Project Summary

Overview: This project will enhance fundamental understanding of the large-scale dynamics and forcing of sub-seasonal to seasonal (S2S; 14 days to 3 months) extreme precipitation events in the U.S. and improve our capability to model and predict such events. Extreme precipitation events (i.e., precipitation that far exceeds climate norms for a given period) pose a significant societal and economic risk to the U.S. In 2015 and 2016, five of the eighteen multi-billion dollar natural disasters were the result of excessive precipitation. However, past studies have focused on sub-daily to sub-weekly precipitation extremes only and there is currently little physical understanding of S2S extreme precipitation events. Additionally, the S2S timescale is emerging as highly important for stakeholders for planning and ultimately resilience-building to extreme weather hazards. This project assembles an expert team of scientists and stakeholders to narrow the prediction gap of S2S extreme precipitation events by answering four scientifically and societally relevant research questions: 1) What are the synoptic patterns associated with, and characteristic of, S2S extreme precipitation events in the contiguous U.S.? 2) What role, if any, do large-scale modes of climate variability play in modulating these events? 3) How predictable are S2S extreme precipitation events across temporal scales? 4) How do we create an informative prediction of S2S extreme precipitation events for policymaking and planning? The team will answer these questions through seven integrated research activities to understand and improve prediction of 14-day, 30-day, and 3-month extreme precipitation events. For the first time, these activities will combine observations with novel machine-learning techniques, high-resolution radar composites, dynamical climate models (the National Multi-Model Ensemble (NMME) and the Coupled Model Intercomparison Project phase 5 (CMIP5)), and workshops that engage stakeholders in co-production of knowledge. The co-production will steer the science to focus on useful characteristics that matter most to the people who use and rely on predictions, thus contributing to knowledge-sharing and improving the capability to predict what is meaningful.

Intellectual Merit: Through the combination of novel statistical analysis of observations and model output, this project will identify and understand the fundamental weather and climate processes that are tied to S2S extreme precipitation events across the U.S. from scales as small as individual storms to those as large as ocean basins. Predictability of S2S extreme precipitation events will be quantified through analysis of the statistical predictive model developed for this project and the NMME models. The prediction skill for S2S extreme precipitation events will be improved through an increased mechanistic understanding of historical events and a quantification of NMME and CMIP5 model performance for simulating these events and their characteristic patterns. The statistical and co-production frameworks developed in this project will have the flexibility to be applied across meteorological extremes and timescales, in other global regions, and with future climate model simulations to reduce the impact of and increase resilience to extreme meteorological events.

Broader Impacts: This work advances discovery of S2S extreme precipitation events, a critical timescale for the weather-to-climate interface. Workshops, a testbed activity, and webinars, will promote geographical, organizational, and business sector diversity through the co-production of knowledge with stakeholders. Participation in science will be broadened by including federal, tribal, and local stakeholders, with three targeted user communities; water resource managers, emergency managers, and tribal environmental professionals, as tribes are consistently and considerably under-represented in provision of weather information. The team is led by an early-career female and includes three other females and one African American. Post-docs and students will engage directly with stakeholders. Societal benefits include the development of predictive models that have the potential to reduce mortality, morbidity, and damages caused by S2S extreme precipitation events. Research infrastructure will be strengthened by the addition of significant storage capacity. The project will develop a pipeline of experts spanning research-to-operations who will aid in the eventual translation, with support by other programs and organizations, of results into actionable products and predictions.