In this paper, we develop a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) for in situ measurements of seasonal mean and extreme daily precipitation. Critically, we isolate the individual effects of modes of climate variability while resolving these relationships to their native scales and using a data-driven procedure to robustly determine statistical significance.
We detect significant relationships in all seasons in spite of extremely large (>90-95%) background variability in both mean and extreme daily precipitation. We also demonstrate how our analysis reduces uncertainty, increases detection of significance, and discovers new results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation. It should also be noted that this high-impact paper was only made possible by the large, interdisciplinary research team within the CASCADE SFA. The eight co-authors of this paper pooled their broad expertise in statistical methods, climate modeling, oceanic and atmospheric processes, climate variability, and detection and attribution to produce a thorough and detailed analysis of gauge measurements of seasonal precipitation. Furthermore, this paper lays the foundation for a forthcoming high-profile study that conducts attribution of anthropogenic influence on seasonal mean and extreme precipitation based on in situ measurements from the historical record.
While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for an 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 this paper 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.