Timescale Matters: Finer Temporal Resolution Influences Driver Contributions to Global Soil Respiration

Understanding the dynamics of soil respiration (R_s) 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 R_s predictions and their environmental drivers. Here, we employ deep learning models to predict global R_s at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting R_s, including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global R_s 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 R_s-environment relationships that annual models may obscure. High temporal resolution R_s predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between R_s 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 R_s variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate.