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Within a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic‐wide scale is based on passive microwave satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N‐ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower‐frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher‐frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong. Key Points: The uncertainty of snow depth retrievals from satellite microwave radiometers is estimated using field measurements and model simulationsSnow properties have the strongest contribution to the uncertainty of snow depth retrievals compared to the influence of atmosphere and iceThe snow depth retrieval based on 19 and 7 GHz has a lower uncertainty compared to the 37 and 19 GHz one [ABSTRACT FROM AUTHOR]

The article presents a study which evaluates the ice and snow content in the numerical weather prediction (NWP) model GME with CloudSat Cloud Profiling Radar (CPR). Observations from the CloudSat CPR offer unique opportunity of vertically resolving clouds at a near-global scale. Conclusions of the study are highlighted which revealed that snow is the dominant contributor to ice water content (IWC) and ice water path (IWP).

The article discusses the global numerical weather prediction model GME using CloudSat with respect to frozen particles, both ice and snow. It compares the observation-to-model and the model-to-observation approach for further evaluation of the global numerical weather prediction model. In addition, the comparison reveals that the prognostic scheme reproduces the shape of the CloudSat frequency distributions for both ice water content (IWC) and reflectivity factor well.