Uncertainty Estimate of Surface Irradiances Comput

Authors: Seiji Kato Norman G Loeb David A Rutan Fred G Rose Sunny SunMack Walter F Miller Yan Chen

Publish Date: 2012/02/23

Volume: 33, Issue: 3-4, Pages: 395-412

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Abstract

Differences of modeled surface upward and downward longwave and shortwave irradiances are calculated using modeled irradiance computed with active sensorderived and passive sensorderived cloud and aerosol properties The irradiance differences are calculated for various temporal and spatial scales monthly gridded monthly zonal monthly global and annual global Using the irradiance differences the uncertainty of surface irradiances is estimated The uncertainty 1σ of the annual global surface downward longwave and shortwave is respectively 7 W m−2 out of 345 W m−2 and 4 W m−2 out of 192 W m−2 after known bias errors are removed Similarly the uncertainty of the annual global surface upward longwave and shortwave is respectively 3 W m−2 out of 398 W m−2 and 3 W m−2 out of 23 W m−2 The uncertainty is for modeled irradiances computed using cloud properties derived from imagers on a sunsynchronous orbit that covers the globe every day eg moderateresolution imaging spectrometer or modeled irradiances computed for nadir view only active sensors on a sunsynchronous orbit such as CloudAerosol Lidar and Infrared Pathfinder Satellite Observation and CloudSat If we assume that longwave and shortwave uncertainties are independent of each other but up and downward components are correlated with each other the uncertainty in global annual mean net surface irradiance is 12 W m−2 Onesigma uncertainty bounds of the satellitebased net surface irradiance are 106 W m−2 and 130 W m−2Estimating the surface irradiance is important in understanding the energy cycle of the globe for several reasons The sum of surface net irradiance and other surface enthalpy sensible and latent heat fluxes is the energy flux through the lower boundary of the atmospheric column and the upper boundary of an ocean column Therefore the global mean net surface irradiance balances with the sum of the surface latent and sensible heat fluxes and ocean heating rate Wong et al 2006 In addition the radiative net energy deposition in the atmosphere and vertical and horizontal profiles of the energy deposition determine the dynamics in the atmosphere Understanding the topofatmosphere TOA surface and atmospheric irradiances quantitatively is therefore necessary to quantitatively understand the dynamics which in turn controls cloud feedback processes Wielicki et al 1995 The global mean surface irradiance estimate is only possible through modeling surface irradiances In earlier studies cloud properties derived from passive satellite instruments combined with radiative transfer models have been used to estimate surface irradiance eg Pinker and Laszlo 1992 Zhang et al 1995 and a summary is given by Kandel and Viollier 2010While surface irradiances computed with satellitederived and modeled cloud properties have been compared in earlier studies eg Hatzianastassiou et al 2005 Su et al 2008 Stephens 2011 the uncertainty of surface irradiances averaged over different temporal and spatial scales such as monthly or annual and regional zonal or global is not well understood The purpose of this paper is to extend the study by Kato et al 2011 to estimate uncertainties of surface irradiance components in various spatial and temporal scales 1° × 30° or 1° × 1° gridded 1° zonal and global spatial scales and monthly and annual temporal scalesIn this study we define the uncertainty as a range of surface irradiances in which the true value resides at a 68 probability Our goal is different from estimating the error of a specific surface irradiance estimate although we need to have a specific surface irradiance estimate to attach the uncertainty Taylor and Kuyatt 1994 describe the difference nicely the result of a measurement modeled irradiance could have a negligible error because it can unknowably be very close to the truth even though it may have a large uncertainty There are two possible ways of estimating the modeled surface irradiance uncertainty One way is to estimate the uncertainty of the input variables perturb inputs by the uncertainty amount and compute the irradiance using a radiative transfer model The irradiance change by the sensitivity study is considered as the uncertainty due to input variables Some earlier studies eg Zhang et al 1995 Zhang et al 2004 Kim and Ramanathan 2008 used this approach A second approach is to use surface observations and compute the root mean square RMS difference of modeled and observed surface irradiances We primarily take the former approach in this study but briefly examine how uncertainties derived by the two approaches differSections 2 and 3 present a brief overview of surface longwave and shortwave irradiance estimates respectively to understand the range of the global annual mean values Section 4 briefly discusses the computation method using CALIPSO Winker et al 2010 CloudSat Stephens et al 2008 moderate resolution imaging spectrometer MODIS and Clouds and the Earth’s Radiant Energy System CERES data Section 5 analyzes the uncertainty of surface irradiances for various temporal and spatial scales by comparing two modeled irradiances sensitivity approach Section 6 uses surface observations to estimate modeled surface irradiance uncertainties surface observation approach Section 7 combines uncertainties of all surface irradiance components and discusses the uncertainty in the global annual net surface irradianceStephens et al 2011 provide a summary of the global annual mean surface longwave upward and downward irradiance estimated from satellite observations reanalysis and groundbased observations The global annual mean downward longwave irradiance estimated from satellite observations GEWEX SRB ISCCPFD CERES ranges from 342 to 348 W m−2 Stephens et al 2011 The global annual mean downward longwave irradiance estimated by reanalyses ranges from 324 to 340 W m−2 Stephens et al 2011Wild et al 2001 compared the global annual mean surface downward longwave irradiance computed in general circulation models GCMs with surface observations in Global Energy Balance Archive GEBA and Baseline Surface Radiation Network BSRN Ohmura et al 1998 data sets Their results show that the modeled global annual mean surface downward longwave irradiance by GCMs varies more than 40 W m−2 ranging from 303 to 344 W m−2 GCMderived surface downward longwave irradiances are less than observed irradiances especially under dry and cold conditions Their results suggest that the difference is caused by the underestimation of surface downward longwave irradiances under cloudfree conditionsThe global annual mean surface downward longwave irradiance under clearsky conditions computed with satellitebased observations passive sensorderived clouds and that from reanalyses shown in Stephens 2011 range from 309 to 326 W m−2 which is a smaller variation than allsky values 324–348 W m−2 In addition among values given by Stephens 2011 all but two clearsky values agree to within a few W m−2 Allsky global annual mean surface downward longwave irradiances from reanalyses tend to be smaller than satellitebased estimates Stephens 2011 indicating that the difference is caused by clouds especially lowlevel clouds This suggests that cloud properties used in reanalyses are different from those derived from satellites and that the cloud difference is a source of the difference between satellitebased and reanalysisbased estimates for allsky conditions

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