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Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis

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Abstract

The Mediterranean region is identified as one of the two main hot-spots of climate change and also known to have the highest concentration of cyclones in the world. These atmospheric features contribute significantly to the regional climate but they are not reproduced by the Atmosphere–Ocean General Circulation Models (AOGCM), due to their coarse horizontal resolution, which have recently been run in the frame of the 5th Climate Model Intercomparison Project. This article investigates the benefit of dynamically downscaling the Institut Pierre Simon Laplace (IPSL) AOGCM (IPSL-CM5) historical simulation by the weather and research forecasting (WRF) for the representation of the Mediterranean surface winds and cyclonic activity. Indeed, when considering IPSL-CM5 atmospheric fields, the dramatic underestimation of the cyclonic activity in the most cyclogenetic region of the world jeopardizes our ability to investigate in-depth the Mediterranean regional climate and trend in the context of global change. The WRF model shows remarkable skill to reproduce regional cyclogenesis. Indeed, cyclones occurrence is quasi-absent in IPSL-CM5 data but when applying dynamical downscaling their spatial–temporal variability is very close to the re-analysis. This is a clear benefit of dynamical downscaling in regions of strong topographic forcing. This “steady” source of forcing allows the production of lee cyclogenesis and the development of strong cyclones, whatever the quality of the large-scale circulation provided at the WRF’s boundaries by IPSL-CM5. However, dynamical downscaling still presents disadvantages as for instance the fact that large-scale inaccurate features of the IPSL-CM5 regional circulation are replicated by WRF due to the boundary controlled (small domain) simulation. The advantages and disadvantages of dynamical downscaling are thoroughly discussed in this paper revealing its importance for climate research, especially in the context of future scenarios and wind impacts.

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Acknowledgments

This work has been conducted in the IPSL group for regional climate and environment studies. It received support partly from the national ANR project MEDUP and the GIS projects MORCE-MED and MED-ICCBIO. The research leading to these results has also received funding from the European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no 282746. It is a contribution to IMPACT2C, MED-CORDEX and HyMeX, supported in France by the INSU/MISTRALS program. The authors are grateful to S. Denvil and M.A. Foujols for providing the CMIP5 simulations and to C. Lebeaupin-Brossier and H. Omrani for fruitful discussions and help for the development of the WRF/LMDz interface.

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Authors and Affiliations

  1. Institut Pierre Simon Laplace/LaBoratoire de Météorologie Dynamique, CNRS and Ecole Polytechnique, Palaiseau, France

    Emmanouil Flaounas & Philippe Drobinski

  2. Institut Pierre Simon Laplace/LaBoratoire Atmosphères, Milieux, Observations Spatiales, CNRS and Université Versailles Saint Quentin, Guyancourt, France

    Sophie Bastin

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  1. Emmanouil Flaounas
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  2. Philippe Drobinski
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  3. Sophie Bastin
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Correspondence to Emmanouil Flaounas.

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This paper is a contribution to the special issue on the IPSL and CNRM global climate and Earth System Models, both developed in France and contributing to the 5th coupled model intercomparison project.

Appendix

Appendix

In this appendix we present the functionality of the algorithm of cyclones detection we developed for this study. Figure 12a shows the 850 hPa wind field and relative vorticity of a strong cyclone, detected over the Adriatic Sea on January 1995. The algorithm will detect all cyclonic features in the domain, i.e. all the coloured “traces” in Fig. 12a, which correspond to values of relative vorticity above the threshold value of 10−5 s−1. However, only the “traces” which present values above 8 × 10−5 s−1 will be retained and will be identified as a cyclone. In this time step there is only one such cyclonic feature. This cyclonic feature shows a maximum value of 14.6 × 10−5 s−1 (its location is depicted by the arrow in Fig. 12a). Then, in order to determine the cyclones representative surface and center we connect all of the “trace” grid points and calculate their average longitude and latitude (center is depicted by the arrow in Fig. 12b). Finally, within the limits of the cyclone’s representative surface, we detect its minimum MSLP (depicted by the arrow in Fig. 12c) of the cyclone and its maximum 10-m wind speed (depicted by the arrow in Fig. 12d).

Fig. 12
figure 12

ERA-I fields corresponding to the 13th of January 1995 at 18 h a relative vorticity field (multiplied by 10−5) and 850 hPa wind circulation b grid points (red crosses) corresponding to the identified cyclonic feature with maximum relative vorticity above 8 × 10−5 s−1 c as in a but for mean sea level pressure, black crosses represent the cyclone grid points d as in a but for the module of the 10-m wind speed, black crosses represent the cyclone grid points

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Flaounas, E., Drobinski, P. & Bastin, S. Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis. Clim Dyn 40, 2497–2513 (2013). https://doi.org/10.1007/s00382-012-1606-7

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