ARM SGP Observations Help Validate Soil Temperature Simulations

Huang, M., Pacific Northwest National Laboratory

Surface Properties

Cloud-Aerosol-Precipitation Interactions

Xia Y, M Ek, J Sheffield, B Livneh, M Huang, H Wei, S Feng, L Luo, J Meng, and E Wood. 2012. "Validation of Noah-simulated soil temperature in the North American Land Data Assimilation System Phase 2." Journal of Applied Meteorology and Climatology, 52, 10.1175/jamc-d-12-033.1.

The role of soil temperature and its influence on weather and climate, especially its effect on short-range weather processes, has been underestimated in the past. Recent studies show that soil temperature has significant effects on short-term model forecasts of near-surface variables, such as precipitation and lower atmospheric circulation fields. ASR researchers assessed soil temperature for different soil depths and timescales simulated by the Noah land surface model in the North American Land Data Assimilation System Phase 2 using observations from the Oklahoma Mesonet and DOE’s Atmospheric Radiation Measurement Southern Great Plains site.

The research team evaluated long-term and short-term soil temperatures against measurements from 137 cooperative stations over the United States and those from the Oklahoma Mesonet stations, and downward shortwave radiation, downward longwave radiation, upward longwave radiation, and ground heat fluxes against observations from 14 ARM sites in Oklahoma. They found that the simulations from the Noah land surface model generally match well with the observed soil temperature for different soil layers and timescales.

Accurate long-term land surface soil temperature data sets are needed to improve weather and climate simulation and prediction, which are also crucial for the simulations of agricultural crop yield and ecological processes. The difference in monthly mean diurnal cycles between simulated and observed soil temperatures revealed large midnight biases in the cold season due to small downward longwave radiation and issues related to model parameters, which will be addressed in follow-up studies.