| Summary: | Statistical Approaches to Data Assimilation and Downscaling During the next year, we plan to incorporate additional observations of soil moisture, humidity and cloudiness as well as atmospheric circulation indices corresponding to the ENSO, Arctic Oscillation (AO), and its close counterpart the North Atlantic Oscillation (NAO) to assess the degree to which large-scale climate patterns and localized climate feedbacks may reveal and/or mask the climate change signal in the Midwest. This is of interest since an upward trend has been detected in the AO (Delworth and Dixon, 2000) and a significant shift has been detected in the mean ENSO indices (Trenberth and Hoar, 1997). Cook, et al. (2004) recently assessed the statistical link between NAO indices and temperature through a classification technique very similar to our approach for ENSO. However, this simple simulation does not take into account the complex interactions captured by global model-derived indices and the interactions between multiple oscillations. For this reason, we plan to improve on this method as well as apply it to ENSO patterns in order to assess the impact of large-scale atmospheric features on surface conditions as have already been demonstrated to exist for heat waves (Meehl and Tebaldi, 2004). We also plan to expand the spatial area covered by the analysis. A sophisticated gridding program that averages randomly-spaced geographic locations into a uniform grid, similar to model output, has been used to produce gridded historical observed temperature and precipitation fields on an identical scale to global model output. Moving outwards from the Aurora station, we will evaluate the correlation between synoptic-scale observed temperature and precipitation fields and model simulations. We will also focus on neighboring weather stations surrounding Aurora to assess the degree to which point-source observed/simulated climate correlations hold over a broader area. Finally, we propose to build on our current work in order to quantify the contribution of multiple members of modeled ensembles to improving the correlation between modeled and observed climate statistics over spatial and temporal scales. The impact of human-induced change on climate over the next few decades will then be assessed relative to the changes implied from natural variability alone. Statistical methods will be refined and a paper presenting these results prepared for submission to a peer-reviewed journal. Statistical Applications for Regional Simulations of Climate Change We plan to develop a statistical weather typing approach to classify projected changes in large-scale circulation features and assess the impact on surface temperature and precipitation. Although statistical mixture models have frequently been applied to climate studies, mixtures of distributions have rarely been applied to clustering in climate studies. The proposed approach can be used either with or without regional climate simulations, but, for simplicity, we describe a version that only requires global-scale PCM model output. For the region of interest, the first step is to define clusters based on model output and assign actual weather patterns to these clusters. The clusters must be defined in terms of quantities available from model output, but the goal of the clusters is to define groups of days for which the difference between the average observed weather and the average model output has large across-cluster variation. Defining clusters based on one set of variables, such that they produce meaningful clustering for another set of variables, will require close interaction between the statisticians and atmospheric scientists on this project in terms of variable selection, choice of metric, and evaluation strategies. In addition to applying principal components to geopotential heights, we plan to explore clusters based on vertical atmospheric profiles using copula functions to model joint dependencies between profiles. The second critical step is selection of the set of variables to be used in the clustering algorithm. One possible approach is to try the first few principal components of geopotential heights at some pressure level (e.g., 500 or 700 mb) in the region of interest. The clustering would be carried out on the daily loadings for these principal components using some appropriately chosen metric and using a historical model simulation or ensemble. Representative days from a future model simulation would then be categorized into the same set of clusters, and the changes in frequencies of these clusters between the present and future scenarios would be evaluated. Observed weather patterns would be assigned to the clusters, and their effect on surface conditions would be evaluated using historical surface observations and National Centers for Environmental Prediction (NCEP) reanalysis to obtain geopotential heights and assign each day to a cluster based on the loadings of the PCM-based principal components. Downscaled estimates of surface conditions, such as average daily maximum temperature at a specific site in the future scenario, would then be obtained by computing the observed average maximum daily temperature for days assigned to each cluster by NCEP minus the average maximum daily temperature for the PCM output under present conditions for all days assigned to the cluster, to produce a downscaling adjustment for each cluster. To get an overall adjustment, a weighted average of these adjustments would be used, based on the cluster frequencies obtained from the future model simulation(s). This procedure will thus give greater weight to adjustments for clusters that are more frequent in the future simulation(s). Regional simulations could be brought to bear on this problem in a number of ways. First, even a short run of a high-resolution regional simulation in the future would provide for a more direct assessment of how well the clustering method is working. Longer regional simulations, driven by both present and future condition PCM runs, would make it possible to use the clustering method to estimate differences between the observed weather and regional climate simulations, which should be considerably smaller than the differences between observed conditions and PCM output. The issue of judiciously selecting the time periods on which the regional climate simulations should be run, which was a main proposed focus of this subproject, will be critical here as well. Once the clustering approach to downscaling global-scale climate projections has been developed and evaluated against historical data, it will be applied to future projections corresponding to the Special Report on Emissions Scenarios (SRES) A1fi (high) and B1 (low) emission scenarios, as projected by the PCM model, to evaluate the spatial diversity in climate change impacts on the U.S. Midwest. A paper describing the method and its application will be prepared for submission to a peer-reviewed journal.
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