The Decadal State of the Terrestrial Carbon Cycle: Global Retrievals of Terrestrial Carbon Allocation, Pools, and Residence Times. Bloom, A. A., Exbrayat, J., van der Velde, I. R., Feng, L., & Williams, M. 113(5):1285–1290.
The Decadal State of the Terrestrial Carbon Cycle: Global Retrievals of Terrestrial Carbon Allocation, Pools, and Residence Times [link]Paper  doi  abstract   bibtex   
[Significance] Quantitative knowledge of terrestrial carbon pathways and processes is fundamental for understanding the biosphere's response to a changing climate. Carbon allocation, stocks, and residence times together define the dynamic state of the terrestrial carbon cycle. These quantities are difficult to measure and remain poorly quantified on a global scale. Here, we retrieve global 1° × 1° carbon state and process variables by combining a carbon balance model with satellite observations of biomass and leaf area (where and when available) and global soil carbon data. Our results reveal emergent continental-scale patterns and relationships between carbon states and processes. We find that conventional land cover types cannot capture continental-scale variations of retrieved carbon variables; this mismatch has strong implications for terrestrial carbon cycle predictions. [Abstract] The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85-88%) in contrast to higher latitudes (73-82%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42-0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64-0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types. [Excerpt] [] [...] Given an increasing number of C cycle observations, what remains an outstanding challenge is to produce a data-consistent analysis of terrestrial C cycling – including retrievals of C fluxes, C pools, autotrophic respiration, allocation fractions, and residence times – based on multiple global-scale earth observations and datasets. Current global-scale terrestrial biosphere models, because of their complexity and structures, are ill-equipped to ingest an ever-increasing volume of earth observations to estimate (rather than prescribe) model parameters based on the currently available observations. To overcome this challenge, we use a model-data fusion (MDF) approach to retrieve terrestrial C state and process variables during the period 2001-2010 without invoking plant functional type or steady-state assumptions. We bring together global MODIS LAI, a tropical biomass map (24), a soil C dataset (23), MODIS burned area (34), and a diagnostic ecosystem C balance model [Data Assimilation Linked Ecosystem Carbon Model version two (DALEC2)] (19, 35) to retrieve C state and process variables by producing a novel data-consistent and spatially explicit analysis of terrestrial C cycling on a global 1° × 1° grid (Fig. 1) [we henceforth refer to this MDF setup as the C data model framework (CARDAMOM)]. Specifically, we address the following questions: [::] How is C uptake partitioned between the live biomass pools and respiration? [::] What is the residence time of C within the major ecosystem C pools? [::] How do estimates of C cycle states and processes vary spatially, and to what degree do emergent variable patterns match land cover maps? [] We use a Markov Chain Monte Carlo MDF algorithm to retrieve C state and process variables – and their associated uncertainty – within each 1° × 1° grid cell (Materials and Methods). The MDF approach retrieves the state and process variables that minimize the model mismatch against any available C cycle observations. Therefore, in the absence of extratropical biomass data or wintertime MODIS LAI observations, estimates of 2001-2010 C cycle state and process variables are achievable, albeit more uncertain. [] [...] [Discussion] Typically, C allocation and residence time parameters are based on land cover types in global-scale terrestrial C cycle studies (refs. 9 and 22 among others); here, spatially broad allocation and residence patterns emerge instead as a result of the MDF approach. For example, high-biomass ecosystems throughout the wet tropics display similar C allocation, residence time, and LCMA configurations (Figs. 2-5). Similarly, we find that dead organic matter (DOM) C residence is generally longer in high latitudes (Fig. 3). Compared with conventional land cover types, EOFs 1-4 account for a larger degree of the spatial structures in retrieved C variables (Fig. 6); for most variables, the two dominant EOF modes – which together reflect first-order variations in latitude and global precipitation patterns (Fig. S5) – explain more spatial variability than GLOBCOVER land cover types. The mismatch between land cover types and retrieved variables has major implications for the estimation and prediction of terrestrial C cycling, which is currently based on small sets of globally applied parameters linked to land cover types. The importance of climate, biodiversity, fire, and anthropogenic disturbance in generating these mismatches needs to be explored in additional research (42). [] [...] The CARDAMOM approach provides a framework to test alternative model structures (54): in this manner, combined C cycle model parametric and structural uncertainties can be characterized, while ensuring consistency between models and global-scale datasets. This assessment would amount to a major step forward from conventional C cycle model intercomparison studies. Ultimately, an ensemble of models can be used to determine the degree to which retrievals of key C state and process variables are model-dependent. Moreover, alternative model structures could be used in CARDAMOM to assimilate globally spanning plant traits related to C cycling (55) and satellite observations, such as solar-induced fluorescence (27), vegetation optical depth (56), soil moisture (57, 58), and changes in aboveground biomass (25, 59, 60). We anticipate that the incorporation of additional datasets and alternative model structures into CARDAMOM will generate quantifiable reductions in retrieved C variable uncertainties and new ecological insights on the state of the terrestrial C cycle. [] [...]
@article{bloomDecadalStateTerrestrial2016,
  title = {The Decadal State of the Terrestrial Carbon Cycle: Global Retrievals of Terrestrial Carbon Allocation, Pools, and Residence Times},
  author = {Bloom, Anthony A. and Exbrayat, Jean-François and van der Velde, Ivar R. and Feng, Liang and Williams, Mathew},
  date = {2016-02},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {113},
  pages = {1285--1290},
  issn = {1091-6490},
  doi = {10.1073/pnas.1515160113},
  url = {http://mfkp.org/INRMM/article/13924544},
  abstract = {[Significance]

Quantitative knowledge of terrestrial carbon pathways and processes is fundamental for understanding the biosphere's response to a changing climate. Carbon allocation, stocks, and residence times together define the dynamic state of the terrestrial carbon cycle. These quantities are difficult to measure and remain poorly quantified on a global scale. Here, we retrieve global 1° × 1° carbon state and process variables by combining a carbon balance model with satellite observations of biomass and leaf area (where and when available) and global soil carbon data. Our results reveal emergent continental-scale patterns and relationships between carbon states and processes. We find that conventional land cover types cannot capture continental-scale variations of retrieved carbon variables; this mismatch has strong implications for terrestrial carbon cycle predictions. [Abstract]

The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85-88\%) in contrast to higher latitudes (73-82\%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42-0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64-0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types.

[Excerpt] 

[] [...]

Given an increasing number of C cycle observations, what remains an outstanding challenge is to produce a data-consistent analysis of terrestrial C cycling -- including retrievals of C fluxes, C pools, autotrophic respiration, allocation fractions, and residence times -- based on multiple global-scale earth observations and datasets. Current global-scale terrestrial biosphere models, because of their complexity and structures, are ill-equipped to ingest an ever-increasing volume of earth observations to estimate (rather than prescribe) model parameters based on the currently available observations. To overcome this challenge, we use a model-data fusion (MDF) approach to retrieve terrestrial C state and process variables during the period 2001-2010 without invoking plant functional type or steady-state assumptions. We bring together global MODIS LAI, a tropical biomass map (24), a soil C dataset (23), MODIS burned area (34), and a diagnostic ecosystem C balance model [Data Assimilation Linked Ecosystem Carbon Model version two (DALEC2)] (19, 35) to retrieve C state and process variables by producing a novel data-consistent and spatially explicit analysis of terrestrial C cycling on a global 1° × 1° grid (Fig. 1) [we henceforth refer to this MDF setup as the C data model framework (CARDAMOM)]. Specifically, we address the following questions: 

[::] How is C uptake partitioned between the live biomass pools and respiration? [::] What is the residence time of C within the major ecosystem C pools? [::] How do estimates of C cycle states and processes vary spatially, and to what degree do emergent variable patterns match land cover maps? 

[] We use a Markov Chain Monte Carlo MDF algorithm to retrieve C state and process variables -- and their associated uncertainty -- within each 1° × 1° grid cell (Materials and Methods). The MDF approach retrieves the state and process variables that minimize the model mismatch against any available C cycle observations. Therefore, in the absence of extratropical biomass data or wintertime MODIS LAI observations, estimates of 2001-2010 C cycle state and process variables are achievable, albeit more uncertain. 

[] [...]

[Discussion]

Typically, C allocation and residence time parameters are based on land cover types in global-scale terrestrial C cycle studies (refs. 9 and 22 among others); here, spatially broad allocation and residence patterns emerge instead as a result of the MDF approach. For example, high-biomass ecosystems throughout the wet tropics display similar C allocation, residence time, and LCMA configurations (Figs. 2-5). Similarly, we find that dead organic matter (DOM) C residence is generally longer in high latitudes (Fig. 3). Compared with conventional land cover types, EOFs 1-4 account for a larger degree of the spatial structures in retrieved C variables (Fig. 6); for most variables, the two dominant EOF modes -- which together reflect first-order variations in latitude and global precipitation patterns (Fig. S5) -- explain more spatial variability than GLOBCOVER land cover types. The mismatch between land cover types and retrieved variables has major implications for the estimation and prediction of terrestrial C cycling, which is currently based on small sets of globally applied parameters linked to land cover types. The importance of climate, biodiversity, fire, and anthropogenic disturbance in generating these mismatches needs to be explored in additional research (42). 

[] [...]

The CARDAMOM approach provides a framework to test alternative model structures (54): in this manner, combined C cycle model parametric and structural uncertainties can be characterized, while ensuring consistency between models and global-scale datasets. This assessment would amount to a major step forward from conventional C cycle model intercomparison studies. Ultimately, an ensemble of models can be used to determine the degree to which retrievals of key C state and process variables are model-dependent. Moreover, alternative model structures could be used in CARDAMOM to assimilate globally spanning plant traits related to C cycling (55) and satellite observations, such as solar-induced fluorescence (27), vegetation optical depth (56), soil moisture (57, 58), and changes in aboveground biomass (25, 59, 60). We anticipate that the incorporation of additional datasets and alternative model structures into CARDAMOM will generate quantifiable reductions in retrieved C variable uncertainties and new ecological insights on the state of the terrestrial C cycle.

[] [...]},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13924544,~to-add-doi-URL,carbon-cycle,featured-publication,forest-resources,global-scale,integrated-modelling,integration-techniques,knowledge-integration,land-cover,open-data},
  number = {5},
  options = {useprefix=true}
}
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