The surface radiation budget over North America: gridded data assessment and evaluation of regional climate models

Abstract

AbstractWhile surface station observations of downwelling radiation offer accuracy at high temporal resolution, they do not easily allow an evaluation of model surface radiation budgets (SRB) over a wide geographical area. We evaluate three gridded SRB data sets against detailed observations from six surface radiation sites from the US surface radiation (SURFRAD) network. We subsequently use the most accurate surrogate observational data set for evaluation of model‐simulated SRB. The data sets assessed are: ERA40—reanalysis of European Centre for Medium‐Range Weather Forecasts (ECMWF), North American Regional Reanalysis (NARR)—regional reanalysis of National Centres for Environmental Prediction (NCEP) and the surface radiative budget (SRB) from the International Satellite Cloud Climatology Project (ISCCP). Due to varying constraints with respect to temporal coverage of each data set, the evaluation period used in this study is 1996–2001, inclusive.The ERA40 downwelling longwave radiation (DLR) appears the most accurate surrogate observation, while both ERA40 and ISCCP show accurate results when the incoming shortwave radiation (ISR) is considered across the annual cycle. Winter DLR is less accurate in ISCCP with a positive bias and lack of very low (<200 Wm−2) flux values. The NARR SRB shows a large positive bias in the ISR throughout the annual cycle, linked to a significant underestimate of cloud cover.The ERA40 data are subsequently used to evaluate the simulated SRB in three regional climate models across North America. With respect to solar radiation, cloud cover biases are seen to be crucial, while for longwave fluxes both cloud fraction and in‐cloud water content are important to simulate correctly. Inclusion of trace gases beyond H2O, CO2 and O3 appears necessary for an accurate calculation of clear‐sky longwave radiation. Error compensation frequently occurs between the various components contributing to a model total‐sky SRB. This is important to consider when trying to identify the underlying causes of errors in the simulated total SRB. Copyright © 2009 Royal Meteorological Society