Algorithm produces more precise projections of extreme precipitation events.
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.
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.
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.
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MIT Joint Program on the Science and Policy of Global Change
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.
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)
MIT News: Study finds more extreme storms ahead for California
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