Atmospheric forcing uncertainty contributes to divergent estimates of China’s terrestrial carbon dynamics

Dados Bibliográficos

ID15548365
AutoresYue Cheng (0009-0000-2936-5546, Chengdu Institute of Biology, autor correspondente), Peng Luo (0000-0002-3680-8509, Chengdu Institute of Biology), Hao Yang (0000-0003-2138-7307), Hao Frank Yang (0000-0001-6431-8956, Chengdu Institute of Biology), Mingwang Li (Qinghai University), Honglin Li (0000-0002-8632-2056, Chengdu Institute of Biology), Yu Huang (0000-0003-4378-387X, Chengdu Institute of Biology), Ming Ni (0000-0001-9465-2787, Chengdu Institute of Biology), Wenwen Xie (Chengdu Institute of Biology), Chuan Luo (0000-0001-5028-1064, Sichuan Academy of Forestry), Zhigang Hu (0000-0003-3421-0674, University of Chinese Academy of Sciences)
Ano2026
Volume21
Fascículo1
Páginas014033-014033
Data de publicação2026-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnvironmental Research Letters (JOURNAL)
Identificadores do periódicoISSN: 1748-9326 • E-ISSN: 1748-9326
EditoraIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ae2af8
OpenAlexW7123441341
IdiomaEN
Referências citadas75

Accurate quantification of terrestrial carbon dynamics is essential for assessing ecosystem–climate feedbacks and informing climate mitigation in the Anthropocene. China, as both the world’s largest emitter and a region of rapid ecological change, plays a key role in the global carbon cycle. Yet uncertainties in atmospheric forcing datasets remain a significant source of uncertainty in land surface model simulations and are rarely assessed systematically. Here, we assess China’s terrestrial carbon budget (1979–2014) using the Community Land Model (CLM 5.0 ) driven by three widely used meteorological datasets: CRUNCEP, the Global Soil Wetness Project Phase 3 (GSWP3), and the China Meteorological Forcing Dataset (CMFD), and evaluated against more than 800 FLUXNET site-months and nine eddy covariance towers. Forcing choice strongly alters the magnitude and trend of carbon fluxes, and China’s terrestrial ecosystems acted as either a weak carbon sink or a net source, depending on the forcing. GSWP3 performed best overall in simulating gross primary productivity (GPP), CRUNCEP relatively poorly, and CMFD best in high-altitude and cold-dry regions. Total ecosystem carbon storage was estimated at 86.30–90.00 PgC, primarily in soil (84.1%) and vegetation (15.9%). Interannual GPP variability was mainly controlled by precipitation (29.4%), followed by temperature (17.2%), while shortwave radiation had negative effects (11.5%). Shapley additive explanations analysis further showed that moisture-related variables (precipitation and humidity) dominate interannual GPP variability. By combining process-based modeling and machine learning, this study shows how uncertainties in meteorological inputs affect terrestrial carbon dynamics. These findings underscore the limitations of single-forcing simulations in Earth system modeling and highlight the need for improved meteorological inputs to support robust carbon budgeting and climate neutrality goals

Carbon cycle · Carbon sink · Ecosystem · Eddy covariance · FluxNet · Forcing (mathematics · Primary production · Radiative forcing · Shortwave radiation · Terrestrial ecosystem · Climate variability and models · Plant Water Relations and Carbon Dynamics · Remote Sensing in Agriculture