year = {2009},
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 author = {Hamilton, J F and Allen, G and Watson, N M and Lee, J D and Saxton, J E and Lewis, A C and Vaughan, G and Bower, K N and Flynn, M J and Crosier, J and Carver, G D and Harris, N R P and Parker, R J and Remedios, J J and Richards, N A D},
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