Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Previously, substantial uncertainties have been reported in NH snow water equivalent (SWE) estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared NH SWE estimates between CMIP6 (Coupled Model Intercomparison Project Phase 6) models and observation-based SWE reference data north of 40∘ N for the period 1982–2014 and analyzed with a regression approach whether model biases in temperature (T) and precipitation (P) could explain the model biases in SWE. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 data for the mountainous regions. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ snow depth data. The analysis shows that CMIP6 models tend to overestimate SWE; however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0∘ C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

Robust melt season timing and length estimates are important for hydrological and climatological applications; due to the large area and sparse in situ measurements, snow melt monitoring at the continental scale is only possible from satellites. We intercompared melt onset date (MOD) estimates obtained from optical and microwave satellite sensors over the Northern Hemisphere between 1982 and 2015 and subsequently analyzed the causes of the similarities and dissimilarities found. The optical satellite data are based on the mean surface albedo from the Satellite Application Facility for Climate Monitoring (CM SAF) CLouds, Albedo and RAdiation second release Surface ALbedo (CLARA‐A2 SAL) data set. The microwave satellite data are based on temporal variations in the differences of the brightness temperature from satellite passive microwave radiometers. The analysis shows that the microwave‐based method detects melt onset on average 10 days later than the albedo‐based method, which results from the different melt detection methods; the albedo‐based method observes the point when the spring snow metamorphism begins to have a detectable effect on snow albedo, whereas the microwave‐based method detects the appearance of meltwater in snowpack. The difference in MOD decreases in forests, because canopy protects snow from sunlight delaying snow metamorphism. Additionally, we analyzed the MOD estimates for trends across the Northern Hemisphere and separately for Eurasia and North America. A statistically significant negative trend toward earlier melt onset exists in all cases, which is consistent with previous studies.

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) greatly influences surface energy balance, hydrological cycle, and many human activities, such as tourism and agriculture. Monitoring snow cover at continental scale is only possible from satellites or using reanalysis data. The aim of this study is to analyze timeseries of surface albedo, snow water equivalent (SWE), and snow cover extent (SCE) in spring in ERA5 and ERA5-Land reanalysis data and to compare the timeseries with several satellite-based datasets. As satellite data for the SWE intercomparison, we use bias-corrected SnowCCI v1 data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 datasets for the mountainous regions. For surface albedo, we use the black-sky albedo datasets CLARA-A2 SAL, based on AVHRR data, and MCD43D51 based on MODIS data. Additionally, we use Rutgers and JAXA JASMES SCE products. Our study covers land areas north of 40° N and the period between 1982 and 2018 (spring season from March to May). The analysis shows that both ERA5 and ERA5-Land overestimate SWE. ERA5-Land shows larger overestimation, which is mostly due to very high SWE values over mountainous regions. The analysis revealed a discontinuity in ERA5 around year 2004, since adding IMS (Interactive Multisensor Snow and Ice Mapping System) from year 2004 onwards considerably improves SWE estimates but makes the trends less reliable. The negative NH SWE trends in ERA5 range from −249 Gt decade−1 to −236 Gt decade−1 in spring, which is two to three times larger than the trends detected by the other datasets (ranging from −124 Gt decade−1 to −77 Gt decade−1). Albedo estimates are more consistent between the datasets with a slight overestimation in ERA5 and ERA5-Land. SCE is accurately described in ERA5-Land, whereas ERA5 shows notably larger SCE than the satellite-based datasets. The negative trends in albedo and SCE are strongest in May, when albedo trend varies from −0.011 decade−1 to −0.006 decade−1 depending on the dataset. The negative SCE trend detected by ERA5 in May (−1.22 million km2 decade−1) is about twice as large as the trends detected by other datasets (ranging from 0.66 million km2 decade−1 to −0.50 million km2 decade−1). The analysis also shows that there is a large spatial variability in the trends, which is consistent with other studies.

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