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A limited number of complex numerical models that simulate the Earth's atmosphere, ocean, and land processes are the primary tool to study how climate may change over the next century due to anthropogenic emissions of greenhouse gases. A standard assumption is that these climate models are random samples from a distribution of possible models centered(More)
0 Abstract A Bayesian statistical model is proposed that combines information from a multi-model ensemble of atmosphere-ocean general circulation models and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of(More)
Many geophysical problems are characterized by high-dimensional, nonlinear systems and pose difficult challenges for real-time data assimilation (updating) and forecasting. The present work builds on the ensemble Kalman filter (EnsKF) with the goal of producing ensemble filtering techniques applicable to non-Gaussian densities and high-dimensional systems.(More)
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return-level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply to a Colorado region. Separate hierarchical models are constructed for the intensity and the frequency(More)
A recent report of the U.S. Climate Change Science Program (CCSP) identified a 'potentially serious inconsistency' between modelled and observed trends in tropical lapse rates (Karl et al., 2006). Early versions of satellite and radiosonde datasets suggested that the tropical surface had warmed more than the troposphere, while climate models consistently(More)
Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (GCMs). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate(More)
[1] Tebaldi et al. [2005] present a Bayesian approach to determining probability distribution functions (PDFs) of temperature change at regional scales, from the output of a multi-model ensemble, run under the same scenario of future anthropogenic emissions. The main characteristic of the method is the formalization of the two criteria of bias and(More)
1 Abstract Spatio-temporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many diierent scales of spatial and temporal variability. The complexity of these processes and large number of observation/prediction locations preclude the use of traditional(More)