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- Author or Editor: Franklin R. Robertson x
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Despite the general agreement that clouds cool the earth–atmosphere, there are substantial differences in estimated magnitudes of the annual global mean of cloud radiative forcing. Recent estimates of globally averaged net cloud radiative forcing range from −2 to −27 W m−2. The reasons for these differences have not been clarified in spite of the important role of clouds in maintaining global heat balance. Here, three estimation methods [Earth Radiation Budget Experiment (ERBE), Regression I, and Regression II] are compared using the same data source and analysis period.
Intercomparison has been done for the time period of February and March 1985 over which major satellite radiation budget and cloudiness datasets (ERBE radiation budget, Nimbus-7, and ISCCP cloudiness) are contemporaneous. The global averages of five sets of net cloud radiative forcing by three independent methods agree to within 3.5 W m−2; four of five cases agree to within 1 W m−2. This suggests that differences in published global mean values of net cloud radiative forcing are mainly due to different data sources and analysis periods and a best estimated annual mean among all previous estimates appears to be the ERBE measurement, that is, −17.3 W m−2. In contrast to the close agreement in the net cloud radiative forcing estimates, both longwave and shortwave cloud radiative forcing show more dependence on the chosen method and dataset. The bias of regression-retrieved values between Nimbus-7 and ISCCP cloud climatology is largely attributed to the difference in total cloudiness between two climatologies whereas the discrepancies between the ERBE and regression method appear to be, in part, due to the conceptually different definition of clear-sky flux.
Despite the general agreement that clouds cool the earth–atmosphere, there are substantial differences in estimated magnitudes of the annual global mean of cloud radiative forcing. Recent estimates of globally averaged net cloud radiative forcing range from −2 to −27 W m−2. The reasons for these differences have not been clarified in spite of the important role of clouds in maintaining global heat balance. Here, three estimation methods [Earth Radiation Budget Experiment (ERBE), Regression I, and Regression II] are compared using the same data source and analysis period.
Intercomparison has been done for the time period of February and March 1985 over which major satellite radiation budget and cloudiness datasets (ERBE radiation budget, Nimbus-7, and ISCCP cloudiness) are contemporaneous. The global averages of five sets of net cloud radiative forcing by three independent methods agree to within 3.5 W m−2; four of five cases agree to within 1 W m−2. This suggests that differences in published global mean values of net cloud radiative forcing are mainly due to different data sources and analysis periods and a best estimated annual mean among all previous estimates appears to be the ERBE measurement, that is, −17.3 W m−2. In contrast to the close agreement in the net cloud radiative forcing estimates, both longwave and shortwave cloud radiative forcing show more dependence on the chosen method and dataset. The bias of regression-retrieved values between Nimbus-7 and ISCCP cloud climatology is largely attributed to the difference in total cloudiness between two climatologies whereas the discrepancies between the ERBE and regression method appear to be, in part, due to the conceptually different definition of clear-sky flux.
The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.
This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.
Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.
The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.
This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.
Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.
