Changes to Atmospheric River Related Extremes Over the United States West Coast Under Anthropogenic Warming

March 17, 2025

A new study, “Changes to Atmospheric River Related Extremes Over the United States West Coast Under Anthropogenic Warming” was recently published in Geophysical Research Letters by CU Boulder PhD Candidate Tim Higgins along with Aneesh Subramanian (CU Boulder), Peter Watson (University of Bristol), and Sarah Sparrow (Oxford University). This research utilized large ensemble simulations from two climate models (CESM2 and HadAM4). Given the immense size of the datasets and the absence of IVT in the HadAM4 runs, a machine learning approach – trained on human expert hand labels, IWV, mean sea level pressure, and wind velocity at 850 mb – was used for atmospheric river (AR) tracking (Higgins et al. 2023). The study focused on extreme ARs that surpass IWV thresholds occurring, on average, less than once per year on the coast and within ARs under early 21st century forcing. This work aligns with CW3E’s goal to advance understanding and predictability of extreme events and was supported by the California Department of Water Resources’ AR Program and the US Army Corps of Engineers’ Forecast Informed Reservoir Operations.

The forced response of rare extreme AR events to increased global temperatures was first quantified in three warming scenarios (Figure 1). In California, the SSP 3-7.0 scenario in CESM2 creates the potential for unprecedented peak AR IWV on the coast, with already destructive events becoming significantly more frequent. Even mild warming scenarios show a strong sensitivity of rare extreme AR events to temperature increases. The HadAM4 simulations indicate that in just a 2°C increase from preindustrial levels, an event with a 100-year return period under early 21st century conditions could occur every 10-20 years. Similar trends also emerge in the Pacific Northwest, where 100-year return period events in the early 21st century become roughly 20-year events in the 2°C warming.

Five distinct weather regimes of mean sea level pressure (MSLP) anomalies were identified using self-organizing maps, an unsupervised machine learning method. On average, MSLP anomalies exhibited minimal change under warming scenarios for all regimes. Changes to frequencies of extreme ARs during the 2° increase scenario in comparison to the early 21st century forcing scenario were then examined under varying weather regimes (Figure 2). The frequencies of extreme ARs increased under all regimes, but there was a trend of disproportionately higher percentage increases during regimes that historically had higher AR activity (Regimes 2, 3, and 5). These regimes were all characterized by anomalously low pressure over the North Pacific that likely facilitated poleward vapor transport near the coast.

Figure 1. Maximum winter severity of (a) California IWV and (b) Pacific Northwest IWV occurring within Atmospheric river events at varying periods. The solid lines represent simulations from HadAM4, and the dashed lines represent simulations from Community Earth System Model version 2 (CESM2). The HIST scenario in HadAM4 represents historical forcing from 2006 to 2015 and the 1.5°C and 2°C scenarios represent model runs from the same years with temperature differences based on the Half a degree Additional warming, Prognosis, and Projected Impacts framework. The HIST scenario in CESM2 represents historical forcing from 2007 to 2014 and the SSP 3‐7.0 scenario represents possible future forcing from 2092 to 2099.

Figure 2. Changes in frequencies of extreme Atmospheric river (AR) events under each winter weather regime (a–e, filled contours, with increments of 0.25 events per year) and mean for all regimes (f). Mean sea level pressure anomalies during each weather regime are shown in line contours with increments of 6 mb. The number of detected events in each scenario and the percentage change in the number of events per season is shown in the corner of each panel. Dotted areas represent locations in which the difference between the number of extreme AR events is statistically significant at the 95% level using bootstrapping.

Higgins, T. B., Subramanian, A. C., Graubner, A., Kapp‐Schwoerer, L., Watson, P. A. G., Sparrow, S., et al. (2023). Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set. Journal of Advances in Modeling Earth Systems, 15(4), e2022MS003495. https://doi.org/10.1029/2022MS003495

Higgins, T. B., Subramanian, A. C.,Watson, P. A. G., & Sparrow, S. (2025).Changes to atmospheric river related extremes over the United States west coast under anthropogenic warming. Geophysical Research Letters, 52 e2024GL112237. https://doi.org/10.1029/2024GL112237