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Abstract
The diurnal cycle of outgoing longwave radiation (OLR) from the earth is analyzed by decomposing satellite observations into a set of empirical orthogonal functions (EOFs). The observations are from the Earth Radiation Budget Experiment (ERBE) scanning radiometer aboard the Earth Radiation Budget Satellite, which had a precessing orbit with 57° inclination. The diurnal cycles of land and ocean differ considerably. The first EOF for land accounts for 73% to 85% of the variance, whereas the first EOF for ocean accounts for only 16% to 20% of the variance, depending on season. The diurnal cycle for land is surprisingly symmetric about local noon for the first EOF, which is approximately a half-sine during day and flat at night. The second EOF describes lead–lag effects due to surface heating and cloud formation. For the ocean, the first EOF and second EOF are similar to that of land, except for spring, when the first ocean EOF is a semidiurnal cycle and the second ocean EOF is the half-sine. The first EOF for land has a daytime peak of about 50 W m−2, whereas the first ocean EOF peaks at about 25 W m−2. The geographical and seasonal patterns of OLR diurnal cycle provide insights into the interaction of radiation with the atmosphere and surface and are useful for validating and upgrading circulation models.
Abstract
The diurnal cycle of outgoing longwave radiation (OLR) from the earth is analyzed by decomposing satellite observations into a set of empirical orthogonal functions (EOFs). The observations are from the Earth Radiation Budget Experiment (ERBE) scanning radiometer aboard the Earth Radiation Budget Satellite, which had a precessing orbit with 57° inclination. The diurnal cycles of land and ocean differ considerably. The first EOF for land accounts for 73% to 85% of the variance, whereas the first EOF for ocean accounts for only 16% to 20% of the variance, depending on season. The diurnal cycle for land is surprisingly symmetric about local noon for the first EOF, which is approximately a half-sine during day and flat at night. The second EOF describes lead–lag effects due to surface heating and cloud formation. For the ocean, the first EOF and second EOF are similar to that of land, except for spring, when the first ocean EOF is a semidiurnal cycle and the second ocean EOF is the half-sine. The first EOF for land has a daytime peak of about 50 W m−2, whereas the first ocean EOF peaks at about 25 W m−2. The geographical and seasonal patterns of OLR diurnal cycle provide insights into the interaction of radiation with the atmosphere and surface and are useful for validating and upgrading circulation models.
Abstract
Five years of measurements from the Earth Radiation Budget Satellite (ERBS) have been analyzed to define the diurnal cycle of albedo from 55°N to 55°S. The ERBS precesses through all local times every 72 days so as to provide data regarding the diurnal cycles for Earth radiation. Albedo together with insolation at the top of the atmosphere is used to compute the heating of the Earth–atmosphere system; thus its diurnal cycle is important in the energetics of the climate system. A principal component (PC) analysis of the diurnal variation of top-of-atmosphere albedo using these data is presented. The analysis is done separately for ocean and land because of the marked differences of cloud behavior over ocean and over land. For ocean, 90%–92% of the variance in the diurnal cycle is described by a single component; for land, the first PC accounts for 83%–89% of the variance. Some of the variation is due to the increase of albedo with increasing solar zenith angle, which is taken into account in the ERBS data processing by a directional model, and some is due to the diurnal cycle of cloudiness. The second PC describes 2%–4% of the variance for ocean and 5% for land, and it is primarily due to variations of cloudiness throughout the day, which are asymmetric about noon. These terms show the response of the atmosphere to the cycle of solar heating. The third PC for ocean is a two-peaked curve, and the associated map shows high values in cloudy regions.
Abstract
Five years of measurements from the Earth Radiation Budget Satellite (ERBS) have been analyzed to define the diurnal cycle of albedo from 55°N to 55°S. The ERBS precesses through all local times every 72 days so as to provide data regarding the diurnal cycles for Earth radiation. Albedo together with insolation at the top of the atmosphere is used to compute the heating of the Earth–atmosphere system; thus its diurnal cycle is important in the energetics of the climate system. A principal component (PC) analysis of the diurnal variation of top-of-atmosphere albedo using these data is presented. The analysis is done separately for ocean and land because of the marked differences of cloud behavior over ocean and over land. For ocean, 90%–92% of the variance in the diurnal cycle is described by a single component; for land, the first PC accounts for 83%–89% of the variance. Some of the variation is due to the increase of albedo with increasing solar zenith angle, which is taken into account in the ERBS data processing by a directional model, and some is due to the diurnal cycle of cloudiness. The second PC describes 2%–4% of the variance for ocean and 5% for land, and it is primarily due to variations of cloudiness throughout the day, which are asymmetric about noon. These terms show the response of the atmosphere to the cycle of solar heating. The third PC for ocean is a two-peaked curve, and the associated map shows high values in cloudy regions.
Abstract
Satellite and reanalysis data are used to observe interannual variations in atmospheric diabatic heating and circulation within the ascending and descending branches of the Hadley circulation (HC) during the past 12 yr. The column-integrated divergence of dry static energy (DSE) and kinetic energy is inferred from satellite-based observations of atmospheric radiation, precipitation latent heating, and reanalysis-based surface sensible heat flux for monthly positions of the HC branches, determined from a mass weighted zonal mean meridional streamfunction analysis. Mean surface radiative fluxes inferred from satellite and surface measurements are consistent to 1 W m−2 (<1%) over land and 4 W m−2 (2%) over ocean. In the ascending branch, where precipitation latent heating dominates over radiative cooling, discrepancies in latent heating among different precipitation datasets reach 22 W m−2 (17%), compared to 3–6 W m−2 in the descending branches. Whereas direct calculations of DSE divergence from two reanalyses show opposite trends, the implied DSE divergence from the satellite observations of atmospheric diabatic heating exhibits no trend in all three HC branches and is strongly correlated (reaching 0.90) with midtropospheric vertical velocity. The implied DSE divergence from satellite observations thus provides a useful independent measure of HC circulation strength variability. The sensitivity to circulation change is 4–5 times larger for precipitation latent heating compared to atmospheric radiative cooling in the descending branches and 20 times larger in the ascending branch. The difference in sensitivity is due to cloud radiative effects, which enhance atmospheric radiative cooling in the descending branches in response to an increase in HC strength but decrease it in the ascending branch.
Abstract
Satellite and reanalysis data are used to observe interannual variations in atmospheric diabatic heating and circulation within the ascending and descending branches of the Hadley circulation (HC) during the past 12 yr. The column-integrated divergence of dry static energy (DSE) and kinetic energy is inferred from satellite-based observations of atmospheric radiation, precipitation latent heating, and reanalysis-based surface sensible heat flux for monthly positions of the HC branches, determined from a mass weighted zonal mean meridional streamfunction analysis. Mean surface radiative fluxes inferred from satellite and surface measurements are consistent to 1 W m−2 (<1%) over land and 4 W m−2 (2%) over ocean. In the ascending branch, where precipitation latent heating dominates over radiative cooling, discrepancies in latent heating among different precipitation datasets reach 22 W m−2 (17%), compared to 3–6 W m−2 in the descending branches. Whereas direct calculations of DSE divergence from two reanalyses show opposite trends, the implied DSE divergence from the satellite observations of atmospheric diabatic heating exhibits no trend in all three HC branches and is strongly correlated (reaching 0.90) with midtropospheric vertical velocity. The implied DSE divergence from satellite observations thus provides a useful independent measure of HC circulation strength variability. The sensitivity to circulation change is 4–5 times larger for precipitation latent heating compared to atmospheric radiative cooling in the descending branches and 20 times larger in the ascending branch. The difference in sensitivity is due to cloud radiative effects, which enhance atmospheric radiative cooling in the descending branches in response to an increase in HC strength but decrease it in the ascending branch.
Abstract
The zonal mean atmospheric cloud radiative effect, defined as the difference between the top-of-the-atmosphere (TOA) and surface cloud radiative effects, is estimated from 3 yr of Clouds and the Earth’s Radiant Energy System (CERES) data. The zonal mean shortwave effect is small, though it tends to be positive (warming). This indicates that clouds increase shortwave absorption in the atmosphere, especially in midlatitudes. The zonal mean atmospheric cloud radiative effect is, however, dominated by the longwave effect. The zonal mean longwave effect is positive in the tropics and decreases with latitude to negative values (cooling) in polar regions. The meridional gradient of the cloud effect between midlatitude and polar regions exists even when uncertainties in the cloud effect on the surface enthalpy flux and in the modeled irradiances are taken into account. This indicates that clouds increase the rate of generation of the mean zonal available potential energy. Because the atmospheric cooling effect in polar regions is predominately caused by low-level clouds, which tend to be stationary, it is postulated here that the meridional and vertical gradients of the cloud effect increase the rate of meridional energy transport by the dynamics of the atmosphere from the midlatitudes to the polar region, especially in fall and winter. Clouds then warm the surface in the polar regions except in the Arctic in summer. Clouds, therefore, contribute toward increasing the rate of meridional energy transport from the midlatitudes to the polar regions through the atmosphere.
Abstract
The zonal mean atmospheric cloud radiative effect, defined as the difference between the top-of-the-atmosphere (TOA) and surface cloud radiative effects, is estimated from 3 yr of Clouds and the Earth’s Radiant Energy System (CERES) data. The zonal mean shortwave effect is small, though it tends to be positive (warming). This indicates that clouds increase shortwave absorption in the atmosphere, especially in midlatitudes. The zonal mean atmospheric cloud radiative effect is, however, dominated by the longwave effect. The zonal mean longwave effect is positive in the tropics and decreases with latitude to negative values (cooling) in polar regions. The meridional gradient of the cloud effect between midlatitude and polar regions exists even when uncertainties in the cloud effect on the surface enthalpy flux and in the modeled irradiances are taken into account. This indicates that clouds increase the rate of generation of the mean zonal available potential energy. Because the atmospheric cooling effect in polar regions is predominately caused by low-level clouds, which tend to be stationary, it is postulated here that the meridional and vertical gradients of the cloud effect increase the rate of meridional energy transport by the dynamics of the atmosphere from the midlatitudes to the polar region, especially in fall and winter. Clouds then warm the surface in the polar regions except in the Arctic in summer. Clouds, therefore, contribute toward increasing the rate of meridional energy transport from the midlatitudes to the polar regions through the atmosphere.
Abstract
The diurnal cycle of outgoing longwave radiation (OLR) computed by a climate model provides a powerful test of the numerical description of various physical processes. Diurnal cycles of OLR computed by version 3 of the Hadley Centre Atmospheric Model (HadAM3) are compared with those observed by the Earth Radiation Budget Satellite (ERBS) for the boreal summer season (June–August). The ERBS observations cover the domain from 55°S to 55°N. To compare the observed and modeled diurnal cycles, the principal component (PC) analysis method is used over this domain. The analysis is performed separately for the land and ocean regions. For land over this domain, the diurnal cycle computed by the model has a root-mean-square (RMS) of 11.4 W m−2, as compared with 13.3 W m−2 for ERBS. PC-1 for ERBS observations and for the model are similar, but the ERBS result has a peak near 1230 LST and decreases very slightly during night, whereas the peak of the model result is an hour later and at night the OLR decreases by 7 W m−2 between 2000 and 0600 LST. Some of the difference between the ERBS and model results is due to the computation of convection too early in the afternoon by the model. PC-2 describes effects of morning/afternoon cloudiness on OLR, depending on the sign. Over ocean in the ERBS domain, the model RMS of the OLR diurnal cycle is 2.8 W m−2, as compared with 5.9 W m−2 for ERBS. Also, for the model, PC-1 accounts for 66% of the variance, while for ERBS, PC-1 accounts for only 16% of the variance. Thus, over ocean, the ERBS results show a greater variety of OLR diurnal cycles than the model does.
Abstract
The diurnal cycle of outgoing longwave radiation (OLR) computed by a climate model provides a powerful test of the numerical description of various physical processes. Diurnal cycles of OLR computed by version 3 of the Hadley Centre Atmospheric Model (HadAM3) are compared with those observed by the Earth Radiation Budget Satellite (ERBS) for the boreal summer season (June–August). The ERBS observations cover the domain from 55°S to 55°N. To compare the observed and modeled diurnal cycles, the principal component (PC) analysis method is used over this domain. The analysis is performed separately for the land and ocean regions. For land over this domain, the diurnal cycle computed by the model has a root-mean-square (RMS) of 11.4 W m−2, as compared with 13.3 W m−2 for ERBS. PC-1 for ERBS observations and for the model are similar, but the ERBS result has a peak near 1230 LST and decreases very slightly during night, whereas the peak of the model result is an hour later and at night the OLR decreases by 7 W m−2 between 2000 and 0600 LST. Some of the difference between the ERBS and model results is due to the computation of convection too early in the afternoon by the model. PC-2 describes effects of morning/afternoon cloudiness on OLR, depending on the sign. Over ocean in the ERBS domain, the model RMS of the OLR diurnal cycle is 2.8 W m−2, as compared with 5.9 W m−2 for ERBS. Also, for the model, PC-1 accounts for 66% of the variance, while for ERBS, PC-1 accounts for only 16% of the variance. Thus, over ocean, the ERBS results show a greater variety of OLR diurnal cycles than the model does.
Abstract
NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project is responsible for operation and data processing of observations from scanning radiometers on board the Tropical Rainfall Measuring Mission (TRMM), Terra, Aqua, and Suomi National Polar-Orbiting Partnership (NPP) satellites. The clouds and radiative swath (CRS) CERES data product contains irradiances computed using a radiative transfer model for nearly all CERES footprints in addition to top-of-atmosphere (TOA) irradiances derived from observed radiances by CERES instruments. This paper describes a method to constrain computed irradiances by CERES-derived TOA irradiances using Lagrangian multipliers. Radiative transfer model inputs include profiles of atmospheric temperature, humidity, aerosols and ozone, surface temperature and albedo, and up to two sets of cloud properties for a CERES footprint. Those inputs are adjusted depending on predefined uncertainties to match computed TOA and CERES-derived TOA irradiance. Because CERES instantaneous irradiances for an individual footprint also include uncertainties, primarily due to the conversion of radiance to irradiance using anisotropic directional models, the degree of the constraint depends on CERES-derived TOA irradiance as well. As a result of adjustment, TOA computed-minus-observed standard deviations are reduced from 8 to 4 W m−2 for longwave irradiance and from 15 to 6 W m−2 for shortwave irradiance. While agreement of computed TOA with CERES-derived irradiances improves, comparisons with surface observations show that model constrainment to the TOA does not reduce computation bias error at the surface. After constrainment, shortwave down at the surface has an increased bias (standard deviation) of 1% (0.5%) and longwave increases by 0.2% (0.1%). Clear-sky changes are negligible.
Abstract
NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project is responsible for operation and data processing of observations from scanning radiometers on board the Tropical Rainfall Measuring Mission (TRMM), Terra, Aqua, and Suomi National Polar-Orbiting Partnership (NPP) satellites. The clouds and radiative swath (CRS) CERES data product contains irradiances computed using a radiative transfer model for nearly all CERES footprints in addition to top-of-atmosphere (TOA) irradiances derived from observed radiances by CERES instruments. This paper describes a method to constrain computed irradiances by CERES-derived TOA irradiances using Lagrangian multipliers. Radiative transfer model inputs include profiles of atmospheric temperature, humidity, aerosols and ozone, surface temperature and albedo, and up to two sets of cloud properties for a CERES footprint. Those inputs are adjusted depending on predefined uncertainties to match computed TOA and CERES-derived TOA irradiance. Because CERES instantaneous irradiances for an individual footprint also include uncertainties, primarily due to the conversion of radiance to irradiance using anisotropic directional models, the degree of the constraint depends on CERES-derived TOA irradiance as well. As a result of adjustment, TOA computed-minus-observed standard deviations are reduced from 8 to 4 W m−2 for longwave irradiance and from 15 to 6 W m−2 for shortwave irradiance. While agreement of computed TOA with CERES-derived irradiances improves, comparisons with surface observations show that model constrainment to the TOA does not reduce computation bias error at the surface. After constrainment, shortwave down at the surface has an increased bias (standard deviation) of 1% (0.5%) and longwave increases by 0.2% (0.1%). Clear-sky changes are negligible.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
Abstract
The Clouds and the Earth’s Radiant Energy System Synoptic (SYN1deg), edition 3, product provides climate-quality global 3-hourly 1° × 1°gridded top of atmosphere, in-atmosphere, and surface radiant fluxes. The in-atmosphere surface fluxes are computed hourly using a radiative transfer code based upon inputs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), 3-hourly geostationary (GEO) data, and meteorological assimilation data from the Goddard Earth Observing System. The GEO visible and infrared imager calibration is tied to MODIS to ensure uniform MODIS-like cloud properties across all satellite cloud datasets. Computed surface radiant fluxes are compared to surface observations at 85 globally distributed land (37) and ocean buoy (48) sites as well as several other publicly available global surface radiant flux data products. Computed monthly mean downward fluxes from SYN1deg have a bias (standard deviation) of 3.0 W m−2 (5.7%) for shortwave and −4.0 W m−2 (2.9%) for longwave compared to surface observations. The standard deviation between surface downward shortwave flux calculations and observations at the 3-hourly time scale is reduced when the diurnal cycle of cloud changes is explicitly accounted for. The improvement is smaller for surface downward longwave flux owing to an additional sensitivity to boundary layer temperature/humidity, which has a weaker diurnal cycle compared to clouds.
Abstract
The Clouds and the Earth’s Radiant Energy System Synoptic (SYN1deg), edition 3, product provides climate-quality global 3-hourly 1° × 1°gridded top of atmosphere, in-atmosphere, and surface radiant fluxes. The in-atmosphere surface fluxes are computed hourly using a radiative transfer code based upon inputs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), 3-hourly geostationary (GEO) data, and meteorological assimilation data from the Goddard Earth Observing System. The GEO visible and infrared imager calibration is tied to MODIS to ensure uniform MODIS-like cloud properties across all satellite cloud datasets. Computed surface radiant fluxes are compared to surface observations at 85 globally distributed land (37) and ocean buoy (48) sites as well as several other publicly available global surface radiant flux data products. Computed monthly mean downward fluxes from SYN1deg have a bias (standard deviation) of 3.0 W m−2 (5.7%) for shortwave and −4.0 W m−2 (2.9%) for longwave compared to surface observations. The standard deviation between surface downward shortwave flux calculations and observations at the 3-hourly time scale is reduced when the diurnal cycle of cloud changes is explicitly accounted for. The improvement is smaller for surface downward longwave flux owing to an additional sensitivity to boundary layer temperature/humidity, which has a weaker diurnal cycle compared to clouds.
Abstract
A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.
Abstract
A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.
Abstract
Spectral and broadband radiances and irradiances (fluxes) were measured from surface, airborne, and spaceborne platforms in the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) campaign. The radiation data obtained on the 4 clear days over ocean during CLAMS are analyzed here with the Coupled Ocean–Atmosphere Radiative Transfer (COART) model. The model is successively compared with observations of broadband fluxes and albedos near the ocean surface from the Clouds and the Earth's Radiant Energy System (CERES) Ocean Validation Experiment (COVE) sea platform and a low-level OV-10 aircraft, of near-surface spectral albedos from COVE and OV-10, of broadband radiances at multiple angles and inferred top-of-atmosphere (TOA) fluxes from CERES, and of spectral radiances at multiple angles from Airborne Multiangle Imaging Spectroradiometer (MISR), or “AirMISR,” at 20-km altidude. The radiation measurements from different platforms are shown to be consistent with each other and with model results. The discrepancies between the model and observations at the surface are less than 10 W m−2 for downwelling and 2 W m−2 for upwelling fluxes. The model–observation discrepancies for shortwave ocean albedo are less than 8%; some discrepancies in spectral albedo are larger but less than 20%. The discrepancies between low-altitude aircraft and surface measurements are somewhat larger than those between the model and the surface measurements; the former are due to the effects of differences in height, aircraft pitch and roll, and the noise of spatial and temporal variations of atmospheric and oceanic properties. The discrepancy between the model and the CERES observations for the upwelling radiance is 5.9% for all angles; this is reduced to 4.9% if observations within 15° of the sun-glint angle are excluded.
The measurements and model agree on the principal impacts that ocean optical properties have on upwelling radiation at low levels in the atmosphere. Wind-driven surface roughness significantly affects the upwelling radiances measured by aircraft and satellites at small sun-glint angles, especially in the near-infrared channel of MISR. Intercomparisons of various measurements and the model show that most of the radiation observations in CLAMS are robust, and that the coupled radiative transfer model used here accurately treats scattering and absorption processes in both the air and the water.
Abstract
Spectral and broadband radiances and irradiances (fluxes) were measured from surface, airborne, and spaceborne platforms in the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) campaign. The radiation data obtained on the 4 clear days over ocean during CLAMS are analyzed here with the Coupled Ocean–Atmosphere Radiative Transfer (COART) model. The model is successively compared with observations of broadband fluxes and albedos near the ocean surface from the Clouds and the Earth's Radiant Energy System (CERES) Ocean Validation Experiment (COVE) sea platform and a low-level OV-10 aircraft, of near-surface spectral albedos from COVE and OV-10, of broadband radiances at multiple angles and inferred top-of-atmosphere (TOA) fluxes from CERES, and of spectral radiances at multiple angles from Airborne Multiangle Imaging Spectroradiometer (MISR), or “AirMISR,” at 20-km altidude. The radiation measurements from different platforms are shown to be consistent with each other and with model results. The discrepancies between the model and observations at the surface are less than 10 W m−2 for downwelling and 2 W m−2 for upwelling fluxes. The model–observation discrepancies for shortwave ocean albedo are less than 8%; some discrepancies in spectral albedo are larger but less than 20%. The discrepancies between low-altitude aircraft and surface measurements are somewhat larger than those between the model and the surface measurements; the former are due to the effects of differences in height, aircraft pitch and roll, and the noise of spatial and temporal variations of atmospheric and oceanic properties. The discrepancy between the model and the CERES observations for the upwelling radiance is 5.9% for all angles; this is reduced to 4.9% if observations within 15° of the sun-glint angle are excluded.
The measurements and model agree on the principal impacts that ocean optical properties have on upwelling radiation at low levels in the atmosphere. Wind-driven surface roughness significantly affects the upwelling radiances measured by aircraft and satellites at small sun-glint angles, especially in the near-infrared channel of MISR. Intercomparisons of various measurements and the model show that most of the radiation observations in CLAMS are robust, and that the coupled radiative transfer model used here accurately treats scattering and absorption processes in both the air and the water.
