Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models with satellite-based SnowCCI data during 1982–2014

Abstract

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.

Why Do CMIP6 Models Fail to Simulate Snow Depth in Terms of Temporal Change and High Mountain Snow of China Skillfully?

“…For most of the grids, model M1 presented clearly higher R ‐squared values than model M2, though both models showed low R ‐squared values in the southeastern part of China possibly related with the low SD there (Figure 4a). Precipitation was found to be more important than air temperature for CMIP6's winter SD simulation, according to the comparison of the results from error models M3 (only considering precipitation errors) and M4 (only considering temperature errors), which is consistent with previous studies (Kouki et al., 2021). However, this study further revealed the importance of simultaneously considering both errors of precipitation and of temperature.…”

“…The error model M1 explained on average 67.7% of the variance of SD errors (Figure 4b), indicating that precipitation and air temperature were major error sources in the winter SD simulated by CMIP6 models. Previous studies have also noted the effects of precipitation and air temperature on the snow simulation by CMIP6 models (Kouki et al., 2021; Zhong et al., 2021), but they have not previously been identified as major factors. Here, new insights into the error attribution of CMIP6 snow simulations reveal that the accumulated errors in precipitation and temperature in prior months play a key role, in addition to those errors in the current month under consideration.…”

“…• Hotspot score was proposed to evaluate performances of Coupled Model Intercomparison Project Phase 6 (CMIP6) models more efficiently in predicting significant trends of snow depth change • The accumulated errors of precipitation and temperature of prior months lead to a notable time lag of peak snow depth month of CMIP6 • CMIP6 models have larger snow depth errors in the Tibetan Plateau mainly due to more serious wet bias and cooling bias than other regions mass. Based on a comparison with microwave-based SWE, Kouki et al (2021) further attributed the winter SWE overestimation to excess precipitation and found that the errors of precipitation and temperature cannot explain major SD discrepancies for some models. Only errors in precipitation or temperature for the current month were considered in previous studies, however, it has been found that winter snow can also be affected by the precipitation and temperature of the months prior to the winter (Qiao et al, 2021).…”

“…Based on a comparison with microwave‐based SWE, Kouki et al. (2021) further attributed the winter SWE overestimation to excess precipitation and found that the errors of precipitation and temperature cannot explain major SD discrepancies for some models. Only errors in precipitation or temperature for the current month were considered in previous studies, however, it has been found that winter snow can also be affected by the precipitation and temperature of the months prior to the winter (Qiao et al., 2021).…”

Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

“…475 Changes in snow have implications for the society of Arctic communities where snow may impact many resources (Huntington et al, 2004), and for global climate change (Overland et al, 2019) and carbon cycles (Rogers et al, 2011;Arndt et al, 2020). Thus, understanding how to better model snow distribution and the important features involved in snow distribution is fundamental to improving how we interpret, and plan for, changing Arctic snow in the future (Zhu et al, 2021;Kouki et al, 2021;Mudryk et al, 2020). 480…”