U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


New Technique Improves Understanding of Changes in the Potential Frequency of 21st Century Heavy Precipitation
Published: March 13, 2017
Posted: May 10, 2017

Algorithm produces more precise projections of extreme precipitation events.

The Science
Extreme weather and climate events such as floods and droughts, are among the costliest natural disasters our society faces. Yet climate-model simulations of extreme precipitation events are not directly comparable to what we measure in our rain gauges. This is primarily due to the models’ relatively coarse spatial resolution, which precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. Overcoming these drawbacks, a new algorithm produces more precise projections by pinpointing telltale large-scale atmospheric patterns associated with the occurrence of these smaller-scale events.

The Impact
More precise projections of extreme precipitation can strengthen assessments of impacts, adaptation and vulnerability (IAV), and thereby improve public safety and better target infrastructure investment. In addition, the algorithm that enables these projections can serve to diagnose climate model deficiencies and identify model subcomponents where extreme-event processes can be better represented.

Summary
Extreme precipitation events pose a threat to public safety, natural and managed resources, and infrastructure. Informing stakeholders and the public on how such high-impact, low-probability events will change in the future is important as we prepare for consequences of climate change. However, any projected change in extreme precipitation events based on climate model-simulated precipitation, especially on the local scale, lacks informative details, mainly due to the models’ coarse spatial resolution, which precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. To address this challenge, a team of researchers at the MIT Joint Program on the Science and Policy of Global Change and allied MIT departments has developed an algorithm that detects the occurrence of heavy precipitation events based on climate models’ well-resolved, large-scale atmospheric circulation conditions associated with those events—rather than relying on the models’ representation of precipitation. The algorithm significantly reduces the uncertainty of extreme storm predictions in comparison with model-simulated precipitation. In multiple tests over different U.S. regions during different seasons, the algorithm provides more reliable estimates of late 20th-century heavy precipitation frequency than model-simulated precipitation. Applying the algorithm to project extreme precipitation events under a business-as-usual scenario in which the average global temperature rises by four degrees Celsius by 2100, the researchers found that California will undergo three more extreme precipitation events than the current average, per year.

BER PM Contact
Bob Vallario

PI Contact
Xiang Gao
MIT Joint Program on the Science and Policy of Global Change
xgao304@mit.edu

Funding
This work was funded by the National Aeronautics and Space Administration, National Science Foundation, and U.S. Department of Energy (DOE) Office of Science under the grant DE-FG02-94ER61937.

Publication
X. Gao, C. A. Schlosser, P. O’Gorman, E. Monier and D. Entekhabi, 2016: 21st Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns, Journal of Climate, doi: 10.1175/JCLI-D-16-0544.1 (Reference link)

Related Links
MIT News: Study finds more extreme storms ahead for California

Topic Areas:

  • Research Area: Multisector Dynamics (formerly Integrated Assessment)

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)