Evaluation of soil temperature in CMIP6 multimodel simulations

Abstract

Soil temperature plays a crucial role in understanding land-atmosphere interactions. The Sixth Phase of Coupled Model Intercomparison Project (CMIP6) provides valuable information on soil temperature, however, the performance of these models in capturing soil temperature variations remains unclear. To comprehensively assess the performance of CMIP6 in simulating soil temperature, we employed a set of in-situ observation data, observation-derived gridded data (TS-GCB) and reanalysis data (ERA5L) to build various evaluating metrics for the surface (0–5 cm) and subsurface (5–15 cm) soils. At the global scale, the multimodel ensemble mean (MME) of CMIP6 generally captured the spatial, annual and seasonal variations of soil temperature, but overestimated measured soil temperature by 1.86 °C (vs. TS-GCB, surface) and 2.16 °C (vs. TS-GCB, subsurface), indicating more heat accumulated in soils than the reality. Surface soil temperature was better represented by MME compared to the subsurface soil layer (vs. TS-GCB). In addition, we found the largest simulation bias in the tropical zone, and both positive and negative bias in the arid regions. The large model spread of the individual models in representing soil temperatures in cold regions or periods highlights the needs of improved understanding of how snow and freeze-thaw affect soil thermal dynamics. Overall, the performance of MME is superior to that of the majority of individual models (vs. TS-GCB). Locally, the large discrepancy among observation-derived data, reanalysis data and CMIP6 simulations suggested that it is imperative to acquire more ground-truth soil temperature to better inform model simulation and forecasting.