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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 2
Ocean Sci., 8, 197–209, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ocean Sci., 8, 197–209, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Mar 2012

Research article | 29 Mar 2012

An empirical model for the statistics of sea surface diurnal warming

M. J. Filipiak1, C. J. Merchant1, H. Kettle1,*, and P. Le Borgne2 M. J. Filipiak et al.
  • 1Institute of Atmospheric and Environmental Science, School of GeoSciences, The University of Edinburgh, Crew Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JN, UK
  • 2Météo-France/Centre de Météorologie Spatiale, BP 50747, 22307 Lannion Cedex, France
  • *now at: Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Mayfield Road, Edinburgh EH9 3JZ, UK

Abstract. A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

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