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Evaluation of the Radiative Energy Budget in Climate and NWP Models Using Satellite Data, Reanalyses and Ground-based Observations

Evaluation of the Radiative Energy Budget in Climate and NWP Models Using Satellite Data, Reanalyses and Ground-based Observations

Richard P. Allan(1), Anthony Slingo(1), Sean F. Milton(2), Malcolm E. Brooks(2)
(1) ESSC, University of Reading, UK
(2) Met Office, Exeter, UK

Results from three projects are presented in which satellite and ground-based measurements of radiative fluxes and water vapor are utilised in evaluations of a suite of models including detailed radiative transfer codes, operational weather forecasting models and climate simulations.

1) The top of atmosphere radiative fluxes simulated by the Met Office global forecast model are evaluated using new data from the Geostationary Earth Radiation Budget (GERB) instrument and other data from the Meteosat-8 satellite. Large errors in the top of atmosphere energy balance arise over the Sahara, relating to poor representation of surface albedo and mineral dust aerosol. Marine stratocumulus cloud is too reflective in the model.

2) Simultaneous measurment of radiative fluxes at the surface and top of the atmosphere are presented as part of the RADAGAST project. The radiative imprint of a major dust storm during March 2006 is detailed; radiative transfer schemes underestimate the absorption of solar radiation in the dusty atmosphere.

3) Reanalysis and satellite observations are combined to evaluate the IPCC AR4 climate model simulations of the clear-sky longwave radiative energy balance and the atmospheric hydrological cycle. The influence of forecast lead-time on reanalyses quality are examined and the sensitivity of satellite clear-sky fluxes to footprint size are estimated using data from GERB/SEVIRI. There is a significant positive relationship between clear-sky longwave radiative cooling of the atmosphere and surface temperature of 3.6 to 4.6 Wm-2K-1 over tropical ocean descent regimes in the reanalyses and satellite-based datasets. Comparison with model simulations and links with precipitation variability are investigated.