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  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

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    Fig. 1.

    (a) Temporal distribution of the significant severe hail events for different months, and (b) spatial distribution for the peak month of significant severe hail events in the NGP region (confined by the black bold line in map) from the hail reports during 2004–16.

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    Fig. 2.

    Time series of hail frequencies for (a) significant severe hail and (b) severe hail from the hail reports (black line; left axis) and MESH (magenta line; right axis) in the summer (June–August) from 2004 to 2016, and (c) significant severe hail and (d) severe hail from the hail occurrence frequency (black line; left axis) and hail days (gray line; right axis) from 1994 to 2016. The asterisk (*) with the correlation coefficient (r) indicates statistically significant (p < 0.05) in (a) and (b).

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    Fig. 3.

    Spatial distributions of the annual trend of significant severe hail for (a) the hail reports and (b) MESH, and the severe hail from (c) the hail reports and (d) MESH during 2004–16, and (e) significant severe hail and (f) severe hail during 1994–2016 from the hail reports. The hail trend is estimated using Theil–Sen regression and the statistical significance exceeding the two-tailed 95% confidence interval from Kendall’s τ test is marked by black dots.

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    Fig. 4.

    (a) Time series of SOI (magenta bars in positive and cyan bars in negative) and hail frequency in summer for the significant severe hail from the hail reports (black line; right axis) and MESH (gray line; secondary axis), and the scatterplots of SOI and significant severe hail during 2004–16 for (b) the hail reports and (c) MESH. The asterisk (*) with the correlation coefficient (r) indicates statistically significant (p < 0.05). The black line denotes the least squares regression fits the data. Shading shows 95% confidence intervals from 10 000 bootstrapped resamples in (b) and (c).

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    Fig. 5.

    (a) Time series of SOI (magenta bars in positive and cyan bars in negative) for the number of SSH events (black line; axis right) and hail days (gray line; secondary axis) from 1994 to 2016, and the scatterplots of SOI against (b) the number of significant severe hail events and (c) the hail days. The black line denotes the least squares regression fits the data. Shading shows 95% confidence intervals from 10 000 bootstrapped resamples in (b) and (c).

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    Fig. 6.

    Composited large-scale environmental variables for significant severe hail events in summer for (left) La Niña and (center) El Niño, and (right) their differences: (a)–(c) wind speed (shading; m s−1) and geopotential height (contour; dam) at 200 hPa, (d)–(f) wind speed (shading; m s−1) and geopotential height (contour; dam) at 500 hPa, and (g)–(i) specific humidity (shading; g kg−1), winds (arrow; m s−1), and geopotential height (contour; dam) at 850 hPa. The NGP region is confined by the black bold line.

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    Fig. 7.

    As in Fig. 6, but for (a)–(c) CAPE (shading; J kg−1), (d)–(f) SRH (shading; m2 s−2) over the 0–3-km layer, (g)–(i) wind shear (shading; m s−1) over the 0–6-km layer, and (j)–(l) the lapse rate (shading; °C km−1),

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    Fig. 8.

    Spatial distribution of mean geopotential height (shading; gpm) at 850 hPa for the hail events during the summer from 2004 to 2016. The black line indicates the 1560-gpm isoline and areal coverage of the NASH highlighted by cyan contour, and time series of (b) the areal coverage and (c) the intensity of the NASH over CONUS, overlaid with the number of significant severe hail events in summer from the hail reports (black line) and MESH (gray line) at the secondary axis.

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    Fig. 9.

    Scatterplots of the significant severe hail occurrence vs the areal coverage of the NASH over CONUS for (a) the hail reports and (b) MESH, and vs the intensity of the NASH for (c) hail reports and (d) MESH. Shading indicates 95% confidence intervals from 10 000 bootstrapped resamples.

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    Fig. 10.

    Time series of (a) the areal coverage and (b) the intensity of the NASH over CONUS overlaid with the number of significant severe hail events (black line; axis right) and hail days (gray line; secondary axis) from 1994 to 2016. The scatterplots of the hail occurrence frequency vs (c) the areal coverage of the NASH and (d) the intensity of the NASH, and the hail days vs (e) the areal coverage of the NASH and (f) the intensity of the NASH over CONUS.

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    Fig. 11.

    (a) Spatial distribution of mean meridional wind (shading; m s−1) at 875 hPa for the hail events in the summer from 2004 to 2016, (b) mean vertical profile of areal averaged meridional wind (gray contour; m s−1) for each year, and (c) time series of LLJ index overlaid with the significant severe hail from the hail reports (black line) and MESH (gray line) at the secondary axes. The black box in (a) marks the three states for the meridional wind analysis.

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    Fig. 12.

    Scatterplots of the significant severe hail occurrence vs LLJ index for (a) the hail reports and (b) MESH.

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    Fig. 13.

    (a) Time series of LLJ index overlaid with the number of significant severe hail events (black line) and the hail days (gray line) from the hail reports from 1994 to 2016. The scatterplots of LLJ index vs for (b) the hail frequency and (c) the hail days.

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    Fig. 14.

    (a) Spatial distribution of mean IVT at low levels (1000–750 hPa) for the hail events in the summer from 2004 to 2016, time series of mean IVT at (b) low levels (1000–750 hPa) and (c) high levels (750–300 hPa) for the hail events in summer for three states as marked by the black bold line in (a), overlaid with the significant severe hail events from the hail reports (black line) and MESH (gray line) at the secondary axes.

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    Fig. 15.

    Scatterplots of the significant severe hail occurrence vs the IVT magnitude at low levels (1000–750 hPa) for (a) the hail reports and (b) MESH and IVT magnitude at high levels (750–300 hPa) for (c) the hail reports and (d) MESH.

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    Fig. 16.

    Time series of (a) the IVT at low levels (1000–750 hPa) and (b) the IVT at high levels (750–300 hPa) overlaid with the number of significant severe hail events (black line) and hail days (gray line) from 1994 to 2016. The scatterplots of the hail occurrence frequency vs (c) the IVT at low levels (1000–750 hPa) and (d) the IVT at high levels (750–300 hPa), and the hail days vs (e) the IVT at low levels (1000–750 hPa) and (f) the IVT at high levels (750–300 hPa).

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    Fig. 17.

    Differences of large-scale environmental variables for significant severe hail events in summer between 2012 and mean states of 6 years (3 years before and 3 years after): (a) specific humidity (shading; g kg−1), winds (arrow; m s−1), and geopotential height (contour; gpm) at 850 hPa, (b) wind speed (shading; m s−1) and geopotential height (contour; gpm) at 200 hPa, (c) wind shear (shading; m s−1) over the 0–6-km layer, and (d) CAPE (shading; J kg−1). Solid contour lines denote positive values and dashed contour lines in (a) and (b) denote negative values. The NGP region is confined by the black bold line.

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Spatial and Temporal Trends and Variabilities of Hailstones in the United States Northern Great Plains and Their Possible Attributions

Jong-Hoon JeongaAtmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Jiwen FanaAtmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Cameron R. HomeyerbSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Abstract

Following on our study of hail for the southern Great Plains (SGP), we investigated the spatial and temporal hail trends and variabilities for the northern Great Plains (NGP) and the contributing factors for summers (June–August) focusing on the period of 2004–16 using two independent hail datasets. Analysis for an extended period (1994–2016) with the hail reports was also conducted to more reliably investigate the contributing factors. Both severe hail (diameter between 1 and 2 inches) and significant severe hail (SSH; diameter > 2 inches) were examined and similar results were obtained. The occurrence of hail over the NGP demonstrated a large interannual variability, with a positive slope overall. Spatially, the increase is mainly located in the western part of Nebraska, South Dakota, and North Dakota. We find the three major dynamical factors that most likely contribute to the hail interannual variability in the NGP are El Niño–Southern Oscillation (ENSO), the North Atlantic subtropical high (NASH), and the low-level jet (LLJ). With a thermodynamical variable integrated water vapor transport that is strongly controlled by LLJ, the four factors can explain 78% of the interannual variability in the number of SSH reports. Hail occurrences in the La Niña years are higher than the El Niño years since the jet stream is stronger and NASH extends farther into the southeastern United States, thereby strengthening the LLJ and in turn water vapor transport. Interestingly, the important factors impacting hail interannual variability over the NGP are quite different from those for the SGP, except for ENSO.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiwen Fan, jiwen.fan@pnnl.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-19-0606.1.

Abstract

Following on our study of hail for the southern Great Plains (SGP), we investigated the spatial and temporal hail trends and variabilities for the northern Great Plains (NGP) and the contributing factors for summers (June–August) focusing on the period of 2004–16 using two independent hail datasets. Analysis for an extended period (1994–2016) with the hail reports was also conducted to more reliably investigate the contributing factors. Both severe hail (diameter between 1 and 2 inches) and significant severe hail (SSH; diameter > 2 inches) were examined and similar results were obtained. The occurrence of hail over the NGP demonstrated a large interannual variability, with a positive slope overall. Spatially, the increase is mainly located in the western part of Nebraska, South Dakota, and North Dakota. We find the three major dynamical factors that most likely contribute to the hail interannual variability in the NGP are El Niño–Southern Oscillation (ENSO), the North Atlantic subtropical high (NASH), and the low-level jet (LLJ). With a thermodynamical variable integrated water vapor transport that is strongly controlled by LLJ, the four factors can explain 78% of the interannual variability in the number of SSH reports. Hail occurrences in the La Niña years are higher than the El Niño years since the jet stream is stronger and NASH extends farther into the southeastern United States, thereby strengthening the LLJ and in turn water vapor transport. Interestingly, the important factors impacting hail interannual variability over the NGP are quite different from those for the SGP, except for ENSO.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiwen Fan, jiwen.fan@pnnl.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-19-0606.1.

1. Introduction

Hailstones that are produced by severe convective storms cause significant property and crop damages and economic loss across the United States each year (Bouwer 2011, 2013). The U.S. average annual losses from severe convective storms are comparable to those from hurricanes, and these losses have been increasing in recent years (Gunturi and Tippett 2017). Indeed, insured losses have increased due to severe hail since 1992 (Changnon 2009), and severe hail alone causes insured losses of more than $850 million annually (Brown et al. 2015). Understanding and quantifying changes in hail occurrences and their temporal and spatial characteristics are important to mitigate their impact and improve weather and climate science.

It has been challenging to quantify the changes in intensity and frequency of severe hailstorm events due to incomplete observational records. Hail reports from the National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC) are the most comprehensive publicly available observational dataset. However, this dataset is known to have some nonmeteorological biases (Ortega et al. 2005; Blair et al. 2017). For example, the hail data are heavily influenced by the population density, with more hail reports in densely populated areas compared to nonpopulated regions (Schaefer et al. 2004). Also, Allen and Tippett (2015) pointed out that the numbers of reports of 1-inch hail (1 inch, hereafter indicated by ″, equals 25.4 mm) increased significantly from 2010 onward after the minimum threshold for severe hail was increased from 0.75″ to 1″. The Next Generation Weather Radar (NEXRAD) network aids in issuing warnings by identifying locations where severe hail was likely occurring (Allen and Tippett 2015) and provides nearly complete radar coverage over the contiguous United States (CONUS) starting in 1998 (Crum et al. 1998).

The radar-retrieved maximum expected size of hail (MESH) is another hail dataset, which has a relatively short period (a decade or so) but has coherent spatiotemporal coverage (Lukach et al. 2017). MESH is used to determine the occurrence and size of hail by using ground-based radar from the National Weather Surveillance Radar-1988 Doppler (WSR-88D) network (Witt et al. 1998). The radar network provides information with a high temporal and spatial resolution for severe hailstorm detection. A composite radar dataset provides an essential supplementary data source in minimally populated regions with fewer biases by nonmeteorological factors compared to hail reports (Melick et al. 2014; Nisi et al. 2016; Schlie et al. 2019). However, the original MESH calculation exhibits a considerable uncertainty in estimating hailstone size; that is, it tends to overestimate hail size in regions of strong reflectivity aloft (Cintineo et al. 2012; Picca and Ryzhkov 2012). Recently, Murillo and Homeyer (2019) improved the method to estimate the hailstone size using Gridded NEXRAD WSR-88D Radar (GridRad; Bowman and Homeyer 2017) data, for which an hourly archive exists from 2004 to 2016 of radar coverage over the central and eastern United States.

Based on the observational hail dataset, past studies analyzed the frequency, strength, temporal variability, and spatial patterns of severe hail for many decades in the United States (Changnon 1999; Changnon and Changnon 2000; Schaefer et al. 2004; Doswell et al. 2005; Changnon 2009; Allen et al. 2015b; Allen and Tippett 2015). For example, Changnon and Changnon (2000) noted that the hail temporal frequency has decadal fluctuations during a 100-yr period, 1896–1995. They found that short-term (5- and 10-yr) fluctuations have a larger regional variation across the United States than long-term (20-yr) fluctuations. Many studies have also examined the relationships between the hail annual variability over parts of the CONUS and the climatic conditions during the winter and spring. These teleconnections include El Niño–Southern Oscillation (ENSO; Marzban and Schaefer 2001; Lee et al. 2013; Allen and Karoly 2014; Allen et al. 2015a; Cook et al. 2017; Molina et al. 2018), the Madden–Julian oscillation (MJO; Barrett and Gensini 2013; Barrett and Henley 2015), and sea surface temperature in the Gulf of Mexico (Molina et al. 2016, 2018). These teleconnections could affect the large-scale atmospheric conditions conducive to intense severe convective storms over the United States. For instance, the cooling of the equatorial Pacific Ocean leads to the development of a global circulation pattern in which the midlatitude jet stream crosses the northeastern United States, which in turn could enhance the severe convective storm development (Marzban and Schaefer 2001). Lee et al. (2013) show that the majority of the extreme U.S. tornadoes in April and May during 1950–2010 was associated with a positive trans-Niño index (TNI; Trenberth and Stepaniak 2001). They found that increased moisture transport from the Gulf of Mexico to the United States in spring was linked to a positive TNI. The phases of the MJO were also connected to the convective environmental parameters associated with the hail and tornado occurrences (Barrett and Gensini 2013; Barrett and Henley 2015).

Severe hail occurrence varies seasonally and regionally within the CONUS. Brooks et al. (2003) found that severe storm occurrences had a strong seasonal cycle in the central United States. Spatially, the occurrence of severe storms had their peak in the southern Great Plains (SGP) by the end of May and the peak shifted toward the northern Great Plains (NGP) by late July. Brimelow et al. (2017) examined regional differences of the severe hail occurrences under climate change and found that decreases of both hail frequency and size in the SGP while an opposite hail frequency over the NGP in comparing present (1971–2000) and future (2041–70) climates.

To date, there has been no study that has focused on the hail characteristics over the NGP and the associated contributing factors. In our previous study, we conducted such a study for the SGP between 2004 and 2016 and found that severe and significant severe hail (SSH) occurrences have a considerable year-to-year temporal variability. The interannual variabilities over the SGP have strong correlations with three factors: sea surface temperature anomalies over the northern Gulf of Mexico, ENSO (based on the oceanic Niño index), and aerosol loading (Jeong et al. 2020). This is a follow-on study by extending our analysis to the NGP using both hail reports and MESH, to 1) gain a better understanding of hail temporal and spatial trends and variabilities over the NGP and the factors most likely contributing to them and 2) understand the differences in the hail characteristics and their associated environmental conditions between SGP and NGP. This study further considers an additional 10 years (1994–2016) to provide a longer-term trend and a more robust correlation with long-term climate variables such as ENSO.

2. Data and methods

Two hail observational datasets are used for this study: 1) hail reports from the NOAA SPC database and 2) MESH. We focus on the period of 2004–16 as Jeong et al. (2020) because of the availability of MESH data. However, given the high correlation between the MESH and hail reports over the period of 2004–16, we extended the period to 1994–2016 by adding 10 years for the analysis of hail reports to provide a more robust correlation analysis with climate variables such as ENSO.

The National Weather Service (NWS) collects the hail reports from point sources (Doswell et al. 2005). MESH was derived from the gridded radar reflectivity data in radar network over the entire continental United States on a regular longitude–latitude Cartesian grid with a horizontal resolution of 0.02° × 0.02° (about 2 km × 2 km), 1-km vertical resolution, and an hourly temporal resolution. The method for MESH, from Witt et al. (1998), was initially proposed based on an empirically driven power-law relationship between the 75th percentile of hail size and the radar-derived parameter [e.g., severe hail index (SHI)]. Murillo and Homeyer (2019) improved the power-law fit to the 75th and 95th percentiles of hail size using large sample sizes of nearly 6000 hail reports. In this paper, we use the newly improved MESH with the 95th percentile of hail size, which had a better agreement with hail reports than the 75th percentile. However, the MESH data have limitations in giving a greater number of false alarms for smaller sizes (~1″) than the large sizes (>2″). This issue is most significant in summertime environments where high reflectivities aloft in thunderstorms can cause high values of MESH but are not supportive of severe hail production. Coupling radar detection with environmental information will help reduce false detections. Also, there is still room for improvement in how the MESH discriminates between hail sizes (Murillo and Homeyer 2019).

The NGP region we defined includes Montana, Wyoming, Nebraska, South Dakota, and North Dakota (bold black contour in Fig. 1a). To facilitate a consistent analysis for spatial distribution using both hail observational datasets, we processed both datasets at 1° × 1° longitude–latitude grids and also applied a 2D Gaussian kernel smoother with a σ = 1.5 (1.5° smoother) kernel bandwidth based on the study from Doswell et al. (2005), which can reduce the influence of the spatiotemporal inhomogeneities in the gridded hail dataset. The hail trend at each 1° × 1° grid point is calculated using the Theil–Sen slope estimator (Sen 1968) and the statistical significance of the Theil–Sen slope is assessed using Kendall’s τ statistics. The Theil-Sen slope estimator with Kendall’s τ test assesses whether a monotonic trend exists in the variable of interest over time. The Theil–Sen slope is especially useful for linear trend analysis with outliers or large variabilities. The statistical significance of the correlations is determined from 10 000 bootstrapped resamples, the same as in Jeong et al. (2020).

Fig. 1.
Fig. 1.

(a) Temporal distribution of the significant severe hail events for different months, and (b) spatial distribution for the peak month of significant severe hail events in the NGP region (confined by the black bold line in map) from the hail reports during 2004–16.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

The atmospheric meteorological data used in this study are from the North American Regional Reanalysis (NARR). The reanalysis has a horizontal resolution of 32 km and 45 vertical layers every 3 h (Mesinger et al. 2006). NARR data reasonably represent the severe weather environment, with realistic wind fields compared with soundings (Gensini et al. 2014). Walters et al. (2014) and Berg et al. (2015) also noted the suitability of using NARR data to study the wind field during the warm season. The atmospheric variables considered for analysis include specific humidity, geopotential height, and winds. The environmental parameters relevant to severe convective storms that we analyzed are the midtropospheric lapse rates, the surface-based convective available potential energy (CAPE), 0–3-km storm-relative helicity (SRH), and 0–6-km bulk vertical wind shear (S06). The lapse rate is computed by taking the absolute value of the difference between the 750- and 500-hPa temperature, divided by the thickness of the layer. CAPE measures the vertically integrated buoyant energy available to the convective storm. The S06 quantifies the vertical changes of the horizontal wind, and it can represent storm-scale rotation the sustainability of a deep updraft (Trapp et al. 2007). The SRH describes the generation of streamwise vorticity for severe convective storm environments. Those variables have been studied and proven useful for studying severe convective storms related to hail and tornadoes (e.g., Johns and Doswell 1992; Rasmussen and Blanchard 1998) and for estimating hail probabilities (Manzato 2013; Allen et al. 2015a,b; Gagne et al. 2017; Prein and Holland 2018). All parameters were calculated based on the NARR data. SRH and S06 were calculated by vertically interpolating winds at constant pressure levels to an above ground level height coordinate (Gensini and Ashley 2011). For a spatial composite, we used 3-hourly NARR data and chose the nearest available time prior to the occurrences of the hail events. For multiple reports of SSH at different times, we chose the time for the first appearance of SSH.

To explore the possible relationship between the hail interannual variability and ENSO, this study used three ENSO indices, including the Southern Oscillation index (SOI), the multivariate ENSO index (MEI), and the oceanic Niño index (ONI). This is because several different indices can be informative and beneficial in measuring and monitoring ENSO phases (Liu et al. 2018). The SOI is defined as the normalized difference in sea level pressure at two locations: one near Tahiti and the other near Darwin, Australia (Chen 1982; Trenberth 1984). The warm phase of ENSO (El Niño) is associated with a negative SOI value, whereas a sustained positive SOI value indicates the cold phase of ENSO (La Niña). The MEI is a multivariate measure of the ENSO signal (Wolter and Timlin 2011). It has the principal component of six main observed variables over the tropical Pacific: sea level pressure, zonal and meridional surface winds, sea surface temperature, surface air temperature, and cloudiness of the sky. ONI is defined as the three-month anomaly of mid-Pacific sea surface temperatures (SSTs) corresponding to the Niño-3.4 region (5°S–5°N, 170°–120°W), and it can effectively capture the Pacific warm pool during El Niño events (Trenberth 1997). The SOI and ONI data were obtained from the Climate Prediction Center (CPC) and revised MEI index (MEI.v2; Zhang et al. 2019) data were from the NOAA Physical Sciences Laboratory.

We perform the analysis for both SSH with a diameter larger than 2″ (50.8 mm) and severe hail (1″ < diameter ≤ 2″) using the hail reports and MESH. The size thresholds used here follow Allen and Tippett (2015). The observations for the size smaller than 1″ may be less consistent during the period, since the NWS changed the criteria for severe hail from 0.75″ to 1″ in 2010 (Allen and Tippett 2015) and thus they are excluded from the analysis.

3. Results

a. Temporal and spatial trends and variability

Hail in the NGP occurs predominantly during the summer months (June–August; Fig. 1a). These summer hail events account for approximately 78% of the total annual hail occurrences. Therefore, we focus on the summer season for the NGP, in contrast with the spring season (March–May) focused on in Jeong et al. (2020) for the SGP. The occurrence of severe hail events has seasonality varying with the regions as shown in Fig. 1b. Severe hail events mostly occur in spring over the SGP, and shift toward the NGP during the summer, leading to the spatial evolution of hail.

Despite sizably different numbers of hail events between the two datasets (the number of hail occurrences for MESH is about 185 times larger than those of the hail reports), the two datasets give very similar results. For SSH, the Pearson correlation coefficient (r) is 0.74 between the two datasets with a statistical significance (p value = 0.004) based on Student’s t tests (Fig. 2a). For severe hail, r is 0.89 (p value = 0.001; Fig. 2b). Given that the hail reports and MESH are highly correlated in a recent 13-yr (2004–16) set of data, we further examine the data from the hail reports by including an additional 10 years (1994–2016) as shown in Figs. 2c and 2d.

Fig. 2.
Fig. 2.

Time series of hail frequencies for (a) significant severe hail and (b) severe hail from the hail reports (black line; left axis) and MESH (magenta line; right axis) in the summer (June–August) from 2004 to 2016, and (c) significant severe hail and (d) severe hail from the hail occurrence frequency (black line; left axis) and hail days (gray line; right axis) from 1994 to 2016. The asterisk (*) with the correlation coefficient (r) indicates statistically significant (p < 0.05) in (a) and (b).

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Over the 13-yr period (2004–16), the SSH has a positive slope using the Theil–Sen slope estimator with a statistical significance at the 90% confidence level (p value = 0.077 for hail reports and p value = 0.087 for MESH) according to Kendall’s τ test. The frequency of occurrence of hail reports increases at a rate of 3.4 per year in hail reports and 400 per year in MESH. The large interannual variability for SSH is prominent (Fig. 2a). However, the amplitude is less than that in the SGP. Over the 23-yr period (1994–2016), the increase of SSH occurrences is still seen with a slightly lower rate (1.8 per year) with higher statistical significance (p value = 0.028) compared with the 13-yr results (Fig. 2c). The results for severe hail resemble those of SSH. We also added the hail days in Figs. 2c and 2d, which show a statistically significant increase as well.

Spatially, we see a statistically significant increase for both SSH and severe hail in the middle of the NGP region from both the hail reports and MESH over the 13-yr period (Fig. 3), based on the Theil–Sen slope analysis and the Kendall’s τ test. About 81% of SSH and 75% severe hail over the NGP occurred in three states (Nebraska, South Dakota, and North Dakota). The SSH in the western part of Nebraska, South Dakota, and North Dakota has a positive slope with statistical significance at the 95% confidence level in both hail reports and MESH (Figs. 3a,b). In northeastern South Dakota, the two hail datasets have inconsistent trends, with the negative slope in the hail reports and the positive slope in MESH, even though these trends are not statistically significant. This discrepancy could be influenced by the nonmeteorological factors in the hail reports, such as less population density in northeastern South Dakota or the retrieved uncertainty in MESH. The temporal trend of severe hail shows a consistent result with SSH (Figs. 3c,d). Over the 23-yr period, the SSH also shows a positive slope in the western part of the three states with statistical significance (Fig. 3e). For severe hail, the increase is observed in most of the NGP region, which is more widespread compared with 13-yr results (Fig. 3f). Interestingly, Brimelow et al. (2017) found that severe hail occurrence frequencies in the same three states (Nebraska, South Dakota, and North Dakota) are increased in the future (2041–70) summers, which is a result of the expected warming and moistening in the lower troposphere.

Fig. 3.
Fig. 3.

Spatial distributions of the annual trend of significant severe hail for (a) the hail reports and (b) MESH, and the severe hail from (c) the hail reports and (d) MESH during 2004–16, and (e) significant severe hail and (f) severe hail during 1994–2016 from the hail reports. The hail trend is estimated using Theil–Sen regression and the statistical significance exceeding the two-tailed 95% confidence interval from Kendall’s τ test is marked by black dots.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Since the temporal variability is very large and the increase has a low rate, it could be very complicated to figure out the drivers responsible for the small temporal increase. The time period we focused on is too short to infer a relation to anthropogenic forcing. Thus, here we mainly focused on investigating the contributing factors to the hail interannual variability and leave the detailed investigation of drivers for the hail increase to future studies. Bromley et al. (2020) found that, for summers over the NGP from 1970 to 2015, the 2-m air temperature has warmed by ~0.2°C decade−1 and surface soil moisture has increased, both of which could contribute to the hail increase over this region. Brimelow et al. (2017) showed a warming climate led to increased hail frequencies in the same three states (Nebraska, South Dakota, and North Dakota) as discussed above.

b. Influence of environmental conditions on hail interannual variability

The analyses shown in this section are for SSH only since the results are similar for severe hail.

1) Correlation with ENSO

Several previous studies have attempted to link teleconnection patterns with oscillating phases to hail and tornado occurrence frequencies (Marzban and Schaefer 2001; Lee et al. 2013; Allen and Karoly 2014; Allen et al. 2015a; Cook et al. 2017; Molina et al. 2018). These studies found that the ENSO could influence the hail and tornado occurrences in the United States during winter and spring, but none have explicitly focused on summer over the NGP.

We examined three indices introduced earlier to investigate the potential effects of ENSO on hail interannual variability. The SOI has a relatively high correlation with early warm season (April–June) ENSO phases, but we did not find a significant correlation with winter and summer ENSO phases. Thus, we used the mean SOI during April–June for the analysis. The time series of SOI values over 2004–16 is shown in Fig. 4a. The SOI values are well correlated with SSH with r of 0.61 for the hail reports (0.57 for MESH) with a statistical significance (Figs. 4b,c). The goodness of fit, R2, is 0.37 for the hail reports (and 0.32 for MESH). It suggests that the ENSO phase can explain 37% (32%) of hail interannual variabilities in the hail reports (MESH) based on the SOI index. The cold phase of ENSO (i.e., positive SOI) corresponds to higher hail occurrences in the NGP, whereas the warm phase of ENSO (negative SOI) has relatively lower hail occurrences (Fig. 4). The hail occurrences are roughly 73% larger for the positive SOI years compare to the negative SOI years. Positive SOI suggests the La Niña phase of ENSO, and past studies also showed more hail occurrence over the United States during La Niña years (Cook and Schaefer 2008; Allen et al. 2015a; Cook et al. 2017).

Fig. 4.
Fig. 4.

(a) Time series of SOI (magenta bars in positive and cyan bars in negative) and hail frequency in summer for the significant severe hail from the hail reports (black line; right axis) and MESH (gray line; secondary axis), and the scatterplots of SOI and significant severe hail during 2004–16 for (b) the hail reports and (c) MESH. The asterisk (*) with the correlation coefficient (r) indicates statistically significant (p < 0.05). The black line denotes the least squares regression fits the data. Shading shows 95% confidence intervals from 10 000 bootstrapped resamples in (b) and (c).

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

The other two indices have a much lower correlation coefficient and R2 compared with SOI. The MEI has r of 0.17 (0.29) and R2 of 0.03 (0.08) for the hail reports (MESH). Similar to MEI, the ONI has a weak correlation, with r of 0.13 (0.26) for the hail reports (MESH) and R2 of 0.02 (0.07) for hail reports (MESH). This result is different from SGP, where the spring hail occurrences are well correlated with the winter ENSO phase based on ONI as shown in Jeong et al. (2020). The reason for a moderate correlation with SOI but not ONI can be that ONI is an SST-based index ensuring commensurate persistence and atmospheric response time lags, whereas SOI is based on sea level pressure and it more directly reflects changes in atmospheric circulation than ONI. Thus, the atmospheric part of the ENSO phenomenon is more influential than the oceanic part on the hail occurrence over the NGP, which may have an important implication for seasonal predictability.

The 13-yr data might be too short for deriving a robust relationship with ENSO. We further looked at the correlation of hail frequency from the hail reports with SOI over the 23-yr period (Fig. 5). The hail frequency is still well correlated with the SOI (r = 0.49, R2 = 0.24; Figs. 5a,b), with a slightly lower coefficient than the 13-yr data. This confirms the relationship of ENSO with the hail frequency over the NGP. The relationship of hail days with ENSO is also examined because the hail days are less affected by nonmeteorological factors when long periods of the tornado or hail reports are used (Brooks et al. 2003; Doswell et al. 2005). The hail days are defined with three or more hail events are reported in a day over the NGP. The correlation appears to be similar to the hail frequency (r = 0.44, R2 = 0.20; Fig. 5c), suggesting that ENSO phases could contribute to both hail frequency and hail days based on the 23-yr data.

Fig. 5.
Fig. 5.

(a) Time series of SOI (magenta bars in positive and cyan bars in negative) for the number of SSH events (black line; axis right) and hail days (gray line; secondary axis) from 1994 to 2016, and the scatterplots of SOI against (b) the number of significant severe hail events and (c) the hail days. The black line denotes the least squares regression fits the data. Shading shows 95% confidence intervals from 10 000 bootstrapped resamples in (b) and (c).

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

To understand how ENSO phases impact the large-scale environment and storm activity over the NGP, we compared the synoptic conditions between three La Niña years (1999, 2000, and 2011) and El Niño years (1994, 1997, and 2015). The three La Niña and El Niño years are determined based on three indices (SOI, ONI, and MEI): the La Niña years with ONI and MEI smaller than −0.5 and SOI larger than 0.4, while the El Niño years with ONI and MEI larger than 0.5 and SOI smaller than −0.4. The SSH occurrences are 14% higher during the three La Niña years compared to the three El Niño years. During the La Niña years, the upper-level jet stream at 200 hPa was more intense over the NGP (Figs. 6a–c). The core of the jet stream (>29 m s−1) extended northeastward to the NGP in the three states (Nebraska, South Dakota, and North Dakota) where the majority of SSH occurred. Past studies also showed that the ENSO phase modulated the mean latitudinal position of the upper-level jet stream across North America, which is an essential factor for the development of hail- and tornado-producing storms (Allen et al. 2015a). The midtropospheric flow in the La Niña years also reveals a stronger westerly jet (>15 m s−1) over the NGP relative to the El Niño years (Figs. 6d–f), with a northward extension of the ridge into Wyoming, Nebraska, and South Dakota. The mean ridge was comparatively stronger and displaced farther northward across the NGP in the La Niña years. In contrast, a negative geopotential height anomaly existed in Montana and North Dakota in response to a low-amplitude shortwave trough. The northward displacement of the ridge and the approaching trough are important contributors to the relatively strong westerly midlevel jet over the NGP.

Fig. 6.
Fig. 6.

Composited large-scale environmental variables for significant severe hail events in summer for (left) La Niña and (center) El Niño, and (right) their differences: (a)–(c) wind speed (shading; m s−1) and geopotential height (contour; dam) at 200 hPa, (d)–(f) wind speed (shading; m s−1) and geopotential height (contour; dam) at 500 hPa, and (g)–(i) specific humidity (shading; g kg−1), winds (arrow; m s−1), and geopotential height (contour; dam) at 850 hPa. The NGP region is confined by the black bold line.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Underneath the core of the mid- and upper-level jets, the southerly or southwesterly low-level jet (LLJ) (Figs. 6g–i), which elongated the corridor of strong winds (>12.5 m s−1), extended poleward from the low latitudes into the NGP. The LLJ appeared stronger over the central United States in the La Niña years, which means more moisture advection into the NGP via the LLJ. The intensity of LLJ over the central United States is coincident with the pressure gradient between the surface low pressure in the western NGP and North Atlantic subtropical high (NASH). Wei et al. (2019) suggested that the meridional gradient of pressure could increase associated with the northwestward extension of the NASH. In the La Niña years, the NASH (defined isopleth 1560 gpm; Li et al. 2011) extended its western ridge farther into the southeast of the United States, leading to an increase of the meridional gradient of pressure over the central United States, which causes the strengthening of the LLJ. The stronger LLJ transported more moisture along the western flank of the NASH to the NGP. The enhanced low-level moisture led to a more unstable atmosphere, indicated by higher CAPE over the NGP in the La Niña years (Figs. 7a–c).

Fig. 7.
Fig. 7.

As in Fig. 6, but for (a)–(c) CAPE (shading; J kg−1), (d)–(f) SRH (shading; m2 s−2) over the 0–3-km layer, (g)–(i) wind shear (shading; m s−1) over the 0–6-km layer, and (j)–(l) the lapse rate (shading; °C km−1),

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

The SRH showed stronger over the NGP during the La Niña years (Figs. 7d–f), which could intensify the low-level mesocyclones associated with severe convective storms. The S06 was also larger over the NGP in the La Niña years relative to the El Niño years (Figs. 7g–i), which is a crucial component in the development of severe convective storms (Weisman and Klemp 1982; Rotunno et al. 1988). The reason for higher wind shear (0–6 km) is because of the larger increase of wind speed at 6 km compared with the El Niño years because of a strong midlevel jet. Brooks (2013) suggested that once a relatively low threshold of CAPE is reached, large hail is mostly a function of deep shear. Both larger CAPE and stronger S06 in the La Niña years indicate a higher likelihood of the development of severe convective storms.

We also find that in the La Niña years the mean lapse rates at 750–500 hPa were steeper over the central Rocky Mountains region compared to the El Niño years (Figs. 7j–l). The steep midlevel lapse rate (>6.5°C km−1) is linked to environments promoting severe convective storms (Brooks 2013; Tang et al. 2019; Taszarek et al. 2017, 2021). The lapse rates with values larger than 7.4°C km−1 extended from Colorado northeastward across the NGP (Figs. 7j,k). The presence of the high terrain of the Rocky Mountains and the Gulf of Mexico as a source of warm, moist air at low levels is critical for the steep midlevel lapse rates (Brooks et al. 2007). In the La Niña years, the increased transport of warm, moist air from the Gulf of Mexico to the NGP is one of the factors leading to the steeper lapse rates. The stronger ridge and the negative geopotential height anomaly in the middle troposphere (Fig. 6f) can be another contributor. In addition, the steeper lapse rates appeared over a large area of Southwest through an anomalous large-scale southwesterly flow (Fig. 7l), which was noted in Lanicci and Warner (1991). Thus, the stronger advection of the midlevel dry air from the southwestern deserts contributes to the steeper lapse rates as well.

2) Correlations with NASH

As abovementioned, Li et al. (2011) showed that the westward extension of NASH generally increases the meridional gradient of pressure over the central United States, affecting LLJ intensity and moisture advection over the central United States. We further examined the direct correlation of hail interannual variability with the extension of the NASH. The extension of the NASH in Li et al. (2011) was defined with the location of the western boundary of the NASH, that is, where the 1560 geopotential height (gpm) contour at 850 hPa intersects the ridgeline of the NASH. When the NASH is more intense, the western displacement of the ridge causes an increase in the meridional flow over the Great Plains (Li et al. 2011). Here we defined the NASH extension over the CONUS with both the areal coverage and intensity. The areal coverage means the numbers of the grid with the geopotential height larger than 1560 gpm at 850 hPa in the CONUS (blue contour in Fig. 8a). This definition may better represent the range of the NASH extension over the CONUS compared to the position of the western ridge. The intensity of the NASH was calculated as the average geopotential height at 850 hPa larger than 1560 gpm over the CONUS. We first examined the correlations of SSH frequency with areal coverage and intensity of NASH over the CONUS during the 2004–16 period (Figs. 8b, c). The most notable feature in 2012, which had the lowest number of hail events in the NGP over the 23-yr period, was that the NASH was very weak and did not extend over the southeastern United States.

Fig. 8.
Fig. 8.

Spatial distribution of mean geopotential height (shading; gpm) at 850 hPa for the hail events during the summer from 2004 to 2016. The black line indicates the 1560-gpm isoline and areal coverage of the NASH highlighted by cyan contour, and time series of (b) the areal coverage and (c) the intensity of the NASH over CONUS, overlaid with the number of significant severe hail events in summer from the hail reports (black line) and MESH (gray line) at the secondary axis.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Figure 9 shows that the correlation of SSH with the areal coverage of the NASH has r of 0.50 (0.39) and R2 of 0.25 (0.14) for hail reports (MESH). We find a slightly lower correlation with the intensity of the NASH (r = 0.49, R2 = 0.24 for hail reports and r = 0.35, R2 = 0.12 for MESH) compared to the areal coverage. When we perform the multivariate linear regression using both the areal coverage and intensity of the NASH, a higher correlation with r of 0.61 (0.43) and R2 of 0.38 (0.16) for hail reports (MESH) is obtained compared with the regression analysis of the single quantity. With the 23-yr hail reports, the correlation is slightly lower compared to 13-yr data, with r of 0.36 and R2 of 0.13 for the areal coverage and r of 0.35 and R2 of 0.12 for the intensity of the NASH (Figs. 10c,d). This might be expected because the hail reports with the additional 10-yr data could include more uncertainties than the reports in the most recent decade or so (Allen and Tippett 2015; Blair et al. 2017). The hail days have a slightly higher correlation with the NASH (r = 0.40, R2 = 0.16 for the areal coverage and r = 0.38, R2 = 0.14 for the intensity; Figs. 10e,f) because hail days are likely modulated by the large-scale environment (e.g., NASH). The joint correlation of both the areal coverage and intensity of the NASH using multivariate linear regression has r of 0.50 (0.45) and R2 of 0.25 (0.20) for hail frequency (hail days) with the 23-yr data. Thus, a moderate correlation of NASH with hail occurrences and hail days over NGP is seen from both 13- and 23-yr data.

Fig. 9.
Fig. 9.

Scatterplots of the significant severe hail occurrence vs the areal coverage of the NASH over CONUS for (a) the hail reports and (b) MESH, and vs the intensity of the NASH for (c) hail reports and (d) MESH. Shading indicates 95% confidence intervals from 10 000 bootstrapped resamples.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Fig. 10.
Fig. 10.

Time series of (a) the areal coverage and (b) the intensity of the NASH over CONUS overlaid with the number of significant severe hail events (black line; axis right) and hail days (gray line; secondary axis) from 1994 to 2016. The scatterplots of the hail occurrence frequency vs (c) the areal coverage of the NASH and (d) the intensity of the NASH, and the hail days vs (e) the areal coverage of the NASH and (f) the intensity of the NASH over CONUS.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

3) Correlations with LLJ and IVT

It is well known that the LLJ plays an important role in the severe storm initiation and development over the central United States (Raymond 1978; Maddox 1983; Augustine and Caracena 1994; Stensrud 1996; French and Parker 2010; Pu and Dickinson 2014). Here we examined the relationship between the LLJ and the interannual variability of SSH. The LLJ index is used, constructed from the areal averaging of the meridional wind at the height of the maximum wind speed over the three states that had the most significant changes in SSH over the NGP (delineated by the black box in Fig. 11a). The vertical profile of the average meridional wind each year exhibits the typical LLJ structure (Fig. 11b). The average southerly jet is the strongest at 875 hPa, and this height is used to define the LLJ index (Fig. 11b). The composite southerly jet at 875 hPa for the hail events during the summer from 2004 to 2016 is presented in Fig. 11a, showing that the wind distribution is remarkably similar to the typical summer LLJ from 1979 to 2017 as shown in Hodges and Pu (2019).

Fig. 11.
Fig. 11.

(a) Spatial distribution of mean meridional wind (shading; m s−1) at 875 hPa for the hail events in the summer from 2004 to 2016, (b) mean vertical profile of areal averaged meridional wind (gray contour; m s−1) for each year, and (c) time series of LLJ index overlaid with the significant severe hail from the hail reports (black line) and MESH (gray line) at the secondary axes. The black box in (a) marks the three states for the meridional wind analysis.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

The LLJ index has a substantial interannual variability during the time period of 2004–16 (Fig. 11c). The correlation between the interannual variability of the LLJ index and the hail frequency of SSH has an r of 0.59 for the hail reports (0.65 for MESH), and the Pearson correlation test shows the correlations are statistically significant (Fig. 12). The LLJ variability explains 35% (42%) of the interannual variability in the hail reports (MESH). The strong (weak) LLJ index corresponds to more (less) SSH frequencies over the NGP. However, a linear regression model does not provide a good fit to all data, because the LLJ index distributes more frequently around its median value (14.41 for hail reports and 14.52 for MESH). The majority of the LLJ occurs within a narrow range of wind speed, typically between 15 and 21 m s−1 (Whiteman et al. 1997). Whiteman et al. (1997) also noted that the southerly LLJs have the median 14.9 m s−1 below the 3-km level during the warm season from 2 years of rawinsonde observations. Over the study period 1994–2016 with additional 10 years is considered (Fig. 13), the correlation is similar to that obtained for the 2004–16 period (r = 0.62, R2 = 0.39 for hail frequency and r = 0.50, R2 = 0.25 for hail days).

Fig. 12.
Fig. 12.

Scatterplots of the significant severe hail occurrence vs LLJ index for (a) the hail reports and (b) MESH.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Fig. 13.
Fig. 13.

(a) Time series of LLJ index overlaid with the number of significant severe hail events (black line) and the hail days (gray line) from the hail reports from 1994 to 2016. The scatterplots of LLJ index vs for (b) the hail frequency and (c) the hail days.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Thus, both NASH and LLJ can contribute to the interannual variability of SSH. However, these two environmental variables may be closely related to each other. Wei et al. (2019) found that the LLJ intensity is strongly correlated with the westward extent of the NASH western ridge.

The influence of LLJ on hail occurrences is mainly through transporting warm, moist air from the low latitudes to the NGP (Schubert et al. 2004; Gimeno et al. 2010, 2012). The presence of large moisture transport by the LLJ destabilizes the lower troposphere, which can lead to the initiation of deep convection and hailstorms. Therefore, we expect the correlation of the hail frequency with moisture transport to be strong. We use the vertically integrated water vapor transport (IVT) as a measure of the total horizontal water vapor transport in the troposphere (e.g., Newell and Zhu 1994; Zhu and Newell 1998). IVT is defined as
IVT=1gpsfcpqVdp,
where q is the specific humidity, V is the horizontal wind, psfc is 1000 hPa, p is 300 hPa, and g is the acceleration due to gravity. To investigate the relationship between hail and the IVT, we modified the IVT calculation and calculate IVT for two layers at low levels (1000–750 hPa) and high levels (750–300 hPa), respectively (Figs. 14b,c). The IVT for each year is computed from NARR using the closest time prior to the hail events in the NGP and averaged over the summer for the three states (Nebraska, South Dakota, and North Dakota) as shown in Fig. 14a.
Fig. 14.
Fig. 14.

(a) Spatial distribution of mean IVT at low levels (1000–750 hPa) for the hail events in the summer from 2004 to 2016, time series of mean IVT at (b) low levels (1000–750 hPa) and (c) high levels (750–300 hPa) for the hail events in summer for three states as marked by the black bold line in (a), overlaid with the significant severe hail events from the hail reports (black line) and MESH (gray line) at the secondary axes.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

The mean IVT at the low levels (1000–750 hPa) during 2004–16 shows a large northward water vapor transport, with the values exceeding 450 kg m−1 s−1 stretching more than 1000 km across three states over the NGP (Fig. 14a). The three states with the most significant increase in hail frequency have the largest IVT. IVT varies significantly year to year (see Figs. 14b,c). The four years with the lowest IVT at the low levels are 2004, 2007, 2012, and 2013, corresponding to relatively lower hail frequencies. Indeed, IVT at the low levels has a similar correlation with SSH frequency as the LLJ, with r of 0.55 for hail reports and 0.63 for MESH (Figs. 15a,b). The contribution (R2) of IVT is also similar to the LLJ. This corroborates our finding that the contribution of the LLJ to hail frequency is mainly through moisture transport. Although the magnitude of IVT at the high levels (750–300 hPa) is lower than that at the low levels (Figs. 14b,c), the year-to-year variation is very similar, and the correlation with SSH is also similar (Fig. 15). In general, the source of moisture would be different between the low level and high level, with the low-level moisture directly tied to the LLJ whereas the high-level moisture is likely tied to phenomena occurring upstream across the Rocky Mountains. The highly similar interannual variation and correlation with SSH between the low-level and high-level IVT suggest a potential role of convective transport in contributing to the high-level IVT in convective environments.

Fig. 15.
Fig. 15.

Scatterplots of the significant severe hail occurrence vs the IVT magnitude at low levels (1000–750 hPa) for (a) the hail reports and (b) MESH and IVT magnitude at high levels (750–300 hPa) for (c) the hail reports and (d) MESH.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Over the 23-yr period, the correlations with hail occurrence are slightly weaker than the 13-yr period, with an r of 0.47, R2 of 0.22 for the low-level IVT, and r of 0.56, R2 of 0.31 for the high-level IVT (Figs. 16c,d). For hail days, the correlation is lower than the hail frequency, with r of 0.35 (R2 of 0.13) for the low-level IVT and r of 0.52 (R2 of 0.27) for the high-level IVT (Figs. 16e,f), suggesting that hail occurrences are more strongly modulated by the magnitude of moisture than the hail days.

Fig. 16.
Fig. 16.

Time series of (a) the IVT at low levels (1000–750 hPa) and (b) the IVT at high levels (750–300 hPa) overlaid with the number of significant severe hail events (black line) and hail days (gray line) from 1994 to 2016. The scatterplots of the hail occurrence frequency vs (c) the IVT at low levels (1000–750 hPa) and (d) the IVT at high levels (750–300 hPa), and the hail days vs (e) the IVT at low levels (1000–750 hPa) and (f) the IVT at high levels (750–300 hPa).

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

Therefore, the three dynamical factors ENSO, NASH, and LLJ may notably influence the interannual variability of SSH over the NGP over 2004–16. The analysis with an extended period (23-yr) corroborates the results with slightly weaker correlations for all these factors. IVT is a thermodynamical variable that can be strongly modulated by the LLJ as discussed above. With the multivariate linear regression analysis, the joint contribution of all three dynamical factors is 66% (59%) for the hail reports (MESH) with the LLJ contributing the most (Table 1). By including the low-level IVT in the multivariate linear regression analysis, the joint contribution increases to 78% (63%) for the hail reports (MESH), with IVT of the largest contribution (Table 2). The contributions from LLJ, NASH, and ENSO are similar. We also found that, in the La Niña years, the position of the western ridge of the NASH extended farther into the southeast of the United States, which enhanced southwesterly LLJ and thus the water vapor transport. Thus, ENSO modulates NASH, which affects LLJ and IVT.

Table 1.

Summary of the statistical analysis (r and R2) with the multivariate model from least squares regression for three dynamical factors: ENSO (based on SOI), NASH (based on areal coverage), and LLJ index for the hail reports and MESH.

Table 1.
Table 2.

As in Table 1, but for four factors including IVT (based on 1000–750 hPa).

Table 2.

As concluded in Jeong et al. (2020), the major factors that may impact the hail interannual variability over the SGP are ENSO, sea surface temperatures (SSTs) in the Gulf of Mexico (GoM), and aerosol loading from northern Mexico, Here we also looked at the correlation with the SSTs over GoM and aerosol loading over the NGP. Overall the correlations with these two factors are low. For the SSTs in the GoM, the r is 0.23 (0.21) and R2 of 0.05 (0.05) for hail reports (MESH). The correlation with aerosol loading is very weak over the NGP (r = 0.14, R2 = 0.02 for hail reports and r = 0.15, R2 = 0.03 for MESH). Thus, although NGP hail occurrences have a large interannual variability, the same as SGP, the temporal trend and the variability amplitude are different from those over the SGP. Therefore, the physical factors that contribute to hail frequencies are also different.

4) Extremely low hail occurrences in 2012

The year 2012 has a sharp drop in hail occurrence during the 13-yr period (Fig. 2). In fact, 2012 had the lowest hail events over the NGP since 1990 based on the hail reports. To understand why 2012 had a uniquely low hail occurrence, we first examined synoptic environment differences between 2012 and the mean states of six years (three years before and three years after). The water vapor mixing ratio in the lower troposphere (850 hPa) is significantly lower, and the upper-level jet stream is about 10 m s−1 weaker over the NGP than the mean states (Figs. 17a,b). The variables for indicating severe storm environments such as CAPE and S06 are also a lot smaller in 2012 (Figs. 17c, d), suggesting that the synoptic environment in 2012 is much less favorable for severe storm development in the NGP. Since 2012 was ENSO neutral, ENSO was unlikely to have played a role in the conditions over the NGP. As discussed earlier, 2012 had the lowest NASH intensity among the 13 years and the NASH did not extend over the southeastern CONUS (Figs. 8b,c). This suggests that the extremely weak NASH in 2012 may have had some influence on the low hail activity over the NGP. The LLJ and IVT in 2012 were not especially weak (Figs. 11c and 14b,c), with the weakest IVT occurring in 2013. These data suggest that the 2012–13 drought may have played a significant role in reducing moisture. The drought started from the southern United States and migrated northward, and central North America experienced one of its most severe droughts on record (Blunden and Arndt 2013). The NGP states were in severe and extreme droughts in all summer months based on NOAA Climate Monitoring (https://www.ncdc.noaa.gov/sotc/drought/201208). The droughts resulted from a shortage of snowmelt in spring followed by a severe summer heatwave, yielding year-round negative soil moisture anomalies (Blunden and Arndt 2013).

Fig. 17.
Fig. 17.

Differences of large-scale environmental variables for significant severe hail events in summer between 2012 and mean states of 6 years (3 years before and 3 years after): (a) specific humidity (shading; g kg−1), winds (arrow; m s−1), and geopotential height (contour; gpm) at 850 hPa, (b) wind speed (shading; m s−1) and geopotential height (contour; gpm) at 200 hPa, (c) wind shear (shading; m s−1) over the 0–6-km layer, and (d) CAPE (shading; J kg−1). Solid contour lines denote positive values and dashed contour lines in (a) and (b) denote negative values. The NGP region is confined by the black bold line.

Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0245.1

4. Summary and discussion

Following on our study of the hail interannual variability and contributing factors over the SGP (Jeong et al. 2020), we have studied the hail temporal and spatial trends and variabilities for the NGP summers (June–August) focusing on a recent 13-yr period (2004–16) using two independent hail datasets. We have also examined an extended period (1994–2016) with the hail reports to more reliably investigate the correlations with physical factors. We have used the Theil–Sen method with Kendall’s τ significance test for the trends and performed univariate and multivariate model analyses for the factors that may impact hail frequency.

It is found that the number of hail events for both severe hail and SSH demonstrates a positive slope during the last decade or so (+3.4 per year with the hail reports and +400 per year with MESH). The increase with time is also evident in the 23-yr period. The increase is statistically significant over the central areas of the NGP, particularly in the western part of Nebraska, North Dakota, and South Dakota. Hail in the NGP occurred mainly in three states (Nebraska, North Dakota, and South Dakota), accounting for ~81% of SSH and ~75% of severe hail over the region. This result is consistent with previous studies (e.g., Allen 2017; Brimelow et al. 2017). However, there is a notable interannual variability and there is also a sharp drop in hail frequency in 2012. The year 2012 is unique with the extremely weak NASH, and the extreme and severe droughts starting from spring in the southern United States. It is worth mentioning that the large interannual variability is also the feature of hail over the SGP, as revealed in Jeong et al. (2020).

We further investigated possible factors contributing to the large hail interannual variability over the NGP. Three important dynamical factors are ENSO, NASH, and LLJ. Together, the three factors can explain 66% in the hail reports (59% in MESH) of the hail interannual variability over 2004–16 based on the multivariate linear regression. Both hail reports and MESH consistently suggest that the LLJ has the largest contribution in the multivariate linear regression (28% in both the hail reports and MESH). The influence of LLJ occurs through transporting moisture from the low latitudes into the NGP region. The LLJ is stronger with the enhanced pressure gradient between the surface low pressure over the central United States and the NASH western ridge. Therefore, NASH areal coverage and intensity over the CONUS are well correlated with hail interannual variability. The multivariate linear regression with both the areal coverage and intensity shows a correlation of 0.61 (0.43) and a contribution of 38% (16%) in the hail reports (MESH). As mentioned above, the more extensive areal coverage and more intensified NASH leads to the enhanced southerly or southwesterly LLJ over the central United States, suggesting that the LLJ enhances the clockwise circulation associated with NASH westward extension (Gimeno et al. 2010, 2012). In addition, the moisture in the atmosphere can be impacted by other factors such as surface soil moisture. Bromley et al. (2020) found that surface air temperature and soil moisture have increased during the summer in the NGP during 1970–2015. By accounting for the vertically integrated vapor transport (IVT) in the multivariate linear regression analysis, the total contribution of ENSO, NASH, LLJ, and IVT is 78% in the hail reports (63% in MESH), which increased from 66% (59%) with the three dynamical factors considered only. When the four contributing factors are considered, IVT becomes the largest contributor with 23% (19%) in hail reports (MESH) in the multivariate linear regression.

The ENSO phases represented by the SOI are moderately correlated with hail occurrence frequency in the NGP, with a contribution of 37% (32%) in the hail reports (MESH) in the univariate linear regression model. The multivariate linear regression model with four factors considered shows a 21% (17%) relative contribution in the hail reports (MESH). The cold phase of ENSO (positive SOI) corresponds to a higher hail occurrence than the warm phase of ENSO (negative SOI). The hail occurrence frequency averaged over the three strong La Niña is ~14% higher than that over the three strong El Niño years. This is because, in the La Niña years, the jet stream was stronger. Also, the western ridge of the NASH extended farther into the southeast of the United States, which enhances southwesterly LLJ thus the water vapor transport from the Gulf of Mexico to the Great Plains. This suggests that the position and strength of the NASH can be influenced by ENSO. However, the modes of NASH can be also influenced by other factors such as the global modes of oceanic and atmospheric variability including the Pacific decadal oscillation (PDO) and SSTs in both the Pacific Ocean and Atlantic Ocean (Wang et al. 2010; Li et al. 2012), which can influence diabatic heating anomalies over the Gulf of Mexico, forcing a low-level, anomalous cyclonic flow that influences NASH strength and westward extent.

With additional 10-yr data with the hail reports (1994–2016), the analysis for each factor gives consistent results like that for the 13-yr period, except for the slightly weaker correlation. This is expected considering a larger uncertainty with the hail reports before 2000. In addition, both hail frequency and hail days have similar correlations with those contributing factors, which further supports their contributions.

It is interesting that the major factors likely impacting the hail interannual variability over the NGP are different from those over the SGP, except ENSO. The SSTs over the Gulf of Mexico and aerosol loadings, which strongly correlate with hail frequencies over the SGP (Jeong et al. 2020), have very low correlations with the hail occurrences in the summer over the NGP. Instead, NASH and the LLJ are shown to be important factors.

We note that it was not our intention here to provide a hail climatology over the NGP. We intend to provide reliable results with the most reliable hail datasets available, which is for the recent decade or so. Although the analysis of the hail reports over the extended period (23 years) corroborates our conclusions, lower correlations with ENSO, NASH, LLJ, and IVT are seen.

The environmental factors most likely contributing to the hail interannual variability were identified based on the linear regression analysis only. To build a more rigorous analysis of causality, model simulations are needed, which we plan to carry out in the near future. These results may be useful to improve the statistical prediction of hail occurrences over the NGP by constructing a probabilistic forecast using those major contributing factors.

Acknowledgments

This study is supported by the U.S. Department of Energy Office of Science Early Career Award Program. PNNL is operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract DE-AC05-76RL01830. C. Homeyer was supported by NSF under Grant AGS-1522910 and NASA under Award NNX15AV81G. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract DE-AC02-05CH11231.

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