Anticipating responses to climate change and planning for resilience in California’s freshwater ecosystems

National Academy of Sciences (NAS) | July 29th, 2024

Arctic sea-ice loss drives a strong regional atmospheric response over the North Pacific and North Atlantic on decadal scales

Nature Portfolio (Springer Nature) | March 11th, 2025

Previous studies have suggested that Arctic sea-ice loss can have a profound influence on atmospheric circulation far away from the Arctic. However, there is litt

Previous studies have suggested that Arctic sea-ice loss can have a profound influence on atmospheric circulation far away from the Arctic. However, there is little scientific consensus on the features of these remote responses, with the opposite impacts reported. Here we present a multi-model analysis of the decadal climate response to Arctic sea-ice loss using state-of-the-art energy conserving methodologies to isolate the impacts of sea-ice decline. We observe weakening of the Aleutian Low and development of a geopotential ridge in the North Pacific, associated with drier winter conditions over the southwest United States. Over the Atlantic, a negative NAO-like response drives wetter winter conditions across the western Mediterranean. These decadal-scale impacts substantially differ from reported centennial-scale responses to Arctic sea-ice loss simulated using non-energy conserving methodologies. Factors such as the timescale of the response and methodologies used to isolate the impacts of disappearing sea-ice cover should be carefully considered when consolidating scientific understanding on the future impacts of changing Arctic.

Arctic sea-ice loss is projected to lead to more frequent strong El Niño events

Nature Portfolio (Springer Nature) | August 23rd, 2022

Arctic sea ice has decreased substantially and is projected to reach a seasonally ice-free state in the coming decades. Little is known about whether dwindling Arctic sea

Arctic sea ice has decreased substantially and is projected to reach a seasonally ice-free state in the coming decades. Little is known about whether dwindling Arctic sea ice is capable of influencing the occurrence of strong El Niño, a prominentmode of climate variability with global impacts. Based on time slice coupled model experiments, here we show that no significant change in the occurrence of strong El Niño is found in response to moderate Arctic sea-ice loss that is consistent with satellite observations to date. However, as the ice loss continues and the Arctic becomes seasonally ice-free, the frequency of strong El Niño events increases bymore than one third, as defined by gradientbased indices that remove mean tropical Pacific warming induced by the seasonally ice-free Arctic. By comparing our time slice experiments with greenhouse warming experiments, we conclude that at least 37–48% of the increase of strong El Niño near the end of the 21st century is associated specifically with Arctic sea-ice loss. Further separation of Arctic sea-ice loss and greenhouse gas forcing only experiments implies that the seasonally ice-free Arctic might play a key role in driving significantly more frequent strong El Niño events.

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

American Meteorological Society (AMS) | July 16th, 2024

ARTMIP-early start comparison of atmospheric river detection tools: how many atmospheric rivers hit northern California’s Russian River watershed?

Climate Dynamics (Springer) | September 10th, 2018

Many atmospheric river detection tools (ARDTs) have now been developed. However, their relative performance is not well documented. This paper compares a diverse set of A

Many atmospheric river detection tools (ARDTs) have now been developed. However, their relative performance is not well documented. This paper compares a diverse set of ARDTs by applying them to a single location where a unique 12-year-long time-series from an atmospheric river observatory at Bodega Bay, California is available. The study quantifies the sensitivity of the diagnosed number, duration, and intensity of ARs at this location to the choice of ARDT, and to the choice of reanalysis data set. The ARDTs compared here represent a range of methods that vary in their use of different variables, fixed vs. percentile-based thresholds, geometric shape requirements, Eulerian vs. Lagrangian approaches, and reanalyses. The ARDTs were evaluated first using the datasets documented in their initial publication, which found an average annual count of 19 ± 7. Applying the ARDTs to the same reanalysis dataset yields an average annual count of 19 ± 4. Applying a single ARDT to three reanalyses of varying grid sizes (0.5°, 1.0°–2.5°) showed little sensitivity to the choice of reanalysis. While the annual average AR event count varied by about a factor of two (10–25 per year) depending on the ARDT, average AR duration and maximum intensity varied by less than ± 10%, i.e., 24 ± 2 h duration; 458 ± 44 kg m− 1 s− 1maximum IVT. ARDTs that use a much higher threshold for integrated vapor transport were compared separately, and yielded just 1–2 ARs annually on average. Generally, ARDTs that include either more stringent geometric criteria or higher thresholds identified the fewest AR events.

Assessing and communicating the impacts of climate change on the Southern California coast

California Natural Resources Agency (CNRA) | August 27th, 2018

 Over the course of this and the next century, the combination of rising sea levels, severe storms, and coastal erosion will threaten the sustainability of coastal commu

 Over the course of this and the next century, the combination of rising sea levels, severe storms, and coastal erosion will threaten the sustainability of coastal communities, development, and ecosystems as we currently know them. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies for projected increased levels of coastal flooding and erosion, coastal managers need user-friendly planning tools based on the best available climate and coastal science. In anticipation of these climate change impacts, many communities are in the early stages of climate change adaptation planning but lack the scientific information and tools to adequately address the potential impacts. In collaboration with leading scientists worldwide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections and translate that information using sophisticated physical process models into planning-scale physical, ecological, and economic exposure, shoreline change, and impact assessments, all delivered in two simple, user-friendly, online tools. Our results show that by the end of the 21st century, over 250,000 residents and nearly $40 billion in building value across Southern California could be exposed to coastal flooding from storms, sea level rise, and coastal change. Results for the other major population center in California (the greater San Francisco Bay Area) are also available but not explicitly discussed in this report. Together, CoSMoS has now assessed the exposure of 95% of the 26 million coastal residents of the State (17 million in Southern California) 

Assessing Environmental Drivers and Protist Community Dynamics that Shaped the Historic August 2022 Heterosigma akashiwo Bloom in San Francisco Bay, California

Springer Nature | December 22nd, 2025

Assessing future hydrologic extremes using an integrated hydrology and river operations model in the Russian River watershed

Elsevier | January 6th, 2026

This paper introduces an integrated hydrology and river operations model for the Russian River watershed. This model is distinct from models in previous studies because i

This paper introduces an integrated hydrology and river operations model for the Russian River watershed. This model is distinct from models in previous studies because it represents surface-groundwater interactions and uses climate forcings to estimate dynamic water use demands that are superimposed onto both reservoir operations and water supply constraints. The model was used to examine three historical (1990–2015) and eight future (2016–2099) water use and climate change scenarios.

Assessing Regional Strategies to Cope with Drought, Climate, and Environmental Flow Policy Impacts for a Sustainable Water Supply: A Case Study of the Bay Area

American Chemical Society (ACS) | December 27th, 2024

Assessing risk of groundwater pollution exposure from sea level rise in California

Publisher not available | June 12th, 2025

Sea level rise (SLR) will cause a groundwater table rise in coastal aquifers, and this can trigger exposure to toxic chemicals via direct contact with contaminated water

Sea level rise (SLR) will cause a groundwater table rise in coastal aquifers, and this can trigger exposure to toxic chemicals via direct contact with contaminated water or through vapor intrusion. This study presents deep learning- and explainable artificial intelligence (XAI) models to assess risk of polluted groundwater exposure due to SLR in California. Our method quantifies risk based on (1) SLR-related groundwater rise projections and (2) a prioritization of 2296 hazardous sites according to a geochemical indexing algorithm applied to groundwater data from the California State Water Resources Control Board. Our results indicate that nearly half a million people in California may be potentially exposed to marginal or poor groundwater by the end of the century. We show that feature collinearity renders numerous indicators on socioeconomics, demography, health and environment as dependent- and thus insignificant risk predictors. Our XAI models furthermore indicate that features on total population and features related to demographics (households of color and low income) and pollution source (proximity to wastewater dischargers) exert strongest controls on risk. However performance differences between randomly- and spatially cross-validated models highlight generalization challenges due to spatial autocorrelation. While the former model yields better results for California-specific analysis, only the latter model should be applied for unbiased risk assessments in other coastal regions.