EAGER: CAS-Climate: AI-driven Probabilistic Technique, Quantile Regression based Artificial Neural Network Model, for Bias Correction and Downscaling of CMIP6 Projections

North Carolina State University, Raleigh NC

Investigators

Abstract

Global Climate Models (GCMs) are typically used to develop climate projections to predict extreme events (e.g., droughts, floods). Spatial resolution of GCM projections has improved due to increasing computational power, but is still inadequate for watershed-scale applications where extreme event prediction is needed to enable planning. The research undertaken in this project will develop an AI-based technique to improve hydroclimatic projections at the watershed scale. AI techniques are quite powerful in modeling global climate data and could develop finer spatial and temporal future climatic projections. The potential impact is improved planning for, and resilience to, extreme events at the watershed scale. This research will develop an AI-based probabilistic approach that uses a Quantile Regression based Artificial Neural Network (ANN) (QR-AI) model for bias-corrected and statistically downscaled (BCSD) Coupled Model Intercomparison Projects (CMIP6) projections. Specifically, the research will develop three BCSD data products of CMIP6 projections over the continental U.S. (CONUS): 1) Historical simulations (1950-2014) of precipitation and temperature of GCMs; 2) Near-term (30 year) hindcasts of precipitation and temperature from relevant GCMs and 3) Near-term (30 year) projections of precipitation and temperature for four different Shared Socioeconomic Pathways, which are represented by CO2 emission and mitigation scenarios. Developing BCSD of both hindcasts and historical projections will provide an opportunity to validate the QR-AI methodology by comparing the uncertainty in the estimated climate variables with the observed marginal density of precipitation and temperature over the CONUS. The BCSD CMIP6 products on precipitation and temperature will be developed using the AI method for the entire CONUS and disseminated through the project website. BCSD data will also be archived in figshare and github for dissemination. Additionally, the investigators will work with focused user groups, such as reservoir management and social media, for active dissemination of the developed BCSD products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.