scholarly journals Causes of variation in soil carbon predictions from CMIP5 Earth system models and comparison with observations

2012 ◽  
Vol 9 (10) ◽  
pp. 14437-14473 ◽  
Author(s):  
K. E. O. Todd-Brown ◽  
J. T. Randerson ◽  
W. M. Post ◽  
F. M. Hoffman ◽  
C. Tarnocai ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Net Primary Productivity ◽  
Carbon Stocks ◽  
Earth System ◽  
System Models ◽  
Soil Database ◽  
Empirical Estimates ◽  
Global Soil ◽  
Soil Carbon Stocks ◽  
Earth System Models

Abstract. 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.

scholarly journals Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations

2013 ◽  
Vol 10 (3) ◽  
pp. 1717-1736 ◽  
Author(s):  
K. E. O. Todd-Brown ◽  
J. T. Randerson ◽  
W. M. Post ◽  
F. M. Hoffman ◽  
C. Tarnocai ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Soil Carbon ◽  
Pearson Correlation ◽  
Carbon Stocks ◽  
Earth System ◽  
Northern Latitudes ◽  
Global Soil ◽  
Soil Carbon Stocks ◽  
Reduced Complexity ◽  
Earth System Models

Abstract. 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.

scholarly journals Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models

2017 ◽  
Vol 14 (22) ◽  
pp. 5143-5169 ◽  
Author(s):  
Sarah E. Chadburn ◽  
Gerhard Krinner ◽  
Philipp Porada ◽  
Annett Bartsch ◽  
Christian Beer ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Nutrient Limitation ◽  
Vascular Plants ◽  
Carbon Stocks ◽  
The Arctic ◽  
Earth System ◽  
Area Index ◽  
System Models ◽  
Soil Carbon Stocks ◽  
Earth System Models

Abstract. It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.

scholarly journals Carbon stocks and fluxes in the high latitudes: Using site-level data to evaluate Earth system models

2017 ◽  
Author(s):  
Sarah Chadburn ◽  
Gerhard Krinner ◽  
Philipp Porada ◽  
Annett Bartsch ◽  
Christian Beer ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Nutrient Limitation ◽  
Vascular Plants ◽  
Carbon Stocks ◽  
The Arctic ◽  
Earth System ◽  
Area Index ◽  
System Models ◽  
Soil Carbon Stocks ◽  
Earth System Models

Abstract. It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic, due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth System Models (JSBACH, Germany; JULES, UK and ORCHIDEE, France). We use a site-level approach where comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate soil biological and physical processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus we identify three priority areas for model development: 1. Dynamic vegetation including a. climate and b. nutrient limitation effects. 2. Adding moss as a plant functional type. 3. Improved vertical profile of soil carbon including peat processes.

scholarly journals Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation

2013 ◽  
Vol 10 (6) ◽  
pp. 10229-10269
Author(s):  
J.-F. Exbrayat ◽  
A. J. Pitman ◽  
Q. Zhang ◽  
G. Abramowitz ◽  
Y.-P. Wang
Keyword(s):  
Soil Carbon ◽  
Nutrient Limitation ◽  
Historical Change ◽  
Cmip5 Models ◽  
Earth System ◽  
Microbial Decomposition ◽  
Soil C ◽  
System Models ◽  
C And N ◽  
Earth System Models

Abstract. Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth System Models. There are several sources of uncertainty in how carbon is parameterized in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to diverse changes in land–atmosphere fluxes. Second, while the parameterization of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. We address these sources uncertainty by implementing three soil moisture (SMRF) and three soil temperature (STRF) respiration functions in an Earth System Model that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of a SMRF with a STRF and a biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regions from net sinks to net sources over the historical period (1850–2005). Differences in the soil C balance implied by the various SMRFs and STRFs also change the sign of some regional sinks. Further, although the absolute uncertainty in global carbon uptake is reduced, the uncertainty due to the SMRFs and STRFs grows relative to the inter-annual variability in net uptake when N and P limitations are added. We also demonstrate that the equilibrated soil C also depend on the shape of the SMRF and STRF. Equilibration using different STRFs and SMRFs and nutrient limitation generates a six-fold range of global soil C that largely mirrors the range in available (17) CMIP5 models. Simulating the historical change in soil carbon therefore critically depends on the choice of STRF, SMRF and nutrient limitation, as it controls the equilibrated state to which transient conditions are applied. This direct effect of the representation of microbial decomposition in Earth System Models adds to recent concerns on the adequacy of these simple representations of very complex soil carbon processes.

scholarly journals Sensitivity of global soil carbon stocks to combined nutrient enrichment

2019 ◽  
Vol 22 (6) ◽  
pp. 936-945 ◽  
Author(s):  
T. W. Crowther ◽  
C. Riggs ◽  
E. M. Lind ◽  
E. T. Borer ◽  
E. W. Seabloom ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Nutrient Enrichment ◽  
Carbon Stocks ◽  
Global Soil ◽  
Soil Carbon Stocks

scholarly journals A review of the global soil property maps for Earth system models

SOIL ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 137-158 ◽  
Author(s):  
Yongjiu Dai ◽  
Wei Shangguan ◽  
Nan Wei ◽  
Qinchuan Xin ◽  
Hua Yuan ◽  
...  
Keyword(s):  
Information Needs ◽  
Soil Property ◽  
Biological Properties ◽  
Model Complexity ◽  
Earth System ◽  
System Models ◽  
Uncertainty Sources ◽  
Uncertainty Estimates ◽  
Global Soil ◽  
Earth System Models

Abstract. Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

scholarly journals Evaluating the simulated mean soil carbon transit times by Earth system models using observations

2019 ◽  
Vol 16 (4) ◽  
pp. 917-926 ◽  
Author(s):  
Jing Wang ◽  
Jianyang Xia ◽  
Xuhui Zhou ◽  
Kun Huang ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Soil Carbon ◽  
Earth System ◽  
Soil C ◽  
Global Database ◽  
System Models ◽  
Transit Times ◽  
Negative Linear Correlation ◽  
Earth System Models

Abstract. One known bias in current Earth system models (ESMs) is the underestimation of global mean soil carbon (C) transit time (τsoil), which quantifies the age of the C atoms at the time they leave the soil. However, it remains unclear where such underestimations are located globally. Here, we constructed a global database of measured τsoil across 187 sites to evaluate results from 12 ESMs. The observations showed that the estimated τsoil was dramatically shorter from the soil incubation studies in the laboratory environment (median = 4 years; interquartile range = 1 to 25 years) than that derived from field in situ measurements (31; 5 to 84 years) with shifts in stable isotopic C (13C) or the stock-over-flux approach. In comparison with the field observations, the multi-model ensemble simulated a shorter median (19 years) and a smaller spatial variation (6 to 29 years) of τsoil across the same site locations. We then found a significant and negative linear correlation between the in situ measured τsoil and mean annual air temperature. The underestimations of modeled τsoil are mainly located in cold and dry biomes, especially tundra and desert. Furthermore, we showed that one ESM (i.e., CESM) has improved its τsoil estimate by incorporation of the soil vertical profile. These findings indicate that the spatial variation of τsoil is a useful benchmark for ESMs, and we recommend more observations and modeling efforts on soil C dynamics in regions limited by temperature and moisture.

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