Illinois State Water Survey - CAQIMS - Components, University of Illinois at Urbana-Champaign

A Basis for Achieving Economic, Societal and Environmental Goals in Illinois
Xin-Zhong Liang

CAQIMS Diagnostic Analysis
Diagnostic analysis aims to interpret the physical characteristics of model output in ways that are meaningful to the use of the data for impacts assessments. The impacts of climate and air quality variability and change result principally from changes in the surface climate, such as temperature, precipitation, humidity, severe weather, air quality, wind, and cloudiness. Our effort to diagnostically analyze model output is aimed at examining the surface climate features. However, we also seek to understand the causes of changes in the surface climate. Thus, we also examine other features of the climate system that affect the surface climate of Illinois. Some examples include the location and strength of low-level and high-level jet streams, the tracks of storms, and the general wind flow over the U.S. and surrounding areas. The major thrusts for our diagnostic work are (1) the analysis of simulations of the past historical climate, (2) the analysis of simulations of simulations of future climate scenarios, and (3) the analysis of output from global climate models, which are used to establish the boundary conditions around the edges of the domain for the regional models.

A diagnostic analysis of relationships between central United States climate characteristics and various flow and scalar fields was used to evaluate 9 global coupled ocean-atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). In order to facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses. Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central US, although no model exhibits small differences from the observationally-based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally-based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except HadCM3, appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central US. The CMIP doubled-CO₂ transient runs show warming (1-5°C) for all models and seasons and variable precipitation changes,. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the 20^th Century and the model variations in the control simulations.

 
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