Understanding the factors that determine regional climate variability and change is a challenge with important implications for the economy, security, and environmental sustainability of many regions around the globe. Our understanding and modeling of the large-scale dynamics of the Earth climate system and associated regional-scale climate variability significantly affects our ability to predict and mitigate climatic extremes and hazards. Earth observations and climate model outputs are witnessing an unprecedented increase in data volume, creating new opportunities to advance climate science but also leading to new data science challenges that must be addressed using tools from mathematics, statistics, and computer science. This project focuses on two central challenges at the heart of modern data-enabled climate science: (1) Increasing the predictive capacity of subseasonal forecasts by discovering and quantifying the sources of (un)predictability, including known and emergent climate modes and their interactions and non-stationarities; and (2) Understanding and quantifying the intricate space-time dynamics of the climate system to provide guidance for climate model assessment and regional forecasting. This project brings together an interdisciplinary team that combines expertise in both hydroclimate science and statistical machine learning to create new platforms for climate diagnostics and prognostics. The broader impacts of an enhanced knowledge of the climate system and robust and accurate seasonal forecasts have wide-ranging implications for society as a whole. For example, better seasonal forecasts will allow water resource managers to make sustainable decisions for water allocation.
This TRIPODS+CLIMATE project will develop novel machine learning and network estimation methodologies for analyzing the climate system over a range of space and time scales, to understand climate modes of variability and change and to explore their predictive ability for regional hydroclimatology. The two main objectives of this project are the following. Objective 1: Develop novel classification and regression tools that account for highly-correlated features or covariates, nonlinear interaction terms in high-dimensional settings, and nonstationarity in climate observations. These tools will be used to improve seasonal-to-subseasonal forecasts of regional precipitation using multidimensional climate modes and feature vectors in the presence of evolving dynamics and nonstationarities. Objective 2: Develop network identification methods that leverage recent advances in machine learning and statistics and that can account for the nonstationarity and limited timeframe of climate data. The network representation will be used to analyze the structure and dynamics of the learned dependencies to contextualize and interpret them physically, and to quantify changing patterns in climate modes and their regional predictive capacity. Emphasis will be placed on the western Pacific dynamics where an interhemispheric bi-directional connection has recently been discovered, promising earlier and more accurate seasonal-to-subseasonal forecasts in the southwestern US and other parts of the world.
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.
