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[1] This paper analyzes the sensitivity of simulated climate and energy balance to changes in soil emissivity over Northern Africa and the Arabian Peninsula and considers how this information may be used to improve emissivity parameterizations in climate models. Analysis of satellite observations suggests that the soil emissivity in current models is too high over this region. Sensitivity tests based on the recently developed Community Land Model indicate that this bias could produce significant errors in the model simulated ground and air temperature, net and upward longwave radiation, and sensible heat flux. There is a linear relationship between changes in emissivity and changes in these variables. Statistical results show that, on average for the study region, a decrease of soil emissivity by 0.1 will increase ground and air temperature by about 1.1°C and 0.8°C and decrease net and upward longwave radiation by about 6.6 Wm À2 and 8.1 Wm À2, respectively, at the ground surface. The decreased net longwave radiation (less emission) is mainly balanced by an increase of sensible heat flux of about 5.9 Wm À2 . These relations vary seasonally and diurnally. The temperature increases are slightly higher in winter than in summer and twice as large during nighttime as during daytime, while the sensible heat flux and longwave radiation show more change in summer/daytime than in winter/ nighttime. Our experimental results are consistent with our theoretical energy balance analyses. When a more realistic emissivity value is used, the model cold bias over the Sahara in comparison with land surface air temperature observations could be partially reduced. These results indicate that the simple representations of the land surface emissivity in climate models, especially for bare soil, need improvements based on satellite and in situ observations.

Land surface window emissivity is an important parameter for estimating the longwave radiative budget. This study focuses on estimating the window (8–12 μm) emissivity from the waveband emissivities of the five thermal infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). ASTER data along with the Temperature‐Emissivity Separation (TES) algorithm allows us to estimate surface channel emissivities with 90 m spatial resolution globally. Multiple regression was used to relate window emissivity to the five ASTER emissivities. This regression was developed using spectral libraries. Its residual error was less than 0.005 (RMSE) for values ranging between 0.81 and 1.00. We applied this regression to ASTER emissivities extracted from data acquired in 2001 and 2002 over a 240 × 1200 km area in a desert of North Africa. A comparison against a classification based emissivity map showed significant differences ranging between −0.08 and +0.06.

Surface broadband emissivity in the thermal infrared region is an important parameter for the studies of the surface energy balance. This paper focuses on estimating a broadband window emissivity from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectrometer (MODIS) data. Both sensors are on board the NASA Earth Observing System (EOS) Terra satellite, which was launched in 1999. First, several definitions of the broadband emissivity were investigated, and it was found that the emissivity integrated between 8 and 13.5 lm is the best for estimating the net longwave radiation under clear-sky conditions. Then, a method to estimate broadband emissivity at the continental scale was developed. The method uses two regressions. The first regression is to relate the broadband emissivity to the emissivities for the five ASTER channels using measured emissivities in the laboratory from spectral libraries. The second regression relates the broadband emissivity map from ASTER data to the emissivity and reflectance derived from MODIS data. The first regression was used for mapping the broadband emissivity using ASTER data in a 500 km 3 1400 km area of North Africa, which includes Tunisia, eastern Algeria, and western Libya. This emissivity map was used to calibrate the second regression, which was applied to MODIS data and generated a broadband emissivity map over North Africa and the Arabian Peninsula. The range of the broadband emissivity was found to be between 0.85 and 0.96 for the desert area. The resulting broadband emissivity maps and the methodology for generating them will contribute to future climate modeling studies.

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