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Publication . Article . Other literature type . 2016

Toward more realistic projections of soil carbon dynamics by Earth system models

Yiqi Luo; Anders Ahlström; Steven D. Allison; Niels H. Batjes; Victor Brovkin; Nuno Carvalhais; Adrian Chappell; +34 Authors
Open Access
English
Abstract
©2015. American Geophysical Union. All Rights Reserved. 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-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-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-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- and flux-based data sets through data assimilation is among the highest priorities for near-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.
Subjects by Vocabulary

Microsoft Academic Graph classification: Climate change External variable Environmental science Hydrology Soil carbon Atmospheric sciences Data assimilation Soil texture Soil water Earth system science Sink (geography) geography.geographical_feature_category geography

Subjects

[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, CMIP5, Earth system models, realistic projections, recommendations, soil carbon dynamics, Atmospheric Sciences, Geochemistry, Oceanography, Meteorology & Atmospheric Sciences, Atmospheric Science, General Environmental Science, Environmental Chemistry, Global and Planetary Change, GLOBAL CLIMATE-CHANGE, ORGANIC-CARBON, DATA-ASSIMILATION, HETEROTROPHIC RESPIRATION, TEMPERATURE SENSITIVITY, TERRESTRIAL ECOSYSTEMS, LITTER DECOMPOSITION, PARAMETER-ESTIMATION, MICROBIAL MODELS, LAND MODELS

Funded by
NSF| Collaborative Research: Grassland Sensitivity to Climate Change at Local to Regional Scales: Assessing the Role of Ecosystem Attributes vs. Environmental Context
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1137293
  • Funding stream: Directorate for Biological Sciences | Emerging Frontiers Office
,
NSF| Development of a Data Assimilation Capability Towards Ecological Forecasting in a Data-Rich Era
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0850290
  • Funding stream: Directorate for Biological Sciences | Division of Biological Infrastructure
,
NSF| Collaborative Research: EPSCoR RII Track 2 Oklahoma and Kansas: A cyberCommons for Ecological Forecasting
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0919466
  • Funding stream: Office of the Director | Experimental Program to Stimulate Competitive Research
,
NSF| RCN: Forecasts Of Resource and Environmental Changes: data Assimilation Science and Technology (FORECAST)
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0840964
  • Funding stream: Directorate for Biological Sciences | Division of Environmental Biology
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