Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of extreme winter weather in California, including ARs, SAWs and temperature extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1-4 lead. This methodology (i) uses dynamical model information considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters for dynamical model error/uncertainty at longer lead times, and (iii) increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.

Water years (WY) 2017 and 2023 were anomalously wet for California, each helping to terminate multiyear droughts. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier-than-normal winters. We analyze over seven decades of precipitation and snow records along with mid-tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical ENSO-precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO-independent weather patterns was a major cause of anomalous precipitation in WY2017, record-setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983,1998 and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal forecasts.

Skillfully forecasting hydrologic outcomes of rain-on-snow (ROS) events is critical for water management and flood mitigation not only in the western U.S. but globally. This study applies methods for a Snowpack Runoff Decision Support System (SR-DSS) to the unimpaired Upper Carson watershed in the eastern Sierra Nevada of California and Nevada by leveraging hourly Natural Resource Conservation Service SNOw TELemetry (SNOTEL) data and compares results to observed soil moisture, streamflow, and an existing operational snowpack-runoff model framework used by the National Oceanic and Atmospheric Administration’s River Forecast Centers. Information provided by the SR-DSS can be disseminated to forecasters in real-time to adjust the SNOW-17 model as conditions change in ways that the model alone might not capture. Our results indicate that SR-DSS can enhance situational awareness by providing detailed snowpack and weather conditions in a time-relevant manner for forecasting and decision-making. We provide case studies to demonstrate how the SR-DSS alone captures the onset of terrestrial water input and how it can help assess the performance of operational models (SNOW-17 and SAC-SMA). The study suggests that the SR-DSS can be a valuable tool for operational hydrologists by helping to refine flood forecasts by identifying specific aspects of models that can be improved or adjusted and enhance decision-making during ROS events by providing additional situational awareness. Further development and testing of the SR-DSS could lead to its adoption in operational forecasting, enhancing the resilience of water management systems in the face of growing extreme precipitation concerns.

The 1997 New Year’s flood event was the most costly in California’s history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events inform practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14km down to 3.5km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are prominently influenced by horizontal resolution. The finest resolution RRM-E3SM simulation best represents storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.

The elevation of the mountain rain-snow transition is critical for short-term hazard forecasting and longer-term water supply considerations. Despite the transition’s importance, direct in-situ observations are rare. Here we present two new methods that utilize “anomalous” snow observations to detect rainfall during rain-on-snow: (1) a mass fluctuation at snow pillow sites, and (2) inflated remotely sensed snow grain sizes. Using auxiliary data, we show snow pillows respond to rain-on-snow with distinct perturbations that appear as pulses, collapses and declines within the snow water equivalent. We use these responses to identify mountain-scale rain-snow transitions across California’s Sierra Nevada. We also show how a threshold approach (>200 mm) for remotely sensed snow grain size can identify rain-on-snow as snow grain sizes artificially inflate due to a liquid water film. While the methods are not predictive, if paired retroactively with hydrometeorological models, these new methods have the potential to improve predictive streamflow capabilities.

Observational networks enhance real-time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multi-tiered observational network in California provided insights into hydrometeorological processes and impacts during a three-day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows for: 1) the validation of record cool season precipitable water observations over southern California, 2) the identification of phenomena that produce natural hazards and present difficulties for short-term weather forecast models, such as extreme precipitation amounts and snow level variability, 3) the use of soil moisture data to improve hydrologic model forecast skill in northern California’s Russian River basin, and 4) the combination of meteorological data with seismic observations to “observe” a large avalanche on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of diverse observations to characterize climatological context and advance understanding of processes modulating extreme weather.

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detection with snow seasonality classifications to examine fire activity in California’s seasonal and ephemeral snow areas. We find a nearly tenfold increase in fire activity during 2020 and 2021 compared to 2001-2019 as measured by satellite data. Accumulation season snow albedo declined 17-77% in two burned sites as measured by in-situ data relative to un-burned conditions, with greater declines associated with increased soil burn severity. By enhancing snowpack susceptibility to melt, decreased snow albedo drove mid-winter melt during a multi-week midwinter dry spell in 2022. Despite similar meteorological conditions in 2013 and 2022, which we link to persistent high pressure weather regimes, minimal melt occurred in 2013. Post-fire differences are confirmed with satellite measurements. Our findings suggest larger areas of California’s snowpack will be increasingly impacted by the compounding effects of dry spells and wildfire.