Tropical Pacific sea surface temperatures (SST) and the accompanying El Nino Southern Oscillation (ENSO) phenomenon are recognized as significant components of climate behaviour. The atmospheric and oceanic processes involved display highly complicated variability over both space and time. Researchers have applied both physically derived modeling and statistical approaches to develop long-lead predictions of tropical Pacific SSTs. The comparative successes of these two approaches are the subject of substantial inquiry and some controversy. A new procedure for long-lead forecasting of Pacific SST fields, that expresses qualitative aspects of scientific paradigms for SST dynamics in a statistical manner, is presented. The investigators (Berliner, Wikle, and Cressie) would like to acknowledge the help of National Center for Atmospheric Research (NCAR) scientists in developing this procedure. Through its combining of substantial physical understanding and statistical modeling and learning, the procedure acquires considerable predictive skill. Specifically, a Markov model, applied to a low-order (EOF-based) dynamical system of tropical Pacific SSTs, with a stochastic regime transition is considered. The approach accounts explicitly for uncertainty in the formulation of the model, which leads to realistic error bounds on forecasts. The methodology that makes this possible is Bayesian hierarchical dynamic (HiDyn) modeling.

This research is featured in an article on The Ohio State University Research Communications webpage.

The Antarctic ozone-hole event has become a symbol of global ozone depletion since its discovery in 1985. During the 1990s, the patterns in the ozone-hole during the Antarctic winter were similar from year to year. Unfortunately, Total Ozone Mapping Spectrometer (TOMS) ozone datasets are not complete because of restrictions in sunlight availability, the coverage of satellite orbits, and other engineering problems. In order to address this deficiency in spatial coverage, Johannesson and Cressie (2004) proposed the Multi-resolution Spatial Model (MRSM), which is an effective statistical method for estimation of spatial processes based on the change-of-resolution Kalman filter (Chou et al. 1994; Huang et al. 2002) and variance-covariance likelihood inference.

In September 2002, the ozone hole split, unlike in any previous years where data were available. Researchers have proposed several theories to explain the 2002 ozone-hole splitting based on a diverse collection of ozone datasets. TOMS is one of the most important resources of total column ozone (TCO) data.

This TCO website shows complete TCO estimates based on the TOMS data and the MRSM, along with a measure of each value's uncertainty. The website also contains brief background descriptions of the MRSM, the atmospheric ozone distribution, and the 2002 ozone-hole splitting event.

This research is featured in an article on The Ohio State University Research Communications webpage.

The mass balance and equilibrium state of the polar ice sheets are complex functions of external climate forcings and internal dynamical processes. To understand the behavior of vast ice sheets and to assess future behavior, we seek to understand the dominant forces controlling ice flow and how these forces have responded and will respond to changes in climate and external forcings. We study ice-stream dynamics via a fully Bayesian statistical analysis that incorporates physical models that are not perfectly known, and using data that are both incomplete and noisy. The physical-statistical models we propose account for these uncertainties in a coherent, hierarchical manner. Use of Bayes' Theorem allows us to make inference on all unknowns given the data. The result of that inference is a (posterior) distribution of possible values that can be summarized in a number of possible ways. For example, the posterior mean of the stress field gives average behavior at any location in the field, and the posterior standard deviation associated with a posterior mean value shows how variable the possible values are. There are no direct measurements on stress; we infer it from basal-elevation data, surface-elevation data, and velocity data. Forward smoothing methods can be used, but their disadvantage is that they lack a coherent accounting of uncertainties. This Ice Streams website analyzes data from the Northeast Ice Stream in Greenland and indicates how scientific conclusions may be drawn from Bayesian analyses. It also includes a Tutorial on Bayesian Statistics for Geophysicists.