Abstract
Abstract. Accurate subseasonal-to-seasonal (S2S) atmospheric forecasts and hydrological forecasts have considerable socioeconomic value. This study conducts a multimodel comparison of the Tibetan Plateau snow cover (TPSC) prediction skill using three models (ECMWF, NCEP and CMA) selected from the S2S project database to understand their performance in capturing TPSC variability during wintertime. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Compared with the observational snow cover analysis, all three models tend to overestimate the area of TPSC. Another remarkable issue regarding the TPSC forecast is the increasing TPSC with forecast lead time, which further increases the systematic positive biases of TPSC in the S2S models at longer forecast lead times. All three S2S models consistently exaggerate the precipitation over the Tibetan Plateau. The exaggeration of precipitation is prominent and always exists throughout the model integration. Systematic bias of TPSC therefore occurs and accumulates with the model integration time. Such systematic biases of TPSC influence the forecasted surface air temperature in the S2S models. The surface air temperature over the Tibetan Plateau becomes colder with increasing forecast lead time in the S2S models. Numerical experiments further confirm the causality.
Highlights
Anomalous weather- and climate-related natural disasters are among the most common disasters and are associated with severe socioeconomic consequences
The China Meteorological Administration (CMA) shows largest biases of approximately 25 %–40 %. Another remarkable issue regarding the forecast of Tibetan Plateau snow cover (TPSC) is the increasing TPSC with forecast lead time, which further increases the overestimation of TPSC in models at longer forecast lead times
This study evaluates the Tibetan Plateau snow cover (TPSC) prediction capabilities of three S2S forecast models (ECMWF, National Centers for Environmental Prediction (NCEP) and CMA) during wintertime
Summary
Anomalous weather- and climate-related natural disasters are among the most common disasters and are associated with severe socioeconomic consequences. Snow cover may provide a potential source of S2S predictability via its variability and atmospheric effects at the subseasonal timescale Tibetan Plateau snow cover (TPSC) is a key component of the climate system. Since the implementation of the S2S prediction project database (Vitart et al, 2016), many studies have evaluated the skill of S2S models for atmospheric elements and variables, such as the Madden–Julian Oscillation (Vitart, 2017), surface air temperature (Yang et al, 2018; Wulff and Domeisen, 2019) and precipitation (de Andrade et al, 2019). Understanding the forecasting skills of the S2S model on the TPSC is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau.
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