The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA), Edition 4.0 (Ed4.0), data product is described. EBAF Ed4.0 is an update to EBAF Ed2.8, incorporating all of the Ed4.0 suite of CERES data product algorithm improvements and consistent input datasets throughout the record. A one-time adjustment to shortwave (SW) and longwave (LW) TOA fluxes is made to ensure that global mean net TOA flux for July 2005–June 2015 is consistent with the in situ value of 0.71 W m−2. While global mean all-sky TOA flux differences between Ed4.0 and Ed2.8 are within 0.5 W m−2, appreciable SW regional differences occur over marine stratocumulus and snow/sea ice regions. Marked regional differences in SW clear-sky TOA flux occur in polar regions and dust areas over ocean. Clear-sky LW TOA fluxes in EBAF Ed4.0 exceed Ed2.8 in regions of persistent high cloud cover. Owing to substantial differences in global mean clear-sky TOA fluxes, the net cloud radiative effect in EBAF Ed4.0 is −18 W m−2 compared to −21 W m−2 in EBAF Ed2.8. The overall uncertainty in 1° × 1° latitude–longitude regional monthly all-sky TOA flux is estimated to be 3 W m−2 [one standard deviation (1 σ)] for the Terra-only period and 2.5 W m−2 for the Terra– Aqua period both for SW and LW fluxes. The SW clear-sky regional monthly flux uncertainty is estimated to be 6 W m−2 for the Terra-only period and 5 W m−2 for the Terra– Aqua period. The LW clear-sky regional monthly flux uncertainty is 5 W m−2 for Terra only and 4.5 W m−2 for Terra– Aqua.

The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.

Abstract. We compared CALIPSO column aerosol optical depths at 0.532 µm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13 % relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97 % confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3 % and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27 %. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29 % and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of R = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 µm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt.

Abstract. The top-of-atmosphere (TOA) radiative fluxes are critical components to advancing our understanding of the Earth's radiative energy balance, radiative effects of clouds and aerosols, and climate feedback. The Clouds and the Earth's Radiant Energy System (CERES) instruments provide broadband shortwave and longwave radiance measurements. These radiances are converted to fluxes by using scene-type-dependent angular distribution models (ADMs). This paper describes the next-generation ADMs that are developed for Terra and Aqua using all available CERES rotating azimuth plane radiance measurements. Coincident cloud and aerosol retrievals, and radiance measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from Goddard Earth Observing System (GEOS) data assimilation version 5.4.1 are used to define scene type. CERES radiance measurements are stratified by scene type and by other parameters that are important for determining the anisotropy of the given scene type. Anisotropic factors are then defined either for discrete intervals of relevant parameters or as a continuous functions of combined parameters, depending on the scene type. Significant differences between the ADMs described in this paper and the existing ADMs are over clear-sky scene types and polar scene types. Over clear ocean, we developed a set of shortwave (SW) ADMs that explicitly account for aerosols. Over clear land, the SW ADMs are developed for every 1 • latitude × 1 • longitude region for every calendar month using a kernel-based bidirectional reflectance model. Over clear Antarctic scenes, SW ADMs are developed by accounting the effects of sastrugi on anisotropy. Over sea ice, a sea-ice brightness index is used to classify the scene type. Under cloudy conditions over all surface types, the longwave (LW) and window (WN) ADMs are developed by combining surface and cloud-top temperature, surface and cloud emissivity, cloud fraction, and precipitable water. Compared to the existing ADMs, the new ADMs change the monthly mean instantaneous fluxes by up to 5 W m −2 on a regional scale of 1 • latitude × 1 • longitude, but the flux changes are less than 0.5 W m −2 on a global scale.

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