Abstract
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.
Highlights
Extreme precipitation in the observational record has been shown to contain nonstationarities over the past fifty to one hundred years (Hartmann et al 2013; Donat et al 2016; Papalexiou and Montanari 2019), and this result has been verified in numerous studies over the contiguous United States (CONUS; Kunkel 2003; Easterling et al 2017; Risser et al 2019a)
The literature contains a large number of studies that explore the relationship between climate variability and extreme precipitation, for example the El Niño–Southern Oscillation (ENSO; Gershunov 1998; Cayan et al 1999; Gershunov and Cayan 2003; Cannon 2015), the Pacific Decadal Oscillation (PDO; McCabe and Dettinger 1999), the Atlantic Multidecadal Oscillation (AMO; Enfield et al 2001), the North Atlantic Oscillation (NAO; Durkee et al 2008), the Pacific North American pattern (PNA; Archambault et al 2008), and the Artic Oscillation (AO; Goswami et al 2006)
We have developed a spatial analysis for in situ measurements of seasonal mean and extreme precipitation that quantifies joint relationships with a set of natural and anthropogenic climate indices
Summary
Extreme precipitation in the observational record has been shown to contain nonstationarities over the past fifty to one hundred years (Hartmann et al 2013; Donat et al 2016; Papalexiou and Montanari 2019), and this result has been verified in numerous studies over the contiguous United States (CONUS; Kunkel 2003; Easterling et al 2017; Risser et al 2019a). The literature contains a large number of studies that explore the relationship between climate variability and extreme precipitation, for example the El Niño–Southern Oscillation (ENSO; Gershunov 1998; Cayan et al 1999; Gershunov and Cayan 2003; Cannon 2015), the Pacific Decadal Oscillation (PDO; McCabe and Dettinger 1999), the Atlantic Multidecadal Oscillation (AMO; Enfield et al 2001), the North Atlantic Oscillation (NAO; Durkee et al 2008), the Pacific North American pattern (PNA; Archambault et al 2008), and the Artic Oscillation (AO; Goswami et al 2006) Almost all of these studies explore individual relationships between a single climate index and extreme precipitation; such analyses often compare years from high/positive phases of the index versus low/ negative phases of the index (similar to the so-called “composite analysis” in Zhang et al 2010), which discretizes the fundamentally continuous relationships between the indices and extreme precipitation. These drivers are used as a proxy for natural variability in an assessment to determine where there is a meaningful anthropogenic influence on the frequency of extreme precipitation
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