Abstract: The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance ...

Abstract: Statistical principles underlying ?fingerprint? methods for detecting a climate change signal above natural climate variations and attributing the potential signal to specific anthropogenic forcings are discussed. The ...

Abstract: Suppose that one has the freedom to adapt the observational network by choosing the times and locations of observations. Which choices would yield the best analysis of the atmospheric state or the best subsequent forecast? ...

Abstract: Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global, and limited area domains, discretized for applications of numerical models of the ...

Abstract: Tropical Pacific sea surface temperatures (SSTs) and the accompanying El Niño?Southern Oscillation phenomenon are recognized as significant components of climate behavior. The atmospheric and oceanic processes involved ...

Abstract: A Bayesian fingerprinting methodology for assessing anthropogenic impacts on climate was developed. This analysis considers the effect of increased CO2 on near-surface temperatures. A spatial CO2 fingerprint based on control ...

Abstract: Analyses of atmospheric sciences data and models are heavily dependent upon statistical and probabilistic reasoning. Statistical methods have played an important role in establishing physical relationships of atmosphere-ocean-land ...