Evaluations of Land–Ocean Skin Temperatures of the ISCCP Satellite Retrievals and the NCEP and ERA Reanalyses

Abstract

This study evaluates the skin temperature (ST) datasets of the International Satellite Cloud Climatology Project (ISCCP) D satellite product, the ISCCP FD satellite product, the 40-yr ECMWF Re-Analysis (ERA-40), the NCEP?NCAR Reanalysis, and the NCEP?Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis. The monthly anomalies of all the datasets are correlated to each other and to most of the ground-truth stations with correlation coefficients >0.50. To evaluate their qualities, the 5 ST datasets are used to calculate clear-sky (CS) outgoing longwave radiation (OLR) and upward surface longwave radiation (USLR); the results are compared with the Earth Radiation Budget Experiment (ERBE) satellite observation and 14 surface stations. The satellite-derived STs and ERA-40 ST tend to bias high on hot deserts (e.g., Sahara Desert), and the reanalyzed STs tend to bias low in mountain areas (e.g., Tibet). In Northern Hemisphere high-latitude regions (tundra, wetlands, deciduous needle-leaf forests, and sea ice), the CS OLR anomalies calculated using the satellite-derived STs have higher correlations and lower root-mean-squared errors with the ERBE satellite observation than those derived from using the reanalyzed STs. ERA-40 underestimates the amplitude of the seasonal ST over glaciers. All the reanalysis products (ERA-40, NCEP?NCAR, and NCEP?DOE AMIP-II) overestimate the ST during partial sea ice?covered periods in the middle-high-latitude oceans. Nonetheless, suspected spurious noises with an amplitude of 2 K in the satellite-derived STs produce a physically unviable anomaly over earth?s surface where the amplitude of the anomaly is weak (such as open-water bodies, croplands, rain forest, grasslands, hot deserts, and cold deserts). Better land?ocean?ice schemes for a reanalysis should be developed for desert regions, high plateaus, fractional sea ice?covered oceans, and seasonally snow-covered lands, where the largest ST errors are identified.