2.1. Time period of study
Our time period of study spans 1949–2021. However, some variables have shorter records due to data availability. The season of focus is extended winter (November-February; hereafter winter) to coincide with the NP4 dataset (described in Sect. 2.8), and which is also the part of the season most active with respect to both AR activity and SAW activity in California (Gershunov et al. 2017, and Guzman-Morales and Gershunov 2019, respectively).
2.2. Atmospheric Variables
Daily 500 mb geopotential height (Z500) and wind fields for the period spanning Nov-Feb 1949–2021 are from NCEP/NCAR 2.5° global reanalysis (Kalnay et al. 1996). Daily anomalies were calculated relative to the annual/semiannual seasonal cycle fit using least squares regression.
2.3. Atmospheric River Detection and AR Intensity Scale
The SIO-R1 catalog of Gershunov et al. (2017) is used to identify landfalling ARs along the coast of western North America. The detection method uses threshold criteria for integrated water vapor (IWV > 15 mm) and integrated vapor transport (IVT > 250 kg m-1s-1) to identify elongated objects of high moisture content that intersect the coast. The AR scale of Ralph et al. (2019) is used to categorize the magnitude of storms. This 5-scale classification is based on IVT magnitude and duration where the scale 1–5 describes ARs as weak, moderate, strong, extreme, and exceptional, respectively. AR scales 1–2 are mostly beneficial providing needed precipitation without damage, whereas AR scales 4–5 are mostly hazardous with peak IVT in the range of 750 to more than 1250 kg m-1s-1. Flood damages in the western U.S. increase exponentially with the AR scale (Corringham et al. 2019). The SIO-RI AR catalog is available at https://weclima.ucsd.edu/data-products/.
2.4. Santa Ana Winds
Historical SAW events are identified using the daily Santa Ana Winds Regional Index (SAWRI) of Guzman-Morales (2016, 2019). This data product is based on 65 years of hourly data from the dynamically downscaled CaRD10 dataset (Kanamitsu and Kanamaru, 2007) and validated against station observations. SAWs are defined by strong, sustained, northerly to easterly winds (Guzman-Morales et al., 2016). This study uses the updated daily version of the Guzman-Morales and Gershunov (2019) SAW index, regionalized over Southern California (~ 32–35 °N, 116–118 °W) and available at https://weclima.ucsd.edu/data-products/. We define moderate (extreme) SAWs as those exceeding the 50th (90th ) percentile.
2.5. Historical Temperature and Precipitation
Daily precipitation and maximum temperature (Tmax) data are from Livneh et al. (2013, 2015), which is a gridded data product available at a 1/16° spatial resolution over the period 1950–2015 (available at https://climatedataguide.ucar.edu/climate-data). The source data are from approximately 20,000 stations in the Global Historical Climatology Network (GHCN). Extreme precipitation is defined as daily accumulation above the 95th percentile. Heat waves (cold extremes) are defined following Gershunov and Guirguis (2012) as daily temperatures persisting for at least one day above the 95th (below the 5th ) percentile after removing the seasonal cycle.
2.6. Sierra Nevada Snowpack
We use monthly snow water equivalent data from a single snow course in the Northern Sierra Nevada. Specifically, we selected Donner Summit (39.3 °N, -120.3 °W, elevation 2100 meters, station ID 20K10) from the Natural Resources Conservation Service (https://wcc.sc.egov.usda.gov/nwcc/rgrpt?report=snowcourse&state=CA). This location sits at the intersection of three key headwater regions (Yuba, Truckee, and American Rivers) involved in managing California and Nevada water resources. The Donner Summit snow course has a long (1913–2021) and complete record.
2.7. Wildfire
Wildfire information is from the California Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters database (https://frap.fire.ca.gov/frap-projects/fire-perimeters/). We focus on fire start dates for historical wildfires with burn sizes exceeding 1000 acres in the Southern California (SoCal) counties of Santa Barbara, Ventura, Los Angeles, Orange, Riverside, San Bernardino, and San Diego. Our sample includes 76 wildfires spanning 1949–2018, with most occurring in November (n = 40) or December (n = 21). It is worth noting that most wildfires in California occur in October, which is outside of our extended winter focus season. However, the wildfire season can and does extend into winter during drought years or when delayed rains extend dry vegetation into the peak of SAW season.
2.8. North Pacific Atmospheric Circulation Regimes
We use daily amplitudes of four North Pacific atmospheric circulation regimes (NP4 modes), which were identified and shown to be essential drivers of vapor transport and AR activity along the coast of western North America by Guirguis et al. (2018, hereinafter GGR’18) and GGR’20. These four modes are named according to their geographic centers of action: Baja-Pacific (BP), Alaskan-Pacific (AP), Canadian-Pacific (CP), and Offshore-California (OC) modes. The NP4 modes were identified using rotated Empirical Orthogonal Function (EOF) analysis applied to Z500 anomaly fields over a large domain spanning the northern Pacific Ocean and western North America. While the NP4 modes are not the most important in terms of field variance (i.e., they correspond to higher-order rotated principal components), they were found to be the most important modes for coastal vapor transport, AR landfalls, and extreme precipitation in California (GGR’18, GGR’20). The EOF loading patterns (maps) associated with the positive phase of each mode are shown in Fig. 1a. The portion of the domain where these modes are most influential is shown in Figs. 1b and 1c. Specifically, Fig. 1b shows the locations where one of the four modes is by itself responsible for more than 50% of the Z500 variance. Figure 1c shows the collective influence of the four modes, where we see that together these modes explain 25–90% variance in the portion of the domain most relevant for west coast weather. The NP4 dataset used in this study is described in Guirguis et al. (2020b) and is available at https://doi.org/10.6075/J0154FJJ.
2.9. Weather regime Classification
Daily weather regimes are classified based on the joint phase combination of the NP4 modes. As the NP4 modes oscillate over the North Pacific, their daily interactions determine the position and strength of ridges and troughs along the coast of western North America. Unique NP4 phase combinations result in distinct weather patterns that drive extreme weather in California (GGR’18 GGR’20). On a given day, each of the four NP4 modes can be positive or negative, which yields 16 possible phase combinations, and 16 corresponding weather regimes (introduced and described in Sect. 3).
We use the terminology “weather pattern” and “weather regime” somewhat interchangeably. “Weather pattern” is taken to mean the Z500 anomaly field itself while “weather regime” refers to the classification of that pattern. A catalog of historical weather regimes observed over 1949–2021 is developed as an accompaniment to this publication and is available at https://weclima.ucsd.edu/data-products.