05 Apr 2022
05 Apr 2022
Status: this preprint is currently under review for the journal TC.

Comparing rain-on-snow representation across different observational methods and a regional climate model

Hannah Ming Siu Vickers1, Priscilla Mooney1, Eirik Malnes1, and Hanna Lee1,2 Hannah Ming Siu Vickers et al.
  • 1NORCE Norwegian Research Centre AS, Postboks 22 Nygårdstangen, 5838 Bergen, Norway
  • 2Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Abstract. Rain on snow events (ROS) have the potential to cause wide-ranging ecological and societal impacts. Current knowledge and understanding of ROS and their future development are dependent on both observational and model datasets. However, different types of data provide insights into different aspects of ROS and carry limitations that may lead to contrasting results and conclusions, which need to be understood. This study examines the similarities and differences in ROS frequency over mainland Norway, estimated using a regional climate model (Weather Research and Forecasting (WRF)), a remote sensing method (Synthetic Aperture Radar (SAR)), and a gridded observational method (seNorge). Similarities in the geographical occurrence of ROS were obtained from both the WRF model and seNorge, with highest ROS frequency located predominantly along the western and southern coastal regions from autumn through to early spring, but greater ROS activity in the WRF model over inland mountainous areas during late spring and summer. We found significant differences in the spatial occurrence of ROS detected using the remote sensing approach, with much fewer ROS occurrences along the western coast but many more events inland from late autumn through to spring. Ground observations indicated the WRF model has an average accuracy for ROS detection of > 80 % for the period studied due to a high rate of detection of non-ROS days and low rate of false positives. However, the WRF model also missed on average > 50 % of the ROS days detected in ground-based data. For the SAR datasets, both the correct detection and false detection of ROS days was greater, producing a lower overall accuracy of 50–60 %. On the other hand, the timing of wet snow occurrence detected by SAR agreed qualitatively well with the onset of ROS detected from ground-based data, but the overall duration of a ROS event was frequently overestimated by SAR due to the persistence of liquid water in the snowpack. The similarities and differences across modeling and observational datasets shown in our study suggests that cross data validation is necessary and there is a need to analyse data collected at a much greater number of sites and future studies should take this into account.

Hannah Ming Siu Vickers et al.

Status: open (until 22 Jun 2022)

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  • RC1: 'Comment on tc-2022-57', Achut Parajuli, 24 May 2022 reply

Hannah Ming Siu Vickers et al.

Hannah Ming Siu Vickers et al.


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Short summary
Rain-on-snow (ROS) events are becoming more frequent as a result of a warming climate, and can have significant impacts on nature and society. Accurate representation of ROS events is need to identify where impacts are greatest both now and in the future. We compare rain-on-snow climatologies from a climate model, ground and satellite radar observations and show how different methods can lead to contrasting conclusions and interpretation of the results should take into account their limitations.