2011-12 UQ Program: Climate Modeling Opening Workshop

General Information

The Opening Workshop for the Climate theme of the SAMSI program on Uncertainty Quantification was held on Monday-Wednesday, August 29-31, 2011, at the Marriott Pleasanton, in Pleasanton, CA. The location is in close proximity with Lawrence Livermore National Laboratory (LLNL) which co-sponsored the event.

From Monday to mid-day Wednesday, invited speakers gave presentations. For each of the five themes described below, one presentation was introductory. There was a poster session and reception on Monday, August 29. Additional activities were organized later during the week, in conjunction with LLNL.

The workshop focused on four complementary themes at the forefront of current research in climate modeling.

(1) Observations - Observations are key to uncertainty quantification in climate research because they provide a corroborating source of information about physical processes being modeled. However, observations have uncertainties and this poses a set of methodological and practical issues for comparing them to model simulations: (i) quantifying observational uncertainty when the observations are themselves inferences based on other quantities, (ii) change of support between model resolution and the resolution of remote sensing or in-situ data, (iii) rectifying or accounting for spatial and temporal inconsistencies, (iv) coping with dependence between observations used in model construction and observations used for UQ, and (v) leveraging massive volumes of distributed data.

(2) Climate models - Climate models remain our best tool for understanding past, present and future climate change. However, state-of-the-art climate models still contain many sources of uncertainty, including uncertainties from physical processes that are poorly known or are not resolved at the temporal and spatial scales represented in climate models. These uncertainties may cloud the analysis and interpretation of climate simulations. This theme focused on the applications of UQ to characterize uncertainties in climate model simulations.

(3) Assimilation/calibration/forward UQ - With computational models that simulate climate 10s to 100s of years in the future, comes the need to quantify the uncertainties of the predictions they produce. Uncertainties in these large-scale computational models can stem from a variety of sources including numerical approximations, unknown initial conditions, unknown model parameter settings, missing physics and other inadequacies in the model. Some of these uncertainties can be reduced by constraining the model be consistent with physical observations. This theme focuses on approaches for uncertainty propagation, data assimilation and model calibration that help estimate and constrain uncertainties in model-based predictions. The use of large-scale models make such approaches challenging due to their computational burden, their complexity, and their inadequacies. The incorporation of physical data leads to challenges as well - data are recorded at different scales, the volume of data can be quite large, while their spatial and temporal coverage can be quite small.

(4) Multiscale inference - Uncertainty in climate model projections is often represented by an ensemble of plausible simulations, which can either be a collection of simulations from multiple models or from a single climate model. Drawing inference from such ensembles can be challenging; for example, conclude about changes in trends and extreme events. The theme of this topic is statistical analysis of climate model simulations, in particularly methods to draw inference from an ensemble of simulations and across different spatial and temporal scales.

The workshop culminated in the formation of research working groups in the afternoon of Wednesday, August 31. The participants defined not only specific research objectives to be addressed by the working group over the ensuing year but also established modes of cooperation for the working groups, via web or teleconference, to facilitate full participation of all members, regardless of residence status at SAMSI.

Organizers: Amy Braverman (JPL, California Institute of Technology, and UCLA), Don Estep (Colorado State), Dave Higdon (LANL), Gardar Johanneson (LLNL), Donald Lucas (LLNL)

Schedule

Monday, August 29, 2011
Marriott Pleasanton

Tuesday, August 30, 2011
Marriott Pleasanton

Wednesday, August 31, 2011
Marriott Pleasanton