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Abstract:
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Understanding earth's changing climate is vital to informed decision-making. Thus, numerous physical models exist which predict climate at various resolutions across the globe. With so many models, it is of interest to find which models produce "accurate" climate predictions. An added complication associated with these physical models is that the predictions may change based upon the initial conditions assumed. Remote sensing instruments provide global measurements of quantities associated with climate, such as temperature, specific humidity, and precipitation, at a high resolution. Although these measurements are variable due to discrepancies in atmospheric composition, they comprise a dataset that may be considered as observed climate. We use the Wild Scale-Enhanced (WiSE) bootstrap to evaluate existing physical model predictions with respect to the observational remote sensing data. This method allows for a comparison of two time series (i.e. within a grid-cell, the observational data and a physical model) where each series has intrinsic heteroscedastic variability. The developing methodology also addresses spatial relationships between grid cells.
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