14 Jan 2022

14 Jan 2022

Review status: this preprint is currently under review for the journal ACP.

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

Annakaisa von Lerber1,2, Mario Mech1, Annette Rinke3, Damao Zhang4, Melanie Lauer1, Ana Radovan5, Irina Gorodetskaya6, and Susanne Crewell1 Annakaisa von Lerber et al.
  • 1University of Cologne, Cologne, Germany
  • 2Finnish Meteorological Institute, Helsinki, Finland
  • 3Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
  • 4Pacific Northwest National Laboratory, Washington, USA
  • 5Deutscher Wetterdienst, Germany
  • 6University of Aveiro, Aveiro, Portugal

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 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° N and 81° N. An excellent agreement (difference less than 1 %) in 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 frequency 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 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. 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 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.

Annakaisa von Lerber et al.

Status: open (until 25 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Annakaisa von Lerber et al.

Annakaisa von Lerber et al.


Total article views: 199 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
153 42 4 199 2 2
  • HTML: 153
  • PDF: 42
  • XML: 4
  • Total: 199
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 14 Jan 2022)
Cumulative views and downloads (calculated since 14 Jan 2022)

Viewed (geographical distribution)

Total article views: 193 (including HTML, PDF, and XML) Thereof 193 with geography defined and 0 with unknown origin.
Country # Views %
  • 1


Latest update: 23 Jan 2022
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for the atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. An excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.