High Resolution Sea Surface Temperature Projections using Statistical Downscaling of General Circulation Model Ensembles in the North Pacific.

Abstract:

In this work we develop a general methodology to obtain high-resolution spatial-temporal forecasts of Sea Surface Temperature (SST) using ensembles of general circulation model (GCM) output and historical records as the major driving force. As a case study, we consider SST in the North Pacific Ocean. We use two ensembles of different GCM simulation output, made available in the 4th Assessment Report of the Intergovernmental Panel on Climate Change. One corresponds to 20th century forcing conditions and the other corresponds to the emissions scenario A1B for the 21st century. Given a representation of the SST spatio-temporal fields based on a common set of empirical orthogonal functions (EOFs), we use a hierarchical Bayesian model for the EOF coefficients to estimate a baseline and a set of model discrepancies. These components are all time-varying. The model enables us to extract relevant temporal patterns of variability from both the observations and simulations as well as obtain common patterns from all GCM simulations. This is used to obtain unified 21st century forecasts of relevant oceanic indexes as well as whole fields of forecast North Pacific SST. The unified forecast captures large longterm oceanic behavior, however the coarse resolution prevents us from capturing coastal behaviors. We use the unified forecast to model high resolution SST by establishing a link between large and small scale variability using statistical downscaling techniques. Using a combination of a discrete process convolution and a dynamic linear model, we obtain a smooth high-resolution forecast of SST fields off the coast of California. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract.