Journal article
Strong Linkage Between Observed Daily Precipitation Extremes and Anthropogenic Emissions Across the Contiguous United States
Geophysical research letters, Vol.51(20), e2024GL109553
10/28/2024
DOI: 10.1029/2024GL109553
Appears in UI Libraries Support Open Access
Abstract
The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.
Plain Language Summary We investigate how human‐induced emissions affect daily rainfall extremes across the United States. The attribution of an extreme event to human‐induced emissions depends on the selected extreme event statistics, with setting a threshold to define what counts as an extreme event remaining a major challenge. To overcome this, we used the Extended Generalized Pareto Distribution (ExtGPD) that jointly models both low and heavy rainfall events without defining a threshold, providing a more complete picture of the full distribution including extremes. We fitted the ExtGPD to 12 general circulation models and compared scenarios with and without human‐induced emissions. Our findings suggest that human emissions are responsible for the observed intensity of daily rainfall extremes across the United States, especially in regions with temperate climates, and that these extremes would have been smaller without greenhouse gases.
Key Points We apply the Extended Generalized Pareto Distribution for probabilistic event attribution to bypass issues with threshold specification Anthropogenic emissions alone could exacerbate the observed magnitude of extreme daily precipitation across the United States The study underscores the urgent need for mitigation, revealing a clear link between anthropogenic activities and extreme precipitation
Details
- Title: Subtitle
- Strong Linkage Between Observed Daily Precipitation Extremes and Anthropogenic Emissions Across the Contiguous United States
- Creators
- J. S. Nanditha - University of Iowa, IIHR--Hydroscience and EngineeringGabriele Villarini - Princeton UniversityHanbeen Kim - Princeton UniversityPhilippe Naveau - Laboratoire des Sciences du Climat et de l'Environnement
- Resource Type
- Journal article
- Publication Details
- Geophysical research letters, Vol.51(20), e2024GL109553
- Publisher
- Wiley; WASHINGTON
- DOI
- 10.1029/2024GL109553
- ISSN
- 0094-8276
- eISSN
- 1944-8007
- Grant note
- High Meadows Environmental Institute, Princeton University: 101003469 Earth System Grid Federation - Princeton University: 80 PRIME CNRS-INSU French national programs: ANR-20-CE40-0025-01, ANR-22-EXTR-0005 Agence Nationale de la Recherche (ANR)
We acknowledge the availability of CPC Unified Gauge-Based Analysis of Daily Precipitation over CONUS data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at . We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. This work was in part funded by Princeton University. Part of Philippe Naveau's research work was supported by European H2020 XAIDA (Grant agreement ID: 101003469) and the French national programs: 80 PRIME CNRS-INSU, Agence Nationale de la Recherche (ANR) under reference ANR-20-CE40-0025-01 (T-REX), the ANR EXSTA, and the PEPR TRACCS programme under Grant ANR-22-EXTR-0005, and the Mines Paris/INRAE chair Geolearning. The comments by two anonymous reviewers are gratefully acknowledged.
- Language
- English
- Date published
- 10/28/2024
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984740951202771
Metrics
1 Record Views