Journal Article

Separating Storm Intensity and Arrival Frequency in Nonstationary Rainfall Frequency Analysis

Wasko, Conrad, O’Shea, Declan, Nathan, Rory, Sharma, Ashish

2024

Overview

Nonstationary Rainfall frequency analysis (RFA) is used to assess how climate change is impacting the likelihood of extreme storms. A key limitation of covariate‐based approaches to nonstationary RFA is that without a physical basis, models selected based on the quality of fit to historical data cannot be reliably projected to estimate future quantiles. Here we propose to improve the physical representation of rainfall processes by using a peaks‐over‐threshold approach to separate the processes of storm intensity (impacted by thermodynamic drivers related to changes in atmospheric moisture) and storm arrival frequency (impacted by dynamic drivers that lead to changes in regional weather systems). Through stochastic experiments we demonstrate that quantiles can only be accurately projected beyond the observed climate when nonstationary models reflect the underlying nonstationary process. Through a case study we demonstrate how climate model projections of rainfall can be utilized to deduce nonstationary model structures, showing that changes in both the storm intensity and storm arrival frequency are needed to accurately estimate future quantiles. While here we propose a single simple physically informed approach for storm intensity, structuring the arrival frequency component requires a detailed understanding of atmospheric dynamics in the region of interest. Plain Language Summary We explore how complexity can be added to probability models to estimate how the risks of extreme rainfalls may change in a warming climate. We propose a way to structure these models to separate changes due to increased moisture holding capacity in the atmosphere due to warming temperatures (thermodynamics) from changes in atmospheric circulation patterns that affect weather systems (dynamics). This separation allows us to introduce physically motivated models which represent the impact of climate change on these two processes. We demonstrate the value of using this approach through several stochastic experiments which demonstrate that shifts in extreme rainfall probabilities can only be estimated when the underlying physical processes are correctly represented. A case study is provided to demonstrate how rainfall projections from a climate model can be used to develop physically motivated models for both thermodynamic and dynamic processes. Key Points Different nonstationary probability model structures are tested for their ability to estimate quantiles in future climates A peaks‐over‐threshold approach is utilized to separate thermodynamic and dynamic drivers of rainfall Physically motivated models are shown to be necessary to estimate extreme rainfall quantiles in a warming world

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Atmospheric circulation / Atmospheric circulation patterns / Atmospheric correction / Atmospheric dynamics / Atmospheric moisture / Case studies / climate / Climate and weather / Climate change / Climate models / Environmental impact / Extreme weather / four‐parameter Kappa distribution / Frequency analysis / Global warming / nonstationarity / peaks‐over‐threshold series / Precipitation / Quantiles / Rain / Rainfall / Rainfall frequency / rainfall frequency analysis / Rainfall processes / storm arrival frequency / storm intensity / Storms / Thermodynamics / water / Weather