Evaluation of reanalysis datasets against observational soil temperature data over China

Abstract

Soil temperature is a key land surface variable, and is a potential predictor for seasonal climate anomalies and extremes. Using observational soil temperature data in China for 1981–2005, we evaluate four reanalysis datasets, the land surface reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA-Interim/Land), the second modern-era retrospective analysis for research and applications (MERRA-2), the National Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), and version 2 of the Global Land Data Assimilation System (GLDAS-2.0), with a focus on 40 cm soil layer. The results show that reanalysis data can mainly reproduce the spatial distributions of soil temperature in summer and winter, especially over the east of China, but generally underestimate their magnitudes. Owing to the influence of precipitation on soil temperature, the four datasets perform better in winter than in summer. The ERA-Interim/Land and GLDAS-2.0 produce spatial characteristics of the climatological mean that are similar to observations. The interannual variability of soil temperature is well reproduced by the ERA-Interim/Land dataset in summer and by the CFSR dataset in winter. The linear trend of soil temperature in summer is well rebuilt by reanalysis datasets. We demonstrate that soil heat fluxes in April–June and in winter are highly correlated with the soil temperature in summer and winter, respectively. Different estimations of surface energy balance components can contribute to different behaviors in reanalysis products in terms of estimating soil temperature. In addition, reanalysis datasets can mainly rebuild the northwest–southeast gradient of soil temperature memory over China.