ALBERT

Description: Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land–atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.

Description: Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied for comparing flux estimates in light of the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that appear to have the greatest control over the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1°×1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that control the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of these factors can be linked back to model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the quantitative approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed or a detailed sensitivity analysis was performed.

Description: Scientific understanding of the global carbon cycle is required for developing national and international policy to mitigate fossil-fuel CO2 emissions by managing terrestrial carbon uptake. Toward that understanding and as a contribution to the REgional Carbon Cycle Assessment and Processes (RECCAP) project, this paper provides a synthesis of net land–atmosphere CO2 exchange for North America over the period (1990–2009). This synthesis is based on results from three different methods: atmospheric inversion, inventory-based methods and terrestrial biosphere modeling. All methods indicate that the North America land surface was a sink for atmospheric CO2, with a net transfer from atmosphere to land. Estimates ranged from −890 to −280 Tg C yr−1, where the atmospheric inversion estimate forms the lower bound of that range (a larger land-sink) and the inventory-based estimate the upper (a smaller land sink). Integrating across estimates, "best" estimates (i.e., measures of central tendency) are −472 ± 281 Tg C yr−1 based on the mean and standard deviation of the distribution and −360 Tg C yr−1 (with an interquartile range of −496 to −337) based on the median. Considering both the fossil-fuel emissions source and the land sink, our analysis shows that North America was, however, a net contributor to the growth of CO2 in the atmosphere in the late 20th and early 21st century. The continent's CO2 source to sink ratio for this time period was likely in the range of 4 : 1 to 3 : 1.

Description: Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for Alaska, we provide a baseline of terrestrial carbon cycle structural and parametric uncertainty, defined as the multi-model standard deviation (σ) against the mean (x) for each quantity. Mean annual uncertainty (σ/x) was largest for net ecosystem exchange (NEE) (−0.01± 0.19 kg C m−2 yr−1), then net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), and soil carbon (14.0± 9.2 kg C m−2). The spatial patterns in regional carbon stocks and fluxes varied widely with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Additionally, a feedback (i.e., sensitivity) analysis was conducted of 20th century NEE to CO2 fertilization (β) and climate (γ), which showed that uncertainty in γ was 2x larger than that of β, with neither indicating that the Alaskan Arctic is shifting towards a certain net carbon sink or source. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic.

Description: Robust estimates of regional-scale terrestrial CO2 exchange are needed to support carbon management policies and to improve the predictive ability of models representing carbon-climate feedbacks. Large discrepancies remain, however, both among and between CO2 flux estimates from atmospheric inverse models and terrestrial biosphere models. Improved atmospheric inverse models that provide robust estimates at sufficiently fine spatial scales could prove especially useful for monitoring efforts, while also serving as a validation tool for process-based assumptions in terrestrial biosphere models. A growing network of continental sites collecting continuous CO2 measurements provides the information needed to drive such models. This study presents results from a regional geostatistical inversion over North America for 2004, taking advantage of continuous data from the nine sites operational in that year, as well as available flask and aircraft observations. The approach does not require explicit prior flux estimates, resolves fluxes at finer spatiotemporal scales than previous North American inversion studies, and uses a Lagrangian transport model coupled with high-resolution winds (i.e. WRF-STILT) to resolve near-field influences around measurement locations. The estimated fluxes are used in an inter-comparison with other inversion studies and a suite of terrestrial biosphere model estimates collected through the North American Carbon Program Regional and Continental Interim Synthesis. Differences among inversions are found to be smallest in areas of the continent best-constrained by the atmospheric data, pointing to the value of an expanded measurement network. Aggregation errors in previous coarser-scale inversion studies are likely to explain a portion of the remaining spread. The spatial patterns from a geostatistical inversion that includes auxiliary environmental variables from the North American Regional Reanalysis were similar to those from the median of the biospheric model estimates during the growing season, but diverged more strongly in the dormant season. This could be due to a lack of sensitivity in the inversion during the dormant season, but may also point to a lack of skill in the biospheric models outside of the growing season, particularly in agricultural areas. For the annual continental budget, the boundary conditions used as an input into the inversions were seen to have a substantial impact on the estimated net flux, with a difference of ~0.8 PgC yr−1 associated with results using two different plausible sets of boundary conditions.

Description: Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied to systematically compare flux estimates despite the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that best explain the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1° × 1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that explain the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of the differences in spatial patterns of estimated flux can be linked back to differences in model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the systematic approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed, or a detailed sensitivity analysis performed.

Description: Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.

Description: Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

Description: Scientific understanding of the global carbon cycle is required for developing national and international policy to mitigate fossil fuel CO2 emissions by managing terrestrial carbon uptake. Toward that understanding and as a contribution to the REgional Carbon Cycle Assessment and Processes (RECCAP) project, this paper provides a synthesis of net land–atmosphere CO2 exchange for North America (Canada, United States, and Mexico) over the period 1990–2009. Only CO2 is considered, not methane or other greenhouse gases. This synthesis is based on results from three different methods: atmospheric inversion, inventory-based methods and terrestrial biosphere modeling. All methods indicate that the North American land surface was a sink for atmospheric CO2, with a net transfer from atmosphere to land. Estimates ranged from −890 to −280 Tg C yr−1, where the mean of atmospheric inversion estimates forms the lower bound of that range (a larger land sink) and the inventory-based estimate using the production approach the upper (a smaller land sink). This relatively large range is due in part to differences in how the approaches represent trade, fire and other disturbances and which ecosystems they include. Integrating across estimates, "best" estimates (i.e., measures of central tendency) are −472 ± 281 Tg C yr−1 based on the mean and standard deviation of the distribution and −360 Tg C yr−1 (with an interquartile range of −496 to −337) based on the median. Considering both the fossil fuel emissions source and the land sink, our analysis shows that North America was, however, a net contributor to the growth of CO2 in the atmosphere in the late 20th and early 21st century. With North America's mean annual fossil fuel CO2 emissions for the period 1990–2009 equal to 1720 Tg C yr−1 and assuming the estimate of −472 Tg C yr−1 as an approximation of the true terrestrial CO2 sink, the continent's source : sink ratio for this time period was 1720:472, or nearly 4:1.

Description: Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM model skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model-model and model-observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land-use and land-cover change (LULCC), C3/C4 grasses fractions, major crops, phenology, and soil data into a standard format for global (0.5° x 0.5° resolution) and regional (North American, 0.25° x 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by changing the quality, the spatial and temporal coverage, resolution, or a combination of these. The resulting standardized model driver data sets are being used by over 20 different models participating MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.