Winter Windstorms Research Articles

Projections and uncertainties of winter windstorm damage in Europe in a changing climate

Abstract. Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future.

Natural Hazards in a Changing World: Methods for Analyzing Trends and Non‐Linear Changes

AbstractEstimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long‐term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non‐stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100‐year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms.

Understanding winter windstorm predictability over Europe

Abstract. Winter windstorms belong to the most damaging meteorological events in the extra-tropics. Their impact on society makes it essential to understand and improve seasonal forecasts of these extreme events. Skilful predictions on a seasonal timescale have been shown in previous studies by investigating hindcasts from various forecast centres. This study aims to explain storm forecast skill based on relevant dynamical factors. Therefore, a number of factors which are known to influence either windstorms directly or their synoptic relevant systems, mid-latitude cyclones, are investigated. These factors are analysed for their relation to windstorm forecast performance based on a reanalysis (ERA5) and the seasonal hindcast of the UK Met Office (Global Seasonal forecasting system version 5, GloSea5). Within GloSea5, relevant dynamical factors are (1) validated with respect to their physical connections to windstorms, (2) investigated with respect to the seasonal forecast skill of the factors themselves, and (3) assessed on the relevance and influence of their forecast performance to and on windstorm forecast skill. Although not all investigated factors reveal a clear and consistent influence on windstorm forecast skill over Europe, core factors like mean sea level pressure gradient, sea surface temperature, equivalent potential temperature and Eady growth rate show consistent results within these three steps: their physical connection is well represented in the model; these factors are skilfully predicted in storm-relevant regions, and, consequently, this skill leads to increased forecast skill of winter windstorms over Europe. This study thus explains existing forecast skill in winter windstorms but also indicates potential for further model developments to improve seasonal winter windstorm predictions.

Importance of Bed Liquefaction‐Induced Erosion During the Winter Wind Storm in the Yellow River Delta, China

AbstractData obtained during a 5‐month field observation of the Yellow River Delta show that the storm wave‐related oscillatory flows are mainly in an intermittently turbulent regime (400 < ReΔ < 1,200). Wave‐induced seabed liquefaction causes changes to the bed erodibility and increases the sediment entrainment rate, resulting in the formation of fluid mud layer (FML) during winter wind events. The strong NE winter winds lead to most of the wave‐induced liquefaction events in the study area. It was found the sediment erosion rate is underestimated when we solely focus on the wave‐induced bottom shear stress. To parameterize wave‐induced excess pore pressure buildup and sediment liquefaction, a series of experiments were conducted in a large wave flume and a new liquefaction‐erosion method is proposed to calculate the sediment erosion flux. Analysis of model results shows that the FML thickness is about 2–12 cm at the storm‐arrival stage during the entire observation period. The thickness of FML significantly increases with the increase of wave height and decrease of water depth. Once the FML is formed, strong currents can remove fluid mud 10 hr after the storm‐arrival stage, while the background currents on calm days can hardly cause suspension. The erosion rate in liquefaction zones can increase 5–10 times compared with the erosion rate in nonliquefaction conditions. The whole Bohai Sea is highly turbid after intense NE winter wind, and the liquefaction zones during winter winds would be the major sediment source. Enhanced sediment erosion can cause the subsequent degradation of the subaqueous Yellow River Delta.

FARMER’S PERCEPTIONS REGARDING NATURAL HAZARDS IN COTTON-WHEAT CROPPING SYSTEM OF PUNJAB, PAKISTAN

The present research study was conducted during 2020 in Institute of Agricultural Resource Economics, University of Agricutlure, Faisalabad, Punjab, Pakistan to measure the farmers’ understanding regarding the impact of Climatic and Natural Hazards (CNHs) in the CottonWheat Cropping System of Punjab, Pakistan. The study also ranked the impact of climatic and natural hazards. The primary data were collected from 540 farmers of Punjab, Pakistan’s major cotton-growing districts. Questions regarding CNHs were inquired by using the Likert Scale (one being the strongly disagree and five strongly agree). Using descriptive statistics, the influence of climate and natural risks on agricultural yield was assessed. The replies were grouped into percentages and graphs. Approximately 61% of farmers strongly agreed that floods and droughts reduced agricultural yield. Moreover, 56% of respondents strongly agreed that frost, hailstorms and excessive heat harm agricultural output. In addition, 65 percent strongly agreed that biological illnesses, extreme cold, winter and summer winds, fog, smog, heatwaves, humidity, typhoons, and insect infestations had negative consequences. The impact of frequent rain was ranked as first by the farmers. In contrast, biological illnesses, hailstorms, bug infestations, floods and droughts, late rains, intense heat and frost, summer and winter wind storms, typhon, early showers and extreme cold were ranked 2nd through 17th. It was concluded that majority of farmers well aware of the climatic and natural hazards. Educating farmers on the impacts of CNHs on agricultural productivity was proposed.

Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set

Abstract. PRIMAVERA (process-based climate simulation: advances in high-resolution modelling and European climate risk assessments) was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The data are available at https://doi.org/10.5281/zenodo.6492182. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3 s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduce uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion and bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.

Large-scale circulation patterns and their influence on European winter windstorm predictions

Severe winter windstorms are amongst the most damaging weather events for Europe and show significant interannual variability. While surface variables (temperature, precipitation) have been successfully predicted for some time now, predictability of severe windstorms caused by extra-tropical cyclones remains less well explored. This study investigates windstorm prediction skill of the UK Met Office Global Seasonal Forecast System Version 5 (GloSea5) for the Northeast-Atlantic and European region. Based on an objective Lagrangian tracking of severe, damage relevant windstorms, three storm parameters are analysed: windstorm frequency and two intensity measures. Firstly, skill based on direct tracking of simulated windstorms is diagnosed. Significant positive skill for storm frequency and intensity is found over an extended area at the downstream end of the storm track, i.e., from the UK to southern Scandinavia. The skill for frequency agrees well with previous studies for older model versions, while the results of event-based intensity are novel. Receiver Operating Characteristic Curves for three smaller regions reveal significant skill for high and low storm activity seasons. Second, skill of windstorm characteristics based on their multi-linear regressions to three dominant large-scale circulation patterns [i.e., the North Atlantic Oscillation (NAO), the Scandinavian Pattern (SCA), and the East-Atlantic Pattern (EA)] are analysed. Although these large-scale patterns explain up to 80% of the interannual variance of windstorm frequency and up to 60% for intensity, the forecast skill for the respectively linear-regressed windstorms do not show systematically higher skill than the direct tracking approach. The signal-to-noise ratio of windstorm characteristics (frequency, intensity) is also quantified, confirming that the signal-to-noise paradox extends to windstorm predictions.

Towards operational impact forecasting of building damage from winter windstorms in Switzerland

AbstractNational meteorological and hydrological services issue warnings for severe weather events, typically based on stakeholder‐agreed fixed thresholds of meteorological parameters such as wind speeds or precipitation amounts. Yet societal decisions on preventive actions depend on the expected impacts of the weather event. In order to better inform such preventive actions, meteorological services are currently working towards including expected impacts into their warnings. We develop an open‐source impact forecasting system for building damage due to winter windstorms in Switzerland. It combines a numerical ensemble weather prediction model with exposure and vulnerability data. This system forecasts expected building damage in Swiss Francs with a 2‐day lead time on a 500‐m grid or aggregated to administrative regions. We compare the forecasted building damage with insurance claims in the canton of Zurich. The uncertainty of the impact forecasts is large. For the majority of days with severe winter windstorm damage, the mean forecasted damage was in the right order of magnitude, with one missed event and one false alarm. For thunderstorms and foehn storms, the rate of missed events and false alarms is much higher, most likely related to the limited meteorological forecast skill. Such impact forecasts can inform decision makers on preventive actions, such as allocating emergency response and other assets. Additionally, impact forecasts could also help communicating the severity of the upcoming event to the general public as well as indirectly help meteorological forecasters with taking warning decisions.

Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Abstract. With access to claims, insurers have a long tradition of being knowledge leaders on damages caused by windstorms. However, new opportunities have arisen to better assess the risks of winter windstorms in Europe through the availability of historic footprints provided by the Windstorm Information Service (Copernicus WISC). In this study, we compare how modelling of building damages complements claims-based risk assessment. We describe and use two windstorm risk models: an insurer's proprietary model and the open source CLIMADA platform. Both use the historic WISC dataset and a purposefully built, probabilistic hazard event set of winter windstorms across Europe to model building damages in the canton of Zurich, Switzerland. These approaches project a considerably lower estimate for the annual average damage (CHF 1.4 million), compared to claims (CHF 2.3 million), which originates mainly from a different assessment of the return period of the most damaging historic event Lothar–Martin. Additionally, the probabilistic modelling approach allows assessment of rare events, such as a 250-year-return-period windstorm causing CHF 75 million in damages, including an evaluation of the uncertainties. Our study emphasizes the importance of complementing a claims-based perspective with a probabilistic risk modelling approach to better understand windstorm risks. The presented open-source model provides a straightforward entry point for small insurance companies.

Variation in Phytoplankton Community Due to an Autumn Typhoon and Winter Water Turbulence in Southern Korean Coastal Waters

We evaluated changes in the phytoplankton community in Korean coastal waters during October 2016 and February 2017. Typhoon Chaba introduced a large amount of freshwater into the coastal areas during autumn 2016, and there was a significant negative relationship between salinity and nutrients in the Nakdong estuarine area, particularly in the northeastern area (Zone III; p < 0.001). The abundance of diatom species, mainly Chaetoceros spp., increased after this nutrient loading, whereas Cryptomonas spp. appeared as opportunists when there was relatively low diatom biomass. During winter, biotic and abiotic factors did not differ among the surface, middle, and lower layers (p > 0.01; ANOVA), implying that water mixing by winter windstorms and low surface temperature (due to the sinking of high-density water) physically accelerated mixing of the whole water column. Diatoms predominated under these conditions. Among diatoms, the centric diatom Eucampia zodiacus remained at high density at the inshore area and its abundance had a negative correlation with water temperature, implying that this species can grow at cold temperatures. On the other hand, the harmful freshwater diatom Stephanodiscus hantzschii mainly appeared in conditions with low salinity and high nutrients, implying that it can persist even in the saltwater conditions of the Nakdong Estuary. Our results indicate that hydro-oceanographic characteristics, such as river discharge after an autumn typhoon and winter water turbulence, have major effects on the composition of phytoplankton communities and can potentially affect the occurrence and characteristics of harmful algal blooms in southern Korean coastal waters.

Improvement in the decadal prediction skill of the North Atlantic extratropical winter circulation through increased model resolution

Abstract. In this study the latest version of the MiKlip decadal hindcast system is analyzed, and the effect of an increased horizontal and vertical resolution on the prediction skill of the extratropical winter circulation is assessed. Four different metrics – the storm track, blocking, cyclone and windstorm frequencies – are analyzed in the North Atlantic and European region. The model bias and the deterministic decadal hindcast skill are evaluated in ensembles of five members in a lower-resolution version (LR, atm: T63L47, ocean: 1.5∘ L40) and a higher-resolution version (HR, atm: T127L95, ocean: 0.4∘ L40) of the MiKlip system based on the Max Planck Institute Earth System model (MPI-ESM). The skill is assessed for the lead winters 2–5 in terms of the anomaly correlation of the quantities' winter averages using initializations between 1978 and 2012. The deterministic predictions are considered skillful if the anomaly correlation is positive and statistically significant. While the LR version shows common shortcomings of lower-resolution climate models, e.g., a storm track that is too zonal and southward displaced as well as a negative bias of blocking frequencies over the eastern North Atlantic and Europe, the HR version counteracts these biases. Cyclones, i.e., their frequencies and characteristics like strength and lifetime, are particularly better represented in HR. As a result, a chain of significantly improved decadal prediction skill between all four metrics is found with the increase in the spatial resolution. While the skill of the storm track is significantly improved primarily over the main source region of synoptic activity – the North Atlantic Current – the other extratropical quantities experience a significant improvement primarily downstream thereof, i.e., in regions where the synoptic systems typically intensify. Thus, the skill of the cyclone frequencies is significantly improved over the central North Atlantic and northern Europe, the skill of the blocking frequencies is significantly improved over the Mediterranean, Scandinavia and eastern Europe, and the skill of the windstorms is significantly improved over Newfoundland and central Europe. Not only is the skill improved with the increase in resolution, but the HR system itself also exhibits significant skill over large areas of the North Atlantic and European sector for all four circulation metrics. These results are particularly promising regarding the high socioeconomic impact of European winter windstorms and blocking situations.

It's in your glass: a history of sea level and storminess from the Laphroaig bog, Islay (southwestern Scotland)

Severe winter windstorms have become an increasingly common occurrence over recent decades in northwestern Europe. Although there exists considerable uncertainty, storminess is projected to increase in the future. On centennial to millennial time scales in particular, the mechanisms forcing storminess remain unsettled. We contribute to available palaeostorm records by reconstructing changes over the last 6670 years using a coastal peat sequence retrieved from the ombrotrophic Laphroaig bog on Islay, southwestern Scotland. We use a combination of ash content, grain size and elemental chemistry to identify periods of greater storminess, which are dated to 6605, 6290–6225, 5315–5085, 4505, 3900–3635, 3310–3130, 2920–2380, 2275–2190, 2005–1860, 1305–1090, 805–435 and 275 cal. a BP. Storm signals in the first half of the record up to ~3000 cal. a BP are mainly apparent in the grain‐size changes. Samples from this time period also have a different elemental signature than those later in the record. We speculate that this is due to receding sea levels and the consequent establishment of a new sand source in the form of dunes, which are still present today. The most significant events and strongest winds are found during the Iron Ages Cold Epoch (2645 cal. a BP), the transition into, and in the middle of, the Roman Ages Warm Period (2235 and 1965 cal. a BP) and early in the Little Ice Age (545 cal. a BP). The Laphroaig record generally agrees with regionally relevant peat palaeostorm records from Wales and the Outer Hebrides, although the relative importance of the different storm periods is not the same. In general, stormier periods are coeval with cold periods in the region as evidenced by parallels with increased ice‐rafted debris in the North Atlantic, highlighting that sea‐ice conditions could impact future storminess and storm track position.

Human influence on European winter wind storms such as those of January 2018

Abstract. Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe.

Loss potentials based on an ensemble forecast: How likely are winter windstorm losses similar to 1990?

In this paper we investigate the feasibility and added value of using the seasonal hindcasts of the ECMWF System 4 as a hazard event set for European winter windstorms damage calculations. The windstorms are identified for every ensemble member and every year by an objective windstorm tracking algorithm. The damages are calculated directly from the obtained wind footprints via the open source natural catastrophe damage model CLIMADA for Germany, the UK, France and Spain and compared to the loss from ERA‐Interim. The results show that the ensembles of losses in System 4 nicely capture the inter‐annual loss variability of the reanalysis. Due to more than 1,500 years of “virtual reality” windstorm data from the hindcasts, the return levels of extreme losses can be estimated fairly accurately. Based on System 4, the losses in the scale of 1990 (January, February, March and December including the prominent windstorm Daria) represent a 20‐year event in Germany whereas they represent a 100‐year event for the UK. Thus, a considerably shorter return period compared to return periods calculated from ERA‐Interim alone.Further we investigate the link between the annual losses and large‐scale drivers derived from mean‐sea‐level‐pressure (MSLP) data in System 4. We can show that within System 4 there is a significant link between increased loss potentials for strongly positive North Atlantic Oscillation (NAO) phases for Germany and the UK as well as a reduced loss potential for Spain. The link between the other analysed indices is weak bar the East Atlantic (EA) pattern index. Thus, if the NAO in System 4 is correct we can assume that the windstorms in System 4 are useable. If this premise is given our study shows that the loss estimates and ultimately the return levels of losses from System 4 can be used in an operational way.

Factors Influencing the Seasonal Predictability of Northern Hemisphere Severe Winter Storms

AbstractWe investigate the role of the tropics, the stratosphere, and atmosphere‐ocean coupling for seasonal forecasts of strong, potentially damaging, Northern Hemisphere extratropical winter wind storm frequencies. This is done by means of relaxation experiments with the European Centre for Medium‐Range Weather Forecasts model, which allow us to prescribe perfect forecasts for specific parts of the coupled atmosphere‐ocean system. We find that perfect predictions of the Northern Hemisphere stratosphere significantly enhance winter storm predictive skill between eastern Greenland and Northern Europe. Correct seasonal predictions of the occurrence of stratospheric sudden warmings play a decisive role. The importance of correctly predicting the tropics and of two‐way atmosphere‐ocean coupling, both for forecasting stratospheric sudden warming risk and, correspondingly, severe winter storm frequency, is noted.

Modelling serial clustering and inter-annual variability of European winter windstorms based on large-scale drivers.

Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large‐scale indices retrieved from the ERA‐20C reanalysis. Significant large‐scale drivers accountable for the inter‐annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter‐annual variability in certain regions.Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.

Probabilistic evaluation of decadal prediction skill regarding Northern Hemisphere winter storms

Winter wind storms related to intense extra-tropical cyclones are meteorological extreme events, often with major impacts on economy and human life, especially for Europe and the mid-latitudes. Hence, skillful decadal predictions regarding the frequency of their occurrence would be of great socio-economic value. The present paper extends the study of Kruschke et al. (2014) in several aspects. First, this study is situated in a more impact oriented context by analyzing the frequency of potentially damaging wind storm events instead of targeting at cyclones as general meteorological features which was done by Kruschke et al. (2014). Second, this study incorporates more data sets by analyzing five decadal hindcast experiments – 41 annual (1961–2001) initializations integrated for ten years each – set up with different initialization strategies. However, all experiments are based on the Max-Planck-Institute Earth System Model in a low-resolution configuration (MPI-ESM-LR). Differing combinations of these five experiments allow for more robust estimates of predictive skill (due to considerably larger ensemble size) and systematic comparisons of the underlying initialization strategies. Third, the hindcast experiments are corrected for model bias and potential drifts over lead time by means of a novel parametric approach, accounting for non-stationary model drifts. We analyze whether skillful probabilistic three-category forecasts (enhanced, normal or decreased) can be provided regarding winter (ONDJFM) wind storm frequencies over the Northern Hemisphere (NH). Skill is assessed by using climatological probabilities and uninitialized transient simulations as reference forecasts. It is shown that forecasts of average winter wind storm frequencies for winters 2–5 and winters 2–9 are skillful over large parts of the NH. However, most of this skill is associated with external forcing from transient greenhouse gas and aerosol concentrations, already included in the uninitialized simulations. Only over East Asia and the Northwest Pacific, the Northwest Atlantic as well as the Eastern Mediterranean the initialized hindcasts perform significantly better than the uninitialized simulations. While no significant differences are evident between anomaly- and full-field-initialization, initializing the model's ocean component from GECCO2-ocean-reanalysis yields slightly better results than from ORA-S4, especially over the Northeast Pacific. Additionally, it is shown that the novel parametric drift-correction approach – estimating potential cubic drifts with parameters linearly changing in time – is more appropriate than the standard procedure – estimating constant model drifts via the lead-time-dependent bias – and, hence, yields higher skill estimates.

Modelling economic losses of historic and present-day high-impact winter windstorms in Switzerland

This study investigates the wind gusts and associated economic loss patterns of high-impact winter windstorms in Switzerland between 1871 and 2011. A novel approach for simulating windstorm-related gusts and losses at regional to local scales is applied to a sample of 84 windstorms. The approach involves the dynamical downscaling of the Twentieth Century Reanalysis (20CR) ensemble mean to 3-km horizontal grid size using the Weather Research and Forecasting (WRF) model. Economic losses are simulated at municipal level for present-day asset distribution based on the downscaled (parameterised) wind gusts at high spatiotemporal resolution using the open-source impact model climada. A comparison with insurance loss data for two recent windstorms (“Lothar” in 1999, “Joachim” in 2011) indicates that the loss simulation allows to realistically simulate the spatial patterns of windstorm losses. The loss amplitude is strongly underestimated for ‘Lothar’, while it is in reasonable agreement for ‘Joachim’. Possible reasons are discussed. Uncertainties concerning the loss simulation arise from the wind gust estimation method applied; estimates can differ considerably among the different methods, in particular over high orography. Furthermore, the quality of the loss simulation is affected by the underlying simplified assumptions regarding the distribution of assets and their susceptibilities to damage. For the whole windstorm sample, composite averages of simulated wind gust speed and loss are computed. Both composites reveal high values for the densely populated Swiss Plateau and lower values for south-eastern Switzerland; metropolitan areas stand out in the loss composite. Eight of the top 10 events concerning the losses simulated for present-day asset distribution and summed over all Swiss municipalities occurred after 1950. It remains uncertain whether this is due to decadal-scale changes of winter windstorms in Switzerland or merely due to a possible bias of the 20CR ensemble mean towards lower wind speeds in the period before around 1950.

Interactions between apparently ‘primary’ weather-driven hazards and their cost

A statistical analysis of the largest weather-driven hazards in the UK contradicts the typical view that each predominates in distinct events that do not interact with those of other hazard types (i.e., are ‘primary’); this potentially has implications for any multi-hazard environments globally where some types of severe event are still thought to occur independently. By a first co-investigation of long (1884–2008) meteorological time-series and nationwide insurance losses for UK domestic houses (averaging £1.1 billion/yr), new systematic interactions within a 1 year timeframe are identified between temporally-distinct floods, winter wind storms, and shrink–swell subsidence events (P < 0.03); this increases costs by up to £0.3 billion/yr (i.e., 26%), although impacts will be spatially variable depending upon the interplay of hazards. ‘Memory’ required in the environmental system to cause these intra-annual links between event types appears to reside in soil moisture and, tentatively, sea surface temperatures. Similar, unidentified interactions between non-synchronous events are likely worldwide, and the analytical methods we have developed to identify and quantify them are suitable for application to meteorological, geological (e.g., volcanic) and cryospheric (e.g., avalanches) hazards.

The XWS open access catalogue of extreme European windstorms from 1979 to 2012

Abstract. The XWS (eXtreme WindStorms) catalogue consists of storm tracks and model-generated maximum 3 s wind-gust footprints for 50 of the most extreme winter windstorms to hit Europe in the period 1979–2012. The catalogue is intended to be a valuable resource for both academia and industries such as (re)insurance, for example allowing users to characterise extreme European storms, and validate climate and catastrophe models. Several storm severity indices were investigated to find which could best represent a list of known high-loss (severe) storms. The best-performing index was Sft, which is a combination of storm area calculated from the storm footprint and maximum 925 hPa wind speed from the storm track. All the listed severe storms are included in the catalogue, and the remaining ones were selected using Sft. A comparison of the model footprint to station observations revealed that storms were generally well represented, although for some storms the highest gusts were underestimated. Possible reasons for this underestimation include the model failing to simulate strong enough pressure gradients and not representing convective gusts. A new recalibration method was developed to estimate the true distribution of gusts at each grid point and correct for this underestimation. The recalibration model allows for storm-to-storm variation which is essential given that different storms have different degrees of model bias. The catalogue is available at http://www.europeanwindstorms.org .