PNNL

Abstract

In this study we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007 - 2010. For this purpose, both approaches, i.e. the assessment of surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the south-eastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes North of 66o. An excellent agreement (difference less than 1%) in mean annual snowfall rate between HIRHAM5 and CloudSat is found whereas ERA Interim reanalysis shows an underestimation of 45% and significant deficits in the representation of the snowfall frequency distribution. From the statistical analysis it can be seen that the largest differences in the mean annual snowfall amounts are an overestimation near the coastlines of Greenland and other regions with large orographical variations, as well as an underestimation in the northern North Atlantic ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. HIRHAM5 underestimation within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks as could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the PAMTRA forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72 - 73?N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model most likely due to the small size of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes as illustrated by comparison of different schemes available for HIRHAM5.