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The 7th European Storm Workshop gathered scientists and insurance industry experts from 10 countries to facilitate an interdisciplinary exchange regarding novel scientific advances and developments in risk modeling, allowing specialists with different backgrounds working in European windstorm research to discuss the priorities for future research.
What: The seventh European Storm Workshop
gathered scientists and insurance industry
experts from 10 countries to facilitate an
interdisciplinary exchange regarding novel
scientific advances and developments in risk
modeling, allowing specialists with different
backgrounds working in European windstorm
research to discuss the priorities for future
When: 10–12 October 2018
Where: Karlsruhe, Germany
An Interdisciplinary Workshop on European Storms
Joaquim G. Pinto, Florian Pantillon, Patrick ludWiG, madeleine-SoPhie déroche,
Giovanni leoncini, chriStoPh c. raible, len c. ShaFFrey, and david b. StePhenSon
AFFILIATIONS: Pinto and lu dWiG Institute of Meteorology
and Climate Research, Karlsruhe Institute of Technology,
Karlsruhe, Germany; Pantillon —Institute of Meteorology and
Climate Research, Karlsruhe Institute of Technology, Karlsruhe,
Germany, and Laboratoire d’Aérologie, Centre National de la
Recherche Scientifique and Université de Toulouse, Toulouse,
France; déroch eGroup P&C Risk Management, AXA , Paris,
France; leoncini —Zurich Insurance Company Ltd, Zurich,
Switzerland; raib leClimate and Environmental Physics, and
Oeschger Centre for Climate Change Research, University
of Bern, Bern, Switzerland; ShaFFre yNational Centre for
Atmospheric Science, and Department of Meteorology, University
of Reading, Reading, United Kingdom; StePhe nSon —Department
of Mathematics, University of Exeter, Exeter, United Kingdom
DOI:10.1175/BAMS -D-19-0026.1
In final form 8 March 2019
©2019 American Meteorological Society
For information regarding reuse of this content and general copyright
information, consult the AMS Copyright Policy.
W indstorms are extreme midlatitude cyclones
and one of the major natural hazards that
cause damage and losses in Europe. However,
the processes involved in their intensification and
generation of disastrous impacts, such as widespread
wind damage and f looding, are not fully understood.
Initiated in 2011, the European Storm Workshop
series ( brings together
the academic community, weather services, and risk
model developers from insurance and engineering
consulting companies. The goals are to stimulate
interdisciplinary research on midlatitude storms and
to bridge the gap between fundamental research and
practical implementations.
The seventh European Storm Workshop took
place in October 2018 at the Karlsruhe Institute of
Technology (KIT) in Germany. Over 60 partici-
pants from 10 countries discussed the latest results
and developments in windstorm research and its
industry applications, including 26 nonacadem-
ics. The workshop featured a total of 30 oral and
poster presentations split into three sessions, allowing
plenty of opportunities for exchange and discussion.
Presentations and the discussions they generated
focused on the dynamics of European windstorms
(extreme midlatitude cyclones), their predictability
and variability from weather to climate time scales,
risk assessments, and academic–insurance industry
collaborations. The workshop included keynote
lectures given by speakers from both academia and
the insurance industry. Highlights of each session are
discussed below.
STORMS. The aim of this session was to discuss
new progress in understanding European wind-
storm dynamics. A major discussion topic was the
combination of perils associated with windstorms
such as extreme precipitation, severe wind gusts and/
or storm surges (so-called compound events). The
complexity of these events in terms of impacts makes
them a crucial topic for both researchers and the in-
surance industry. For example, Margarida Liberato
(University of Trás-os-Montes and Alto Douro)
presented a consistent catalog of exceptional, high-
impact windstorms for Iberia, which lead to both
wind and rainfall extremes. Extreme events were also
the focus of the keynote by Helen Dacre (University
of Reading), who reported on advances in the under-
standing of the relationship between warm conveyor
belts and atmospheric rivers (Dacre et al. 2019). She
showed the importance of low-level cyclone airflow,
known as the feeder airstream, which originates
ahead of the cyclone and flows rearward toward the
cyclone center. Some of the moisture transported
by the feeder airstream is supplied to the base of
the warm conveyor belt where it ascends to form
precipitation, while the rest remains at low levels,
forming the leading edge of an atmospheric river. Lea
Eisenstein (KIT) presented a modeling study of the
first detected sting-jet windstorm over continental
Europe (“Egon” in January 2017). Devastating sting
jets are associated with strong wind gusts lasting for
a few hours over a distinct region located between
the cold and warm jet of Shapiro–Keyser cyclones
(Hewson and Neu 2015). Accurately modeling the
sting jet, which is essential to assess its loss poten-
tial, requires high spatial resolution. Convection-
permitting simulations show that the characteristics
of this storm were largely consistent with other
known cases over the North Atlantic and the British
Isles (Clark and Gray 2018), but the cyclone was
also clearly affected by topography over continental
Europe. Possible changes in the characteristics of
windstorms in a future climate were the subject of
Dominik Büeler’s (ETH Zurich; KIT) presentation.
Based on idealized studies, he reported that, while
the intensity of moderate cyclones may decrease in
a warmer world, an intensification is expected for
strong cyclones, which is partly associated w ith latent
heating effects. Such results are of great importance
for the insurance industry, as more windstorms have
the potential to cause higher losses.
SCALES. This session discussed the current state
of knowledge of the predictability of cyclones on dif-
ferent time scales. Aiko Voigt (KIT) illustrated with
various examples the importance of cloud–radiative
interactions on the midlatitude atmospheric cir-
culation and cyclone activity. Understanding these
interactions is crucial for an adequate assessment
of climate change projections, as clouds are one
of the largest sources of uncertainty (Bony et al.
2015). Recent studies suggest that thermal radiation
effects can weaken idealized cyclones by modifying
potential vorticity (Schäfer and Voigt 2018). Given
that the cloud–radiative impact is important for
both weather and climate, a better understanding of
cloud–circulation coupling is needed to quantify the
response of cyclone activity to global warming. Len
Shaffrey (University of Reading) presented a critical
evaluation of the significant increase in Northern
Hemisphere storminess detected in ECMWF’s first
atmospheric reanalysis of the twentieth century
(ERA20C), which had not been reported in this form
by other studies. This century-long trend is appar-
ently related to a significant and unrealistic decrease
in surface pressure over the Arctic. This decreasing
trend in pressure is not seen in observational data and
leads to an increase of the meridional pressure gradi-
ent between the high and midlatitudes and therefore
of midlatitude storminess (Bloomfield et al. 2018).
Hence, the long-term storminess trends present in
ERA20C should be regarded with caution. Finally,
Florian Pantillon (Centre National de la Recherche
Scientifique; KIT) presented recent advances on
the prediction of wind gusts over central Europe
based on statistical postprocessing of an operational
convection-permitting weather forecast ensemble.
While ensemble model output statistics (EMOS)
substantially improve the average gust forecasts,
there are still a few cases which are poorly forecast
despite the use of EMOS (Pantillon et al. 2018). For
these cases, it is crucial to accurately represent frontal
convection, which is the source of some of the most
destructive gusts during windstorms over central
Europe (Ludwig et al. 2015).
COLLABORATIONS. Applications of wi ndstorm
research were discussed focusing on the insurance
industry. One key aspect to improve the assessment
ES176 JUNE 2019
of windstorm risk is access to both observational and
model data. Alan Whitelaw (CGI IT U.K. Limited)
presented the operational windstorm service for
the insurance sector provided by the Copernicus
Climate Change Service. Expanding upon previous
efforts (Roberts et al. 2014), it provides an extended
database of windstorm tracks and high-resolution
wind footprints (
/wisc/#/). The new developments combine dynamical
and statistical downscaling to cover a larger number
of events. The use of numerical prediction models for
windstorm risk purposes has increased in recent years,
but they remain computationally very expensive and
are not easily implemented by private companies. As
an example of collaboration with the scientific com-
munity, Robin Locatelli (AXA) and Bernd Becker (Met
Office) presented a research partnership aiming at
providing hig h-resolution gust footprints for historical
events. These footprints were combined with claims
data to develop vulnerability curves for the European
market. Using statistical modeling, Dav id Stephenson
(University of Exeter) discussed various approaches to
quantify the dominant extremal dependence class for
realist ic windstorm footprints and found lit tle evidence
of asymptotic extremal dependency. When fitting the
data with statistical dist ributions, the Gaussian copula
appears to perform well, which allows the statistical
simulation of windstorm footprints (Dawkins and
Stephenson 2018). This approach opens the possibil-
ity of using geostatistical models for fast simulation
of windstorm hazard maps, which can complement
dynamical modeling approaches. Finally, one crucial
issue for the estimation of aggregated insurance losses
is the occurrence of multiple windstorms within a
season, a phenomenon known as storm clustering.
Based on high-resolution climate model simulations,
Matthew Priestley (University of Reading) showed
that serial clustering leads to an increase in annua l ag-
gregated losses of 10%–20% for return periods longer
than 3 years (Priestley et al. 2018). This was another
successful example of how basic research can have
important industrial applications.
PERSPECTIVES. Future directions and emerging
topics were debated in three breakout groups, which
covered a wide range of areas including new oppor-
tunities to further our understanding of European
windstorm risk. For example, the potential to reassess
historical windstorm risk through international proj-
ects on data discovery and the development of new
multidecadal reanalysis was discussed to overcome
the current limitations associated with the short his-
toric record. Similarly, new opportunities to under-
stand climate change impacts on future windstorm
risk will arise with the upcoming phase 6 of the
Coupled Model Intercomparison Project (CMIP6)
and the High Resolution Model Intercomparison
Project (HighResMIP) climate model projections
(e.g., Haarsma et al. 2016). A better understanding
of the importance of the different cyclone relative
air flows (“conveyor belts”; Hewson and Neu 2015)
for total property damage was also identified as
a key priority. Other emerging areas of scientific
interest include improved seasonal forecasts of the
North Atlantic Oscillation (e.g., Scaife et al. 2014),
which might help windstorm risk estimation for
the insurance industry. Additionally, validation and
calibration methods of extreme storms were debated.
Given the limited sample size of the historic records,
there is a need to develop methods beyond the stan-
dard quantile mapping approaches in order to cor-
rect biases of extreme events. Overall, the breakout
discussions highlighted the strong synergy between
academia and the insurance industry in terms of open
research questions, providing compelling evidence of
the need for sustained collaboration and dialogue. A
general consensus was reached to keep organizing
future workshops. The program and presentations
are available on our website (www.stormworkshops
ACKNOWLEDGMENTS. The authors thank all
the workshop participants for their contributions and
discussions. The workshop was made possible thanks
to support from the AXA Research Fund and the
Transregional Collaborative Research Center SFB/TRR
165 “Waves to Weather” funded by the German Research
Foundation (DFG).
Bloomfield, H. C., L. C. Shaffrey, K. I. Hodges, and P. L.
Vidale, 2018: A critical assessment of the long-term
changes in the wintertime surface Arctic Oscilla-
tion and Northern Hemisphere storminess in the
ERA20C reanalysis. Environ. Res. Lett., 13, 094004,
Bony, S., and Coauthors, 2015: Clouds, circulation and
climate sensitivity. Nat. Geosci., 8, 261–268, https://
Clark, P. A., and S. L. Gr ay, 2018: Sting jets i n extratropica l
cyclones: A review. Quart. J. Roy. Meteor. Soc., 144,
Dacre, H. F., O. Martinez-Alvarado, a nd C. O. Mbengue,
2019: Linking atmospheric rivers a nd warm conveyor
belt airflows. J. Hydrometeor.,
/JHM-D-18-0175.1, in press.
Dawkins, L. C., and D. B. Stephenson, 2018: Quanti-
fication of extremal dependence in spatial natural
hazard footprints: independence of windstorm
gust speeds and its impact on aggregate losses. Nat.
Hazards Earth Syst. Sci., 18, 2933–2949, https://doi
Haarsma, R. J., and Coauthors, 2016: High Resolution
Model Intercomparison Project (HighResMIP v1.0)
for CMIP6. Geosci. Model Dev., 9, 4185–4208, https://
Hewson, T. D., and U. Neu, 2015: Cyclones, windstorms
and the IM ILAST project. Tellus, 67A, 27 128, https://
Ludwig, P., J. G. Pinto, S. A. Hoepp, A. H. Fink, and
S. L. Gray, 2015: Secondary cyclogenesis along an
occluded front leading to damaging wind gusts:
Windstorm Kyrill, January 2007. Mon. Wea. Rev.,
143, 1417–1437,
Pantillon, F., S. Lerch, P. Knippertz, and U. Corsmeier,
2018: Forecasting wind gusts in winter storms using
a calibrated convection-permitting ensemble. Quart.
J. Roy. Meteor. Soc., 144, 1864–1881,
Priestley, M. D. K., H. F. Dacre, L. C. Shaffrey, K. I.
Hodges, and J. G. Pinto, 2018: European windstorm
clustering and seasonal losses. Nat. Hazards Earth
Syst. Sci., 18, 2991–3006,
Roberts, J. F., and Coaut hors, 2014: The XWS open access
catalogue of extreme European windstorms from
1979 to 2012. Nat. Hazards Earth Syst. Sci., 14, 2487–
Scaife, A. A., and Coauthors, 2014: Skillful long-range
prediction of European and North American
winters. Geophys. Res. Lett., 41, 2514–2 519, https://doi
Schäfer, S. A. K., and A. Voigt, 2018: Radiation weakens
ideal ized midlat itude cyclones. Geophys. Res. Lett., 45,
ES178 JUNE 2019
... This was achieved by using a loss model that required training with records of local insurance data. Beyond these published studies, several companies in the insurance sector (e.g., Willis Towers Watson, Aon, Guy Carpenter, AIR, RMS) provide loss estimates of impending or current windstorm events as a service for their clients (see Pinto et al., 2019, for an overview). These models link freely available forecasts from the weather services to in-house company loss models. ...
... These impact estimates and their uncertainty provide a clear added value to the clients, as they enable them to take measures to minimize potential impacts of an impending storm, for example, to assign staff or to buy short-term additional windstorm damage coverage (Welker et al., 2020). Unfortunately, such information is not widely accessible, which calls for enhanced communication between public and private research (Pinto et al., 2019). To the authors' knowledge, there is no published study on the quantitative benefits of windstorm impact forecasts yet. ...
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Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe.
... Gusts represent the wind hazard most likely to be associated with serious and harmful windstorm damage, ranging from human fatalities and injuries to losses of livelihoods (Sheridan 2018;Pinto et al. 2019). They affect a wide range of infrastructures including buildings, power distribution outages, road and air traffic. ...
The devastating winds in extra-tropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the non-local transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations.
... In the following, we will summarize the efforts for forecasting the impacts of storm events. According to Merz et al. (2020), there are storm impact forecasting systems for insurance losses in the private domain, for example, proprietary models run by insurance companies (Pinto et al., 2019). In the public domain, there have been studies that show the skill of impact forecasting for storm impacts on a theoretical level (Pantillon et al., 2017;Pardowitz et al., 2016), but they do not focus on the communication of these forecasted impacts as warnings to specific users or the general public. ...
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National 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. We develop an open‐source impact forecasting system to better inform societal decisions on preventive actions. It combines a numerical ensemble weather prediction model with exposure and vulnerability data. The figure shows mean forecasted building damage of the storm Burglind/Eleanor hitting Switzerland on 3 January 2018, at a lead time of 2 days
... Intense extratropical cyclones belong to the main meteorological hazards in midlatitudes due to the associated windstorms and they strongly affect regions located downstream of the North Atlantic storm track (Lamb and Frydendahl 1991). Therefore, European windstorms have been widely studied in both academia and industry due to large insurance losses associated with extreme events (e.g., Pinto et al. 2019). Overall, the dynamics and life cycle of extratropical cyclones are well understood at the synoptic scale thanks to a century of research on the topic [see Schultz et al. (2018) for a historical review]. ...
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Damaging gusts in windstorms are represented by crude subgrid-scale parameterizations in today’s weather and climate models. This limitation motivated the Wind and Storms Experiment (WASTEX) in winter 2016–17 in the Upper Rhine Valley over southwestern Germany. Gusts recorded at an instrumented tower during the passage of extratropical cyclone “Thomas” on 23 February 2017 are investigated based on measurements of radial wind with ≈70-m along-beam spacing from a fast-scanning Doppler lidar and realistic large-eddy simulations with grid spacings down to 78 m using the Icosahedral Nonhydrostatic model. Four wind peaks occur due to the storm onset, the cold front, a precipitation line, and isolated showers. The first peak is related to a sudden drop in dewpoint and results from the downward mixing of a low-level jet and a dry layer within the warm sector characterized by extremely high temperatures for the season. While operational convection-permitting forecasts poorly predict the storm onset overall, a successful ensemble member highlights the role of upstream orography. Lidar observations reveal the presence of long-lasting wind structures that result from a combination of convection- and shear-driven instability. Large-eddy simulations contain structures elongated in the wind direction that are qualitatively similar but too coarse compared to the observed ones. Their size is found to exceed the effective model resolution by one order of magnitude due to their elongation. These results emphasize the need for subkilometer-scale measuring and modeling systems to improve the representation of gusts in windstorms.
Recent advances in numerical weather prediction, combined with the new generation, high‐resolution climate simulations, and open‐source loss modeling frameworks, herald a move beyond the limited statistical representation of catastrophe risk based on past observations. In this new forward‐looking view of risk, an appreciation that our observed record of past natural catastrophes represents a limited sample of possible events, and that the statistics of weather and climate are changing as the planet warms, highlights a key limitation in traditional catastrophe modeling approaches that are built on defining statistical relationships using the observed record. Instead, ensembles of new spatially and dynamically consistent simulations of weather and climate provide physically plausible, but as‐yet‐unseen events at scales appropriate for making effective risk management and risk transfer decisions. This approach is especially useful in locations around the world where observational records are unobtainable or of short historical duration, such as in low‐income countries. We take a forward‐looking approach at the way that future catastrophe modeling and insurance underwriting could occur in response to these technological and scientific advances, using open‐source loss model frameworks.
Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s ⁻¹ ) and damaging (≥25.7 m s ⁻¹ ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.
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Natural hazards, such as European windstorms, have widespread effects that result in insured losses at multiple locations throughout a continent. Multivariate extreme-value statistical models for such environmental phenomena must therefore accommodate very high dimensional spatial data, as well as correctly representing dependence in the extremes to ensure accurate estimation of these losses. Ideally one would employ a flexible model, able to characterise all forms of extremal dependence. However, such models are restricted to a few dozen dimensions, hence an a priori diagnostic approach must be used to identify the dominant form of extremal dependence. Here, we present various approaches for exploring the dominant extremal dependence class in very high dimensional spatial hazard fields: tail dependency measures, copula fits, and conceptual loss distributions. These approaches are illustrated by application to a data set of high-dimensional historical European windstorm footprints (6103 spatial maps of 3-day maximum gust speeds at 14872 locations). We find there is little evidence of asymptotic extremal dependency in windstorm footprints. Furthermore, empirical extremal properties and conceptual losses are shown to be well reproduced using Gaussian copulas but not by extremally dependent models such as Gumbel copulas. It is conjectured that the lack of asymptotic dependence is a generic property of turbulent flows. These results open up the possibility of using geostatistical Gaussian process models for fast simulation of windstorm hazard fields.
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Windstorms associated with low‐pressure systems from the North Atlantic are the most important natural hazards for central Europe. Although their predictability has generally improved over the last decades, forecasting wind gusts is still challenging due to the multiple scales involved. One of the first ensemble prediction systems at convection‐permitting resolution, COSMO‐DE‐EPS offers a novel 2.8‐km dataset over Germany for the 2011–2016 period. The high resolution allows representing mesoscale features that are hardly captured by global models, while the long period allows both investigating rare storms and applying statistical post‐processing. Ensemble model output statistics based on a truncated logistic distribution substantially improve forecasts of wind gusts in the whole dataset. However, some winter storms exhibit uncharacteristic forecast errors that cannot be reduced by post‐processing. During the passage of the most severe storm, gusts related to a cold jet are relatively well predicted at the time of maximum intensity whereas those related to a warm jet are poorly predicted at an early phase. Wind gusts are overestimated during two cases of frontal convection, which suggests that even higher resolution is needed to fully resolve the downward mixing of momentum and the stabilization resulting from convective dynamics. In contrast, extreme gusts are underestimated during a rare case involving a possible sting jet but this arises from the representation of the synoptic‐ rather than the mesoscale. The synoptic scale also controls the ensemble spread, which is inherited from the initial and boundary conditions mostly. This is unsurprising but leads to high forecast uncertainty in a case of small, fast‐moving cyclone crossing the model domain. These results illustrate how statistical post‐processing can help identify the limits of predictability across scales in convection‐permitting ensemble forecasts. They may guide the development of regime‐dependent statistical methods to further improve forecasts of wind gusts in winter storms. This article is protected by copyright. All rights reserved.
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This study investigates the robustness of the long-term changes in the wintertime surface Arctic Oscillation (AO) in the ERA20C reanalysis. A statistically significant trend in the AO is found in ERA20C over the period 1900–2010. These long-term changes in the AO are not found in two other observational datasets. The long-term change in the AO in ERA20C is associated with statistically significant negative trend (approximately −6 hPa per century) in mean-sea level pressure (MSLP) over the Northern Hemisphere polar regions. This is not seen in the HADSLP2 observational dataset, suggesting that the trends in the ERA20C AO index may be spurious. The spurious long-term changes in MSLP and the AO index in ERA20C result in a strengthening of the meridional MSLP gradient in ERA20C. The strengthening of the meridional MSLP gradient is consistent with increases in wintertime storminess in Northern Europe and the NH high latitudes.
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Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20% larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10%–20% relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25% and 50%. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.
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Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950–2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.
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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 .
Extreme precipitation associated with extratropical cyclones can lead to flooding if cyclones track over land. However, the dynamical mechanisms by which moist air is transported into cyclones is poorly understood. In this paper we analyze airflows within a climatology of cyclones in order to understand how cyclones redistribute moisture stored in the atmosphere. This analysis shows that within a cyclone’s warm sector the cyclone-relative airflow is rearwards relative to the cyclone propagation direction. This low-level airflow (termed the feeder airstream) slows down when it reaches the cold front, resulting in moisture flux convergence and the formation of a band of high moisture content. One branch of the feeder airstream turns toward the cyclone center, supplying moisture to the base of the warm conveyor belt where it ascends and precipitation forms. The other branch turns away from the cyclone center exporting moisture from the cyclone. As the cyclone travels, this export results in a filament of high moisture content marking the track of the cyclone (often used to identify atmospheric rivers). We find that both cyclone precipitation and water vapor transport increase when moisture in the feeder airstream increases, thus explaining the link between atmospheric rivers and the precipitation associated with warm conveyor belt ascent. Atmospheric moisture budgets calculated as cyclones pass over fixed domains relative to the cyclone tracks show that continuous evaporation of moisture in the precyclone environment moistens the feeder airstream. Evaporation behind the cold front acts to moisten the atmosphere in the wake of the cyclone passage, potentially preconditioning the environment for subsequent cyclone development.
This paper reviews the current state of knowledge of sting jets in extratropical cyclones. Sting jets were formally identified in 2004 by the pioneering work of Keith Browning. Reviewing this and subsequent studies, we define the sting jet as a coherent air flow that descends from mid‐levels inside the cloud head into the frontal‐fracture region of a Shapiro‐Keyser cyclone over a period of a few hours leading to a distinct region of near‐surface stronger winds. It therefore lies above the cold conveyor belt during some stage its life, but, at least in some cases, descends to reach the top of boundary layer ahead of the cold conveyor belt. It is not attributed to a specific mechanism in this definition. We conclude that it is likely that a continuum of sting jet descent and speed‐up mechanisms exists. At one extreme is balanced descent partly associated with frontolysis in the frontal‐fracture region. More horizontally small‐scale and stronger frontolytic descent may occur associated with weak stability to slantwise convective downdraughts. Instability to slantwise convective downdraughts may occur in some systems, leading to multiple slantwise convective downdraughts associated with the release of conditional symmetric instability and even, possibly, symmetric instability. The global climatology of sting jets and the interaction between the atmospheric boundary layer and sting jets are revealed as specific areas where more research is needed. Finally, we describe eight myths and misunderstandings that exist in the current literature with the aim of guiding future research into the sting jet phenomenon.
Mid-latitude cyclones are strongly affected by diabatic processes. While the importance of latent heating is well established, the role of radiation has received little attention. Here, we address this question for idealized cyclones by performing baroclinic life cycle simulations in the global atmosphere model ICON with and without radiation, and with transparent clouds. Radiation substantially weakens the simulated cyclone: peak eddy kinetic energy reduces by 50%, and minimum storm central pressure increases by 17hPa. An analysis of the Lorenz energy cycle shows that the radiative weakening is not due to changes in the large-scale environment alone, but involves radiative processes within the cyclone. In fact, radiation warms the lower-tropospheric part of the cyclone's warm conveyor belt, and cools the upper-tropospheric part. We hypothesize that radiation weakens the cyclone by destroying mid-tropospheric potential vorticity in the warm conveyor belt.