Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation
While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United State...
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2021
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ftcdlib:oai:escholarship.org/ark:/13030/qt46d779cf 2023-05-15T17:31:37+02:00 Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation Risser, MD Wehner, MF O’Brien, JP Patricola, CM O’Brien, TA Collins, WD Paciorek, CJ Huang, H 3205 - 3230 2021-05-01 application/pdf https://escholarship.org/uc/item/46d779cf unknown eScholarship, University of California qt46d779cf https://escholarship.org/uc/item/46d779cf public Climate Dynamics, vol 56, iss 9-10 Extreme value analysis Spatial statistics Station data Natural variability El Nino/Southern Oscillation Pacific-North American pattern North Atlantic Oscillation Meteorology & Atmospheric Sciences Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience article 2021 ftcdlib 2021-08-02T17:10:05Z While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation. Article in Journal/Newspaper North Atlantic North Atlantic oscillation University of California: eScholarship Pacific |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
Extreme value analysis Spatial statistics Station data Natural variability El Nino/Southern Oscillation Pacific-North American pattern North Atlantic Oscillation Meteorology & Atmospheric Sciences Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience |
spellingShingle |
Extreme value analysis Spatial statistics Station data Natural variability El Nino/Southern Oscillation Pacific-North American pattern North Atlantic Oscillation Meteorology & Atmospheric Sciences Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience Risser, MD Wehner, MF O’Brien, JP Patricola, CM O’Brien, TA Collins, WD Paciorek, CJ Huang, H Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
topic_facet |
Extreme value analysis Spatial statistics Station data Natural variability El Nino/Southern Oscillation Pacific-North American pattern North Atlantic Oscillation Meteorology & Atmospheric Sciences Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience |
description |
While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation. |
format |
Article in Journal/Newspaper |
author |
Risser, MD Wehner, MF O’Brien, JP Patricola, CM O’Brien, TA Collins, WD Paciorek, CJ Huang, H |
author_facet |
Risser, MD Wehner, MF O’Brien, JP Patricola, CM O’Brien, TA Collins, WD Paciorek, CJ Huang, H |
author_sort |
Risser, MD |
title |
Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
title_short |
Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
title_full |
Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
title_fullStr |
Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
title_full_unstemmed |
Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
title_sort |
quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation |
publisher |
eScholarship, University of California |
publishDate |
2021 |
url |
https://escholarship.org/uc/item/46d779cf |
op_coverage |
3205 - 3230 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Climate Dynamics, vol 56, iss 9-10 |
op_relation |
qt46d779cf https://escholarship.org/uc/item/46d779cf |
op_rights |
public |
_version_ |
1766129285933826048 |