PNNL

Abstract

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North-American Land Data Assimilation (NLDAS) Phase 2 (NLDAS-2) has generated 31-years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of 1/8o. This dataset has not been comprehensively evaluated to date. Thus, the ultimate purpose of the present work is to assess Noah-simulated soil temperature for different soil depths and timescales. We used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10 cm, 10-40 cm, 40-100 cm) for annual and monthly timescales. We used short-term (1997-1999) observed soil temperature from 72 Oklahoma Mesonet stations to validate simulated soil temperatures for three soil layers and for daily and hourly timescales. The results showed that the Noah land surface model (Noah LSM) generally matches observed soil temperature well for different soil layers and timescales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season due to small downward longwave radiation and issues related to model parameters.