Global-Scale Seasonal Variability Profiles of EPIC-Derived vs GISS ModelE-Simulated All-Cloud and Ice-Cloud Fraction Distributions

Gary L Russell¹Andrew Lacis^1*Barbara E Carlson¹Wenying Su²Juliet A Pilewskie³

• ¹Goddard Institute for Space Studies (NASA), New York, United States
• ²NASA Langley Research Center, Hampton, United States
• ³Colorado State University, Fort Collins, United States

Detailed cloud information over the Earth's sunlit hemisphere is an important EPIC-image biproduct stemming from reflected solar shortwave (SW) flux determination from EPIC-image backscattered radiances. Using MODIS and CERES satellite retrievals EPIC spectral radiances are transformed into pixel-level broadband radiances. Cloud property information gathered from low-Earth-orbit and geostationary retrievals coincident with EPIC-view geometry are selected. CERES angular distribution models (ADMs) are utilized to accomplish the EPIC radiances-to-flux conversion. Cloud, being the principal contributors to Earth's planetary albedo, are also the controlling factor regulating the Earth's global energy balance. With the relatively short and time-variable atmospheric life-cycle, characteristic cloud-process signatures should be appearing in the reflected solar SW radiation from Earth. The prime focus here is to study the global-scale variability of the terrestrial energy balance using global-scale EPIC-derived reflected fluxes and cloud property information obtained with daily time resolution, a unique capability specific only to DSCOVR Mission EPIC data acquired from the Lissajous orbital vantage point around the Lagrangian L1-point. One major sticking point in model/data comparisons is that climate GCMs and the real-world exhibit quasi-chaotic variability. Thus, cloud maps generated from climate GCM output, and satellite data retrievals, can only provide qualitative information in model/data comparisons. Global integration suppresses the ubiquitous weather noise, but issues with viewing geometry, diurnal cycle, and space-time resolution incompatibilities persist in model/data comparisons utilizing traditional monthly-mean GCM output formats and traditional monthly-mean satellite data displays. DSCOVR Mission EPIC data, coupled with DSCOVR satellite ephemeris-enabled GCM data aggregation provide a promising new approach. In this approach, integration over the sunlit hemisphere eliminates the quasi-chaotic weather noise, while assuring identical viewing geometry and consistent diurnal cycle sampling. The Earth's rotation provides precise longitudinal alignment of the variability. Moreover, this approach also makes possible day-by-day model/data comparisons, and brings into model/data scrutiny relevant cloud process timescales that are otherwise excluded in traditional monthly-mean model/data comparisons. Results to-date show that DSCOVR Mission measurements from the Lagrangian L1 vantage point, including the use of ancillary and biproduct data assembled within this format, constitute a new and powerful capability for model/data variability profile comparisons operating with a 1-day time resolution.