Search

Persistent heavy rain on 14-15 July 2021 caused unprecedented floods and the loss of nearly 250 lives in Germany and Belgium. In this study, we investigate the impact of the representation of parameterized warm-rain microphysical processes in the regional configuration of the Met Office Unified Model on the simulated precipitation characteristics. Specifically, we performed simulations with a one- and two-moment microphysics parameterization and investigated the raindrop self-collection and breakup formulations in the latter. Furthermore, we investigated the impact of different aerosol climatologies on precipitation characteristics. Precipitation properties, including the intensity distribution and drop size distributions, were compared against high-quality radar-rain gauge merged precipitation observations and a disdrometer in the vicinity of the most extreme precipitation.It is shown that a two-moment representation of the precipitation size distributions leads to smaller peak rain rates and a larger extent of light rain rates compared to the one-moment configuration. However, peak rain rates are modulated by the representation of the uncertain raindrop breakup process. The aerosol climatology overall has a smaller impact than the number of prognostic moments of raindrop size distributions or the formulations of the warm-rain processes. This study underlines that poor understanding of warm-rain processes continues to contribute to uncertainty in the precipitation characteristics of extreme events., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

It is well known that high latitude zones are very sensitive to climate change. As a result of global warming, ice sheet melting has increased which in turn has an influence on climate through modifications of the thermohaline circulation, feedback of ice albedo, sea level rise, …Svalbard is an archipelago between 74 and 81°lat N and 60 percent of its area (62 248 km2) is covered with glaciers and ice sheets. The impact of global warming on the Svalbard cryosphere can be estimated with climate models. However, we need to use regional climate models as they offer the possibility of a higher resolution than general circulation models. We have ran two regional climate models (MAR and WRF) at a 10-kilometre resolution between 2006 and 2010 over Svalbard and compared their simulated climate to near surface measurements at several weather stations through the archipelago in order to determine which one of them could best represent the Svalbard climate.

In the context of climate change, the Greenland Ice Sheet (GrIS) plays an important role in sea level variation and oceanic thermohaline circulation changes. Unfortunately, Global Climate Models do not illustrate enough the characteristics of Greenland. To solve that, specific RCMs have been developed to take into account the features of polar regions.In this project, we compare three RCMs : the MAR model, the RACMO model and the Weather Research and Forecasting (WRF) model. WRF is an open source model developed by the Mesoscale and Microscale Meteorology Division of NCAR. WRF has been modified for polar regions by the Ohio State University. The key modifications are changes of surface energy balance and heat transfer, and an implementation of sea ice thickness, snow thickness and seasonally-varying sea ice albedo in the surface module (Noah LSM). We use here the standard WRF (version 3.2.1) and its polar optimization (called polar WRF). The MAR version tuned for the GrIS and coupled with a 1D surface scheme called SISVAT (for Soil Ice Snow Vegetation Atmosphere Transfer) is compared here. The version of RACMO is a specific version for the Greenland climate, RACMO2/GR. This model contains a special surface module for snow-ice treatment and other modifications concerning, for example, the surface turbulence heat flux or the surface roughness.The comparison is made on a domain centered on Greenland at a 25-km horizontal resolution over the 1995-2005 period when AutomaticWeather Station (AWS) measurements are available from the Greenland Climate NETwork (GC-NET). Statistics (mean, bias, RMSE, correlation coefficient) are calculated for the near-surface temperature, surface pressure, 10m-wind speed and specific humidity for winter (October to April) and summer (May to September). In addition, the modeled snowfall are evaluated with ice core-based snow accumulation climatologies.Comparison shows a significant improvement from RCMs compared to the reanalyses (NCEP2 and ERAINTERIM) in respect to the AWS measurements. RACMO and MAR seem to compare better with observations than WRF. However, we note a significant improvement between WRF and polarWRF.