Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

• Published in: Climate Dynamics
• Main Authors: Risser, Mark D., Wehner, Michael F., O’Brien, John P., Patricola, Christina M., O’Brien, Travis A., Collins, William D., Paciorek, Christopher J., Huang, Huanping
• Other Authors: Office of Science, U.S. Department of Energy
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: Atmospheric Science; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1007/s00382-021-05638-7; https://link.springer.com/content/pdf/10.1007/s00382-021-05638-7.pdf; https://link.springer.com/article/10.1007/s00382-021-05638-7/fulltext.html

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.