Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in theWorld Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over-and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November-April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (
Matiu, M., Petitta, M., Notarnicola, C., Zebisch, M. (2020). Evaluating snow in EURO-CORDEX regional climate models with observations for the european alps: Biases and their relationship to orography, temperature, and precipitation mismatches. ATMOSPHERE, 11(1) [10.3390/ATMOS11010046].
Evaluating snow in EURO-CORDEX regional climate models with observations for the european alps: Biases and their relationship to orography, temperature, and precipitation mismatches
Petitta M.;
2020-01-01
Abstract
Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in theWorld Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over-and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November-April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
