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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-12
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-12
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 19 Mar 2019

Model evaluation paper | 19 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Snowfall distribution and its response to the Arctic Oscillation: An evaluation of HighResMIP models in the Arctic using CPR/CloudSat observations

Manu Anna Thomas1, Abhay Devasthale1, Tristan L'Ecuyer2, Shiyu Wang1, Torben Koenigk1,3, and Klaus Wyser1 Manu Anna Thomas et al.
  • 1Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 60176 Norrköping, Sweden
  • 2University of Wisconsin-Madison, 1225 West Dayton Street, Madison, WI 53706
  • 3Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden

Abstract. A realistic representation of snowfall in the general circulation models (GCM) is important to accurately simulate snow cover, surface albedo, high latitude precipitation and thus the radiation budget. Hence, in this study, we evaluate snowfall in a range of climate models run at two different resolutions using the latest estimates of snowfall from CloudSat Cloud Profiling Radar over the northern latitudes. We also evaluate if the finer resolution versions of the GCMs simulate the accumulated snowfall better than their coarse resolution counterparts. As the Arctic Oscillation (AO) is the prominent mode of natural variability in the polar latitudes, the snowfall variability associated with the different phases of the AO is examined in both models and in our observational reference. We report that the statistical distributions of snowfall vary considerably between the models and CloudSat observations. While CloudSat shows an exponential distribution of snowfall, the models show a Gaussian distribution that is heavily positively skewed. As a result, the 10 and 50 percentiles, representing the light and median snowfall, are overestimated by a factor of 3 and 1.5 respectively in the models investigated here. The overestimations are strongest during the winter months compared to autumn and spring. The extreme snowfall represented by the 90 percentiles, on the other hand, is positively skewed underestimating the snowfall estimates by a factor of 2 in the models in winter compared to the CloudSat estimates. Though some regional improvements can be seen with increased spatial resolution within a particular model, it is not easy to identify a specific pattern that hold across all models. The characteristic snowfall variability associated with the positive phase of AO over Greenland Sea and central Eurasian Arctic is well captured by the models.

Manu Anna Thomas et al.
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Latest update: 21 Apr 2019
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Short summary
Snow cover significantly influences the surface albedo and radiation budget. Therefore, a realistic representation of snowfall in climate models is important. Here, using a decade long estimates of snowfall derived from the satellite sensor, four climate models are evaluated to assess how well they simulate snowfall in the Arctic. It is found that the light and median snowfall is overestimated by the models in comparison to the satellite observations and the extreme snowfall is underestimated.
Snow cover significantly influences the surface albedo and radiation budget. Therefore, a...
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