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

Area of Science:

• Ecology
• Earth System Science
• Climate Science

Background:

• Accurate modeling of terrestrial carbon fluxes requires understanding soil respiration (R_s) dynamics and environmental drivers.
• Current R_s models often yield divergent estimates and annual predictions, potentially overlooking critical seasonal interactions.
• A significant knowledge gap exists regarding how temporal resolution influences R_s predictions and their key drivers.

Purpose of the Study:

• To employ deep learning models for predicting global R_s at both monthly and annual scales.
• To investigate the influence of temporal resolution on identifying environmental drivers of R_s, including temperature, precipitation, and leaf area index (LAI).
• To highlight the importance of high temporal resolution for refining carbon flux models and Earth system predictions.

Main Methods:

• Utilized deep learning models to predict global soil respiration (R_s) from 1982 to 2018 at monthly (MRM) and annual (ARM) resolutions.
• Incorporated temperature, precipitation, and leaf area index (LAI) as potential environmental drivers.
• Compared the predictive capabilities and driver importance between monthly and annual R_s models.

Main Results:

• Achieved strong global R_s estimations: 79.4 ± 5.7 Pg C year^-1 for MRM and 78.3 ± 7.5 Pg C year^-1 for ARM.
• Identified notable disparities in the spatial contribution of dominant drivers between models.
• MRM highlighted influences of both temperature and LAI, whereas ARM emphasized precipitation as the dominant driver.

Conclusions:

• Temporal resolution critically affects the identification of R_s-environment relationships, with monthly models capturing seasonal nuances missed by annual models.
• High temporal resolution R_s predictions are essential for refining carbon flux models, detecting seasonal thresholds, and improving Earth system predictions.
• Further research into monthly and seasonal R_s variations is needed to advance understanding of ecosystem carbon dynamics in a changing climate.