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A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability
Author(s) -
Yao Yitong,
Ciais Philippe,
Viovy Nicolas,
Li Wei,
CrestoAleina Fabio,
Yang Hui,
Joetzjer Emilie,
BondLamberty Ben
Publication year - 2021
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2020gb006918
Subject(s) - environmental science , soil respiration , global change , soil carbon , carbon cycle , atmospheric sciences , ecosystem , precipitation , soil water , climate change , ecology , soil science , meteorology , geography , biology , geology
Soil heterotrophic respiration (SHR) is important for carbon‐climate feedbacks because of its sensitivity to soil carbon, climatic conditions and nutrient availability. However, available global SHR estimates have either a coarse spatial resolution or rely on simple upscaling formulations. To better quantify the global distribution of SHR and its response to climate variability, we produced a new global SHR data set using Random Forest, up‐scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity. We estimated a global total SHR of46 . 8 38.6 56.3 Pg C yr −1 over 1985–2013 with a significant increasing trend of 0.03 Pg C yr −2 . Among the inputs to generate SHR products, the choice of soil moisture datasets contributes more to the difference among SHR ensemble. Water availability dominates SHR inter‐annual variability (IAV) at the global scale; more precisely, temperature strongly controls the SHR IAV in tropical forests, while water availability dominates in extra‐tropical forest and semi‐arid regions. Our machine‐learning SHR ensemble of data‐driven gridded estimates and outputs from process‐based models (TRENDYv6) shows agreement for a strong association between water variability and SHR IAV at the global scale, but ensemble members exhibit different ecosystem‐level SHR IAV controllers. The important role of water availability in driving SHR suggests both a direct effect limiting decomposition and an indirect effect on litter available from productivity. Considering potential uncertainties remaining in our data‐driven SHR datasets, we call for more scientifically designed SHR observation network and deep‐learning methods making maximum use of observation data.