1. Introduction
Atmospheric rivers (AR) are long, narrow, dynamically forced bands of enhanced poleward atmospheric water vapor transport found in the pre-cold-frontal region of an extratropical cyclone and typically characterized by an embedded low-level jet (e.g., Newell et al. 1992; Ralph et al. 2017, their Fig. 16). ARs are responsible for almost 90% of poleward moisture transport from the subtropics to the midlatitudes (i.e., Zhu and Newell 1998). Where ARs are orographically lifted, such as by coastal mountain ranges like the Sierra Nevada of the western United States, or are forced upward, as part of their parent extratropical cyclone’s warm conveyor belt (e.g., Sodemann and Stohl 2013; Pfahl et al. 2014), extreme precipitation rates can be sustained for extended periods of time, leading to severe flooding (e.g., Neiman et al. 2008; Corringham et al. 2019). Due to their association with extratropical cyclones, ARs are more frequent throughout the globe during the respective cold season, when the midlatitude jet streams are located farthest from the poles. Globally, regions most heavily impacted by ARs include western United States (e.g., Dettinger 2004), central United States (e.g., Lavers and Villarini 2013a), western South America (e.g., Viale et al. 2018), western Europe (e.g., Lavers et al. 2011; Lavers and Villarini 2013b), South Africa (e.g., Blamey et al. 2018), eastern Asia (e.g., Kamae et al. 2017), as well as Australia and New Zealand (e.g., Reid et al. 2022; Kingston et al. 2022). The central role that ARs play in the global water cycle, extreme weather, and regional water resources has made their detection and prediction active topics of investigation within the hydrological science community (e.g., Chen et al. 2019; Dettinger et al. 2011; Gimeno et al. 2016).
Global weather model forecast systems and their associated reanalyses, beginning with the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis 1 (Kalnay et al. 1996) have enabled much of the AR-related research. The most common AR detection approaches apply thresholds of either integrated vapor transport (IVT; Zhu and Newell 1998) or integrated water vapor content (IWV; Ralph et al. 2004) that may be location (e.g., latitude) and time (i.e., season) dependent, and may be combined with additional shape and size criteria. Using atmospheric reanalysis data, the international Atmospheric River Tracking Method Intercomparison Project (ARTMIP; e.g., Shields et al. 2018) is currently working to better understand and quantify uncertainties in AR studies that can be attributed to detection methodology. ARTMIP is principally conducted in the context of AR long-term climatology and interannual variability (e.g., Zhou et al. 2021; Rutz et al. 2019). Some ARTMIP efforts are regionally focused (e.g., Ralph et al. 2019; Chen et al. 2019), whereas others address the global scale (e.g., Lora et al. 2020). The same AR detection algorithms being intercompared and refined in ARTMIP may be applied to climate model projections to investigate how ARs will change in response to climate change and global warming (e.g., Payne et al. 2020; Shields et al. 2019; O’Brien et al. 2022). Other studies have focused on improving AR dynamical understanding and forecasting through environmental sampling and data assimilation. For example, the 2014–16 CalWater2 (Ralph et al. 2016, 2017) and 2016–22 AR Reconnaissance (Ralph et al. 2020) field programs collectively released more than 4300 dropsondes during 112 IOPs over eight seasons (January–March 2014–22, excluding 2017) to investigate the operational forecast added value of additional, targeted soundings of landfalling ARs off the U.S. Pacific Coast (i.e., Cobb et al. 2021; Zheng et al. 2021). An active new area of research addresses the role of ARs in the Arctic hydroclimate (e.g., Nash et al. 2018; Vázquez et al. 2019; Ma et al. 2021).
Research interest in regional ARs is strongly correlated with the scale of their socioeconomic impacts, which are exceptionally large in the case of Pacific Coast ARs that cause roughly $1.1 billion a year in flood damage (i.e., Corringham et al. 2019). Pacific Coast ARs that occur between December–March deliver 20%–50% of California’s annual precipitation and streamflow (Dettinger et al. 2011) and explain 30%–60% of interannual streamflow variability (Chen et al. 2019). However, the central United States is also impacted by Gulf Coast ARs, which have gained less scientific attention. Nayak and Villarini (2017) report that most floods, 30% of annual precipitation accumulation, and 40% of the top 1% of daily precipitation extremes in the central United States are attributable to Gulf Coast ARs. ARs cause severe flooding when they persist over the same geographical location for several days. For example, the historic floods of May–September 1993, June 2008, and May 2010 in the central United States were attributed to persistent Gulf Coast ARs (i.e., Kunkel et al. 1994; Dirmeyer and Kinter 2010; Moore et al. 2012). With such devastating societal impacts documented in the central United States, it is unsurprising that Gulf Coast ARs are garnering increased attention in research.
An open question is whether Gulf Coast ARs that are the focus of newfound research attention are synonymous with Great Plains low-level jets (GPLLJs), which have served as a focal point of mesoscale dynamic meteorological research since the 1940s (e.g., Means 1954; Bonner 1968; Uccellini 1980; Shapiro et al. 2016; Parish and Clark 2017; Gebauer and Shapiro 2019). Mainly occurring between May–September, GPLLJs constitute the primary mechanism for northerly atmospheric moisture transport from the Gulf to Mexico to the central United States (Algarra et al. 2019). They account for 50%–80% of May–June precipitation accumulation in the central United States and 40%–70% of July–August precipitation in the Midwest (Wang and Chen 2009, their Fig. 9). GPLLJs are associated with lifting when they intersect with baroclinic zones (Trier et al. 2006) and can provide an important source of instability in the initiation and maintenance of mesoscale convective systems (e.g., Squitieri and Gallus 2016a,b; Augustine and Caracena 1994). Similar to ARs, GPLLJs play an important role in regional water budgets, wind energy potential, and severe weather generation.
GPLLJs have been defined in prior studies as a maximum in the vertical profile of horizontal wind speed between the surface and 700 hPa that exceeds 10 and is 5 m s−1 greater than minimum wind speed between its level and the 700-hPa level (Bonner 1968; Whiteman et al. 1997; Burrows et al. 2019a). They are forced mechanistically (Shapiro et al. 2016) by differential heating in the Rocky Mountains and Great Plains (Holton 1967) and nocturnal decoupling of the boundary layer from the friction-governed surface layer (Blackadar 1957) and can be linked, by a varying degree of strength, to an upper-level cyclonic circulation (Burrows et al. 2019a). Synoptically coupled GPLLJs are mainly formed due to the southerly flow associated with a trough-and-ridge pattern over western and eastern CONUS (Burrows et al. 2019a). Although the structure is similar, these coupled GPLLJs have a stronger amplitude of precipitation, moisture convergence, and 850-hPa wind speed (except for the foothills of the Rockies) throughout the central and eastern United States relative to the GPLLJs that occur under conditions of an upper-level high (Burrows et al. 2019a, their online supplemental Fig. S4).
The objective of this study is to investigate the degree of correspondence between central U.S. AR and GPLLJ event samples that have historically been compiled and studied by separate and uncoordinated research communities. We apply the ECMWF Coupled Reanalysis of the Twentieth Century (CERA-20C; Laloyaux et al. 2018) to characterize the independent and joint probability space of April–September ARs and GPLLJs, their frequency trends, differences between their supporting synoptic-scale environments, and associated mean and extreme wind and precipitation. The central United States is important at a national scale in terms of agricultural production and wind energy generation, so it is critical to understand the comparative impact of ARs and GPLLJs on the region’s mean and extreme wind and precipitation events. The results will underscore the need for more synergistic research between the AR and GPLLJ events research communities, but also highlight the importance of differentiating the two phenomena in weather forecasts and climate projections. This study specifically addresses the following science questions: 1) How do AR and GPLLJ frequency and seasonality compare? 2) What are the differences in mean and extreme precipitation and 850-hPa wind speed associated with ARs and GPLLJs? 3) What are the differences between AR and GPLLJ synoptic-scale atmospheric environments?
2. Data and methods
a. Study region and period
This study focuses on the GPLLJ corridor extending from 28.125° to 50.625°N and from 91.125° to 102.375°W, as outlined with a black box in Fig. 1. Using CERA-20C, GPLLJs and ARs occurring within this corridor during the peak season of GPLLJ (April–September) 1901–2010 are detected, characterized, and contrasted. Peak GPLLJ timing within the corridor varies with season and latitude but tends to occur between 0600 and 0900 UTC—earlier in the southern Great Plains and later in the northern Great Plains (i.e., Weaver and Nigam 2008; Burrows et al. 2020). Most of the analysis is focused on 0900 UTC instantaneous variables fields because this is the time of day when GPLLJ activity, as measured by 850-hPa meridional wind speed, υ, peaks over most of the detection corridor during April–September (Burrows et al. 2019a, their Fig. S1). Precipitation accumulation between 12 h before and 12 h after 0900 UTC event classification is analyzed to capture precipitation over the full duration of the event. Weather map analyses extend a few degrees outward in all directions beyond the detection corridor to capture associated synoptic-scale circulation and extreme wind and precipitation event differences. Prior studies have shown that the presence of GPLLJs, especially synoptically coupled GPLLJs, often leads to increased afternoon convective precipitation west of the jet core, in addition to increased nocturnal precipitation east of the jet core (Pu and Dickinson 2014; Song et al. 2016).
The 0900 UTC IVT (shaded; kg m−1 s−1), W850 (wind barbs: half barb is 5 m s−1 and full barb is 10 m s−1) for example (a) AR GPLLJ, (b) non-AR GPLLJ, and (c) AR non-GPLLJ event days. The black box outlines the GPLLJ corridor, which is used for daily event classification.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
b. CERA-20C
CERA-20C is a 10-member land–atmosphere–ocean coupled climate-quality reanalysis of the twentieth century. CERA-20C was produced using the TL159L91 Integrated Forecast System, version Cy41r2, with only surface pressure and ocean wind data assimilation. CERA-20C sea surface temperature (SST) is relaxed toward the monthly Met Office Hadley Centre Sea ice and surface temperature, version 2, dataset (HadISST2; Titchner and Rayner 2014). In turn, CERA-20C’s SST constrains any extension of sea ice concentration. Short-term observational records, including modern satellite and upper-air measurements, are not assimilated into CERA-20C as part of a deliberate effort to minimize spurious trends related to data inhomogeneity and produce a long-term consistent reanalysis (e.g., Robertson et al. 2011; Ferguson and Villarini 2014).
CERA-20C outputs are available at 3-hourly temporal resolution and 125 km horizontal resolution for four soil layers and 37 vertical pressure levels. Output pressure levels are spaced at 25-hPa increments between 1000 and 750 hPa and at 50-hPa increments between 750 and 250 hPa. For the detection of ARs, 3-hourly zonal wind, meridional wind, and specific humidity fields at every output pressure level from 1000 to 300 hPa is used. For the detection of GPLLJs, 0900 UTC zonal and meridional winds at all pressure levels from surface to 700 hPa are used. Previously, Burrows et al. (2019a, 2020) demonstrated that GPLLJs are well represented in CERA-20C.
For synoptic weather map diagnoses and impact assessments, we use: 0900 UTC 850-hPa wind speed (W850), 0900 UTC geopotential height at 500 hPa (Z500), and 24-h precipitation accumulation time-centered at 0900 UTC (i.e., from 2100 UTC day −1 to 2100 UTC day 0). We use only outputs from CERA-20C’s first ensemble member because the large-sample composite analyses of our study are unlikely to be sensitive to intraensemble variability. Also, using a single ensemble member substantially eases the computational burden. All analyses are conducted on CERA-20C data that were spatially interpolated to 1.125° × 1.125° resolution using the ECMWF EMOSLIB interpolation library. Coarse-resolution models such as CERA-20C that employ parameterized convection are known to poorly represent local convective precipitation events relative to finer-resolution convection-permitting models or models that directly assimilate precipitation (i.e., Taylor et al. 2012; Prein et al. 2020; Mesinger et al. 2006), but CERA-20C is considered the most advanced century-scale reanalysis available (i.e., Feng et al. 2018). The long record afforded by CERA-20C is critical to ensuring a sufficiently large sample size for robust seasonal statistics.
c. GPLLJ classification
GPLLJs are classified according to the Bonner (1968) and Whiteman et al. (1997) method with an additional requirement of a positive (i.e., southerly) υ-wind component, following Burrows et al. (2019a, 2020). At a grid point, a GPLLJ is defined when the following three criteria are simultaneously met: 1) 3-hourly wind maximum exceeds 10 m s−1 between the surface and 700 hPa because these jets occur at lower part of the troposphere and the peak is usually at or below 850 hPa, 2) the difference between jet maximum wind below 700 hPa and the 700-hPa wind speed exceeds 5 m s−1, and 3) jet maximum wind has a positive υ-component (Burrows et al. 2020). A day is grouped into a GPLLJ event sample if, at 0900 UTC, 15% or more of the grids in the GPLLJ corridor (i.e., boxed region in Fig. 1) meet the GPLLJ criteria. The use of a uniform wind threshold for April–September ensures that, for example, GPLLJs of equal strength in May and July are both detected, despite greater background W850 and 850–700-hPa wind shear in May (Ferguson et al. 2020, their online supplemental material). The choice of 0900 UTC is based on the results from a previous study that showed the jet peaks over most of the detection corridor during April–September (Burrows et al. 2019a, their online supplemental Fig. S1). The choice of a 10 m s−1 threshold is based on its use in prior successful GPLLJ detection studies using CERA-20C (i.e., Burrows et al. 2019a, 2020). However, it is true that a percentile threshold could better facilitate comparison across models of different resolution and bias (e.g., Montini et al. 2019).
d. AR classification
The variable threshold approach of tARget, version 2, is an important improvement upon version 1 of the algorithm that uses only the 85th-percentile IVT threshold (Guan and Waliser 2015), because it captures ARs that are geometrically less well structured according to the 85th-percentile IVT threshold but satisfy AR geometry requirements with their higher IVT core. When assessed with ERA-Interim data, tARget, version 2, resulted in a 17% increase in the number of detected ARs relative to tARget, version 1 (Guan et al. 2018). Another advantage of the tARget, version 2, algorithm is that it filters out tropical cyclone (TC) centers, but not potential ARs emanating from the TC’s during recurvature and extratropical transition (Cordeira et al. 2013; Guan et al. 2018).
e. Event type sample composition
All April–September days between 1901 and 2010 are classified according to 0900 UTC GPLLJ and/or AR presence in the GPLLJ corridor. They are grouped into one or more of the following five samples: 1) all GPLLJ (i.e., days that satisfy GPLLJ criteria), 2) AR GPLLJ (i.e., days that satisfy both GPLLJ and AR criteria), 3) non-AR GPLLJ (i.e., days that satisfy GPLLJ criteria but not AR criteria), 4) AR non-GPLLJ (i.e., days that satisfy AR criteria but not GPLLJ criteria), and 5) all AR (i.e., days that satisfy AR criteria). Some of the events may persist for more than a day, so in this regard, event independence is not enforced.
After the samples are populated, the IBTrACS, version 4, TC track dataset (Knapp et al. 2010) is used to filter and extract days from all sample sets for which a TC center tracks within 5°, or approximately 500 km, of the GPLLJ corridor. This step is implemented to augment the programmed TC filtering of tARget, version 2, algorithm and it should not preclude AR-TC combinations, like predecessor rain events (e.g., Moore et al. 2013; Galarneau et al. 2010), that occur on the order of 1000 km poleward from the TC center. The average number of days filtered from each sample due to TC proximity is as follows: all GPLLJ: 3.9 days yr−1; AR GPLLJ: 1.7 days yr−1; non-AR GPLLJ: 2.2 days yr−1; AR non-GPLLJ: 0.4 days yr−1; and all AR: 2.03 days yr−1. Overall, 3% of April–September days are affected by a TC (not shown). The fact that fewer events are filtered from AR samples when compared with the all GPLLJ sample is evidence that the automated TC exclusion module in the tARget, version 2, algorithm is effective (Guan et al. 2018). Figure 1 shows 0900 UTC IVT and W850 fields associated with representative AR GPLLJ, non-AR GPLLJ, and AR non-GPLLJ event days.
f. Analysis methods
The analysis focuses on differences in midlevel atmospheric circulation (i.e., Z500), W850, precipitation accumulation, and IVT between event type composites. First, Venn diagrams, boxplots, and trend analysis are used to illustrate and quantify the relative frequency of event types, their seasonality, and any long-term trends (Table 1, Fig. 2). Specifically, a Mann–Kendall test (Kendall 1975; Mann 1945) is used to evaluate trend significance, whereas trend magnitude is calculated by the Theil–Sen line (Sen 1968; Theil 1950a,b,c). Second, sample mean composite differencing is used to quantify differences between event samples in terms of: Z500, IVT, mean and 95th-percentile (p95) W850, mean and p95 24-h precipitation, as well as number of rainy days when precipitation accumulation from 2100 UTC day −1 to 2100 UTC day 0 exceeds 1 mm. Only the subset of rainy event days at a grid are used to calculate event type p95 precipitation. The differences between conditional event sample means and total sample (i.e., all days between April and September of 1901–2010) means are evaluated at the α = 0.05 significance level using a Student’s t test. Following the same approach, the statistical significance of differences between AR GPLLJ and non-AR GPLLJ event means are also assessed. Differences in the p95 wind and p95 precipitation values, however, are evaluated for significance at the α = 0.05 level by using 1000 bootstrapped samples. Considering p95 IVT does not reliably translate into socioeconomic impacts like precipitation and wind extremes, we chose to omit its discussion in the interest of conciseness. Interested readers can find p95 IVT event composite differences in the online supplemental material.
April–September 1901–2010 total (days), annual mean (days yr−1), and annual standard deviation (days yr−1) of days in each of the five GPLLJ-AR event sample sets (see section 2e). If significant at the α = 0.05 level, corresponding Theil–Sen slopes (days yr−1) are also provided.
Time series of the number of April–September days (i.e., of 183) each year from 1901 to 2010 that are in the (a) all GPLLJ, (b) AR GPLLJ (c) non-AR GPLLJ, (d) AR non-GPLLJ, and (e) all AR samples. The dashed red and dashed blue lines represent the Theil–Sen trend lines for the 1901–2010 and 1943–2010 periods, respectively. Only statistically significant trends at the α = 0.05 level according to a Mann–Kendall test are plotted. The blue vertical line in each subplot represents the 1942 statistical breakpoint in CERA-20C that was reported by Ferguson (2022).
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
3. Results and discussion
a. Twentieth century AR and GPLLJ climatologies
The results of our classification of April–September days are summarized in Table 1. The table shows that GPLLJs occur on 71.5% of April–September days, and on 36.2% of these days, they accompany an AR. Overall, GPLLJs are 2.5 times more frequent in the central United States than ARs.
ARs, which occur on 28.4% of April–September days, are nearly always characterized by a GPLLJ. AR non-GPLLJ events are likely misclassified AR GPLLJ events—an artifact of our methodology (Fig. S1 in the online supplemental material). They track through the southeast corner of the GPLLJ corridor without satisfying the 15% minimum spatial domain coverage stipulated for GPLLJ classification (see Fig. 1c.).
There has been a significant decrease in GPLLJ and non-AR GPLLJ frequency. However, there has been no significant long-term trend in AR GPLLJ and all AR frequency (Fig. 2). Specifically, Table 1 and Fig. 2 show that all GPLLJ and non-AR GPLLJ frequency is decreasing at a rate of −0.19 and −0.15 days yr−1, respectively. These results are consistent with the findings of Ferguson (2022) who, using CERA-20C, attributed diminished GPLLJ frequency over the twentieth century to decreases in July–September uncoupled GPLLJ occurrences. Using Pettitt’s test for homogeneity (Pettitt 1979), Ferguson (2022) also detected a 1942 statistical breakpoint in GPLLJ frequency coincident with a change in SST measurement technique and bias correction in HadISST that could explain these long-term trends. Therefore, we performed a second trend analysis on only the 1943–2010 record. We find that of the five event samples, only the all GPLLJ event sample has a statistically significant frequency trend of −0.22 days yr−1 (not shown).
Figure 3 includes a boxplot summary of April–September monthly frequency statistics for each event type and Venn diagrams for each month to show the relative number of ARs and GPLLJs. It shows that ARs are uniformly distributed throughout the warm months with a mean range of 8.1–9.6 events month−1, with a slight increase in frequency in May and June. The broad latitudinal range of the GPLLJ corridor as defined in this study is likely precluding detection of any baroclinicity-induced seasonal variability in AR frequency. By contrast, non-AR GPLLJs contribute substantial seasonality to all GPLLJ event distribution. Mean non-AR GPLLJ frequency increases from a minimum of 10.4 events month−1 in April to a maximum of 17.3 events month−1 in July and declines through August and September. The peak frequency of non-AR LLJ events in July and August points to the stronger diurnal mechanisms (e.g., Shapiro et al. 2016) for their formation during the warmest months. AR non-GPLLJ events tend to fail to satisfy the domain spatial coverage requirement for GPLLJ because they track across the detection corridor’s southeast corner (Fig. 1c). Their sample size is very small in comparison with the other four event categories (Table 1), so we will not discuss them further. For the interested reader, plots corresponding to AR non-GPLLJs may be found in the online supplemental material (Figs. S2–S5). Taking the original 0900 UTC grid scale Burrows et al. (2019b) LLJ classification dataset and applying the same GPLLJ detection method used in this study (see section 2), 68% of this study’s May–September 1901–2010 AR GPLLJ days are classified as synoptically coupled GPLLJ days. On a monthly basis, the correspondence between AR GPLLJ and coupled GPLLJ classifications ranges from 60% in July to 76% in September. Given all jets have a fraction of both local and large-scale forcing (i.e., not all coupled and not all uncoupled), the extensive domain used in our study is seen as a limiting factor in classifying jet dynamical coupling. GPLLJs tend to be more uncoupled north of 40°N and more coupled south of 40°N. It is for this reason that Burrows et al. (2019a) performed separate subregional analyses for the southern, central, and northern Great Plains.
Venn diagrams of the number of 1901–2010 (a) April, (b) May, (c) June, (d) July, (e) August, and (f) September days that are classified as GPLLJ-only (red), AR GPLLJ (beige) and AR-only (green) days. The titles for (a)–(f) list the sample sizes for GPLLJ-only, AR GPLLJ, and AR-only (e.g., 1147 GPLLJ-only, 777 AR GPLLJ, and 166 AR-only for April). Also shown are boxplots of 1901–2010 monthly (g) all GPLLJ, (h) AR GPLLJ, (i) non-AR GPLLJ, and (j) all AR event frequency. The boxes span the interquartile range, and the whiskers extend from the box to show the full range of the data. The median and mean values are represented by orange lines and green triangles, respectively. Note that the AR non-GPLLJ sample size is less than 1 event month−1 for all months (see Fig. S2 in the online supplemental material).
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
b. Synoptic analysis
There are distinct differences in the 500-hPa synoptic-scale airflow configuration among the event type samples, but particularly between non-AR GPLLJ and AR GPLLJ events (Fig. 4). The non-AR GPLLJ composite mean features a strong ridge located over the entire central United States, which is an indication of those GPLLJs being uncoupled to the upper-tropospheric circulation (Burrows et al. 2019a, their Fig. 10). On the other hand, all AR and AR GPLLJ composites feature an amplified trough–ridge Z500 pattern, indicative of synoptically coupled GPLLJs (Burrows et al. 2019a, their Fig. 9). A stronger trough located over the western United States and a ridge over the eastern United States drive northward wind and moisture transport from the Gulf of Mexico. Thus, these events have greater associated windspeeds, moisture transport, and precipitation in the Midwest relative to non-AR GPLLJ events. The all AR and AR GPLLJ Z500 anomaly composites share a similar ridge–trough pattern because AR GPLLJ events compose 91% of all AR events (Table 1; Figs. 3a–f). The trough–ridge pattern is also evident near the surface, as shown in corresponding mean sea level pressure anomaly composites (Fig. S6 in the online supplemental material). Monthly Z500 composite plots illustrate strengthening of the ridge over the central United States during June–August for the non-AR GPLLJ sample whereas the trough–ridge anomaly pattern for AR GPLLJ events is greatest in April, May, and September (Fig. S7 in the online supplemental material).
Event sample mean 0900 UTC Z500 (contour lines; m) and its corresponding anomaly (color-shaded contours; m) for the (a) all GPLLJ, (b) non-AR GPLLJ, (c) AR GPLLJ, and (d) all AR samples. The anomaly is calculated as the difference between the marginal [in (a) and (d)] or conditional [in (b) and (c)] sample mean and the 0900 UTC total sample mean calculated from all days between April and September 1901–2010. Stippling denotes regions where a Student’s t test of the difference between composite mean and climatology indicates statistical significance (i.e., p ≤ 0.05). See Table 1 for the corresponding sample sizes.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
c. IVT, precipitation, and wind analysis
1) Mean differences
The GPLLJ corridor-averaged mean W850 values are 9.3, 8.3, 11.1, and 11.0 m s−1, respectively, for all GPLLJ, non-AR GPLLJ, AR GPLLJ, and all AR samples (Table 2). Note AR event types have stronger associated W850, and specifically, W850 is 34% greater for AR GPLLJ days than non-AR GPLLJ days. Figure 5 shows distinct spatial pattern differences in W850, IVT, and precipitation between diurnally forced non-AR GPLLJs and synoptically forced AR GPLLJs. AR GPLLJ and all AR event composite W850 anomalies across the entire plotted region range from 1.2 to 4.8 m s−1, whereas all GPLLJ and non-AR GPLLJ positive maximum W850 anomalies are less than 0.6 and 1.2 m s−1 and confined to a limited area on the western edge of the corridor. East of that location, the mean for all GPLLJ and non-AR GPLLJ W850 can be up to 1.8 m s−1 less than climatology. For non-AR GPLLJ events, negative W850 anomalies are centered approximately 8.5° east of the W850 maximum (Fig. 5d). The highest value of W850 wind contours in our domain extends north to Kansas and Nebraska on all AR and AR GPLLJs days, whereas anomalously high W850 for all GPLLJ and non-AR GPLLJ days is generally confined to the southern Great Plains. W850 12 m s−1 and higher contours encompass all of Oklahoma, Nebraska, Kansas, and almost half of Texas in the AR GPLLJ composite, whereas in the non-AR GPLLJ composite the maximum W850 is isolated to western Texas, Oklahoma, Kansas, and Nebraska (i.e., south of 43°N and west of 99°W). The maximum mean W850 values are confined to the Oklahoma Panhandle area where the AR GPLLJ W850 composite average is shown to exceed 16 m s−1. The mean W850 contour pattern is similar for the AR GPLLJ and all AR composite, although W850 magnitude is 0.1 m s−1 less on average over the detection corridor for the all AR composite.
GPLLJ corridor-averaged April–September, April, May, June, July, August, and September mean and p95 values of W850, IVT, and precipitation for the period from 1901 to 2010. Note that all days and grid points are included in the calculation of mean precipitation, but only grid points with precipitation greater than 1 mm day−1 are included in the calculation of p95 precipitation.
April–September 1901–2010 (a)–(c),(j)–(l) marginal and (d)–(i) conditional event sample mean (contour lines) and its anomaly (color shading) for (left) 0900 UTC W850 (m s−1), (center) 0900 UTC IVT (kg m−1 s−1), and (right) 24 h (i.e., ±12 h from 0900 UTC) precipitation (mm) for the (top) all GPLLJ, (top middle) non-AR GPLLJ, (bottom middle) AR GPLLJ, and (bottom) all AR samples, respectively. Stippling denotes where the difference between sample composite mean and climatological mean is statistically significant at the α = 0.05 level. See Table 1 for the corresponding sample sizes. The black box in each subpanel outlines the GPLLJ corridor for which domain-averaged values are provided in Table 2.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
The GPLLJ corridor-averaged mean IVT values are 187.8, 157.2, 241.3, and 238.4 kg m−1 s−1, respectively for all GPLLJ, non-AR GPLLJ, AR GPLLJ, and all AR samples (Table 2). The all GPLLJ IVT composite pattern closely resembles the climatology, as indicated by the small anomalies in Fig. 5b. Climatological IVT decreases along a southwest to northeast anticyclonic axis from the southern Great Plains to the upper Midwest. ARs enable positive IVT anomalies across the domain, but especially in the central Great Plains. The all AR and AR GPLLJ composites have similar IVT mean and anomaly patterns (Figs. 5h,k). The GPLLJ corridor averaged IVT value is 53.5% larger in the AR GPLLJ composite than in the non-AR GPLLJ composite (Table 2). By contrast, Fig. 5e shows nearly the entire domain has a significant negative IVT anomaly for the non-AR GPLLJ composite. This result is consistent with Fig. 4b, which shows a strong ridge in the central United States during non-AR GPLLJs that induces an anticyclonic circulation that in turn increases northerly wind in the eastern part of our domain potentially causing a negative anomaly of IVT in the east/southeastern part of our domain.
The GPLLJ corridor-averaged daily precipitation accumulation values are 2.3, 1.8, 3.1, and 3.3 mm, respectively, for all GPLLJ, non-AR GPLLJ, AR GPLLJ, and all AR samples (Table 2). Figure 5i shows a positive daily precipitation accumulation anomaly throughout the central and northern Great Plains for AR GPLLJ days whereas Fig. 5f exhibits a negative daily precipitation accumulation anomaly throughout the central and eastern United States for non-AR GPLLJ days. The upper-Midwest precipitation maximum observed in the all GPLLJ composite may be mostly attributed to AR GPLLJ events. Applying the simplified Trenberth quasigeostrophic (QG) approximation (Trenberth 1978), we confirmed anomalously large synoptic-scale forcing for ascent in the northern half of our study domain (Fig. S8 in the online supplemental material), corresponding to the location of positive precipitation anomalies. The same diagnosis applied to the non-AR GPLLJ sample does not reveal any remarkable spatial gradients in QG forcing for ascent (Fig. S9 in the online supplemental material), and therefore, points to the predominance of mesoscale forcing of non-AR GPLLJ precipitation (i.e., Shapiro et al. 2018).
Overall, the large-scale precipitation and Z500 patterns of AR GPLLJ and all AR days are consistent with coupled LLJ cases, as characterized by Agrawal et al. (2021). The composite anomaly patterns of AR GPLLJ and non-AR GPLLJ vertically integrated moisture flux divergence (not shown) and precipitation are consistent with those for coupled and uncoupled LLJ composites, respectively, as presented in Burrows et al. (2019a, their Figs. 9 and 10).
Statistically significant differences between AR GPLLJ and non-AR GPLLJ composite mean W850 and precipitation are called out in Figs. 6a and 6b. Differences respectively range from −1 to 6 m s−1 and from −2 to 4 mm day−1. The W850 difference is greatest over Kansas, where AR GPLLJ W850 exceeds non-AR GPLLJ W850 by more than 5 m s−1. Maximum differences in AR GPLLJ and non-AR GPLLJ precipitation are found in the northeast corner of the GPLLJ corridor (i.e., Minnesota and Iowa) collocated with the common jet exit region of GPLLJs (Fig. 6b).
April–September 1901–2010 difference between AR GPLLJ and non-AR GPLLJ (a),(b) mean and (c),(d) p95 (left) W850 (m s−1) and (right) precipitation from 2100 UTC day −1 to 2100 UTC day 0 (mm day−1). Stippling denotes grid points where the difference between AR GPLLJ and non-AR GPLLJ means and p95 is statistically significant at the α = 0.05 level. Note that p95 precipitation is computed using only rainy days.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
2) Extreme event differences
Figure 7 illustrates the spatial pattern of event composite differences relative to climatology in p95 W850, p95 precipitation, and rainy day frequency. The GPLLJ corridor-averaged p95 0900 UTC W850 is 19.3, 17.2, 21.5, and 21.5 m s−1, respectively, for all GPLLJ, non-AR GPLLJ, AR GPLLJ, and all AR sample composites (Table 2). Notably, domain-averaged AR GPLLJ p95 W850 is 25% greater than non-AR GPLLJ p95 W850. Maximum anomalies in p95 W850 occur 5° east relative to maximum composite mean contours for all samples—and for non-AR GPLLJs—shifted 4° south. Relative to the climatological p95 0900 UTC W850, non-AR GPLLJ p95 W850 is 0–4 m s−1 less, except in the northwest Great Plains where it can be up to 1 m s−1 more (Fig. 7d). The major difference between all AR and all GPLLJ p95 W850 is that all AR events have greater positive anomalies in the eastern Great Plains whereas all GPLLJ events have only moderate anomalies across the Great Plains (Figs. 7a,j).
April–September 1901–2010 (a)–(c) all GPLLJ, (d)–(f) non-AR GPLLJ, (g)–(i) AR GPLLJ, and (j)–(l) all AR p95 0900 UTC (left) W850 (contour lines; m s−1) and anomaly from the 0900 UTC climatological p95 W850 (color shading; m s−1), (center) precipitation accumulation over ±12 h from 0900 UTC and its anomaly (mm), and (right) April–September 1901–2010 annual mean rainy day frequency. Stippling denotes regions where the difference between composite p95 and climatological p95 is statistically significant at the α = 0.05 level. Note that p95 precipitation is computed at each grid independently, using only the rainy days.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
The GPLLJ corridor-averaged p95 precipitation accumulation between ±12 h from 0900 UTC is 21.6, 19.3, 23.7, and 24.6 mm day−1 for all GPLLJ, non-AR GPLLJ, AR GPLLJ, and all AR sample sets, respectively (Table 2). Note that AR GPLLJ and all AR samples have the greatest associated p95 precipitation and that AR GPLLJ p95 precipitation exceeds that of non-AR GPLLJ by 23%. The all AR p95 precipitation is 14% greater than that of all GPLLJ sample. The non-AR GPLLJ sample p95 precipitation is less than climatological p95 precipitation everywhere, but particularly south of 37°N (Fig. 7e). In the central United States, AR GPLLJ p95 precipitation anomalies are greatest, consistent with Nayak and Villarini (2017) (Fig. 7h). Nevertheless, non-AR GPLLJ events still compose a greater fraction of the central and eastern Great Plains’ rainy days (Figs. 7f,i). In the region extending from southern Texas to northern Alabama, p95 precipitation anomalies are negative for all event samples. Likely, this is an indication of the importance of TCs, which have been shown to account for 26%–58% of the region’s summer extreme precipitation (Kunkel et al. 2012, their Fig. 5d) and the unfavorable synoptic conditions for heavy precipitation events (Fig. 4b; Fig. S9 in the online supplemental material). Plots of the p95 IVT for each sample set show a spatial pattern similar to that for p95 precipitation with an anomaly dipole near 35°N (Fig. S10 in the online supplemental material).
Figures 6c and 6d illustrate the spatial pattern of differences between AR GPLLJ and non-AR GPLLJ p95 W850 and p95 precipitation. AR GPLLJ p95 W850 ranges from 0 to 8 m s−1 greater than non-AR GPLLJ p95 W850, exceeding 6 m s−1 within a large swath spanning from central Texas to western Iowa (Fig. 6c). AR GPLLJ p95 precipitation exceeds non-AR GPLLJ p95 precipitation in all locations, except the far south and southeast of our domain (Fig. 6d). In the eastern half of our domain, AR GPLLJ p95 precipitation is 6–8 mm day−1 greater than that of non-AR GPLLJs.
Overall, for a given event sample and variable, the mean and p95 anomaly spatial patterns are similar but still with a few remarkable differences. The greatest p95 W850 anomalies are shifted 3°–5° eastward relative to the greatest mean W850 anomalies for all AR and AR GPLLJ events. The non-AR GPLLJ mean wind anomaly is positive in the western part of Texas, Oklahoma, Kansas, Nebraska, and South and North Dakota and negative in the eastern two third of our domain, whereas the p95 anomaly for non-AR GPLLJ is negative throughout our detection domain. (Figs. 5d and 7d). In terms of precipitation, AR GPLLJ and all AR composites have large positive mean anomalies in the northern Great Plains whereas their greatest p95 anomalies span the northern to southern Great Plains.
The extreme statistics are sensitive to seasonal variability in the position of the midlatitude jet stream and atmospheric moisture content. Thus, in Table 2, we report the monthly values of domain-averaged event composite p95 precipitation and p95 W850. In Figs. 8 and 9, we illustrate the corresponding spatial patterns for AR GPLLJ and non-AR GPLLJ composites, their difference from the climatological p95, and their differences. Figure 8 shows the largest value of p95 W850 occurs during April for both AR GPLLJ days and non-AR GPLLJ days. By comparison, the domain-averaged p95 W850 values are: 23.9 m s−1 (AR GPLLJ) and 19.6 m s−1 (non-AR GPLLJ) in April and 22.5 m s−1 (AR GPLLJ) and 18.1 m s−1 (non-AR GPLLJ) in May. The greatest absolute differences occur in the southern Great Plains in April and the central Great Plains in May and June (Figs. 8m,n). The p95 W850 anomaly pattern remains fairly similar in all months except August when the maximum difference occurs in the northeast corner of our domain (Fig. 8e) During July, AR GPLLJ (19.0 m s−1) and non-AR GPLLJ (15.8 m s−1) W850 differ the least. During July–August, the greatest p95 W850 for non-AR GPLLJ days are within the western half of the GPLLJ corridor whereas the extreme winds on AR GPLLJ event days extend eastward. The eastern part of our domain, including Arkansas, Missouri, and Indiana, tends to experience higher W850 values on AR GPLLJ days than on non-AR GPLLJ days, especially during April, May, and September. Although the differences are relatively minimal in the western portion of our domain, the differences are statistically significant across our domain, for all months (Figs. 8m–r).
The event-conditioned p95 0900 UTC W850 (contours; m s−1) and anomaly from the 0900 UTC climatological p95 W850 (color shading; m s−1) for (a),(g) April; (b),(h) May; (c),(i) June; (d),(j) July; (e),(k) August; and (f),(l) September for (top) AR GPLLJ days and (middle) non-AR GPLLJ days. (m)–(r) The corresponding difference between monthly AR GPLLJ and non-AR GPLLJ 0900 UTC W850. Stippling denotes that the difference is statistically significant at the α = 0.05 level.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
As in Fig. 8, but for event-conditioned p95 daily precipitation (±12 h of 0900 UTC) (contours; mm) and anomaly from p95 daily climatological precipitation (±12 h of 0900 UTC) (color shading; mm). Note that p95 precipitation is computed from the samples of days with 24-h precipitation accumulation exceeding 1 mm day−1.
Citation: Journal of Hydrometeorology 24, 8; 10.1175/JHM-D-22-0086.1
The difference in AR GPLLJ and non-AR GPLLJ p95 precipitation is greatest during April, May, and September, and least during July and August (Fig. 9). For example, the domain-averaged values are 23.7 mm day−1 (AR GPLLJ) and 19.3 mm day−1 (non-AR GPLLJ) in April and 25.3 mm day−1 (AR GPLLJ) and 20.0 mm day−1 (non-AR GPLLJ) in May. The differences between events are less in July and August, with 22.0 mm day−1 (AR GPLLJ) and 18.1 mm day−1 (non-AR GPLLJ) in July, and 20.9 mm day−1 (AR GPLLJ) and 17.4 mm day−1 (non-AR GPLLJ) in August. During May and June, the 28 mm day−1 AR GPLLJ p95 precipitation contour line envelopes the southern Great Plains to the northern Midwest region for AR GPLLJ events (Figs. 9b,c). On the other hand, the 26 mm day−1 for non-AR GPLLJ p95 precipitation does not expand beyond a portion of Kansas and Nebraska in June (Fig. 9i). The maximum p95 precipitation difference values shift northward in the warmest months when statistically significant differences occur mainly in the central and northern Great Plains for all months (Figs. 9m–r).
4. Summary and conclusions
Using the 110-yr CERA-20C reanalysis, we performed a climatological comparison of April–September GPLLJ and AR events in the central United States. Days with either a GPLLJ and/or AR spanning greater than 15% of the study domain were sorted into one of the following five event sample sets: 1) all GPLLJ, 2) non-AR GPLLJ, 3) AR GPLLJ, 4) AR non-GPLLJ, and 5) all AR. These event sample sets were then evaluated in terms of their: frequency; seasonality; mean large-scale synoptic environment, IVT, W850, and 24-h precipitation; and p95 W850 and 24-h precipitation.
The results show that 36.2% of GPLLJs are linked with ARs (Table 1). A statistically significant decreasing trend in the number of April–September GPLLJ days over the 1901–2010 period is found whereas the frequency of AR days during the same period is uniform (Fig. 2). Seasonally, the number of GPLLJs is highest during June, July, and August whereas the number of AR days is roughly equal throughout the six months with 7–8 ARs per month (Fig. 3). An analysis of the 500-hPa geopotential heights showed AR GPLLJ days have a trough–ridge pattern over west–east United States (Fig. 4). This pattern is stronger in April and May. The non-AR GPLLJ sample composite 500-hPa geopotential height field features a strong ridge over the central United States. Relative to non-AR GPLLJ days, AR GPLLJ days have a larger mean W850 and 24-h precipitation accumulation by 2.8 m s−1 and 1.3 mm day−1 on average over the study domain (Table 2). In terms of the p95 W850 and p95 24-h precipitation, AR GPLLJ days are 4.3 m s−1 and 4.4 mm day−1 more intense than non-AR GPLLJs (Table 2).
AR GPLLJs and non-AR GPLLJ events are important to study for different reasons. Non-AR GPLLJs are classical nocturnal LLJs under weak synoptic forcing, which are important to our understanding of land–atmosphere coupling, the nocturnal stable boundary layer, and the impact of the west-to-east sloping terrain effects for the formation of nocturnal GPLLJs. Non-AR GPLLJs are more common than AR GPLLJs and represent the climatological state in the central United States. In contrast, AR GPLLJs can bring extreme precipitation and wind events to the region, and highlight the importance of synoptic forcing on the region’s hydroclimate. Although much previous work has focused on winter western U.S. ARs, the results of this study show that summer central U.S. ARs are also prominent and impactful. Central U.S. ARs, unimpeded by a major mountain range, penetrate farther inland until other dynamical mechanisms provide forcing for ascent, moisture convergence, and precipitation. Accordingly, the impacts of central U.S. ARs are distributed over a larger region relative to western U.S. ARs. The hydrometeorological-focused AR community should take heed that almost all central U.S. ARs have a concomitant GPLLJ, for which there is also a very large and active research community. Future joint AR and GPLLJ studies must take place to further diagnose and understand the underlying mesoscale to synoptic-scale processes, in addition to their hydrometeorological impacts.
One limitation of the current study is the domain size, which encompasses the entire GPLLJ corridor. Over this domain, W850, IVT, and precipitation impact differences are averaged. There are bound to be important regional differences in event dynamics and impacts that are overlooked. To address this shortcoming, a future study could divide the central U.S. domain into separate northern, central, and southern Great Plains regions, as in Burrows et al. (2019a, 2020). Specifically, there is a need to further diagnose and understand the differences between forcing for ascent associated with AR GPLLJ and non-AR GPLLJ precipitation. Similarly, a future study could also complement the climatological analysis presented here with in-depth event case studies that better highlight dynamical differences between AR GPLLJ and non-AR GPLLJ events. Such case studies would ideally employ finer-resolution models and/or modern reanalyses such as the Rapid Refresh (Benjamin et al. 2016) or North American Regional Reanalysis (Mesinger et al. 2006), respectively.
Acknowledgments.
This research was supported by U.S. National Science Foundation Award AGS-1638936 and NASA Awards 80NSSC21K1731 and 80NSSC22K0319. Author Ferguson conceived the idea for the study. Ferguson supervised the design and implementation of the research. Author Gyawali completed all analyses and produced all figures. Ferguson and Gyawali designed and cowrote the paper. All authors contributed to interpretation of the results and edited the paper. The authors are grateful to Bin Guan (bin.guan@jpl.nasa.gov) for sharing the tARget, version 2, code for MATLAB 2018b. Gyawali also thanks Shubhi Agrawal for her helpful suggestions in the initial stage of this research.
Data availability statement.
CERA-20C data may be obtained from the ECMWF data server (https://www.ecmwf.int). The Great Plains Low-Level Jet Occurrence in CERA-20C dataset is available online (https://doi.org/10.5065/KDB5-9X72). The lists of days composing each of the event samples (all GPLLJ, AR GPLLJ, non-AR GPLLJ, AR non-GPLLJ, and all AR) are available upon request to author Gyawali (ngyawali@albany.edu).
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