Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe

Authors

  • M. García-Díez,

    1. Instituto de Física de Cantabria, IFCA (CSIC-UC), Santander, Spain
    2. Department of Applied Mathematics and Computer Science, Universidad de Cantabria, Santander, Spain
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  • J. Fernández,

    Corresponding author
    1. Department of Applied Mathematics and Computer Science, Universidad de Cantabria, Santander, Spain
    • Departamento Matemática Aplicada y CC de la Computación, ETSI de Caminos, Canales y Puertos, Avenida los Castros s/n, Santander 39005, Spain.
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  • L. Fita,

    1. Department of Applied Mathematics and Computer Science, Universidad de Cantabria, Santander, Spain
    2. Climate Change Research Center, University of New South Wales, Sydney, Australia
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  • C. Yagüe

    1. Departamento de Geofísica y Meteorología, Universidad Complutense de Madrid, Madrid, Spain
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Abstract

The seasonal dependence of Weather Research and Forecasting (WRF) model surface temperature biases and sensitivity to planetary boundary layer (PBL) schemes are jointly explored. For this purpose, the year 2001 was simulated using three different PBL schemes in a domain covering all Europe. The simulations were compared with gridded observations, upper-air data and high-frequency station data. Seasonal and daily cycles were analysed, aimed at providing a link between long-term biases and restricted case studies. The results show that the model mean bias significantly depends on the season, being warm in winter and cold in summer. The winter warm bias is related to misrepresented cold extremes, while a systematic cold bias dominates the whole temperature range in summer. Regarding PBL schemes, an overall underestimation of the entrainment is found, with the non-local Yonsei University scheme producing systematically warmer temperatures. It is shown that the opposite seasonal biases and systematic behaviour of the PBL schemes during the year lead to a different best-performing scheme in winter and summer. Moreover, the best-performing PBL scheme in an average sense is a result of the compensation of errors. The average summer results can be partially explained by a detailed case study. It is concluded that short-term studies should be used with caution to decide on the parametrizations to be used in long-term simulations. Copyright © 2012 Royal Meteorological Society

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