Using moisture conservation to evaluate oceanic surface freshwater fluxes in climate models View Full Text


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Article Info

DATE

2010-09-10

AUTHORS

J. M. Rodríguez, T. C. Johns, R. B. Thorpe, A. Wiltshire

ABSTRACT

We apply a diagnostic based on moisture conservation in the atmosphere, integrated over planetary-scale ocean basins and drainage areas to evaluate freshwater fluxes over the ocean surface to three generations of the Hadley Centre climate model (HadCM3, HadGEM1 and HadGEM2-AO). The coherent inclusion of runoff by the diagnostic enables model surface freshwater fluxes to be compared directly with observational estimates of precipitation, evaporation and river discharge. We also introduce a normalised metric, based on model-observation RMS differences, to assess the representation of the fluxes by the model. This methodology could be a powerful tool for evaluating model performance during future model development and model intercomparison exercises. Using this diagnostic, and defining the drainage areas from the global river routing model TRIP, we obtain large-scale surface oceanic fluxes from ERA40 and NCAR-NCEP reanalysis data, which we compare with analogous budgets computed from a set of individual observational estimates of evaporation, precipitation and river discharge. The sum of errors in the Hadley Centre climate model in all ocean basins suggests a steady improvement over the three generations of the model. However, an analysis of sources and sinks of water vapour shows common errors in the models, like an excess of evaporation in the tropical-subtropical Atlantic, and a surplus of water vapour export from tropical-subtropical areas to the mid-latitude regions, making the oceanic surface fluxes too fresh at mid latitudes. Errors in the models are consistent with an excessively strong hydrological cycle. More... »

PAGES

205-219

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-010-0899-7

DOI

http://dx.doi.org/10.1007/s00382-010-0899-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035499901


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