Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

Journal Article · Tue Jun 07 00:00:00 EDT 2022 · Atmospheric Chemistry and Physics (Online)

DOI:https://doi.org/10.5194/acp-22-7287-2022· OSTI ID:1872315

von Lerber, Annakaisa  ^[1]; Mech, Mario  ^[2]; Rinke, Annette  ^[3]; Zhang, Damao  ^[4]; Lauer, Melanie ^[2]; Radovan, Ana ^[2]; Gorodetskaya, Irina  ^[5]; Crewell, Susanne  ^[2]

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1. Univ. of Cologne (Germany); Finnish Meteorological Inst. (FMI), Helsinki (Finland)
2. Univ. of Cologne (Germany)
3. Alfred Wegener Inst. for Polar and Marine Research, Potsdam (Germany)
4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
5. Univ. of Aveiro (Portugal)

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 assessments of the 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 southeastern 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 between 66 and 81°N. Excellent agreement (difference less than 1%) in the Arctic-averaged 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 rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic 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. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (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 sizes 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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE; German Research Foundation (DFG); Academy of Finland; Fundaçao para a Ciencia e Tecnologia (FCT); Ministry of Science, Technology and Higher Education (MCTES)
• Grant/Contract Number: AC05-76RL01830; 268020496-TRR 172; 333901; UIDP/50017/2020; UIDB/50017/2020; LA/P/0094/2020
• OSTI ID: 1872315
• Report Number(s): PNNL-SA-169667
• Journal Information: Atmospheric Chemistry and Physics (Online), Vol. 22, Issue 11; ISSN 1680-7324
• Publisher: Copernicus Publications, EGU
• Country of Publication: United States
• Language: English

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54 ENVIRONMENTAL SCIENCES