scholarly journals Response to comment on “Climate legacies drive global soil carbon stocks in terrestrial ecosystem”

2018 ◽  
Vol 4 (3) ◽  
pp. eaat1296
Author(s):  
Manuel Delgado-Baquerizo ◽  
David J. Eldridge ◽  
Fernando T. Maestre ◽  
Senani B. Karunaratne ◽  
Pankaj Trivedi ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Terrestrial Ecosystem ◽  
Carbon Stocks ◽  
Global Soil ◽  
Soil Carbon Stocks

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 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 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 Impact of priming on global soil carbon stocks

2018 ◽  
Vol 24 (5) ◽  
pp. 1873-1883 ◽  
Author(s):  
Bertrand Guenet ◽  
Marta Camino-Serrano ◽  
Philippe Ciais ◽  
Marwa Tifafi ◽  
Fabienne Maignan ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Stocks ◽  
Global Soil ◽  
Soil Carbon Stocks

scholarly journals Climate legacies drive global soil carbon stocks in terrestrial ecosystems

2017 ◽  
Vol 3 (4) ◽  
pp. e1602008 ◽  
Author(s):  
Manuel Delgado-Baquerizo ◽  
David J. Eldridge ◽  
Fernando T. Maestre ◽  
Senani B. Karunaratne ◽  
Pankaj Trivedi ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Terrestrial Ecosystems ◽  
Carbon Stocks ◽  
Global Soil ◽  
Soil Carbon Stocks

Predicted consequences of increased rainfall variability on soil carbon stocks in a semiarid environment

2016 ◽  
Vol 67 (1) ◽  
pp. 61-69
Author(s):  
M Forouzangohar ◽  
R Setia ◽  
DD Wallace ◽  
CR Nitschke ◽  
LT Bennett
Keyword(s):  
Soil Carbon ◽  
Rainfall Variability ◽  
Carbon Stocks ◽  
Semiarid Environment ◽  
Soil Carbon Stocks
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