The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models (ESMs)1,2,3. To assess the climatological temperature sensitivity of soil carbon, we calculate apparent soil carbon turnover times4 that reflect long-term and broad-scale rates of decomposition. Here, we show that the climatological temperature control on carbon turnover in the top metre of global soils is more sensitive in cold climates than in warm climates and argue that it is critical to capture this emergent ecosystem property in global-scale models. We present a simplified model that explains the observed high cold-climate sensitivity using only the physical scaling of soil freeze–thaw state across climate gradients. Current ESMs fail to capture this pattern, except in an ESM that explicitly resolves vertical gradients in soil climate and carbon turnover. An observed weak tropical temperature sensitivity emerges in a different model that explicitly resolves mineralogical control on decomposition. These results support projections of strong carbon–climate feedbacks from northern soils5,6 and demonstrate a method for ESMs to capture this emergent behaviour.
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This research was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy (DOE) under Contract DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling (BGC-Feedbacks SFA), and Terrestrial Ecosystem Science Programs (NGEE-Arctic and NGEE-Tropics), and used resources of the National Energy Research Scientific Computing Center, also supported by the Office of Science of the US Department of Energy, under Contract DE-AC02-05CH11231. National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation (NSF). The CESM project is supported by the NSF and the Office of Science (BER) of the US Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by NSF and other agencies. G.H. acknowledges funding from the Swedish Research Council (grant numbers E0689701 and E0641701), the EU JPI-climate COUP project and Marie Sklodowska Curie Actions, Cofund, Project INCA 600398. D.M.L. is supported by funding from the US Department of Energy BER, as part of its Climate Change Prediction Program, Cooperative Agreement DE-FC03-97ER62402/ A010 and NSF Grants AGS-1048996 and ARC-1048987. W.R.W. is supported by funding from the US Department of Agriculture NIFA 2015-67003-23485 and US Department of Energy TES DE-SC0014374. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 2 of this paper) for producing and making available their model output.
About this article
Nature Climate Change (2017)