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\n \n\n \n \n \n \n \n \n Detecting Paleoclimate Transitions With Laplacian Eigenmaps of Recurrence Matrices (LERM).\n \n \n \n \n\n\n \n James, A.; Emile‐Geay, J.; Malik, N.; and Khider, D.\n\n\n \n\n\n\n Paleoceanography and Paleoclimatology, 39(1): e2023PA004700. January 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Detecting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{james_detecting_2024,\n\ttitle = {Detecting {Paleoclimate} {Transitions} {With} {Laplacian} {Eigenmaps} of {Recurrence} {Matrices} ({LERM})},\n\tvolume = {39},\n\tissn = {2572-4517, 2572-4525},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023PA004700},\n\tdoi = {10.1029/2023PA004700},\n\tabstract = {Abstract \n            Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the “8.2 ka” event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. \n\nKey Points:           \n                                \nLaplacian eigenmaps of recurrence matrices (LERM) is a novel tool for paleoclimate time series analysis \n                 \n                 \nLERM can robustly detect the gradual Mid‐Pleistocene Transition in relatively low signal‐to‐noise ratio scenarios \n                 \n                 \nLERM can also be applied to detect abrupt climate transitions like the 8.2 ka event, though less robustly},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-01-03},\n\tjournal = {Paleoceanography and Paleoclimatology},\n\tauthor = {James, Alexander and Emile‐Geay, Julien and Malik, Nishant and Khider, Deborah},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {e2023PA004700},\n}\n\n

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\n Abstract Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the “8.2 ka” event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. Key Points: Laplacian eigenmaps of recurrence matrices (LERM) is a novel tool for paleoclimate time series analysis LERM can robustly detect the gradual Mid‐Pleistocene Transition in relatively low signal‐to‐noise ratio scenarios LERM can also be applied to detect abrupt climate transitions like the 8.2 ka event, though less robustly\n

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\n \n 2023\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Globally coherent water cycle response to temperature change during the past two millennia.\n \n \n \n \n\n\n \n Konecky, B. L.; McKay, N. P.; Falster, G. M.; Stevenson, S. L.; Fischer, M. J.; Atwood, A. R.; Thompson, D. M.; Jones, M. D.; Tyler, J. J.; DeLong, K. L.; Martrat, B.; Thomas, E. K.; Conroy, J. L.; Dee, S. G.; Jonkers, L.; Churakova, O. V.; Kern, Z.; Opel, T.; Porter, T. J.; Sayani, H. R.; Skrzypek, G.; Iso2k Project Members; Abram, N. J.; Braun, K.; Carré, M.; Cartapanis, O.; Comas-Bru, L.; Curran, M. A.; Dassié, E. P.; Deininger, M.; Divine, D. V.; Incarbona, A.; Kaufman, D. S.; Kaushal, N.; Klaebe, R. M.; Kolus, H. R.; Leduc, G.; Managave, S. R.; Mortyn, P. G.; Moy, A. D.; Orsi, A. J.; Partin, J. W.; Roop, H. A.; Sicre, M.; Von Gunten, L.; and Yoshimura, K.\n\n\n \n\n\n\n Nature Geoscience, 16(11): 997–1004. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Globally$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{konecky_globally_2023,\n\ttitle = {Globally coherent water cycle response to temperature change during the past two millennia},\n\tvolume = {16},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/s41561-023-01291-3},\n\tdoi = {10.1038/s41561-023-01291-3},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-11-17},\n\tjournal = {Nature Geoscience},\n\tauthor = {Konecky, Bronwen L. and McKay, Nicholas P. and Falster, Georgina M. and Stevenson, Samantha L. and Fischer, Matt J. and Atwood, Alyssa R. and Thompson, Diane M. and Jones, Matthew D. and Tyler, Jonathan J. and DeLong, Kristine L. and Martrat, Belen and Thomas, Elizabeth K. and Conroy, Jessica L. and Dee, Sylvia G. and Jonkers, Lukas and Churakova, Olga V. and Kern, Zoltán and Opel, Thomas and Porter, Trevor J. and Sayani, Hussein R. and Skrzypek, Grzegorz and {Iso2k Project Members} and Abram, Nerilie J. and Braun, Kerstin and Carré, Matthieu and Cartapanis, Olivier and Comas-Bru, Laia and Curran, Mark A. and Dassié, Emilie P. and Deininger, Michael and Divine, Dmitry V. and Incarbona, Alessandro and Kaufman, Darrell S. and Kaushal, Nikita and Klaebe, Robert M. and Kolus, Hannah R. and Leduc, Guillaume and Managave, Shreyas R. and Mortyn, P. Graham and Moy, Andrew D. and Orsi, Anais J. and Partin, Judson W. and Roop, Heidi A. and Sicre, Marie-Alexandrine and Von Gunten, Lucien and Yoshimura, Kei},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {997--1004},\n}\n\n

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\n \n\n \n \n \n \n \n \n A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models.\n \n \n \n \n\n\n \n Zhu, F.; Emile-Geay, J.; Anchukaitis, K. J.; McKay, N. P.; Stevenson, S.; and Meng, Z.\n\n\n \n\n\n\n Scientific Data, 10(1): 624. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zhu_pseudoproxy_2023,\n\ttitle = {A pseudoproxy emulation of the {PAGES} 2k database using a hierarchy of proxy system models},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02489-1},\n\tdoi = {10.1038/s41597-023-02489-1},\n\tabstract = {Abstract \n            Paleoclimate reconstructions are now integral to climate assessments, yet the consequences of using different methodologies and proxy data require rigorous benchmarking. Pseudoproxy experiments (PPEs) provide a tractable and transparent test bed for evaluating climate reconstruction methods and their sensitivity to aspects of real-world proxy networks. Here we develop a dataset that leverages proxy system models (PSMs) for this purpose, which emulates the essential physical, chemical, biological, and geological processes that translate climate signals into proxy records, making these synthetic proxies more relevant to the real world. We apply a suite of PSMs to emulate the widely-used PAGES 2k dataset, including realistic spatiotemporal sampling and error structure. A hierarchical approach allows us to produce many variants of this base dataset, isolating the impact of sampling bias in time and space, representation error, sampling error, and other assumptions. Combining these various experiments produces a rich dataset (“pseudoPAGES2k”) for many applications. As an illustration, we show how to conduct a PPE with this dataset based on emerging climate field reconstruction techniques.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-11-17},\n\tjournal = {Scientific Data},\n\tauthor = {Zhu, Feng and Emile-Geay, Julien and Anchukaitis, Kevin J. and McKay, Nicholas P. and Stevenson, Samantha and Meng, Zilu},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {624},\n}\n\n

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\n \n\n \n \n \n \n \n \n cfr (v2023.9.14): a Python package for climate field reconstruction.\n \n \n \n \n\n\n \n Zhu, F.; Emile-Geay, J.; Hakim, G. J.; Guillot, D.; Khider, D.; Tardif, R.; and Perkins, W. A.\n\n\n \n\n\n\n Technical Report Climate and Earth system modeling, September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"cfr$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{zhu_cfr_2023,\n\ttype = {preprint},\n\ttitle = {cfr (v2023.9.14): a {Python} package for climate field reconstruction},\n\tshorttitle = {cfr (v2023.9.14)},\n\turl = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2098/},\n\tabstract = {Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. The climate fields can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and timeconsuming, as they involve: (i) preprocessing of the proxy records, climate model simulations, and instrumental observations, (ii) application of one or more statistical methods, and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. It provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with an extensive documentation of the application interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database: applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the Graphical Expectation-Maximization (GraphEM) algorithm, respectively.},\n\turldate = {2023-11-17},\n\tinstitution = {Climate and Earth system modeling},\n\tauthor = {Zhu, Feng and Emile-Geay, Julien and Hakim, Gregory J. and Guillot, Dominique and Khider, Deborah and Tardif, Robert and Perkins, Walter A.},\n\tmonth = sep,\n\tyear = {2023},\n\tdoi = {10.5194/egusphere-2023-2098},\n}\n\n

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\n \n\n \n \n \n \n \n \n The CoralHydro2k database: a global, actively curated compilation of coral δ $^{\\textrm{18}}$ O and Sr ∕ Ca proxy records of tropical ocean hydrology and temperature for the Common Era.\n \n \n \n \n\n\n \n Walter, R. M.; Sayani, H. R.; Felis, T.; Cobb, K. M.; Abram, N. J.; Arzey, A. K.; Atwood, A. R.; Brenner, L. D.; Dassié, É. P.; DeLong, K. L.; Ellis, B.; Emile-Geay, J.; Fischer, M. J.; Goodkin, N. F.; Hargreaves, J. A.; Kilbourne, K. H.; Krawczyk, H.; McKay, N. P.; Moore, A. L.; Murty, S. A.; Ong, M. R.; Ramos, R. D.; Reed, E. V.; Samanta, D.; Sanchez, S. C.; Zinke, J.; and the PAGES CoralHydro2k Project Members\n\n\n \n\n\n\n Earth System Science Data, 15(5): 2081–2116. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{walter_coralhydro2k_2023,\n\ttitle = {The {CoralHydro2k} database: a global, actively curated compilation of coral \\textit{δ} $^{\\textrm{18}}$ {O} and {Sr} ∕ {Ca} proxy records of tropical ocean hydrology and temperature for the {Common} {Era}},\n\tvolume = {15},\n\tissn = {1866-3516},\n\tshorttitle = {The {CoralHydro2k} database},\n\turl = {https://essd.copernicus.org/articles/15/2081/2023/},\n\tdoi = {10.5194/essd-15-2081-2023},\n\tabstract = {Abstract. The response of the hydrological cycle to anthropogenic climate\nchange, especially across the tropical oceans, remains poorly understood due to the scarcity of long instrumental temperature and hydrological records. Massive shallow-water corals are ideally suited to reconstructing past oceanic variability as they are widely distributed across the tropics,\nrapidly deposit calcium carbonate skeletons that continuously record ambient environmental conditions, and can be sampled at monthly to annual\nresolution. Climate reconstructions based on corals primarily use the stable oxygen isotope composition (δ18O), which acts as a proxy for sea surface temperature (SST), and the oxygen isotope composition of\nseawater (δ18Osw), a measure of hydrological variability. Increasingly, coral δ18O time series are paired with time series of strontium-to-calcium ratios (Sr/Ca), a proxy for SST, from the same coral to quantify temperature and δ18Osw variability\nthrough time. To increase the utility of such reconstructions, we present\nthe CoralHydro2k database, a compilation of published, peer-reviewed coral Sr/Ca and δ18O records from the Common Era (CE). The database contains 54 paired Sr/Ca–δ18O records and 125 unpaired Sr/Ca or δ18O records, with 88 \\% of these records providing data coverage from 1800 CE to the present. A quality-controlled set of metadata with standardized vocabulary and units accompanies each record, informing the use\nof the database. The CoralHydro2k database tracks large-scale temperature\nand hydrological variability. As such, it is well-suited for investigations\nof past climate variability, comparisons with climate model simulations\nincluding isotope-enabled models, and application in paleodata-assimilation projects. The CoralHydro2k database is available in Linked Paleo Data (LiPD) format with serializations in MATLAB, R, and Python and can be downloaded from the NOAA National Center for Environmental Information's Paleoclimate Data Archive at https://doi.org/10.25921/yp94-v135 (Walter et al., 2022).},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-11-17},\n\tjournal = {Earth System Science Data},\n\tauthor = {Walter, Rachel M. and Sayani, Hussein R. and Felis, Thomas and Cobb, Kim M. and Abram, Nerilie J. and Arzey, Ariella K. and Atwood, Alyssa R. and Brenner, Logan D. and Dassié, Émilie P. and DeLong, Kristine L. and Ellis, Bethany and Emile-Geay, Julien and Fischer, Matthew J. and Goodkin, Nathalie F. and Hargreaves, Jessica A. and Kilbourne, K. Halimeda and Krawczyk, Hedwig and McKay, Nicholas P. and Moore, Andrea L. and Murty, Sujata A. and Ong, Maria Rosabelle and Ramos, Riovie D. and Reed, Emma V. and Samanta, Dhrubajyoti and Sanchez, Sara C. and Zinke, Jens and {the PAGES CoralHydro2k Project Members}},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {2081--2116},\n}\n\n

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\n Abstract. The response of the hydrological cycle to anthropogenic climate change, especially across the tropical oceans, remains poorly understood due to the scarcity of long instrumental temperature and hydrological records. Massive shallow-water corals are ideally suited to reconstructing past oceanic variability as they are widely distributed across the tropics, rapidly deposit calcium carbonate skeletons that continuously record ambient environmental conditions, and can be sampled at monthly to annual resolution. Climate reconstructions based on corals primarily use the stable oxygen isotope composition (δ18O), which acts as a proxy for sea surface temperature (SST), and the oxygen isotope composition of seawater (δ18Osw), a measure of hydrological variability. Increasingly, coral δ18O time series are paired with time series of strontium-to-calcium ratios (Sr/Ca), a proxy for SST, from the same coral to quantify temperature and δ18Osw variability through time. To increase the utility of such reconstructions, we present the CoralHydro2k database, a compilation of published, peer-reviewed coral Sr/Ca and δ18O records from the Common Era (CE). The database contains 54 paired Sr/Ca–δ18O records and 125 unpaired Sr/Ca or δ18O records, with 88 % of these records providing data coverage from 1800 CE to the present. A quality-controlled set of metadata with standardized vocabulary and units accompanies each record, informing the use of the database. The CoralHydro2k database tracks large-scale temperature and hydrological variability. As such, it is well-suited for investigations of past climate variability, comparisons with climate model simulations including isotope-enabled models, and application in paleodata-assimilation projects. The CoralHydro2k database is available in Linked Paleo Data (LiPD) format with serializations in MATLAB, R, and Python and can be downloaded from the NOAA National Center for Environmental Information's Paleoclimate Data Archive at https://doi.org/10.25921/yp94-v135 (Walter et al., 2022).\n

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\n \n\n \n \n \n \n \n \n Pyleoclim: Paleoclimate Timeseries Analysis and Visualization with Python.\n \n \n \n \n\n\n \n Khider, D.; Emile-Geay, J.; Zhu, F.; James, A.; Landers, J.; Ratnakar, V.; and Gil, Y.\n\n\n \n\n\n\n Technical Report Climatology (Global Change), July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Pyleoclim:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 11 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{khider_pyleoclim_2022,\n\ttype = {preprint},\n\ttitle = {Pyleoclim: {Paleoclimate} {Timeseries} {Analysis} and {Visualization} with {Python}},\n\tshorttitle = {Pyleoclim},\n\turl = {http://www.essoar.org/doi/10.1002/essoar.10511883.1},\n\tlanguage = {en},\n\turldate = {2022-07-14},\n\tinstitution = {Climatology (Global Change)},\n\tauthor = {Khider, Deborah and Emile-Geay, Julien and Zhu, Feng and James, Alexander and Landers, Jordan and Ratnakar, Varun and Gil, Yolanda},\n\tmonth = jul,\n\tyear = {2022},\n\tdoi = {10.1002/essoar.10511883.1},\n}\n\n

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\n \n\n \n \n \n \n \n A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation.\n \n \n \n\n\n \n Zhu, F.; Emile-Geay, J.; Anchukaitis, K. J.; Hakim, G. J.; Wittenberg, A. T.; Morales, M. S.; Toohey, M.; and King, J.\n\n\n \n\n\n\n Nature communications, 13(1): 1–9. 2022.\n ISBN: 2041-1723 Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zhu_re-appraisal_2022,\n\ttitle = {A re-appraisal of the {ENSO} response to volcanism with paleoclimate data assimilation},\n\tvolume = {13},\n\tcopyright = {All rights reserved},\n\tnumber = {1},\n\tjournal = {Nature communications},\n\tauthor = {Zhu, Feng and Emile-Geay, Julien and Anchukaitis, Kevin J. and Hakim, Gregory J. and Wittenberg, Andrew T. and Morales, Mariano S. and Toohey, Matthew and King, Jonathan},\n\tyear = {2022},\n\tnote = {ISBN: 2041-1723\nPublisher: Nature Publishing Group},\n\tpages = {1--9},\n}\n\n

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\n \n\n \n \n \n \n \n \n PaleoRec: A sequential recommender system for the annotation of paleoclimate datasets.\n \n \n \n \n\n\n \n Manety, S.; Khider, D.; Heiser, C.; McKay, N.; Emile-Geay, J.; and Routson, C.\n\n\n \n\n\n\n Environmental Data Science, 1: e4–undefined. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"PaleoRec:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{manety_paleorec_2022,\n\ttitle = {{PaleoRec}: {A} sequential recommender system for the annotation of paleoclimate datasets},\n\tvolume = {1},\n\tissn = {2634-4602},\n\turl = {https://www.cambridge.org/core/article/paleorec-a-sequential-recommender-system-for-the-annotation-of-paleoclimate-datasets/30B71DFD01C99A3773B4CEAAB2497B2D},\n\tdoi = {10.1017/eds.2022.3},\n\tabstract = {Studying past climate variability is fundamental to our understanding of current changes. In the era of Big Data, the value of paleoclimate information critically depends on our ability to analyze large volume of data, which itself hinges on standardization. Standardization also ensures that these datasets are more Findable, Accessible, Interoperable, and Reusable. Building upon efforts from the paleoclimate community to standardize the format, terminology, and reporting of paleoclimate data, this article describes PaleoRec, a recommender system for the annotation of such datasets. The goal is to assist scientists in the annotation task by reducing and ranking relevant entries in a drop-down menu. Scientists can either choose the best option for their metadata or enter the appropriate information manually. PaleoRec aims to reduce the time to science while ensuring adherence to community standards. PaleoRec is a type of sequential recommender system based on a recurrent neural network that takes into consideration the short-term interest of a user in a particular dataset. The model was developed using 1996 expert-annotated datasets, resulting in 6,512 sequences. The performance of the algorithm, as measured by the Hit Ratio, varies between 0.7 and 1.0. PaleoRec is currently deployed on a web interface used for the annotation of paleoclimate datasets using emerging community standards.},\n\tjournal = {Environmental Data Science},\n\tauthor = {Manety, Shravya and Khider, Deborah and Heiser, Christopher and McKay, Nicholas and Emile-Geay, Julien and Routson, Cody},\n\tyear = {2022},\n\tpages = {e4--undefined},\n}\n\n

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\n \n\n \n \n \n \n \n geoChronR – an R package to model, analyze, and visualize age-uncertain data.\n \n \n \n\n\n \n McKay, N.; Emile-Geay, J.; and Khider, D.\n\n\n \n\n\n\n Geochronology, 3(1): 149–169. March 2021.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

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@article{mckay_geochronr_2021,\n\ttitle = {{geoChronR} – an {R} package to model, analyze, and visualize age-uncertain data},\n\tvolume = {3},\n\tdoi = {10.5194/gchron-3-149-2021},\n\tnumber = {1},\n\tjournal = {Geochronology},\n\tauthor = {McKay, Nicholas and Emile-Geay, Julien and Khider, Deborah},\n\tmonth = mar,\n\tyear = {2021},\n\tkeywords = {GeoChronR},\n\tpages = {149--169},\n}\n\n

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\n \n\n \n \n \n \n \n \n A global database of Holocene paleotemperature records.\n \n \n \n \n\n\n \n Kaufman, D.; McKay, N.; Routson, C.; Erb, M.; Davis, B.; Heiri, O.; Jaccard, S.; Tierney, J.; Dätwyler, C.; Axford, Y.; Brussel, T.; Cartapanis, O.; Chase, B.; Dawson, A.; de Vernal, A.; Engels, S.; Jonkers, L.; Marsicek, J.; Moffa-Sánchez, P.; Morrill, C.; Orsi, A.; Rehfeld, K.; Saunders, K.; Sommer, P. S.; Thomas, E.; Tonello, M.; Tóth, M.; Vachula, R.; Andreev, A.; Bertrand, S.; Biskaborn, B.; Bringué, M.; Brooks, S.; Caniupán, M.; Chevalier, M.; Cwynar, L.; Emile-Geay, J.; Fegyveresi, J.; Feurdean, A.; Finsinger, W.; Fortin, M.; Foster, L.; Fox, M.; Gajewski, K.; Grosjean, M.; Hausmann, S.; Heinrichs, M.; Holmes, N.; Ilyashuk, B.; Ilyashuk, E.; Juggins, S.; Khider, D.; Koinig, K.; Langdon, P.; Larocque-Tobler, I.; Li, J.; Lotter, A.; Luoto, T.; Mackay, A.; Magyari, E.; Malevich, S.; Mark, B.; Massaferro, J.; Montade, V.; Nazarova, L.; Novenko, E.; Pařil, P.; Pearson, E.; Peros, M.; Pienitz, R.; Płóciennik, M.; Porinchu, D.; Potito, A.; Rees, A.; Reinemann, S.; Roberts, S.; Rolland, N.; Salonen, S.; Self, A.; Seppä, H.; Shala, S.; St-Jacques, J.; Stenni, B.; Syrykh, L.; Tarrats, P.; Taylor, K.; van den Bos, V.; Velle, G.; Wahl, E.; Walker, I.; Wilmshurst, J.; Zhang, E.; and Zhilich, S.\n\n\n \n\n\n\n Scientific Data, 7(1): 115. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

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@article{kaufman_global_2020,\n\ttitle = {A global database of {Holocene} paleotemperature records},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {http://www.nature.com/articles/s41597-020-0445-3},\n\tdoi = {10.1038/s41597-020-0445-3},\n\tabstract = {Abstract \n            A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51\\%), marine sediment (31\\%), peat (11\\%), glacier ice (3\\%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-03-29},\n\tjournal = {Scientific Data},\n\tauthor = {Kaufman, Darrell and McKay, Nicholas and Routson, Cody and Erb, Michael and Davis, Basil and Heiri, Oliver and Jaccard, Samuel and Tierney, Jessica and Dätwyler, Christoph and Axford, Yarrow and Brussel, Thomas and Cartapanis, Olivier and Chase, Brian and Dawson, Andria and de Vernal, Anne and Engels, Stefan and Jonkers, Lukas and Marsicek, Jeremiah and Moffa-Sánchez, Paola and Morrill, Carrie and Orsi, Anais and Rehfeld, Kira and Saunders, Krystyna and Sommer, Philipp S. and Thomas, Elizabeth and Tonello, Marcela and Tóth, Mónika and Vachula, Richard and Andreev, Andrei and Bertrand, Sebastien and Biskaborn, Boris and Bringué, Manuel and Brooks, Stephen and Caniupán, Magaly and Chevalier, Manuel and Cwynar, Les and Emile-Geay, Julien and Fegyveresi, John and Feurdean, Angelica and Finsinger, Walter and Fortin, Marie-Claude and Foster, Louise and Fox, Mathew and Gajewski, Konrad and Grosjean, Martin and Hausmann, Sonja and Heinrichs, Markus and Holmes, Naomi and Ilyashuk, Boris and Ilyashuk, Elena and Juggins, Steve and Khider, Deborah and Koinig, Karin and Langdon, Peter and Larocque-Tobler, Isabelle and Li, Jianyong and Lotter, André and Luoto, Tomi and Mackay, Anson and Magyari, Eniko and Malevich, Steven and Mark, Bryan and Massaferro, Julieta and Montade, Vincent and Nazarova, Larisa and Novenko, Elena and Pařil, Petr and Pearson, Emma and Peros, Matthew and Pienitz, Reinhard and Płóciennik, Mateusz and Porinchu, David and Potito, Aaron and Rees, Andrew and Reinemann, Scott and Roberts, Stephen and Rolland, Nicolas and Salonen, Sakari and Self, Angela and Seppä, Heikki and Shala, Shyhrete and St-Jacques, Jeannine-Marie and Stenni, Barbara and Syrykh, Liudmila and Tarrats, Pol and Taylor, Karen and van den Bos, Valerie and Velle, Gaute and Wahl, Eugene and Walker, Ian and Wilmshurst, Janet and Zhang, Enlou and Zhilich, Snezhana},\n\tmonth = dec,\n\tyear = {2020},\n\tkeywords = {PAGES},\n\tpages = {115},\n}\n\n

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\n \n\n \n \n \n \n \n \n Towards automating time series analysis for the paleogeosciences.\n \n \n \n \n\n\n \n Khider, D.; Athreya, P.; Ratnakar, V.; Gil, Y.; Zhu, F.; Kwan, M.; and Emile-Geay, J.\n\n\n \n\n\n\n In San Diego, California, USA, 2020. ACM, New York, NY, USA\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

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@inproceedings{khider_towards_2020,\n\taddress = {San Diego, California, USA},\n\ttitle = {Towards automating time series analysis for the paleogeosciences},\n\turl = {https://github.com/khider/khider.github.io/blob/master/papers/KDD_TimeSeries_Workshop_revised.pdf},\n\tabstract = {There is an abundance of time series data in many domains. Analyz- ing this data effectively requires deep expertise acquired over many years of practice. Our goal is to develop automated systems for time series analysis that can take advantage of proven methods that yield the best results. Our work is motivated by paleogeosciences time series analysis where the datasets are very challenging and require sophisticated methods to find and quantify subtle patterns. We describe our initial implementation of AutoTS, an automated system for time series analysis that uses semantic workflows to rep- resent sophisticated methods and their constraints. AutoTS extends the WINGS workflow system with new capabilities to customize general methods to specific datasets based on key characteristics of the data. We discuss general methods for spectral analysis and their implementation in AutoTS.},\n\tpublisher = {ACM, New York, NY, USA},\n\tauthor = {Khider, Deborah and Athreya, Pratheek and Ratnakar, Varun and Gil, Yolanda and Zhu, Feng and Kwan, Myron and Emile-Geay, Julien},\n\tyear = {2020},\n\tkeywords = {AutoTS},\n}\n

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\n \n 2019\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n Consistent multi-decadal variability in global temperature reconstructions and simulations over the Common Era.\n \n \n \n\n\n \n Neukom, R.; Barboza, L. A.; Erb, M. P.; Shi, F.; Emile-Geay, J.; Evans, M. N.; Franke, J.; Kaufman, D. S.; Lücke, L.; and Rehfeld, K.\n\n\n \n\n\n\n Nature geoscience, 12(8): 643. 2019.\n Publisher: Europe PMC Funders\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{neukom_consistent_2019,\n\ttitle = {Consistent multi-decadal variability in global temperature reconstructions and simulations over the {Common} {Era}},\n\tvolume = {12},\n\tcopyright = {All rights reserved},\n\tnumber = {8},\n\tjournal = {Nature geoscience},\n\tauthor = {Neukom, Raphael and Barboza, Luis A. and Erb, Michael P. and Shi, Feng and Emile-Geay, Julien and Evans, Michael N. and Franke, Jörg and Kaufman, Darrell S. and Lücke, Lucie and Rehfeld, Kira},\n\tyear = {2019},\n\tnote = {Publisher: Europe PMC Funders},\n\tpages = {643},\n}\n\n

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\n \n\n \n \n \n \n \n Climate models can correctly simulate the continuum of global-average temperature variability.\n \n \n \n\n\n \n Zhu, F.; Emile-Geay, J.; McKay, N. P.; Hakim, G. J.; Khider, D.; Ault, T. R.; Steig, E. J.; Dee, S.; and Kirchner, J. W.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 116(18): 8728–8733. 2019.\n ISBN: 0027-8424 Publisher: National Acad Sciences\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zhu_climate_2019,\n\ttitle = {Climate models can correctly simulate the continuum of global-average temperature variability},\n\tvolume = {116},\n\tcopyright = {All rights reserved},\n\tnumber = {18},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Zhu, Feng and Emile-Geay, Julien and McKay, Nicholas P. and Hakim, Gregory J. and Khider, Deborah and Ault, Toby R. and Steig, Eric J. and Dee, Sylvia and Kirchner, James W.},\n\tyear = {2019},\n\tnote = {ISBN: 0027-8424\nPublisher: National Acad Sciences},\n\tpages = {8728--8733},\n}\n\n

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\n \n\n \n \n \n \n \n \n PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data.\n \n \n \n \n\n\n \n Khider, D.; Emile‐Geay, J.; McKay, N. P.; Gil, Y.; Garijo, D.; Ratnakar, V.; Alonso‐Garcia, M.; Bertrand, S.; Bothe, O.; Brewer, P.; Bunn, A.; Chevalier, M.; Comas‐Bru, L.; Csank, A.; Dassié, E.; DeLong, K.; Felis, T.; Francus, P.; Frappier, A.; Gray, W.; Goring, S.; Jonkers, L.; Kahle, M.; Kaufman, D.; Kehrwald, N. M.; Martrat, B.; McGregor, H.; Richey, J.; Schmittner, A.; Scroxton, N.; Sutherland, E.; Thirumalai, K.; Allen, K.; Arnaud, F.; Axford, Y.; Barrows, T.; Bazin, L.; Pilaar Birch, S. E.; Bradley, E.; Bregy, J.; Capron, E.; Cartapanis, O.; Chiang, H.; Cobb, K. M.; Debret, M.; Dommain, R.; Du, J.; Dyez, K.; Emerick, S.; Erb, M. P.; Falster, G.; Finsinger, W.; Fortier, D.; Gauthier, N.; George, S.; Grimm, E.; Hertzberg, J.; Hibbert, F.; Hillman, A.; Hobbs, W.; Huber, M.; Hughes, A. L. C.; Jaccard, S.; Ruan, J.; Kienast, M.; Konecky, B.; Le Roux, G.; Lyubchich, V.; Novello, V. F.; Olaka, L.; Partin, J. W.; Pearce, C.; Phipps, S. J.; Pignol, C.; Piotrowska, N.; Poli, M.; Prokopenko, A.; Schwanck, F.; Stepanek, C.; Swann, G. E. A.; Telford, R.; Thomas, E.; Thomas, Z.; Truebe, S.; Gunten, L.; Waite, A.; Weitzel, N.; Wilhelm, B.; Williams, J.; Williams, J. J.; Winstrup, M.; Zhao, N.; and Zhou, Y.\n\n\n \n\n\n\n Paleoceanography and Paleoclimatology, 34(10): 1570–1596. October 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"PaCTS$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

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@article{khider_pacts_2019,\n\ttitle = {{PaCTS} 1.0: {A} {Crowdsourced} {Reporting} {Standard} for {Paleoclimate} {Data}},\n\tvolume = {34},\n\tissn = {2572-4517, 2572-4525},\n\tshorttitle = {{PaCTS} 1.0},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019PA003632},\n\tdoi = {10.1029/2019PA003632},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2021-03-29},\n\tjournal = {Paleoceanography and Paleoclimatology},\n\tauthor = {Khider, D. and Emile‐Geay, J. and McKay, N. P. and Gil, Y. and Garijo, D. and Ratnakar, V. and Alonso‐Garcia, M. and Bertrand, S. and Bothe, O. and Brewer, P. and Bunn, A. and Chevalier, M. and Comas‐Bru, L. and Csank, A. and Dassié, E. and DeLong, K. and Felis, T. and Francus, P. and Frappier, A. and Gray, W. and Goring, S. and Jonkers, L. and Kahle, M. and Kaufman, D. and Kehrwald, N. M. and Martrat, B. and McGregor, H. and Richey, J. and Schmittner, A. and Scroxton, N. and Sutherland, E. and Thirumalai, K. and Allen, K. and Arnaud, F. and Axford, Y. and Barrows, T. and Bazin, L. and Pilaar Birch, S. E. and Bradley, E. and Bregy, J. and Capron, E. and Cartapanis, O. and Chiang, H.‐W. and Cobb, K. M. and Debret, M. and Dommain, R. and Du, J. and Dyez, K. and Emerick, S. and Erb, M. P. and Falster, G. and Finsinger, W. and Fortier, D. and Gauthier, Nicolas and George, S. and Grimm, E. and Hertzberg, J. and Hibbert, F. and Hillman, A. and Hobbs, W. and Huber, M. and Hughes, A. L. C. and Jaccard, S. and Ruan, J. and Kienast, M. and Konecky, B. and Le Roux, G. and Lyubchich, V. and Novello, V. F. and Olaka, L. and Partin, J. W. and Pearce, C. and Phipps, S. J. and Pignol, C. and Piotrowska, N. and Poli, M.‐S. and Prokopenko, A. and Schwanck, F. and Stepanek, C. and Swann, G. E. A. and Telford, R. and Thomas, E. and Thomas, Z. and Truebe, S. and Gunten, L. and Waite, A. and Weitzel, N. and Wilhelm, B. and Williams, J. and Williams, J. J. and Winstrup, M. and Zhao, N. and Zhou, Y.},\n\tmonth = oct,\n\tyear = {2019},\n\tkeywords = {Standard Development},\n\tpages = {1570--1596},\n}\n\n

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\n \n 2017\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n A Controlled Crowdsourcing Approach for Practical Ontology Extensions and Metadata Annotations.\n \n \n \n \n\n\n \n Gil, Y.; Garijo, D.; Ratnakar, V.; Khider, D.; Emile-Geay, J.; and McKay, N.\n\n\n \n\n\n\n In d'Amato , C.; Fernandez, M.; Tamma, V.; Lecue, F.; Cudré-Mauroux, P.; Sequeda, J.; Lange, C.; and Heflin, J., editor(s), The Semantic Web – ISWC 2017, volume 10588, pages 231–246. Springer International Publishing, Cham, 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@incollection{damato_controlled_2017,\n\taddress = {Cham},\n\ttitle = {A {Controlled} {Crowdsourcing} {Approach} for {Practical} {Ontology} {Extensions} and {Metadata} {Annotations}},\n\tvolume = {10588},\n\tisbn = {9783319682037 9783319682044},\n\turl = {http://link.springer.com/10.1007/978-3-319-68204-4_24},\n\tlanguage = {en},\n\turldate = {2021-03-29},\n\tbooktitle = {The {Semantic} {Web} – {ISWC} 2017},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Gil, Yolanda and Garijo, Daniel and Ratnakar, Varun and Khider, Deborah and Emile-Geay, Julien and McKay, Nicholas},\n\teditor = {d'Amato, Claudia and Fernandez, Miriam and Tamma, Valentina and Lecue, Freddy and Cudré-Mauroux, Philippe and Sequeda, Juan and Lange, Christoph and Heflin, Jeff},\n\tyear = {2017},\n\tdoi = {10.1007/978-3-319-68204-4_24},\n\tkeywords = {LinkedEarth wiki, Standard Development},\n\tpages = {231--246},\n}\n\n

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\n \n 2016\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n The Linked Paleo Data framework–a common tongue for paleoclimatology.\n \n \n \n\n\n \n McKay, N. P.; and Emile-Geay, J.\n\n\n \n\n\n\n Climate of the Past, 12(4): 1093–1100. 2016.\n ISBN: 1814-9324 Publisher: Copernicus GmbH\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mckay_linked_2016,\n\ttitle = {The {Linked} {Paleo} {Data} framework–a common tongue for paleoclimatology},\n\tvolume = {12},\n\tcopyright = {All rights reserved},\n\tnumber = {4},\n\tjournal = {Climate of the Past},\n\tauthor = {McKay, Nicholas P. and Emile-Geay, Julien},\n\tyear = {2016},\n\tnote = {ISBN: 1814-9324\nPublisher: Copernicus GmbH},\n\tpages = {1093--1100},\n}\n\n

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