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Tytuł pozycji:

Verification of the EURO-CORDEX RCM Historical Run Results over the Pannonian Basin for the Summer Season

Tytuł:
Verification of the EURO-CORDEX RCM Historical Run Results over the Pannonian Basin for the Summer Season
Autorzy:
Irida Lazić
Milica Tošić
Vladimir Djurdjević
Temat:
EURO-CORDEX
summer drying problem
regional climate model evaluation
E-OBS
Pannonian Basin
temperature
Meteorology. Climatology
QC851-999
Źródło:
Atmosphere, Vol 12, Iss 6, p 714 (2021)
Wydawca:
MDPI AG, 2021.
Rok publikacji:
2021
Kolekcja:
LCC:Meteorology. Climatology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2073-4433
Relacje:
https://www.mdpi.com/2073-4433/12/6/714; https://doaj.org/toc/2073-4433
DOI:
10.3390/atmos12060714
Dostęp URL:
https://doaj.org/article/7992c59c22ee42f9a17fcf7f559cc5cd  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.7992c59c22ee42f9a17fcf7f559cc5cd
Czasopismo naukowe
In previous projects that focused on dynamical downscaling over Europe, e.g., PRUDENCE and ENSEMBLES, many regional climate models (RCMs) tended to overestimate summer air temperature and underestimate precipitation in this season in Southern and Southeastern Europe, leading to the so-called summer drying problem. This bias pattern occurred not only in the RCM results but also in the global climate model (GCM) results, so knowledge of the model uncertainties and their cascade is crucial for understanding and interpreting future climate. Our intention with this study was to examine whether a warm-and-dry bias is also present in the state-of-the-art EURO-CORDEX multi-model ensemble results in the summer season over the Pannonian Basin. Verification of EURO-CORDEX RCMs was carried out by using the E-OBS gridded dataset of daily mean, minimum, and maximum near-surface air temperature and total precipitation amount with a horizontal resolution of 0.1 degrees (approximately 12 km × 12 km) over the 1971–2000 time period. The model skill for selected period was expressed in terms of four verification scores: bias, centered root mean square error (RMSE), spatial correlation coefficient, and standard deviation. The main findings led us to conclude that most of the RCMs that overestimate temperature also underestimate precipitation. For some models, the positive temperature and negative precipitation bias were more emphasized, which led us to conclude that the problem was still present in most of the analyzed simulations.

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