Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

Abstract

AbstractAssessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. Here, we provide a comprehensive evaluation of simulated spring SCF from 23 and 20 ESMs participating CMIP5 and CMIP6, against satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) and a long‐term Northern Hemisphere daily 5‐km snow cover extent product (JASMES) over the Northern Hemisphere (NH) and its 13 subregions during the period of 1982–2005 and 2072–2095. Our results show that (a) no model performs consistently better in simulating spring SCF from all aspects (mean annual, long‐term trend and climatological monthly of spring SCF); (b) the mean annual of spring SCF that was simulated by CMIP5 and CMIP6 model simulations was underestimated in most of the NH and overestimated over the Tibetan Plateau and eastern Asia; (c) most of the model simulations showed a stronger reduction trend for mean annual of spring SCF; (d) the climatological monthly variation of spring SCF is reasonably captured by all model simulations, except for overestimation over the TIB and EAS regions, and there is underestimation in other regions; (e) compared to CMIP5, most of the model simulations in CMIP6 exhibit an improved ability to simulate spring SCF across all aspects; (f) we confirm that the multi‐model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. Finally, the spring SCF values predicted by models with better simulation abilities over the NH and its 13 subregions under different scenarios show decreasing trends. Specifically, the highest decreasing trend of spring SCF was found under high emission scenarios (RCP8.5 and SSP5‐8.5).