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Cited by 288Climate variability and modelsMeteorological Phenomena and SimulationsClimate Change and Environmental Impact

The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.

Human influence has intensified extreme precipitation in North America

Open access
Kirchmeier-Young, Megan C., Zhang, Xuebin·2020·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1921628117PMC PMC7306817
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Abstract

Significance: Extreme precipitation is relevant to many interests, and observations show an increasing trend that is expected to continue under future projections. Although previous work has identified an anthropogenic influence on extreme precipitation at hemispheric scales, this study finds robust results for a continental scale. We establish that anthropogenic climate change has contributed to the intensification of continental and regional extreme precipitation. Furthermore, we also show that the anthropogenic influence on North American regional precipitation will lead to more frequent and intense precipitation extremes in the future.

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Crossref · 10.1073/pnas.1921628117

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Figures

Fig. 1. Defined North American regions used herein (A). Blue lines separate into three east–west bands and red lines separate into three north–south bands with near-equal distribution of grid boxes. Combining gives nine regions across the domain. The gray shading shows the data mask from HadEX2 for grid boxes with coverage for more than 75% of years, and white lines show the 2.5° × 3.75° grid. Values printed in each region indicate the number of grid boxes. Time series of Rx1day PI for North America (B), the three north–south (NS) regions (C, D, and H) and three east–west (EW) regions (E, F, and G). Smaller regions can be found in SI Appendix, Fig. S1. CanESM2 is shown in blue, CanRCM4 in red, and CESM1 in orange, with the observations from HadEX2 shown in black (5-y means in solid line and annual values in dotted line). A common grid and data mask are used for each dataset. For the models, the spread indicates the 5th to 95th percentile range across the ensemble, and the bold line is 5-y means from the ensemble mean.
Fig. 1. Defined North American regions used herein (A). Blue lines separate into three east–west bands and red lines separate into three north–south bands with near-equal distribution of grid boxes. Combining gives nine regions across the domain. The gray shading shows the data mask from HadEX2 for grid boxes with coverage for more than 75% of years, and white lines show the 2.5° × 3.75° grid. Values printed in each region indicate the number of grid boxes. Time series of Rx1day PI for North America (B), the three north–south (NS) regions (C, D, and H) and three east–west (EW) regions (E, F, and G). Smaller regions can be found in SI Appendix, Fig. S1. CanESM2 is shown in blue, CanRCM4 in red, and CESM1 in orange, with the observations from HadEX2 shown in black (5-y means in solid line and annual values in dotted line). A common grid and data mask are used for each dataset. For the models, the spread indicates the 5th to 95th percentile range across the ensemble, and the bold line is 5-y means from the ensemble mean.CC BY-NC-ND · PubMed Central
Fig. 2. Scaling factors for North American precipitation extremes from a one-signal (ALL forcing) optimal fingerprinting detection and attribution for Rx1day (A) and Rx5day (B). Results from CanRCM4 are shown in red, CanESM2 in blue, and CESM1 in orange. The first point is for a temporal regression of North American mean values, and the next three are for space–time regressions involving the concatenation of means of the designated regions (Fig. 1A). Error bars represent a 90% range. Gray x indicates a failure of the residual consistency test. Dashed lines are shown at 0, which indicates detection, and 1, which implies consistency with the observations.
Fig. 2. Scaling factors for North American precipitation extremes from a one-signal (ALL forcing) optimal fingerprinting detection and attribution for Rx1day (A) and Rx5day (B). Results from CanRCM4 are shown in red, CanESM2 in blue, and CESM1 in orange. The first point is for a temporal regression of North American mean values, and the next three are for space–time regressions involving the concatenation of means of the designated regions (Fig. 1A). Error bars represent a 90% range. Gray x indicates a failure of the residual consistency test. Dashed lines are shown at 0, which indicates detection, and 1, which implies consistency with the observations.CC BY-NC-ND · PubMed Central
Fig. 3. As in Fig. 2 but for temporal regressions for individual regions.
Fig. 3. As in Fig. 2 but for temporal regressions for individual regions.CC BY-NC-ND · PubMed Central
Fig. 4. Return periods for Rx1day 20-y (A), 50-y (B), and 100-y (C) events using CanESM2. For each region, event thresholds are defined from the natural-only simulations, and return periods for these events under different levels of GSAT increase in degrees Celsius relative to preindustrial are shown by colored bars. An increase of +1 °C represents the current climate. Error bars (and shading) indicate the 90% range. Note that different events use different vertical axis scales, and some ranges continue beyond the upper limit of the plot.
Fig. 4. Return periods for Rx1day 20-y (A), 50-y (B), and 100-y (C) events using CanESM2. For each region, event thresholds are defined from the natural-only simulations, and return periods for these events under different levels of GSAT increase in degrees Celsius relative to preindustrial are shown by colored bars. An increase of +1 °C represents the current climate. Error bars (and shading) indicate the 90% range. Note that different events use different vertical axis scales, and some ranges continue beyond the upper limit of the plot.CC BY-NC-ND · PubMed Central

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Show all 38 references

Research works printed but not linked: 2

  1. U. Lall , “Water” in Impacts, Risks, and Adaptation in the United States: Fourth National ClimateAssessment,D. R. Reidmiller , Eds. (US Global Change Research Program, Washington, DC, 2018), vol. II, pp. 145–173.
  2. M. R. Allen , “Framing and Context” in Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, V. Masson-Delmotte , Eds. (Intergovernmental Panel on Climate Change, 2018), in press.

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Metadata

Authors
Kirchmeier-Young, M. C., & Zhang, X.
Affiliations
  • Climate Research Division, Environment and Climate Change Canada, Toronto, ONT M3H 5T4, Canada
  • Environment and Climate Change Canada (CA)
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2020-06
Volume
117
Issue
24
Pages
13308-13313
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1921628117
PMC
PMC7306817
OpenAlex
W3032529621
Cited by
288
References
38
Citations by year
2026: 26 · 2025: 47 · 2024: 52 · 2023: 60 · 2022: 47 · 2021: 48 · 2020: 8
FWCI
15.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
Keywords
heavy rainfall, anthropogenic climate change attribution, North America, annual maximum precipitation, climate change detection, regional climate model, Earth system models, external forcing

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