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: In spite of the importance of phosphorus (P) as a limiting nutrient in terrestrial ecosystems, our understanding of terrestrial P dynamics and our ability to model P cycling are hampered by the lack of consistent measurements of soil P. The Hedley fractionation method provides a comprehensive assessment of soil P and has been widely used in recent decades. Here we expand an earlier study that summarized Hedley P data from the literature to create a larger Hedley P database and further investigate the relationships between distributions of different forms of P and the stages of soil development. Our expanded Hedley P database generally supports what the Walker and Syers (1976) conceptual model predicts: the gradual decrease and eventual depletion of primary mineral P (mainly apatite P); the continual increase and eventual dominance of occluded P; and the overall decrease of total P during soil development. However the analysis disagrees with Walker and Syers (1976) in that we found labile inorganic P(Pi) and secondary Pi (non-occluded P in Walker and Syers' model) to be a significant fraction of total P throughout all soil orders with different weathering stages. By analyzing the Hedley-labile P and vegetation P demand, we found that the amount of labile P is much greater than vegetation demand, even in highly weathered soils commonly considered P limited. We conclude that measured Hedley-labile P should not be defined as plant available P since most of this labile P likely ends up as immobilized by microbes. Our analysis of the database also shows that carbon (C) and nitrogen (N) in soil organic matter are closely linked in all soil orders, but P is decoupled from C and N in highly weathered soils with larger variations of nitrogen:organic P (N:Po) ratio and higher mean values of N:Po ratio, compared to slightly and intermediately weathered soils.

Description: The Unified North American Soil Map (UNASM) was developed to provide more accurate regional soil information for terrestrial biosphere modeling. The UNASM combines information from state-of-the-art US STATSGO2 and Soil Landscape of Canada (SLCs) databases. The area not covered by these datasets is filled with the Harmonized World Soil Database version 1.1 (HWSD1.1). The UNASM contains maximum soil depth derived from the data source as well as seven soil attributes (including sand, silt, and clay content, gravel content, organic carbon content, pH, and bulk density) for the top soil layer (0–30 cm) and the sub soil layer (30–100 cm) respectively, of the spatial resolution of 0.25° in latitude and longitude. There are pronounced differences in the spatial distributions of soil properties and soil organic carbon between UNASM and HWSD, but the UNASM overall provides more detailed and higher-quality information particularly in Alaska and Central Canada. To provide more accurate and up-to-date estimate of soil organic carbon stock in North America, we incorporated Northern Circumpolar Soil Carbon Database (NCSCD) into the UNASM. The estimate of total soil organic carbon mass in the upper 100 cm soil profile based on the improved UNASM is 347.70 Pg, of which 24.7% is under trees, 14.2% is under shrubs, and 1.3% is under grasses and 3.8% under crops. This UNASM data will provide a resource for use in land surface and terrestrial biogeochemistry modeling both for input of soil characteristics and for benchmarking model output.

Description: Phosphorus (P) is a major element required for biological activity in terrestrial ecosystems. Although the total P content in most soils can be large, only a small fraction is available or in an organic form for biological utilization because it is bound either in incompletely weathered mineral particles, adsorbed on mineral surfaces, or, over the time of soil formation, made unavailable by secondary mineral formation (occluded). In order to adequately represent phosphorus availability in global biogeochemistry-climate models, a representation of the amount and form of P in soils globally is required. We develop an approach that builds on existing knowledge of soil P processes and databases of parent material and soil P measurements to provide spatially explicit estimates of different forms of soil P on the global scale. We assembled data on the various forms of phosphorus in soils globally, chronosequence information, and several global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils globally. The amount of P, to 50 cm soil depth, in soil labile, organic, occluded, and secondary pools is 3.5 ± 3, 8.7 ± 6, 13.2 ± 9, and 3.3 ± 2 Pg P respectively. The amount in soil mineral particles to the same depth is estimated at 12.5 ± 9 Pg P for a global soil total of 41.2 ± 20 Pg P. The large uncertainty in our estimates reflects our limited understanding of the processes controlling soil P transformations during pedogenesis and lack of measurements of soil P. In spite of the large uncertainty, the estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization.

Description: Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and century scales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for predicting future carbon-climate feedbacks. We compared soil carbon predictions from 16 ESMs to empirical data from the Harmonized World Soil Database (HWSD) and Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3050 Pg C, compared to an estimate of 890–1660 Pg C from the HWSD. Model predictions for the high latitudes fell between 60 and 800 Pg C, compared to 380–620 Pg C from the NCSCD and 290 Pg C from the HWSD. This 5.3-fold variation in global soil carbon across models compared to a 3.4-fold variation in net primary productivity (NPP) and a 3.8-fold variation in global soil carbon turnover times. The spatial distribution of soil carbon predicted by the ESMs was not well correlated with the HWSD (Pearson's correlations 〈 0.4, RMSE 9.4 to 22.8 kg C m−2), although model-data agreement generally improved at the biome scale. There was poor agreement between the HWSD and NCSCD datasets in northern latitudes (Pearson's correlation = 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained most of the spatial variation in soil carbon predicted by most ESMs (R2 values between 0.73 and 0.93). This result suggests that differences in soil carbon predictions between ESMs are driven primarily by differences in predicted NPP and the parameterization of soil carbon responses to NPP and temperature not by structural differences between the models. Future work should focus on accurately representing these driving variables and modifying model structure to include additional processes.

Description: Tropical forests play a significant role in the global carbon cycle and global climate. However, tropical carbon cycling and the feedbacks from tropical ecosystems to the climate system remain critical uncertainties in current generation carbon-climate models. One of the major uncertainties comes from the lack of representation of phosphorus (P), the most limiting nutrient in tropical regions. Here we introduce P dynamics and C–N–P interactions into the CLM4-CN model and investigate the role of P cycling in controlling the productivity of tropical ecosystems. The newly developed CLM-CNP model includes all major biological and geochemical processes controlling P availability in soils and the interactions between C, N, and P cycles. Model simulations at sites along a Hawaiian soil chronosequence indicate that the introduction of P limitation greatly improved the model performance at the P-limited site. The model is also able to capture the shift in nutrient limitation along this chronosequence (from N limited to P limited), as shown in the comparison of model simulated plant responses to fertilization with the observed data. Model simulations at Amazonian forest sites show that CLM-CNP is capable of capturing the overall trend in NPP along the P availability gradient. This comparison also suggests a significant interaction between nutrient limitation and land use history. Model experiments under elevated atmospheric CO2 ([CO2]) condition suggest that tropical forest responses to increasing [CO2] will interact strongly with changes in the P cycle. We highlight the importance of two feedback pathways (biochemical mineralization and desorption of secondary mineral P) that can significantly affect P availability and determine the extent of P limitation in tropical forests under elevated [CO2]. Field experiments with elevated CO2 are therefore needed to help quantify these important feedbacks. Predictive modeling of C–P interactions will have important implications for the prediction of future carbon uptake and storage in tropical ecosystems and global climate change.

Description: Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon–climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040 Pg C, compared to an estimate of 1260 Pg C (with a 95% confidence interval of 890–1660 Pg C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820 Pg C, compared to 500 Pg C (with a 95% confidence interval of 380–620 Pg C) for the NCSCD and 290 Pg C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model–data agreement was moderate at the biome level (R2 values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1° scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8 kg C m−2). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1° spatial variation in soil carbon within most ESMs (R2 values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model R2 = 0.93), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes.

Description: In spite of the importance of phosphorus (P) as a limiting nutrient in terrestrial ecosystems, our understanding of terrestrial P dynamics and our ability to model P cycling are hampered by the lack of consistent measurements of soil P. The Hedley fractionation method provides a comprehensive assessment of soil P and has been widely used in recent decades. Here we expand an earlier study that summarized Hedley P data from the literature to create a larger Hedley P database and further investigate the relationships between distributions of different forms of P and the stages of soil development. Our expanded Hedley P database generally supports what the Walker and Syers (1976) conceptual model predicts: the gradual decrease and eventual depletion of primary mineral P (mainly apatite P); the continual increase and eventual dominance of occluded P; and the overall decrease of total P during soil development. However the analysis disagrees with Walker and Syers (1976) in that we found labile inorganic P(Pi) and secondary mineral Pi (non-occluded P in Walker and Syers' model) to be a significant fraction of total P throughout all soil orders with different weathering stages. By analyzing the Hedley-labile P and vegetation P demand, we found that the amount of labile P is much greater than vegetation demand, even in highly weathered soils commonly considered P limited. We conclude that labile P measured by Hedley fractionation method should not be defined as plant available P since most of this labile P likely ends up as immobilized by microbes. Our analysis of the database also shows that carbon (C) and nitrogen (N) in soil organic matter are closely linked in all soil orders, but P is decoupled from C and N in highly weathered soils with larger variations of nitrogen:organic P (N:Po) ratio and higher mean values of N:Po ratio, compared to slightly and intermediately weathered soils.

Description: Phosphorus (P) is a major element required for biological activity in terrestrial ecosystems. Although the total P content in most soils can be large, only a small fraction is available or in an organic form for biological utilization because it is bound either in incompletely weathered mineral particles, adsorbed on mineral surfaces, or, over the time of soil formation, made unavailable by secondary mineral formation (occluded). In order to adequately represent phosphorus availability in global biogeochemistry–climate models, a representation of the amount and form of P in soils globally is required. We develop an approach that builds on existing knowledge of soil P processes and databases of parent material and soil P measurements to provide spatially explicit estimates of different forms of naturally occurring soil P on the global scale. We assembled data on the various forms of phosphorus in soils globally, chronosequence information, and several global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils globally. The amount of P, to 50cm soil depth, in soil labile, organic, occluded, and secondary pools is 3.6 ± 3, 8.6 ± 6, 12.2 ± 8, and 3.2 ± 2 Pg P (Petagrams of P, 1 Pg = 1 × 1015g) respectively. The amount in soil mineral particles to the same depth is estimated at 13.0 ± 8 Pg P for a global soil total of 40.6 ± 18 Pg P. The large uncertainty in our estimates reflects our limited understanding of the processes controlling soil P transformations during pedogenesis and a deficiency in the number of soil P measurements. In spite of the large uncertainty, the estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization.

Description: Tropical forests play a significant role in the global carbon cycle and global climate. However, tropical carbon cycling and the feedbacks from tropical ecosystems to the climate system remain critical uncertainties in the current generation of carbon–climate models. One of the major uncertainties comes from the lack of representation of phosphorus (P), currently believed to be the most limiting nutrient in tropical regions. Here we introduce P dynamics and C–N–P interactions into the CLM4-CN (Community Land Model version 4 with prognostic Carbon and Nitrogen) model and investigate the role of P cycling in controlling the productivity of tropical ecosystems. The newly developed CLM-CNP model includes all major biological and geochemical processes controlling P availability in soils and the interactions between C, N, and P cycles. Model simulations at sites along a Hawaiian soil chronosequence indicate that the introduction of P limitation greatly improved the model performance at the P-limited site. The model is also able to capture the shift in nutrient limitation along this chronosequence (from N limited to P limited), as shown in the comparison of model-simulated plant responses to fertilization with the observed data. Model simulations at Amazonian forest sites show that CLM-CNP is capable of capturing the overall trend in NPP (net primary production) along the P availability gradient. This comparison also suggests a significant interaction between nutrient limitation and land use history. Model experiments under elevated atmospheric CO2 ([CO2]) conditions suggest that tropical forest responses to increasing [CO2] will interact strongly with changes in the P cycle. We highlight the importance of two feedback pathways (biochemical mineralization and desorption of secondary mineral P) that can significantly affect P availability and determine the extent of P limitation in tropical forests under elevated [CO2]. Field experiments with elevated CO2 are therefore needed to help quantify these important feedbacks. CO2 doubling model experiments show that tropical forest response to elevated [CO2] can only be predicted if the interactions between C cycle and nutrient dynamics are well understood and represented in models. Predictive modeling of C–nutrient interactions will have important implications for the prediction of future carbon uptake and storage in tropical ecosystems and global climate change.