Discrepancies and Consistencies in Multi-Source Estimates of Eurasian Spring Snowmelt During 1982–2014

Snow alters land–atmosphere energy and moisture exchanges via albedo and hydrological effects, markedly impacting subseasonal-to-seasonal climate variability. While Earth observations and model-based products form the foundation for investigating snow–climate interactions, comprehensive assessments of snowmelt, which directly reflects snow-induced hydrological and thermal anomalies, remain scarce compared to those of conventional snow mass variables. Benchmarked against the satellite–in situ blended GlobSnow data, this study comprehensively evaluates the discrepancies and consistencies of Eurasian spring snowmelt (spring snow water equivalent difference, SSD) across six multi-source datasets (SnowCCI, JRA-3Q, ERA5-Land, MERRA-2, GLDAS-NOAH, and FLDAS) during 1982–2014. The results reveal that satellite-based SnowCCI and JRA-3Q exhibit the highest consistency with GlobSnow in reproducing the spatial distribution, temporal variability, and dominant east–west dipole mode of Eurasian SSD. Specifically, they accurately capture its clear latitudinal zonation and a distinct dipole-like trend, characterized by decreasing SSD over western Eurasia and increasing SSD over eastern Eurasia. Conversely, ERA5-Land and MERRA-2 reasonably capture the temporal evolution of SSD but tend to overestimate variability amplitudes. GLDAS-NOAH exhibits relatively weak regional heterogeneity and underestimates SSD variability, whereas FLDAS tends to overestimate variability amplitudes and shows pronounced regional dependence. Further analysis suggests that uncertainties in snowfall simulations may be a major source of discrepancies in SSD estimation, although their relative contribution remains uncertain. These findings provide crucial insights for selecting appropriate datasets in snow–climate research and underscore the importance of dataset uncertainty quantification in subseasonal-to-seasonal climate prediction.