Perturbation Analysis of TOA and Surface Fluxes using Fu-Liou model with CERES, MODIS, Geos 5.4.1, and MATCH data

Radiative kernels have been used to estimate feedbacks due to water vapor, lapse rate, surface albedo and cloud property changes (e.g. Soden et al. 2008). The approach uses pre-computed top-of-atmosphere (TOA) broadband irradiance changes by the perturbation of these properties. Once atmospheric changes are known from climate model outputs, the contribution to the TOA irradiance by these properties can be computed. A similar approach can be used to estimate contribution of surface, atmospheric and cloud properties to observed TOA (Loeb et al. 2012) and surface irradiance monthly anomalies in order to understand how these properties contribute month-to-month variability of TOA and surface irradiances. In this study, we use a framework of CERES data production and perturb surface, atmospheric and cloud properties to determine the contribution to the TOA and surface irradiance monthly anomalies. We use seven years (2003-2009) of gridded CERES, MODIS, AIRS and MATCH data. Canonical monthly means are formed from CERES SW, LW and Window TOA irradiances AIRS temperature and humidity profiles, MODIS based cloud properties, MATCH aerosol optical depth, as well as surface albedo and surface skin temperature. The LaRC modified version of the Fu-Liou broadband radiative transfer code is run using A) Canonical means of all inputs, B) Selecting individually inputs as actual values keeping others as canonical means. C) A final single run using the actual perturbed values of all inputs. Note that radiative kernels used in this analysis changes depending on monthly gridded atmospheric states, as opposed to pre-computed, i.e. fixed, kernels throughout the time period of the analysis. Global and Tropical mean of the monthly gridded runs are formed. The sum of computed perturbations of TOA irradiances are evaluated by comparing the standard deviation of anomalies with CERES-derived TOA irradiance anomalies, as well as using the correlation coefficient. Individual perturbations are further grouped into cloud, atmosphere and surface property contributions for the analysis. The sum of surface irradiances perturbations are evaluated by comparing against the modeled irradiances with observed inputs. Plots and tables displaying the monthly variability of TOA and surface irradiances as caused by individual inputs and input groups will be shown. In particular, how the contribution of surface, atmospheric and cloud property variability to TOA and surface irradiance variability changes depend on ENSO phase will be investigated. In addition, the contribution to TOA and surface irradiance variability over the Arctic will also be investigated. References

Loeb, N. G., S. Kato, W. Su, T. Wong, F. Rose, D. R. Doelling, and J. Norris, 2012: Advances in Understanding Top-of-Atmosphere Radiation Variability from Satellite Observations. Surveys in Geophysics, doi:10.1007/s10712-012-9175-1.

Soden, B. J., I. M. Held, and R. Colman, 2008: Quantifying climate feedbacks using radiative kernels, J. Climate, 21, doi:10.1175/2007JCLI2110.1.