Abstract
Abstract. Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of the terrestrial carbon cycle, directly reflecting carbon loss from soils to the atmosphere. However, high variations and uncertainties of RH existing in global carbon cycling models require RH estimates from different angles, e.g., a data-driven angle. To fill this knowledge gap, this study applied a Random Forest (RF) algorithm (a machine learning approach) to (1) develop a globally gridded RH dataset and (2) investigate its spatial and temporal patterns from 1980 to 2016 at the global scale by linking field observations from the Global Soil Respiration Database and global environmental drivers (temperature, precipitation, soil water content, etc.). Finally, a globally gridded RH dataset was developed covering from 1980 to 2016 with a spatial resolution of half a degree and a temporal resolution of 1 year. Globally, the average annual RH was 57.2±0.6 Pg C a−1 from 1980 to 2016, with a significantly increasing trend of 0.036±0.007 Pg C a−2. However, the temporal trend of the carbon loss from RH varied in climate zones, and RH showed a significant and increasing trend in boreal and temperate areas. In contrast, such a trend was absent in tropical regions. Temperature-driven RH dominated 39 % of global land and was primarily distributed at high-latitude areas. The areas dominated by precipitation and soil water content were mainly semiarid and tropical areas, accounting for 36 % and 25 % of global land area, respectively, suggesting variations in the dominance of environmental controls on the spatial patterns of RH. The developed globally gridded RH dataset will further aid in the understanding of the mechanisms of global soil carbon dynamics, serving as a benchmark to constrain terrestrial biogeochemical models. The dataset is publicly available at https://doi.org/10.6084/m9.figshare.8882567 (Tang et al., 2019a).
Highlights
Global soils and surface litter store up to 2 or 3 times the amount of carbon present in the atmosphere (Trumbore, 2009), and a small change in soil carbon content could have profound effects on atmospheric CO2 and climate change (Köchy et al, 2015)
A data-derived global RH dataset may be used as a benchmark for terrestrial biogeochemical models; no such study has yet been conducted to assess the global variability in RH using a large dataset of empirical measurements to bridge the knowledge gap between local, regional and global scales
To fill this knowledge gap, we developed a globally gridded RH dataset, which was 0.5◦ × 0.5◦ from 1980 to 2016 with an annually temporal resolution, using a Random Forest (RF) algorithm by linking field observations and global variables
Summary
Global soils and surface litter store up to 2 or 3 times the amount of carbon present in the atmosphere (Trumbore, 2009), and a small change in soil carbon content could have profound effects on atmospheric CO2 and climate change (Köchy et al, 2015). Global carbon flux from soil-to-atmosphere is increasing (Zhao et al, 2017), the degree to which future climate change will stimulate soil carbon loss via heterotrophic respiration (RH) remains highly uncertain (Bond-Lamberty et al, 2018; Friedlingstein et al, 2014; Trumbore and Czimczik, 2008), for areas with a high temperature sensitivity or rapid changes in precipitation frequency and intensity. RH could affect future climate change via the mineralization of long-stored soil carbon, offsetting net primary production (NPP) and even converting terrestrial ecosystems from a carbon sink to a carbon source (Tremblay et al, 2018). Reducing RH uncertainty and clarifying the response of RH to environmental factors are both essential for future projections of the impact of climate change on the terrestrial carbon balance
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