Climatology of severe hail potential in Europe based on a convection-permitting simulation

doi:10.21203/rs.3.rs-3348143/v1

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Climatology of severe hail potential in Europe based on a convection-permitting simulation

https://doi.org/10.21203/rs.3.rs-3348143/v1

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We present a new approach to identify severe hailstorms in a convection-permitting climate model, and build a climatology of severe hail potential in Europe using an ingredients-based approach based on a 20-year long hindcast simulation. Severe hail in Europe occurs mostly in southern regions (up to 40 times a year per 10,000 km² around Northern Italy), and from May to August. It peaks from afternoon to evening hours on land, whilst sea areas are prone to hail at any time of the day. The Mediterranean Sea experiences severe hailstorms mostly in autumn: the central Mediterranean has the highest frequency among all regions studied, and may be considered as an unknown alley for hailstorms in Europe. Results derived from the high-resolution model are in very good agreement with existing hail climatologies from observations, including the fine scale spatial variation. We conclude that our approach provides a reliable proxy for studying changes in severe hail in future convection-permitting simulations.

Hail

hailstorms

severe thunderstorms

convection-permitting simulations

severe weather

Europe.

Hail is a convective weather hazard that is relatively rare at individual locations, but results in significant impacts including damage to property, even sometimes direct fatalities (Allen et al. 2020, Cerveny et al. 2017). It can damage crops, cars, buildings, and animals, especially when it is large and occurs alongside severe winds such as strong downdrafts from thunderstorms. Planes also are affected by hailstorms. Melting hail and blockages from large hail accumulations can also lead to or occur along with flash floods (e.g., Grahame et al. 2009, Chapell and Rodgers 1988), which can halt traffic and cause fatalities.

Since hail is a highly localized meteorological event, with swaths typically a few kilometres wide and one order of magnitude higher in length (the most severe ones are up to a few hundred kilometres), lasting generally only for a few minutes in a given locality, only a small fraction of hailstorms are recorded by meteorological station networks (Allen and Tippett 2015). Historically, those that we know of are generally major hailstorms, resulting in damage to property, or directly witnessed by the people who record them (Kahraman et al. 2016). Hailstorms that occur in rural areas with low population density, or mountains, forests, deserts, or over ocean and sea areas are rarely reported, as are those that occur during night-time (Allen and Tippett 2015). Nevertheless, it is possible to detect storms that likely produce hail via remote sensing tools such as meteorological radars (especially dual-polarisation radars; Kumjian 2013) and satellite products (Bedka et al. 2018). However, the spatial coverage of remotely-sensed products is not homogeneous, and these do not assess whether hail reaches the ground, alongside other limitations (Kunz and Puskeiler 2010, Ortega 2018).

Previous studies of the spatial, temporal, and intensity distributions of hail in Europe mostly rely on national or local datasets (e.g., Tuovinen et al. 2009; Nisi et al. 2018; Sioutas et al. 2009) collected by hydrometeorological services or institutions. Since the European country sizes are relatively small, as well as very few localities with hailpad networks (Manzato 2012; Fraile et al. 1999; Merino et al. 2014), these studies provide an incomplete picture of European hail climatology, although their essential insights are very valuable.

The recent decade has seen a rising interest in a pan-European approach to hail research. Climatologies based on observations / severe weather reports (Pucik et al. 2019; Taszarek et al. 2020a; Hulton and Schultz, 2023), the synthesis of country-based climatologies (Punge and Kunz 2016), remote sensing tools combined with other forms of data (Punge et al. 2017), reanalyses (Rädler et al. 2018; Taszarek et al. 2020b) and climate models with parameterized convection (Rädler et al. 2019) have been used to provide a holistic view of hail hazard across Europe. Generally, observational studies suffer from incomplete datasets due to underreporting issues (as discussed in Taszarek et al. 2020a), while model-based studies rely on proxies that approximate the environmental conditions of hail; and are sensitive to model biases. All in all, there is no perfect hail climatology, with different versions having different strengths and weaknesses.

Climate modelling has undergone a paradigm shift in the recent decade, with an increase in the resolution of projections to kilometre-scale (Kendon et al. 2014; Coppola et al. 2020; Ban et al. 2021, Kendon et al. 2023). Long climate simulations at convection-permitting resolution are now possible, giving an improved representation of convective storms (Prein et al. 2015. These simulations explicitly represent the features of hailstorms, including updrafts, downdrafts, and the formation of ice particles. They add value especially in terms of storm dynamics, local wind systems, and the interaction of atmospheric flows with topography (Prein et al. 2015, Houze 2012). It is possible to produce a multi-decadal climatology of hail without relying on environmental proxies. This study is the first to exploit a CPM to produce a hail climatology for Europe. We use a Met Office Unified Model simulation with 2.2km horizontal grid spacing, spanning 20 years. We describe the data, modelling aspects and the novel hail proxy developed here in Section 2. In Section 3, we present the resulting climatology and its comparison to existing climatologies. Finally, Section 4 concludes the paper.

2.1. The hindcast simulation

A pan-European convection-permitting simulation has been conducted using the Met Office Unified Model v10.1, using a configuration based on the operational forecasting model. The simulation used ERA-Interim (Dee et al. 2011) as initial and boundary conditions every 6 hours, without nudging, providing a downscaled representation of a historical period, termed a “hindcast”. The rotated pole (located at 43°N, 190°E) domain covers most of Europe, with 1536x1536 horizontal grids, and ~ 2.2km spacing. Here, due to the large resolution difference between ERA-Interim (0.75°) driving data and the downscaled regional model, we exclude 70 grid points from each boundary (~ 154km) prior to our analysis corresponding to the region of spin-up of small-scale features.

The Unified Model’s dynamical core is called “Even Newer Dynamics for General atmospheric modelling of the environment” (ENDGame) and is based on a semi-implicit semi-Lagrangian discretization of the governing equations (Wood et al. 2014). It is fully compressible and nonhydrostatic (although a hydrostatic option is available). The model uses Arakawa-C grid staggering. Vertical levels are terrain-following hybrid height coordinates. The model top is set at 40 km, and the lowest grid level is 2.5 m above the surface. There are 70 vertical levels, with vertical grid spacing increasing quadratically with height. The model time step is set as 60 seconds.

As convection is assumed to be resolved by the model dynamics at such km-scale resolutions, no convection parameterization is used. We acknowledge that some small-scale convection might not be resolved, but for the purposes of severe hailstorm research, our focus is on the larger storms that are better resolved. Radiation parameterization is based on Edwards and Slingo (1996), with upgrades described in Stratton et al. (2018). The radiative time step is 15 minutes, with 5-min “correction” updates. A modified Wilson and Ballard microphysics scheme (Wilson and Ballard 1999, Williams et al. 2018) is used, capturing graupel, water vapour, cloud liquid, cloud ice and rain. The boundary layer scheme has been developed for high resolution simulations, as described at Boutle et al. (2014). The land surface parameterization used is the Joint UK Land Environment Simulator (JULES, Best et al. 2011; Clark et al. 2011).

The simulation spans 21 years, the first of which is omitted in the analysis, and is the model spin-up period. All analysis is performed on 20 years of data, from 1 March 1999 to 28 February 2019. Most of the analysis is based on instantaneous data, with 3-hour intervals. Further details about the model and its performance in simulating precipitation extremes is presented in Berthou et al. (2020), which finds an improved diurnal cycle of precipitation, more intense extremes, correction of the biases compared to 12km-grid simulations, and better representation of Mediterranean autumnal intense events with more realistic mesoscale processes.

2.2 The hail proxy

2.2.1 Background: Hail formation theory in brief

Hail requires three ingredients. The first is an embryo, such as graupel, small frozen drops, or sometimes other particles, which make up the nuclei of hailstones (Knight and Knight 1970). The second ingredient is the abundance of supercooled water droplets, which can be collected by the embryo for further growth, as accretion or riming processes. Hail growth occurs at temperatures between − 10 and − 30 C (e.g., Knight et al. 1975; Ziegler et al. 1983), as either “dry” or “wet” growth regimes (or a combination of both), based on available liquid water, ambient temperature, hailstone surface temperature, and its size. This layer is known as the hail growth zone (HGZ). The final ingredient is time for these processes to happen: typically 10 minutes or longer, for the growth to “large” sizes (Allen et al. 2020). “Large” here is usually defined as 2 cm in Europe and, a relatively comparable, 1” in the United States; these are considered “severe” hail.

Supercells are known as the producers of the largest hailstones, and the growth mechanism within them has been known for decades (e.g., Morgan 1972; Browning and Foote 1976). A rotating updraft, called a mesocyclone, is the unique feature of a supercell. Hail embryos may be formed in flanking line convection or grown entirely within the main updraft, more likely at its weaker periphery (Heymsfield & Musil, 1982). The embryos injected into the updraft via environmental storm-relative flow grow rapidly in the liquid-rich updraft, following a path of ascent and then descent. The amount of time spent in the hail growth zone is determined by the balance between the hailstone fall speed and the updraft speed (Browning, 1964, among many others, e.g., Morgan, 1972; Heymsfield, 1983; Miller & Fankhauser,1983; Foote, 1984; Musil et al., 1986). If the fall speed is larger, the particle falls quickly. Whilst if it is much slower, it is ejected by the updraft rapidly out of the growth zone. A balance is necessary to maintain the particle suspended within the hail growth layer, with only horizontal movement within the updraft, sweeping supercooled water droplets. For this balance to be maintained during the growth of the hailstone, the particle should be moving towards the stronger parts of the updraft with time, i.e. the trajectory of the hailstone should follow a path from the periphery of the updraft towards the centre. These settings are best provided by mesocyclones, especially wider (and stronger) ones.

2.2.2. Extracting hail environments from model data

Hail ingredients and processes occur at small scales, and so are typically poorly represented in climate models. The instability indexes and convective available potential energy (CAPE) typically used as proxies within traditional climate models indirectly provide information on possible updraft speeds, but have been found to be inadequate for hail estimation (e.g., Doswell 1982; Groenemeijer and van Delden, 2007; Mohr and Kunz 2013). In the km-scale model, we have the possibility to directly extract the updraft speeds favourable to hail as identified above. We focus on severe hail because such storms are more easily discerned from other thunderstorms, and because these suffer less from under-reporting issues in observations allowing more reliable verification.

When a parameter related to updraft strength is combined with another representing kinematic environments, such as wind shear or storm-relative environmental helicity, it is found to better represent large (or severe) hail environments (e.g. Johnson and Sugden 2014; Kahraman et al. 2017), as these kinematic environments are the crucial determinants of thunderstorm organization (e.g., higher deep layer shear or storm relative helicity favour supercells, ideal settings for hail growth) and wider mid-level updrafts (Trapp et al. 2017; Dennis and Kumjian 2017). Recent research has provided further insight into which segment of the troposphere matters in terms of calculating deep layer shear, and the favourable shape of hodographs -diagrams that depict vertical distribution of horizontal winds- (Kumjian and Lombardo 2020; Kumjian et al. 2021). These studies showed that high wind shear at low levels is unfavourable in terms of rapid advection of hailstones out of the region of hail growth, and straighter hodographs are found to support larger hail production.

Freezing level height is commonly used to identify environments favourable for severe hail (e.g., Billet et al. 1997; Kitzmiller and Breidenbach 1993; Edwards & Thompson, 1998; Prein and Holland, 2018), as it helps to distinguish too warm or too dry environments, which might be unfavourable for hail growth. Other predictors include lapse rates, moisture contents, different versions of wind parameters, and composite parameters such as the Significant Hail Parameter (SPC 2014) or Large Hail Parameter (Johnson and Sugden 2014). Usually, such parameters are found to be most useful for the region in which they have been developed, due to unique local climatological features, and perform poorly when applied elsewhere (Allen et al. 2020).

2.2.3. Severe and significant severe hail potential in convection-permitting simulations

A convection-permitting model output demonstrates more realistic thunderstorm features compared to coarse resolution models, in addition to finer details of their environmental conditions. It is possible to exploit simulated updrafts individually for potential hailstorms. For instance, coupled hail models like HAILCAST (Brimelow et al. 2002) performed better in convection-permitting simulations, albeit with further modifications (Adams-Selin and Ziegler 2016).

We build our proxy on severe hail-focused parameters calculated from the available raw model output: simulated graupel, simulated updrafts in the HGZ (WHGZ), vertical wind shear between the lower troposphere and 500 hPa level (DLS), and the height of 0-degree -freezing- level (FZLV). Grids with HGZ updrafts reaching at least 10 m/s (where graupel exists) are candidates for severe hail production. We use a relatively low threshold, since the data we use does not include each model time-step, but rather instantaneous snapshots with 3-hour intervals. It is likely that updrafts are stronger between these instantaneous snapshots. Therefore, this modest threshold must not be considered as the appropriate value for data from any simulation. The DLS is calculated as the bulk shear between the lowest available pressure level (from 925 hPa, 850 hPa, and 700 hPa) and 500 hPa. Here, we purposely do not use 10 m wind components, as these are contaminated by realised convection in the model. One other benefit here is the fact that the most recent research (Kumjian and Lombardo 2020, Kumjian et al. 2021) suggests a high shear layer from 1–2 km above the surface to the mid-troposphere. We hold the shear threshold at 10 m/s, as this is considered a discriminatory value for severe and non-severe thunderstorms. Finally, the FZLV criteria is set at 2000 m and 4500 m, similar to the range found by Prein and Holland (2020).

To show an example from our hindcast data, we visualize hail-relevant fields as a snapshot from the model simulation (Fig. 1). A band of low-level moist air (with mixed-layer mixing ratio values reaching ~ 0.015 kg/kg, darker green colour in the lower-left panel), and locally high lapse rate values in the 700 − 500 hPa layer (up to 9 K/km, orange colour in the upper-left panel), set up the scene for high latent instability. High environmental wind shear is present (not shown), and the freezing level height is ~ 3 to ~ 4 km above ground (lower-right panel), which is adequate for severe hail based on the literature (e.g., Prein and Holland 2018). The convection-permitting model initiates some storms in the vicinity of these overlapping ingredients, featuring vertical velocities of 10 m/s in the hail growth zone (-10 C to -30 C layer), where graupel is produced (upper-right panel, circled in red).

Another useful example to demonstrate the evolution of storms within the model is given in Fig. 2. Here, strong updrafts in the hail growth zone (where graupel exists) are visualized at 3-hour intervals. With a south-westerly background flow, it is possible that the storm in SW Germany in the left-most subplot propagates towards the central parts of the country within the time-period, while the three storms in E Germany also move slowly. These could also be new storms that form within the “blind” period (without saved model time-steps) while the previous storms are dying. This example demonstrates that the entire hail swath is not sampled with our approach of using instantaneous data with 3-hour intervals. However, given that some supercells live up to 6 hours (and even longer), some long-lived storms are potentially captured more than once. The severe hail potential we extract here is therefore not equal to the number of hailstorms, or severe hail cases, modelled, but from a 20-year hindcast we assume that the overall geographical distribution of such storms represents the hail climatology in a broad sense.

Conventionally, a measure of instability combined with vertical wind shear has been considered a basic tool for assessing severe hail environments (as discussed in Section 2.2.2). In our case, as we can extract the simulated updraft within a convection-permitting model, instead of the instability parameters, we use the vertical velocity itself (along with the shear), to look for the potential for hail. For instance, higher values of the multiplication of vertical velocity in the hail growth zone and wind shear could favour even larger hail. Based on observational studies, 3 to 8% of severe hail (those ≥ 2 cm diameter) is significant severe hail (≥ 5 cm diameter). Recent estimates favour towards the lower end, i.e. 3%, as historical data tend to underrepresent marginal cases more than extreme cases, hence the sig. severe to severe ratio becomes skewed towards higher values in earlier studies. The thresholds for the vertical velocity and shear are both 10 m/s for severe hail potential, producing a multiplication of 100 m² s^− 2 as a minimum. Taking all occasions of severe hail potential in the model dataset into account, a 3% level corresponds to ~ 400 m² s^− 2 (Fig. 3). This can be used as a threshold for significant severe hail for further studies. We understand that the rarity of such hailstorms would mean that most cases would not be captured, and the proxies’ representation skill for significant severe hail is lower than that of severe hail.

2.3. Observational data

The European Severe Storms Laboratory (ESSL) maintains the most extensive dataset of severe storm hazards covering Europe, the European Severe Weather Database (ESWD, Dotzek et al. 2009). The severe hail reports in ESWD are submitted by ESSL staff monitoring local news across Europe, volunteers (including professionals), and by collection from national databases. We use the ESWD severe hail data for Europe for the analysis period from March 1999 to February 2019, extracting those with quality assurance code QC0+ (plausibility checked) or higher confidence level and 2 cm or higher maximum hail diameter. The features of the observational dataset will be discussed in the results section, where we compare them with our findings from the model-derived climatology.

3.1. The CPM-based climatology

The “severe hail potential” is extracted for each grid point in the 20-year simulation data, every 3 hours. This identifies 3-hour periods with 10 m s^− 1 or higher HGZ vertical velocity, 10 m s^− 1 or higher DLS, and between 2000–4500 m FZLV, where graupel exists. Then the total at each grid point is divided by 20, to find the annual frequencies. As these are rare events, we visualise these frequencies per 10,000 km² (i.e. per 100 km x 100 km), by applying a spatial smoothing (Fig. 4). We find frequencies in the range of 0 to 10 y^− 1 10,000 km^− 2 over most land areas, but the frequency exceeds 40 y^− 1 10,000 km^− 2 in some localities, such as parts of Northern Italy (south of the Alps).

Generally, the likelihood of severe hail decreases towards the north of Europe. Hotspots exist in the south, such as Italy, NE Spain, S France, and locally in the Balkans. The British Isles, as well as Scandinavia, have the least severe hail potential. Coastal zones in the north also have very low exposure, such as the Baltic Sea, Brittany and the Channel coasts of France, and the land around the North Sea. The Mediterranean Sea, however, has much higher frequencies of severe hail potential, both along the coasts and offshore. Despite high values in the surrounding region, the high mountains of the Alps themselves have little exposure.

Looking at the seasonality, severe hail potential in Europe is highest from May to August, over most land areas (Fig. 5). The Mediterranean Sea has a higher peak in the autumn, with some local coastal areas having higher frequencies in September and October, and severe hail cases occur in central parts of the Mediterranean Sea during the whole of winter.

We find that hailstorms over the land follow a diurnal cycle (Fig. 6); they mostly occur from the afternoon into the evening. The slightly earlier hour on initiation in the east of the domain reflects the local solar cycle. By contrast, the Mediterranean Sea and coastal areas can experience severe hail at any time of the day in the autumn and winter, as solar radiation forcing is not the dominant factor, and more non-surface-based convection (elevated thunderstorms) exist. These features are in line with previous severe hail climatologies for Europe, with convection following the solar cycle over land, while this is not the case for sea areas (Pucik et al. 2019; Dessens et al. 1986; Hulton and Schultz 2023; Punge and Kunz 2016; Burcea et al. 2016; Kahraman et al. 2016).

In addition to the spatial, seasonal, and diurnal distributions, it is also important to see how the hail proxy parameters are distributed. We find that stronger updrafts in the HGZ typically occur within warmer storms (Fig. 7). In winter, updrafts are less strong, but the wind shear is stronger. In summer, stronger updrafts are present, but the shear is less strong. These are all expected outcomes for observed European hailstorms, and the distributions indicate realistic features compared to observations (e.g. Taszarek et al 2020b).

3.2. Comparison with observations and existing climatologies

We aim to validate our climatology with the current knowledge on European severe hail. The first source is the ESWD observations (Section 2.3). It is important to emphasise here that severe weather observations are not homogeneous in space and time and should be treated accordingly. Due to the locality and short time-frame of hailstorms, and varying reporting practices spatially and temporally, a lack of reports does not mean that there are no severe hailstorms. In fact, reporting efficiency (RE) is the main limiting factor for usage of such databases. Theoretically, the number of reports (for unique events) for a region for a duration can be formulised as

R = RE x E

where R is the number of reports representing unique events, E is the number of events that actually occurred, and RE is a number between 0 and 1: the reporting efficiency. Due to various reasons, such as widespread adoption of relevant technology in the recent decade (internet, smart phones, social media, etc.), purposeful efforts (a rise in scientific interest in severe thunderstorms in Europe, international collaborations, establishment of ESWD, etc.), and changes in population (to a lesser extent), the RE can be considered to have increased nonlinearly over time. This likely overshadows the natural variability of severe hailstorms throughout the decades, which could be order(s) of magnitude lower than the prior, despite the stochastic nature of hailstorms. From the observations only, it is not possible to know if we have reached a RE plateau. Therefore, it is also impossible to detect any trend in the occurrence of hailstorms from observed reports only.

The number of severe hail reports in ESWD increases nonlinearly (Fig. 8). We appreciate the rise in 2021 could be mostly due to an increase in real events but, especially during its early years, the dataset suffers from insufficient RE. It is also important to appreciate different reporting practices in different countries, i.e. the increase of reports in Poland in recent years is likely linked to new volunteers from the Skywarn organization (https://lowcyburz.pl), rather than due to a lack of hailstorms there previously (Fig. 9). This is even though central Europe is best covered by ESWD (due to better reporting practices).

Despite issues with reporting efficiency, the ESWD dataset may be more reliable for assessing the seasonal variation or diurnal cycle of hail for specific countries. These aspects of the dataset may be useful for the validation of a model-derived climatology. For instance, the annual distribution of severe hail reports in seven selected central European countries indicate a May-to-August peak in severe hailstorms, and a considerable reduction off-season (Fig. 10). The different geographies will influence the occurrence of hailstorms between the countries, with reporting efficiency (which will be influenced by different population densities) also contributing to differences, e.g., due to larger mountainous areas in Slovakia, Switzerland, etc. Overall, the captured annual distribution by the model + proxy (Fig. 4) agrees well with the observations’ distribution.

The model-derived severe hail potential frequency in Central Europe and the number of ESWD reports with severe hail (diameter ≥ 2cm) converges with time and seem to be comparable in terms of order of frequency by circa 2010 (Fig. 11). Before 2004, the underreporting issue in the observational dataset is more pronounced. In fact, considering ESWD reports for whole Europe, a relatively stable time-period is suggested as 2010–2020 based both on the diurnal cycle of large hail, and the time accuracy of reports (by 3 hours or less, Hulton and Schultz 2023).

For a rare and local weather event like severe hail, it is not meaningful to perform a trend analysis with two decades of data. But it might be a useful tool to question the order of RE effects vs change in real occurrences. The annual variability of model-derived severe hail potential frequency suggests there is no obvious trend within the 20 years examined up to 2018, or a slightly decreasing one (Figs. 11 and 12). Applying an annual linear trend, the overall decrease in 20 years is 27%. This is a modest indication that most of the enormous increase of severe hail in observational datasets up to 2018 stems from changes in reporting practices, rather than from increases in occurrences.

Regarding the seasonality of severe hail in Central Europe, the model + proxy seems to capture the May-to-August peaks suggested by the observations (Fig. 12). Here, the effects of the improvement in reporting practices over time is realised as comparable peaks in the recent years. It is important to note here that a 20-year-long hindcast simulation is expected to capture the main characteristics of the climate. The model has information about the observed state at the boundaries of the European domain, where it is forced by ERA Interim and observed sea surface temperatures. However, in the interior of the model domain, the model evolves freely and thus the individual occurrence of hailstorms on a month-by-month basis is not expected to absolutely match the observations. Although good agreement may be seen (e.g., for the most recent years) where hailstorm occurrence is strongly constrained by the large-scale conditions.

Compared with our model results, severe hail observations from ESWD also indicate a peak severe hail month of May to August for most of the European continent, while the Mediterranean islands and some coastal zones have autumn, or even locally winter, peaks (Fig. 13). We note that there are a very limited number of reports offshore, so this distribution just reflects the climatology of land areas with better reporting practices, and the lack of sea reports does not imply less frequent severe hail offshore. For comparison, a similar map is produced for peak severe hail potential month based on the model data (Fig. 14). Generally, continental Europe shows similar peak months to the ESWD observations, but autumn peaks (mostly September) exist around the Mediterranean coasts. Some of this comes from spatial smoothing, but the algorithm extracts more autumn Mediterranean storms with severe hail potential compared to those over land in the summer. It is hard to assess if this is the case in reality, with only reporting-based observational data. However, satellite-based studies indicate a severe hail peak over the Central Mediterranean in the autumn (Bang and Cecil 2019; Bedka et al. 2023). Hail occurs on 3 to 5 days on average, at meteorological stations in the Ionian Islands of Greece, and almost all hail days in the western coasts of Greece happen between October-March rather than during the warm season (Sioutas 2019).

Warm season radar-derived hail frequencies for France, Germany, Belgium, and Luxembourg between 2005 and 2014 indicate the peak areas for hail days as the Massif Central and E France, as well as S Germany among the regions studied (Fluck et al. 2021). These areas are also hotspots in our model + proxy (Fig. 4). It is further noteworthy to mention the location of the highest average number of hail days in this region, which is on the leeward side of the Massif Central summits (the Fluck et al. 2021 study links this to low-level convergence in addition to the location of mountains), is captured well by our results in the warm season (Fig. 5). However, the high intensity of cases slightly south in our model in September does not exist in the radar-derived hail frequencies. The radar-derived diurnal distribution is at its maximum between 14:00 and 18:00 local time, which is consistent with peak frequencies at around 18z in our results. Small spatial differences such as the later hour of hailstorms in SW France are also captured by our model + proxy (Fig. 6e, 6f).

A proxy developed for estimating damaging hail hazard using 6-hourly ERA-Interim (0.75°) reanalysis data between 1979–2015 suggested June, July, and August as the peak months for large hail frequency over most of the continent, and September-October for coastal areas in the south and west (Prein and Holland 2018), agreeing well with our results. The spatial distribution is also essentially similar, revealing higher intensities from E Spain through S and E France, Italy, and locally in the Balkans, although this previous study lacks the fine-scale detail provided by our study for the first time (Prein and Holland 2018). An interesting aspect of the Prein and Holland (2018) results are the higher peaks over parts of the Mediterranean Sea (C and W Mediterranean and Adriatic) compared to over European land areas, confirming our spatial patterns over the sea (Fig. 4). A follow-up ERA5-based study (Torralba et al. 2023) focused on Italy with similar outcomes, but providing further details such as south of the Alps in northern Italy having the severe hail maxima, just as our proxy highlights. They also capture up to 3–4 hail days in October along the SW coastline of Italy, which is in line with our findings.

Punge et al. (2017) combined satellite data (overshooting tops of thunderstorms) and ERA-Interim to assess a hail frequency map for Europe, which also highlighted similar areas of severe hail in northern Italy, south-eastern Austria, eastern Spain and the Massif Central. They also showed a peak in NE Algeria and N Tunisia, an area also highlighted by our model, although being near our domain border.

A 4-year satellite-based study has highlighted that the Central Mediterranean experiences up to 4–6 hail occurrences per year, which is higher than anywhere over the European landmass (Bang and Cecil 2019). This is in line with our model + proxy predictions of autumn storms offshore, which cannot be validated by ground observations-based studies, due to reporting issues. Although satellite-based estimates might not reflect the ground truth completely, it is encouraging to retrieve similar results via convection-permitting model output and our derived proxy. This marine region might be a little known alley for hailstorms in Europe.

Our CPM-based severe hail climatology for Europe provides results consistent with the current knowledge of the distribution of European hailstorms, and adds value as follows:

The model + proxy severe hail potential is an objective measure and does not depend on observational biases such as variable reporting efficiencies through space and time. Hence, less densely populated areas, less vulnerable locations, regions without good satellite/radar coverage, and night-time storms, etc. are equally and fairly represented in the climatology built using the CPM-based model output. It can be used to study any trends and provides information on the occurrence of hailstorms over the sea.

Although a 2.2km horizontal grid spacing is not sufficient to resolve the in-storm dynamics of a hailstorm, features such as the updraft speed in the hail growth zone are captured, and therefore the simulated convection permits the development of a better “ingredients-approach” proxy than previous proxies which have relied on large-scale hailstorm-favouring environments only. Additionally, better representation of the topographical details, improved microphysics, and simulation of finer scale features of atmospheric flow by CPMs are crucial in generating thunderstorms. Thus, our CPM-based severe hail measure provides a more reliable estimate of hailstorm occurrence than previous proxies based on coarser-resolution traditional climate models.

The CPM can be run not only for the current climate, but also for future projections. This will allow us to perform a similar analysis for a changing climate, i.e. to examine future changes in severe hailstorms: currently one of the most uncertain aspects of global warming.

Overall, the climatology built by a CPM-dedicated proxy performs well, and represents a significant advance compared to previous studies. However, we acknowledge that going further is possible. The use of thunderstorm-tracking tools (with more frequent precipitation/reflectivity output, such as several times an hour), the analysis of updraft width via multiple neighbouring grids, the use of parameters focused on thunderstorm phase and morphology, using a coupled hail model, updraft helicity, etc., are all potential tools for performing an even more advanced analysis, and are subject to future studies. Of course, these analyses will require even more detailed model outputs and larger computational resource, and much larger disk spaces, not feasible for most of the climate modelling community at this time.

Funding

This work was supported by the FUTURE-STORMS project (NE/R01079X/1). Elizabeth Kendon gratefully acknowledges funding from the EU under H2020 project European Climate Prediction System (EUCP; Grant agreement: 776613) and Joint UK BEIS/Defra MOHC Climate Programme (GA01101). Hayley Fowler was funded by the European Research Council Grant, INTENSE (ERC-2013-CoG-617329), and the Wolfson Foundation and the Royal Society as a Royal Society Wolfson Research Merit Award holder (grant: WM140025).

Acknowledgements

We thank European Severe Storms Laboratory (ESSL) and all ESWD contributors for the severe hail reports used in this study.

Competing Interests

The authors declare no competing interests.

Data availability

European 2.2km model dataset can be used under licence from the Met Office, but restrictions to the use apply, and must respect the work plans of EUCP project partners and of CORDEX-FPS-Convection.

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Climatology of severe hail potential in Europe based on a convection-permitting simulation

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Abstract

Figures

1. Introduction

2. Data and Methods

2.1. The hindcast simulation

2.2 The hail proxy

2.2.1 Background: Hail formation theory in brief

2.2.2. Extracting hail environments from model data

2.2.3. Severe and significant severe hail potential in convection-permitting simulations

2.3. Observational data

3. Results

3.1. The CPM-based climatology

3.2. Comparison with observations and existing climatologies

4. Conclusion

Declarations

References

Status:

Version 1