CAQIMS - Components, Illinois State Water Survey

Climate and Atmospheric Science

Climate, Air Quality and Impact Modeling System (CAQIMS)

A Basis for Achieving Economic, Societal and Environmental Goals in Illinois

Xin-Zhong Liang
Department of Atmospheric Sciences and Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois
Cloud-Aerosol-Radiation Modeling System

The CWRF has incorporated an unprecedented Cloud-Aerosol-Radiation (CAR) ensemble modeling system that consists of 1018 free choices of alternative parameterizations for cloud properties (cover, water, radius, optics, geometry), aerosol properties (type, profile, optics), and radiation transfers (solar, infrared) and their interactions currently available in the literature, including those used by the world leading GCMs. The CAR can be used to determine (via intercomparison across all schemes) or reduce (via the optimized ensemble integration) the range of the uncertainties caused by the likely cloud-aerosol-radiation interactions. That range accounts for most of the spread in climate sensitivities among GCMs and consequently their climate change projections. Thus, the CWRF/CAR system facilitates reliable probabilistic prediction of weather or climate as defined by the likelihood and uncertainty of the physics ensemble forecasts.

1. Liang, X.-Z., M. Xu, W. Gao, K.E. Kunkel, J. Slusser, Y. Dai, Q. Min, P.R. Houser, M. Rodell, C.B. Schaaf, and F. Gao, 2005: Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. J. Geophys. Res., 110, D11107, doi:10.1029/2004JD005579.

A new dynamic-statistical parameterization of snow-free land surface albedo is developed using the Moderate Resolution Imaging Spectroradiometer (MODIS) products of broadband black-sky and white-sky reflectance and vegetation and the North American and Global Land Data Assimilation System (LDAS) outputs of soil moisture during 20002003. The dynamic component represents the predictable albedo dependences on solar zenith angle, surface soil moisture, fractional vegetation cover, leaf plus stem area index, and greenness, while the statistical part represents the correction for static effects that are specific to local surface characteristics. All parameters of the dynamic and statistical components are determined by solving nonlinear constrained optimization problems of a physically based conceptual model for the minimization of the bulk variances between simulations and observations. They all depend on direct beam or diffuse radiation and visible or near-infrared band. The dynamic parameters are also functions of land cover category, while the statistical factors are specific to geographic location. The new parameterization realistically represents surface albedo variations, including the mean, shape, and distribution, around each dependent parameter. For composites of all temporal and spatial samples of the same land cover category over North America, correlation coefficients between the dynamic component of the new parameterization and the MODIS data range from 0.39 to 0.88, while relative errors vary within 842%. The gross (i.e., integrated over all categories) correlations and errors are 0.570.71 and 1726%, changing with direct beam or diffuse radiation and visible or near-infrared band. The static local correction results in a further reduction in relative errors, producing gross values of 1121%. The new parameterization is a marked improvement over the existing albedo scheme of the state-of-the-art Common Land Model (CLM), which has correlation coefficients from _0.57 to 0.71 and relative errors of 18140% for individual land cover categories, and gross values of 0.030.32 and 3771%, respectively.

Geographic distributions
Figure. Geographic distributions of pointwise correlation coefficients (100)
between albedos observed by MODIS and simulated by the new parameterization.
Values below the statistical significant threshold (26) are distinguished by the gray color.

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