IMPACT4SOIL OpenAire Datacite Research@WUR Bielefeld Academic Search Engine (BASE) eScholarship - University of California UnpayWall HAL-INSU Crossref Microsoft Academic Graph HAL-CEA https://escholarship.org/content/qt1pw7g2r2/qt1pw7g2r2.pdf https://doi.org/10.1002/2015gb005239 Global Biogeochemical Cycles Open Access 2015-12-19 2016-01-01 2022-03-03 2020-10-29 Abstract<p>Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real￢ﾀﾐworld processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first￢ﾀﾐorder decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth￢ﾀﾐdependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool￢ﾀﾐ and flux￢ﾀﾐbased data sets through data assimilation is among the highest priorities for near￢ﾀﾐterm research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.</p> 550 LAND MODELS Oceanography HETEROTROPHIC RESPIRATION 01 natural sciences Atmospheric Sciences LITTER DECOMPOSITION ORGANIC-CARBON Geoinformatics GLOBAL CLIMATE-CHANGE DATA-ASSIMILATION Meteorology & Atmospheric Sciences TEMPERATURE SENSITIVITY CMIP5 MICROBIAL MODELS 0105 earth and related environmental sciences 2. Zero hunger [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere 500 Earth system models 04 agricultural and veterinary sciences 15. Life on land [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment 6. Clean water TERRESTRIAL ECOSYSTEMS Climate Action Geochemistry Climate change impacts and adaptation realistic projections 13. Climate action recommendations Earth Sciences 0401 agriculture, forestry, and fisheries soil carbon dynamics [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Climate Change Impacts and Adaptation Environmental Sciences PARAMETER-ESTIMATION Alejandro Salazar, Xiaofeng Xu, Tristram O. West, Junyi Liang, Anders Ahlström, Anders Ahlström, Xia Xu, Victor Brovkin, Oleksandra Hararuk, Niels H. Batjes, Adien Finzi, Jianyang Xia, Chris D. Jones, A. David McGuire, A. David McGuire, Yujie He, Yiqi Luo, Yiqi Luo, Steven D. Allison, Changhui Peng, Kees Jan van Groenigen, William J. Parton, Mark J. Lara, Matthew J. Smith, Philippe Ciais, James T. Randerson, Francesca M. Hopkins, Margaret S. Torn, William R. Wieder, Tao Zhou, Katherine Todd-Brown, Jennifer W. Harden, Lifen Jiang, Charles D. Koven, Carlos A. Sierra, Robert B. Jackson, Bertrand Guenet, Nuno Carvalhais, Nuno Carvalhais, Yaxing Wei, Adrian Chappell, Ying-Ping Wang, Eric A. Davidson, Katerina Georgiou, Katerina Georgiou, Hanqin Tian, 2016-01-01 journalpaper Abstract<p>Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real￢ﾀﾐworld processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first￢ﾀﾐorder decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth￢ﾀﾐdependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool￢ﾀﾐ and flux￢ﾀﾐbased data sets through data assimilation is among the highest priorities for near￢ﾀﾐterm research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.</p> Toward More Realistic Projections Of Soil Carbon Dynamics By Earth System Models 10.1002/2015gb005239