Latent Variable Modeling for Extracting Consensus Estimates of Precipitation in High Mountain Asia

• Activity Number: 251 - Spatial and Spatiotemporal Modeling in Climate and Meteorology
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): William F. Christensen* and Shane Reese and Summer Rupper and Michael F. Christensen and Brenton Mabey and Katie Larson
• Companies: Brigham Young University and Brigham Young University and University of Utah and Brigham Young University and Brigham Young University and Brigham Young University
• Keywords: climate change; spatiotemporal modeling; ensembles

Abstract

With the exception of the earth's polar regions, the High Mountain Asia region (including the Tibetan Plateau) contains more of the world's perennial glaciers than any other. Sometimes called the "third pole" because of its massive storage of ice, High Mountain Asia (HMA) provides water to one-fifth of the world's population. Due to changes in precipitation patterns and temperatures warming faster in HMA than the global average, the region faces increased risk of flooding, crop damage, mudslides, economic instability, and long-term water shortages for the communities down-river. In this talk, we discuss a large, interdisciplinary, multi-institutional research project for characterizing climate change in HMA. We illustrate the use of latent variable models for extracting consensus estimates of spatiotemporally-correlated climate processes from a suite of climate model outputs and remote-sensing observations, and we discuss the uncertainty quantification needed to inform probability-based decision making.