Ridging Associated with Drought Across the Western and Southwestern United States: Characteristics, Trends, and Predictability Sources

January 7, 2020

NASA Jet Propulsion Laboratory postdoctoral researcher Dr. Peter B. Gibson, along with co-authors Dr. Duane E. Waliser (NASA JPL), Dr. Bin Guan (UCLA), CW3E scientist Dr. Mike DeFlorio, CW3E Director Dr. F. Martin Ralph, and Dr. Daniel Swain (UCLA/NCAR), recently published an article in Journal of Climate titled “Ridging Associated with Drought Across the Western and Southwestern United States: Characteristics, Trends, and Predictability Sources”.

This research is an important element of the ongoing partnership between JPL and CW3E. It directly supports the California Department of Water Resources (DWR) goal to improve subseasonal-to-seasonal predictability of precipitation (including atmospheric river events). This research is part of a major effort to develop near-real time subseasonal ridging outlooks to help predict precipitation, or the absence thereof, out to 6-week lead time. Improving sub-seasonal to seasonal forecasting is a major priority in CW3E’s Strategic Plan.

The specific purpose of the recently published study is to identify “wintertime ridging” events that are associated with drought conditions over the western and southwestern United States. Ridging events are defined as periods of time when mid-atmospheric high-pressure anomalies persist near the western U.S. coast and alter pathways for moisture to reach the western U.S. The remote drivers of these anomalies and their precursors are not well understood but they affect the likelihood of precipitation occurring over a given region. This work also examines the relationship of each ridge type with the El Niño-Southern Oscillation (ENSO) and other modes of climate variability. For example, a key question that this research aims to answer is: how does the presence of an El Niño event effect the likelihood of a ridge occurring near the western U.S. coast? Answering this and other questions about ridging processes will provide insights that will help predict the probability of droughts with longer lead times that are needed to better prepare for such events.

Based on this analysis, three dominant ridge types that are associated with drought over the western U.S. are identified (Fig. 1) – the “North” type (40°-55°N; 225°-250°E), the “South” type (28°-40°N; 235°-255°E), and the “West” type (28°-40°N; 210°-235°E). These key regions are used to “track” the centroid locations of ridges in operational weather models.

Figure 1: (a) First 4 EOF loadings corresponding to a combined EOF of daily precipitation anomalies over land over Western/Southwestern U.S. (shading) and Z500 anomalies (contours), see Section 2.2 for further details. The EOF domain panel details the regions used in the combined EOF analysis for Z500 (light-blue shading) and precipitation over the Western/Southwestern U.S. (blue shading). (b) Details of the ridge detection algorithm used in this study, showing an example on a given day from MERRA-2 reanalysis. The 3 labelled boxes (N, S, W) are related to EOFs as follows: N-ridge box from EOF1 (40°-55°N; 225°-250°E), S-ridge box from EOF2 and EOF4 (28°-40°N; 235°-255°E) and W-ridge box from EOF3 (28°-40°N; 210°-235°E). Contours show the daily Z500 anomalies at 50m intervals while the grey shaded regions indicate individual ridge objects (>50m threshold). The red circle in Figure 1b shows the ridge centroid for the date given, while smaller magenta crosses show the ridge centroid on the previous 2 days. From Gibson et al. 2020 (Figure 1).

Our stakeholders at California DWR, along with many other end users in the applications community, are interested in obtaining better forecasts of precipitation at S2S lead times over the Western U.S. to improve water resource management. Accordingly, the relationship of each ridge type to various modes of climate variability is shown below in Figure 2. These modes of variability impact regional weather at S2S lead times, primarily through atmospheric teleconnection patterns (i.e. regional changes in large-scale circulation driven by remote forcing weeks to months in advance).

Figure 2: Pearson correlations between monthly ridge frequency anomalies and various remote drivers/modes of variability (x-axis): the Arctic Oscillation (AO), sea ice variability over the Bering/Chukchi region (BC) and the Barents/Kara region (BK), the Pacific meridional mode (PMM), SSTs in the Western Pacific (SST WP), the Nino4 index, the Nino3.4 index, and the equatorial Southern Oscillation Index (SOI EQ.). Panel (a) shows correlations across all months October to March, while panel (b) shows correlations across January to March. P-values < 0.05 are indicated by open circles and p-values < 0.01 are indicated by filled circles. The time period is 1950-2014 with ridge types generated from 20CRV2c reanalysis. From Gibson et al. 2020 (Figure 8).

During JFM (January-February-March), several moderate-strength correlations are evident: e.g., the North Ridge type and the Pacific Meridional Mode, the West Ridge type and Western Pacific SST, and the West Ridge type and the Nińo-4 index. These simple statistical relationships will help inform future research in terms of the selection of candidate predictor variables when developing empirical S2S precipitation forecast models.

This work has introduced an objective ridge detection algorithm and defines three dominant ridge types that strongly impact precipitation and AR deficits over the Western/Southwestern U.S. region. Ongoing work between JPL/CW3E is focusing on evaluating the representation of each ridge type in operational S2S hindcast systems, which will additionally help in guiding end user interpretation of subseasonal ridging forecasts hosted on the CW3E website.

Gibson, P.B., D.E. Waliser, B. Guan, M.J. DeFlorio, F.M. Ralph, and D.L. Swain (2020): Ridging associated with drought across the Western and Southwestern United States: characteristics, trends and predictability sources. J. Climate, (In Press), https://doi.org/10.1175/JCLI-D-19-0439.1.