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Future changes in regional tropical cyclone wind, precipitation and flooding using event-based downscaling
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Type: | Resource | |
Storage: | The size of this resource is 65.7 MB | |
Created: | Nov 13, 2023 at 2:07 p.m. | |
Last updated: | May 28, 2024 at 3:41 p.m. | |
DOI: | 10.4211/hs.1c4c1016e25e44fd9444273ddd91348c | |
Citation: | See how to cite this resource | |
Content types: | Multidimensional Content |
Sharing Status: | Published |
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Views: | 444 |
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Abstract
Understanding changes in the hazard component of climate risk is important to inform societal resilience planning under a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event-based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2-LENS). We find fewer TCs in the future, but these future TCs have higher wind speeds and are wetter. We also find that TCs produce heavier 3-day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200-year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or statistical downscaling methods. It can also produce very different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event-based simulations in the context of downscaling method limitations.
Subject Keywords
Coverage
Spatial
Temporal
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Data Services
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This resource is shared under the Creative Commons Attribution CC BY.
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