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

Abstract

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 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.

Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic

“…During this detection gap of AR1 and AR2, the precipitation is then associated with fronts only. The temporal development of these ARs is visualized as movies in the supplement (see video supplement Lauer (2022)). All of them reached at least 77°N (Table 2) and have been described…”

“…Although precipitation plays a key role in the Arctic climate system, an accurate Arctic-wide observational assessment of rain and especially snowfall is still a challenge nowadays (von Lerber et al, 2022) which particularly holds for the identification of trends (McCrystall et al, 2021). The consequence of the warming in the Arctic is not only an increase in precipitation but also a phase change from snow to rain (Bintanja and Andry, 2017;Lupikasza and Cielecka-Nowak, 2020).…”

“…Thus, although 260 seven ARs were detected during AFLUX, their contribution to the total precipitation rate is a factor of 2.5 lower compared to ACLOUD (Table 1). In contrast to ACLOUD, the ARs came mainly from the Atlantic or Labrador Sea (Table 2) and were first meridionally orientated with a subsequent zonal alignment over the studied area (see supplement Lauer (2022)). Although the majority of the ARs (5 out of 7) reached 77°N or higher, their contribution did not exceed 16%.…”

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

“…Thus, the main flow directions are S, N, C, and AC and classes in between are assigned to the neighboring main direction following von Lerber et al (2022). In total, 0.6 h were flown during NE and NW flows and 1.8 h during SE and SW conditions, thus, they contribute by less than one percent.…”

“…One of the most important applications of CloudSat is the derivation of snowfall in the Arctic. The Fram Strait is of particular interest as precipitation is most intense in this area (McCrystall et al, 2021) and snowfall estimates between CloudSat and regional climate models differ highly (von Lerber et al, 2022). From the '2C-Snow-Profile', Edel et al (2020) derived a mean snowfall rate S C of 200 to 500 mm yr −1 around Svalbard.…”