• Energy Research

AbstractGlobally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

ABSTRACTUnderstanding the dynamics of soil respiration (Rs) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both Rs predictions and their environmental drivers. Here, we employ deep learning models to predict global Rs at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting Rs, including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global Rs estimation of 79.4 ± 5.7 Pg C year−1 for the MRM and 78.3 ± 7.5 Pg C year−1 for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key Rs‐environment relationships that annual models may obscure. High temporal resolution Rs predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between Rs and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal Rs variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate.

International audience

AbstractQuantifying global soil respiration (RSG) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of RSG range from 68 to 98 Pg C year−1, causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain RSG, we developed RSG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (RS) and climate data to predict RSG. From the resulting RSG estimates (range = 66.62–100.72 Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43–9.46 Pg; however, RSG calculated from daily RS data was only 1.83 Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and −4.36 Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first‐order exponential (93.2 Pg) was greater than the Random Forest (78.76 Pg) or second‐order exponential (76.18 Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to RSG. The results indicated RSG is lower than in recent papers and the current benchmark for land models (98 Pg C year−1), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.

ABSTRACTAerobic soils remove methane from the atmosphere, but global soil methane uptake (SMU) estimates remain highly uncertain due to challenges in scaling local data. We develop a data‐driven approach to refine this global estimate by incorporating local data of 79,800 flux measurements from 198 sites. This novel approach links the global SMU budget to local SMU fluxes by varying its parameters with soil properties. Our 2003–2018 global SMU estimate is ~39.0 Tg CH4 year−1—about 30% higher than existing bottom‐up estimates and consistent with top‐down assessments. Cold grasslands and deserts were found to contribute nearly 30% of the total SMU, while disturbed agricultural biomes have the lowest SMU. The projected future global SMU is shaped by temperature and atmospheric methane, though local SMU is primarily influenced by changes in soil moisture. This study emphasizes the potential of soils in climate regulation and highlights the need to focus on key biomes for a better understanding of the soil‐atmosphere methane feedback and optimizing methane management strategies.

AbstractThe terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean $${149}_{-23}^{+29}$$ 149 − 23 + 29 Pg C yr−1) are significantly higher than most GPP estimates reported in the literature ($${113}_{-18}^{+18}$$ 113 − 18 + 18 Pg C yr−1). Similarly, historical GPP estimates imply a soil respiration flux (RsGPP, bootstrap mean of $${68}_{-8}^{+10}$$ 68 − 8 + 10 Pg C yr−1) statistically inconsistent with most published RS values ($${87}_{-8}^{+9}$$ 87 − 8 + 9 Pg C yr−1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global RS:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.