Abstract
Abstract. Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM) to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea. First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology. Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally) improves the representation of spatial variability, in particular in regions strongly influenced by the complex surrounding orography. The impact of the interactive air-sea coupling is negligible for the temporal scales examined here. Using two different forcing datasets induces differences on the downscaled fields that are directly related to the differences between those datasets. Our results also show that improving the physics of our RCM is still necessary to increase the realism of our simulations. Finally, the choice of the optimal configuration depends on the scientific objectives of the study for which those wind datasets are used.
Highlights
Energy transfers that occur at the air-sea interface drive the dynamics of the surface oceanic mixed layer
This mode follows the setting of Somot et al (2008), except that the ARPEGE-Climate stretched-grid climate model was replaced by the ALADIN-Climate limitedarea regional climate model (RCM) and the former OPAMED8 model was replaced by NEMOMED8 (Madec, 2008; Sevault et al, 2009; Beuvier et al, 2010)
There is no nudging for the atmosphere and ocean in the coupled simulation. For all simulations both components of instantaneous wind velocity are stored every 6 h. We examine those simulations for the period 2000–2001, which corresponds to the period covered simultaneously by QuikSCAT, ERA-40 and ERA-Interim
Summary
Energy transfers that occur at the air-sea interface drive the dynamics of the surface oceanic mixed layer. Performing realistic long-term Mediterranean oceanic simulations without temporal inconsistency, reanalysis of surface atmospheric variables (NCEP; Kalnay et al, 1996, ERA-15; Gibson et al, 1997, ERA-40) are the natural choice despite their low spatial resolution They have been extensively used for Mediterranean Sea modelling (Myers et al, 1998; Lascaratos et al, 1999; Castellari et al, 2000; Rupolo et al, 2003; Demirov and Pinardi, 2007; Beranger et al, 2010). The dynamical downscaling technique is very promising since it provides very good temporal chronology, longterm temporal homogeneity, high spatial and temporal resolution and physical consistency for all the atmospheric variables at the same time This technique can be applied to coupled RCM to take into account air-sea feedbacks (Artale et al, 2009).
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