Parameterization-based uncertainties in RegCM simulations over Hungary in a dry year – a case study

Drought is one of the natural hazard risks that badly affect both agricultural and socio-economic sectors. Hungary, which is located in Eastern Europe, has already been suffering from different drought periods, and the driest year since 1901 was 2011 when the annual precipitation in Hungary was only 72 percent of the normal value. To better understand droughts and to provide information for adaptation strategies and risk-management systems, there is a strong need for a methodological framework to simulate drought events. However, it is uncertain whether climate models can simulate extreme droughts given the well-known model bias of simulating too light rainfall too frequently. So, the aim of the current study is to investigate the effects of the different model settings on the reproduction of drought characteristics.

In order to quantify the impact of the use of different parameterization schemes on regional climate model outputs, hindcast experiments were completed applying RegCM4.7 to the Carpathian region and its surroundings at 10-km horizontal resolution using ERA-Interim reanalysis data as initial and boundary conditions. In this study, we are testing various combinations of the physics schemes (land surface, microphysics, cumulus and boundary layer schemes) for the year 2011. Each parameterization combination leads to different simulated climates, so their spread is an estimate of the model uncertainty arising from the representation of the unresolved phenomena. The analysis of the RegCM-output ensemble indicates systematic precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons.

Based on the results, RegCM is sensitive to the applied convection scheme, but the interactions with the other schemes (e.g., land surface or microphysics) affect the precipitation. Due to the different treatment of moisture in the schemes, there are differences not only between the representation of the precipitation cycle, but also in other climatological variables such as soil moisture, latent and sensible heat fluxes and cloud cover, which affect the drought characteristics.

 

The research was funded by the NKFIH-471-3/2021 project (the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014).