An Observationally Based Global Band-by-Band Surface Emissivity Dataset for Climate and Weather Simulations

Abstract

While current atmospheric general circulation models (GCMs) still treat the surface as a blackbody in their longwave radiation scheme, recent studies suggest the need for taking realistic surface spectral emissivity into account. There have been few measurements available for the surface emissivity in the far IR (<650 cm−1). Based on first-principle calculation, the authors compute the spectral emissivity over the entire longwave spectrum for a variety of surface types. MODIS-retrieved mid-IR surface emissivity at 0.05° × 0.05° spatial resolution is then regressed against the calculated spectral emissivity to determine the surface type for each grid. The derived spectral emissivity data are then spatially averaged onto 0.5° × 0.5° grids and spectrally integrated onto the bandwidths used by the RRTMG_LW—a longwave radiation scheme widely used in current climate and numerical weather models. The band-by-band surface emissivity dataset is then compared with retrieved surface spectral emissivities from Infrared Atmospheric Sounding Interferometer (IASI) measurements. The comparison shows favorable agreement between two datasets in all the bands covered by the IASI measurements. The authors further use the dataset in conjunction with ERA-Interim to evaluate its impact on the top-of-atmosphere radiation budget. Depending on the blackbody surface assumptions used in the original calculation, the globally averaged difference caused by the inclusion of realistic surface emissivity ranges from −1.2 to −1.5 W m−2 for clear-sky OLR and from −0.67 to −0.94 W m−2 for all-sky OLR. Moreover, the difference is not spatially uniform and has a distinct spatial pattern.

Improved Representation of Surface Spectral Emissivity in a Global Climate Model and Its Impact on Simulated Climate

“…This study makes use of a monthly mean global surface spectral emissivity dataset developed by Huang et al (2016). The dataset contains spectral emissivities over the entire LW spectrum for 11 surface types: water, fine snow, medium snow, coarse snow, ice, grass, dry grass, conifer, deciduous forest, desert, and a combination of 55% vegetation and 45% desert.…”

“…Huang et al (2016) also showed the nonnegligible impact of such surface emissivity on the offline calculation of the TOA longwave (LW) radiation budget. Compared to the blackbody surface assumption, using realistic surface emissivity reduces the globally averaged outgoing longwave radiation (OLR) by about 0.7 W m 22 and the regional difference of OLR can be as large as 210 W m 22 , as shown by Huang et al (2016). In addition, Feldman et al (2014) suggested that the far-IR surface emissivity could also impact the simulated climate changes by the CESM in response to an increase of atmospheric CO 2 .…”

“…Figure 1 shows the spectral emissivities for water, ice, coarse snow, and desert surfaces, taken from the global surface spectral emissivity dataset developed by Huang et al (2016). Different surface types exhibit different spectral dependence.…”

“…Chen et al (2014) showed that, in comparison to the blackbody surface assumption, taking the far-IR surface emissivity of snow into account can affect the monthly mean polar radiation budget at the top of the atmosphere (TOA). Huang et al (2016) developed a global surface spectral emissivity dataset based on a hybrid approach using both first-principle calculations and satellite retrievals of surface spectral emissivity. Huang et al (2016) also showed the nonnegligible impact of such surface emissivity on the offline calculation of the TOA longwave (LW) radiation budget.…”

“…Huang et al (2016) developed a global surface spectral emissivity dataset based on a hybrid approach using both first-principle calculations and satellite retrievals of surface spectral emissivity. Huang et al (2016) also showed the nonnegligible impact of such surface emissivity on the offline calculation of the TOA longwave (LW) radiation budget. Compared to the blackbody surface assumption, using realistic surface emissivity reduces the globally averaged outgoing longwave radiation (OLR) by about 0.7 W m 22 and the regional difference of OLR can be as large as 210 W m 22 , as shown by Huang et al (2016).…”