Abstract

We examine 44 years (1958–2001) of model data with the aim of characterizing the low frequency (the seasonal cycle and lower) variability of surface heat fluxes. The data set was produced in the framework of the HIPOCAS project through a dynamical downscaling (1/2˚ × 1/2˚) from the NCEP/NCAR global reanalysis using the atmospheric limited area model REMO. The added value of this data set is the better representation of regional and local aspects related to thermal and dynamical effects resulting from its higher resolution. The basin mean values of the heat fluxes have been estimated in 168 W/m 2 for the solar radiation ( Q S), 73 W/m 2 for the longwave net radiation ( Q B), 8 W/m 2 for the sensible heat ( Q H) and 88 W/m 2 for the latent heat ( Q E), giving a total heat budget of about − 1 W/m 2. The main differences with respect to previous results are the reduced Q S and Q E terms. The seasonal cycle accounts for a significant fraction of the variability (75%, 20% and 10% for Q S, Q E and Q H) except for Q B (less than 1%). The total heat budget has an amplitude of 164 W/m 2 and peaks by middle June, in agreement with previous works and observations. The interannual variability of each component has been first quantified by the standard deviation of the annual mean values, obtaining ± 2.0 W/m 2 for Q S, ± 1.1 W/m 2 for Q B, ± 4.7 W/m 2 for Q E and ± 1.1 W/m 2 for Q H. The dominant modes have been obtained through an EOF analysis, which is shown to be robust with respect to the analysis domain. The correlation between the amplitudes of the radiation terms ( Q S and Q B) and MOI winter values is higher than 0.7 (in absolute value) in the Eastern basin. For the other flux components the correlation with the MOI is less than 0.7 everywhere. The correlation between the heat flux terms and the NAO is smaller than 0.7 for all terms. From the evaluation analysis, HIPCOAS fluxes show stronger correlations with the observation based NOC fields than are obtained with the original NCEP/NCAR fluxes for the full set of interannually varying heat flux estimates. Thus, the downscaling has led to an improved representation of the interannual variability when compared with observations.

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