Exploit high performance computing for data analysis

We are exploring the use of HPC systems for statistical analysis in the climate and atmospheric sciences for a variety of topics. The goal is to enable types of analyses that were previously computationally not feasible or would have taken too much time to be of practical use. One example is a Bayesian model for climate change detection and attribution that we developed recently in collaboration with academic researchers. Detection and attribution is a commonly used multivariate regression methodology to identify if changes in observational trends can be detected beyond natural variability, and if so, if they can be attributed to human actions. One of the issues in current detection and attribution methods is that certain quantities are used in an ad hoc way, e.g., the choice of truncating the empirical orthogonal functions (EOFs), and the uncertainty associated with their estimation is not incorporated properly. The Bayesian framework address these issues.

Temperature data analysis
Results for a tropospheric temperature data analysis. (a) Posterior distributions of the coefficients’ anthropogenic forcing in red and natural forcing in green for each EOF truncation. (b) Posterior probabilities for all values of the EOF truncation. (c) Marginal posterior distributions of the coefficients obtained by Bayesian model averaging. (d) Posterior distribution of p-values for the residual consistency test.

In the spirit of developing statistical methods that rely on the effective use of modern computational infrastructure, we have developed a computationally efficient, parallelizable Bayesian detection and attribution model and created a corresponding software implementation. We have submitted a journal article describing the method and presenting an example application. The newly developed method does not require a specific EOF truncation, but rather evaluates all truncation options and then performs Bayesian model averaging to combine them.

This research is made possible through NSF Core funding.