Tag Archives: European Windstorm

Seasonal Forecast of European Windstorm Activity: Will a Stormier Atlantic Deliver Increased Losses?

Known indicators point to stormier conditions in the North Atlantic this winter. However, what this means for Europe windstorm losses is much less certain.

Our ability to understand and forecast variability of North Atlantic winter storminess continues to improve year-on-year. Research highlights in 2017 include:

  • A new, and skillful, empirical forecast model for winter climate in the North Atlantic revealed that sea ice concentrations in the Kara and Barents Seas are the main source of predictable winter climate variations over the past three decades. Interestingly, a separate 2017 study supports earlier forecasts of either a slowing or reversal of the sea ice reductions in the Barents and Kara Seas between now and 2020, implying an uptick in storminess over the next few years.
  • An innovative tool to analyze sources of predictability in a numerical forecast model revealed strong links between tropical climate anomalies and winter climate in the North Atlantic in that model.

Twelve months ago, the forecasting indicators for the windstorm season broadly pointed to a 2016/17 season characterized by below average storminess — a forecast borne out by subsequent observations. We have already had a fairly active start to the 2017/18 season, with Windstorms Xavier, Herwart, and ex-Hurricane Ophelia causing local damage, but what is the outlook for the rest of the season?

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Europe’s Winter Windstorms – the Only Certainty is Uncertainty

The annual damage from European windstorms can range significantly: from years when there are clusters of severely damaging storms to other years with almost no windstorm loss. How much of this volatility can we predict, and how much remains a roll of the dice? And more specifically, what storm activity can we expect over the next few months?

Forecasting Storminess

Our understanding of the drivers of annual storminess has increased greatly in recent years, allowing us to provide more forecasting insight than ever before. However, there is a cautionary tale for the industry, one that shows the limitations of even the most sophisticated seasonal forecasts.

The Atlantic Multidecadal Oscillation (AMO) is a pattern of long-duration variability in sea surface temperature in the North Atlantic. It is known to influence the climate over much of the northern hemisphere including the level of storminess in Europe1. As north-south gradients of heat in the Atlantic act to fuel extra-tropical storms2 these longer term changes in sea surface temperature tend to alter the odds of extreme storm occurrence over timescales of 60-80 years. Today, the ongoing positive (warm) phase of the AMO favors lower than average storminess this winter.

Annual average values for the AMO Index, 1856-2015 (data from NOAA ESRL3). Positive values (red bars) indicate warmer sea surface temperatures across the North Atlantic, while negative values (blue bars) indicate cooler temperatures.

That’s the multi-decadal perspective. But it will come as no surprise for Europeans to hear that as well as these longer phases of relative activity and inactivity, the continent also experiences variability of storminess from year to year. We know that the jet stream is a main ingredient of storms, and that in turn these storms strengthen the jet itself, in a positive feedback loop that leads to the term “eddy-driven jet.”  This “storms-beget-storms” mechanism typically plays out over a few weeks, and more severe storms are likelier to occur during these periods. The positive feedback between jet and storms amplifies swings in annual damage, and explains a substantial amount of the storm clustering found in longer range historical weather records4. This coupling between storms and jet is reflected in the version 16.0 of the RMS Europe Windstorm Clustering Model.

Researchers have identified various drivers of seasonal storminess in the North Atlantic which, for the coming winter, are ambiguous. For instance: we are three years after the peak of a prolonged but subdued solar cycle and this timing suggests less forcing of storminess. But in contrast the predictions are for neutral to weak La Niña phases of the El Niño–Southern Oscillation (ENSO) which points to a chance of increased forcing of North Atlantic storminess. Whilst, to complicate things further, the anticipated values of tropical stratosphere winds, linked to the Quasi-Biennial Oscillation (QBO), are related to less storminess in the mid-latitude Atlantic – with the caveat that they are in an unusually disrupted pattern.

So is it possible to get off the meteorological fence and make a call? Yes: overall, the multi-decadal and seasonal drivers indicate slightly below average storminess.

Severe Events Can Occur During Any Season

But this does not mean that we as an industry should be entirely relaxed about the new storm season, as the outlook for annual storm damage is blurred by the vagaries of local weather. This is exemplified by storm Kyrill in January 2007.

Then, ahead of the 2006/07 winter, the seasonal and multi-decadal drivers indicated below average storminess, just as they do today. But Kyrill occurred and turned an otherwise innocuous season into a bad one for many. The gusts and damage during this storm were much more extreme than its general circulation, because convection cells embedded in the cold front contributed to extreme damage intensity in some areas5. Storm Kyrill showed how processes on small space and time scales can dominate annual storm damage. These drivers have seriously short predictability windows of just a few hours.

More generally, some of the past variations in annual storminess have no known driver. We are not quite sure how much, but a reasonable ball-park figure is one half. This random part is found in climate models, where the tiniest possible changes at the start of a forecast often grow into large changes in seasonal average storminess.

Although our understanding of the drivers of storminess has greatly increased over the past few years and the odds do favor less storm damage this winter, we should not be complacent. As its tenth anniversary approaches, Storm Kyrill reminds us that major losses can happen in any season, regardless of the forecast.

Web links to references above

1Peings and Magnusdottir (2014)  [ http://iopscience.iop.org/article/10.1088/1748-9326/9/3/034018/pdf ]

2Shaffrey and Sutton (2006)  [ http://journals.ametsoc.org/doi/pdf/10.1175/JCLI3652.1 ]

3NOAA ESRL AMO data [http://www.esrl.noaa.gov/psd/data/timeseries/AMO/ ]

4Cusack (2016)  [ http://www.nat-hazards-earth-syst-sci.net/16/901/2016/nhess-16-901-2016.pdf ]

5Fink et al. (2009)  [ http://centaur.reading.ac.uk/32783/1/nhess-9-405-2009.pdf ]

This post was co-authored by Peter Holland and Stephen Cusack.

European Windstorm: Such A Peculiarly Uncertain Risk for Solvency II

Europe’s windstorm season is upon us. As always, the risk is particularly uncertain, and with Solvency II due smack in the middle of the season, there is greater imperative to really understand the uncertainty surrounding the peril—and manage windstorm risk actively. Business can benefit, too: new modeling tools to explore uncertainty could help (re)insurers to better assess how much risk they can assume, without loading their solvency capital.

Spikes and Lulls

The variability of European windstorm seasons can be seen in the record of the past few years. 2014-15 was quiet until storms Mike and Niklas hit Germany in March 2015, right at the end of the season. Though insured losses were moderate[1], had their tracks been different, losses could have been so much more severe.

In contrast, 2013-14 was busy. The intense rainfall brought by some storms resulted in significant inland flooding, though wind losses overall were moderate, since most storms matured before hitting the UK. The exceptions were Christian (known as St Jude in Britain) and Xaver, both of which dealt large wind losses in the UK. These two storms were outliers during a general lull of European windstorm activity that has lasted about 20 years.

During this quieter period of activity, the average annual European windstorm loss has fallen by roughly 35% in Western Europe, but it is not safe to presume a “new normal” is upon us. Spiky losses like Niklas could occur any year, and maybe in clusters, so it is no time for complacency.

Under Pressure

The unpredictable nature of European windstorm activity clashes with the demands of Solvency II, putting increased pressure on (re)insurance companies to get to grips with model uncertainties. Under the new regime, they must validate modeled losses using historical loss data. Unfortunately, however, companies’ claims records rarely reach back more than twenty years. That is simply too little loss information to validate a European windstorm model, especially given the recent lull, which has left the industry with scant recent claims data. That exacerbates the challenge for companies building their own view based only upon their own claims.

In March we released an updated RMS Europe Windstorm model that reflects both recent and historic wind history. The model includes the most up-to-date long-term historical wind record, going back 50 years, and incorporates improved spatial correlation of hazard across countries together with a enhanced vulnerability regionalization, which is crucial for risk carriers with regional or pan-European portfolios. For Solvency II validation, it also includes an additional view based on storm activity in the past 25 years. Pleasingly, we’re hearing from our clients that the updated model is proving successful for Solvency II validation as well as risk selection and pricing, allowing informed growth in an uncertain market.

Making Sense of Clustering

Windstorm clustering—the tendency for cyclones to arrive one after another, like taxis—is another complication when dealing with Solvency II. It adds to the uncertainties surrounding capital allocations for catastrophic events, especially due to the current lack of detailed understanding of the phenomena and the limited amount of available data. To chip away at the uncertainty, we have been leading industry discussion on European windstorm clustering risk, collecting new observational datasets, and developing new modeling methods. We plan to present a new view on clustering, backed by scientific publications, in 2016. These new insights will inform a forthcoming RMS clustered view, but will be still offered at this stage as an additional view in the model, rather than becoming our reference view of risk. We will continue to research clustering uncertainty, which may lead us to revise our position, should a solid validation of a particular view of risk be achieved.

Ongoing Learning

The scientific community is still learning what drives an active European storm season. Some patterns and correlations are now better understood, but even with powerful analytics and the most complete datasets possible, we still cannot yet forecast season activity. However, our recent model update allows (re)insurers to maintain an up-to-date view, and to gain a deeper comprehension of the variability and uncertainty of managing this challenging peril. That knowledge is key not only to meeting the requirements of Solvency II, but also to increasing risk portfolios without attracting the need for additional capital.

[1] Currently estimated by PERILS at 895m Euro, which aligns with the RMS loss estimate in April 2015

What Is In Store For Europe Windstorm Activity This Winter

From tropical volcanoes to Arctic sea-ice, recent research has discovered a variety of sources of predictability for European winter wind climate. Based on this research, what are the indicators for winter storm damage this season?

The most notable forcings of winds this winter – the solar cycle and the Arctic sea-ice extents – are forcing in opposite directions. We are unsure which forcing will dominate, and the varying amplitude of these drivers over time confuses the situation further: the current solar cycle is much weaker than the past few, and big reductions in sea-ice extent have occurred over the past 20 or so years, as shown in the graph below.

Figure: Standardized anomalies of Arctic sea-ice extent over the past 50 years. (Source: NSIDC)

There are two additional sources of uncertainty, which further undermine predictive skill. First, researchers examine strength of time-mean westerly winds over 3-4 months, whereas storm damage is usually caused by a few, rare days of very strong wind. Second, storms are a chaotic weather process – a chance clash of very cold and warm air – which may happen even when climate drivers of storm activity suggest otherwise.

RMS has performed some preliminary research using storm damages, rather than time-mean westerlies, and we obtain a different picture for East Pacific El Niños. Most of them have elevated storm damage in the earlier half of the storm season (before mid-January) and less later on. Of special note are the two storms Lower Saxony in November 1972 and 87J in October 1987: the biggest autumn storms in the past few decades happened during East Pacific El Niños. The possibility that East Pacific El Niños alter the seasonality of storms, and perhaps raise the chances of very severe autumn storms, highlights potential gaps in our knowledge that compromise predictions.

We have progressed to the stage that reliable, informative forecasts could be issued on some occasions. For instance, large parts of Europe would be advised to prepare for more storm claims in the second winter after an explosive, sulphur-rich, tropical volcano. Especially if a Central Pacific La Niña is occurring [vi] and we are near the solar cycle peak.

However, the storm drivers this coming winter have mixed signals and we dare not issue a forecast. It will be interesting to see if there is more damage before rather than after mid-January, and whatever the outcome, we will have one more data point to improve forecasts of winter storm damage in the future.

Given the uncertainty in windstorm activity levels, any sophisticated catastrophe model should give the user the possibility of exploring different views around storm variability, such as the updated RMS Europe Windstorm Model, released in April this year.

[i] Fischer, E. et al. “European Climate Response to Tropical Volcanic Eruptions over the Last Half Millennium.” Geophys. Res. Lett. Geophysical Research Letters, 2007, .
[ii] Brugnara, Y., et al. “Influence of the Sunspot Cycle on the Northern Hemisphere Wintertime Circulation from Long Upper-air Data Sets.” Atmospheric Chemistry and Physics Atmos. Chem. Phys., 2013.
[iii] Graf, Hans-F., and Davide Zanchettin. “Central Pacific El Niño, the “subtropical Bridge,” and Eurasian Climate.” J. Geophys. Res. Journal of Geophysical Research, 2013.
[iv] Baldwin, M. P., et al. “The Quasi-Biennial Oscillation.” Reviews of Geophysics, 2001.
[v] Budikova, Dagmar. “Role of Arctic Sea Ice in Global Atmospheric Circulation: A Review.” Global and Planetary Change, 2009.
[vi] Zhang, Wenjun, et al. “Impacts of Two Types of La Niña on the NAO during Boreal Winter.” Climate Dynamics, 2014.

The challenges around modeling European windstorm clustering for the (re)insurance industry

In December I wrote about Lothar and Daria, a cluster of windstorms that emphasized the significance of ‘location’ when assessing windstorm risk. This month we have the 25th anniversary of the most damaging cluster of European windstorms on record—Daria, Herta, Wiebke, and Vivan.

This cluster of storms highlighted the need for better understanding the potential impact of clustering for insurance industry.

At the time of the events the industry was poorly prepared to deal with the cluster of four extreme windstorms that struck in rapid succession over a very short timeframe. However, since then we have not seen such a clustering again of such significance, so how important is this phenomena really over the long term?

There has been plenty of discourse over what makes a cluster of storms significant, the definition of clustering and how clustering should be modeled in recent years.

Today the industry accepts the need to consider the impact of clustering on the risk, and assess its importance when making decisions on underwriting and capital management. However, identifying and modeling a simple process to describe cyclone clustering is still proving to be a challenge for the modeling community due to the complexity and variety of mechanisms that govern fronts and cyclones.

What is a cluster of storms?

Broadly, a cluster can be defined as a group of cyclones that occur close in time.

But the insurance industry is mostly concerned with severity of the storms. Thus, how do we define a severe cluster? Are we talking about severe storms, such as those in 1990 and 1999, which had very extended and strong wind footprints. Or is it storms like those in the winter 2013/2014 season, that were not extremely windy but instead very wet and generated flooding in the U.K.? There are actually multiple descriptions of storm clustering, in terms of storm severity or spatial hazard variability.

Without a clearly identified precedence of these features, defining a unique modeled view for clustering has been complicated and brings uncertainty in the modelled results. This issue also exists in other aspects of wind catastrophe modeling, but in the case of clustering, the limited amount of calibration data available makes the problem particularly challenging.

Moreover, the frequency of storms is impacted by climate variability and as a result there are different valid assumptions that could be applied for modeling, depending on the activity time frame replicated in the model. For example, the 1980s and 1990s were more active than the most recent decade. A model that is calibrated against an active period will produce higher losses than one calibrated against a period of lower activity.

Due to the underlying uncertainty in the model impact, the industry should be cautious of only assessing either a clustered or non-clustered view of risk until future research has demonstrated that one view of clustering is superior to others.

How does RMS help?

RMS offers clustering as an optional view that reflects well-defined and transparent assumptions. By having different views of risk model available to them, users can better deepen their understanding of how clustering will impact a particular book of business, and explore the impact of the uncertainty around this topic, helping them make more informed decisions.

This transparent approach to modeling is very important in the context of Solvency II and helping (re)insurers better understand their tail risk.

Right now there are still many unknowns surrounding clustering but ongoing investigation, both in academia and industry, will help modelers to better understand the clustering mechanisms and dynamics, and the impacts on model components to further reduce the prevalent uncertainty that surrounds windstorm hazard in Europe.


Lessons Hidden In A Quiet Windstorm Season

Wind gusts in excess of 100mph hit remote parts of Scotland earlier this month as a strong jet stream brought windstorms Elon and Felix to Europe. The storms are some of the strongest so far this winter; however, widespread severe damage is not expected because the winds struck mainly remote areas.

These storms are characteristic of what has largely been an unspectacular 2014/15 Europe windstorm season. In fact the most chaotic thing to cross the North Atlantic this winter and impact our shores has probably been the Black Friday sales.

This absence of a significantly damaging windstorm in Europe follows on from what was an active winter in 2013/14, but which contained no individual standout events. More detail of the characteristics of that season are outlined in RMS’ 2013-2014 Winter Storms in Europe report.

There’s a temptation to say there is nothing to learn from this year’s winter storm season. Look closer, however, and there are lessons that can help the industry prepare for more extreme seasons.

What have we learnt?

This season was unusual in that a series of wind, flood, and surge events accumulated to drive losses. This contrasts to previous seasons when losses have generally been dominated by a single peril—either a knockout windstorm or inland flood.

This combination of loss drivers poses a challenge for the (re)insurance industry, as it can be difficult to break out the source of claims and distinguish wind from flood losses, which can complicate claim payments, particularly if flood is excluded or sub-limited.

The clustering of heavy rainfall that led to persistent flooding put a focus on the terms and conditions of reinsurance contracts, in particular the hours clause: the time period over which losses can be counted as a single event.

The season also brought home the challenges of understanding loss correlation across perils, as well as the need to have high-resolution inland flood modeling tools. (Re)insurers need to understand flood risk consistently at a high resolution across Europe, while understanding loss correlation across river basins and the impact of flood specific financial terms, such as the hours clause.

Unremarkable as it was, the season has highlighted many challenges that the industry needs to be able to evaluate before the next “extreme” season comes our way.

Location, location, location: what makes a windstorm memorable?

While wind speed can indicate a storm’s damageability, two storms with similar peak wind speeds can cause vastly different levels of damage if they pass over locations with different concentrations of exposure.

This month marks the 15th anniversary of Lothar and Martin. Two powerful storms that tracked violently across Europe on December 26-28, 1999.

The combined European loss of both storms is in excess of $11 billion (2013 values). Since the storms occurred within days of each other it’s difficult to calculate the exact split of damage, however a 70:30 ratio is commonly accepted, ranking Lothar as the second largest Europe windstorm loss on record after Daria (1990).

France was hit hardest by the stormsparticularly Paris, which was right in the bullseye of Lothar’s most extreme physical characteristics. The recorded wind speeds in the low-lying regions of Paris were above 160 km/h and as high as 200 km/h at the top of the Eiffel Tower.

An exceptional storm

While Lothar’s wind speeds are comparable to other historical Europe windstorms, it’s considered an exceptional event for the insurance industry because of its track and the timing of its maximum intensification over Paris. Today, Lothar is a key benchmark used by the industry to understand the potential magnitude of Europe windstorm losses.

Lothar – a one-off for France?

Many industry experts believe Lothar to be higher than a 100-year return period loss event for France; however this should be interpreted as a long-term average and France could potentially experience a similarly extreme storm this winter.

Using current industry exposures, RMS calculated the potential French losses that would result from a Lothar-like storm striking different locations in France. By relocating Lothar’s peak gusts along points up to 500 km in each direction from their original location, our modelers concluded that Lothar was the fourth worst-case storm that could have happened out of a total of 437 scenarios.

The worst-case scenario for France is a Lothar-like storm relocated approximately 100 km west of the original event but which would still significantly impact Paris. The losses from this scenario are not much higher than Lothar’s. At only 15 percent higher the small increase in loss reinforces Lothar as an exceptional benchmark for the insurance industry.

We found that the majority of scenarios in the study produced notably lower losses. This is because the displacement of the storm, by even small distances, meant that the most extreme wind speeds impacted much lower concentrations of insured exposures. The study reinforces our understanding of the sensitivity of windstorm loss to a storm’s path. It also highlights the importance of using a stochastic model containing tens of thousands of events to be able to comprehensively evaluate potential windstorm losses.

London at risk

No European city is immune from damaging windstorms. RMS also re-located Lothar over Londononly a 350 km shift to the northto see what the impacts would be. We calculated the insured loss for Europe could be as much as 25 percent higher than Lothar’s losses and potentially bigger than the $8.6 billion loss caused by Daria.

The uncertainty inherent to the climatic phenomena that drive windstorms makes it impossible to forecast exactly when and where the next strong storm will hit France or Europe. However, catastrophe models can at least help to evaluate the potential financial impact of extreme storms like Lothar.

Matching Modeled Loss Against Historic Loss in European Windstorm Data

To be Solvency II compliant, re/insurers must validate the models they use, which can include comparisons to historical loss experience. In working towards model validation, companies may find their experience of European windstorm hazard does not match the modeled loss. However, this seeming discrepancy does not necessarily mean something is wrong with the model or with the company’s loss data. The underlying timelines for each dataset may simply differ, which can have a significant influence for a variable peril like European windstorm.

Most re/insurers’ claims records only date back 10 to 20 years, whereas European windstorm models use much longer datasets – generally up to 50 years of the hazard. Looking over the short term, the last 15 years represented a relative lull in windstorm activity, particularly when compared to the more extreme events that occurred in the very active 1980s and 1990s.

Netherlands windstorm variability







RMS has updated its European windstorm model specifically to support Solvency II model validation. The enhanced RMS model includes the RMS reference view, which is based on the most up-to-date, long-term historical record, as well as a new shorter historical dataset that is based on the activity of the last 25 years.

By using the shorter-term view, re/insurers gain a deeper understanding of how historical variability can impact modeled losses. Re/insurers can also perform a like-for-like validation of the model against their loss experience, and develop confidence in the model’s core methodology and data. Alternate views of risk also support a deeper understanding of risk uncertainty, which enhances model validation and provides greater confidence in the models that are used for risk selection and portfolio management.

Beyond Solvency II validation, the model also empowers companies to explore the hazard variability, which is vitally important for a variable peril like European windstorm. If a catastrophe model and a company rely on different but equally valid assumptions, the model can present a different perspective to provide a more complete view of the risk.

Lessons Learned from Winter Windstorm Season in Europe

The 2013–2014 winter windstorm season in Europe will be remembered for being particularly active, bringing persistent unsettled weather to the region, and with it some exceptional meteorological features. The insurance industry will have much to learn from this winter.

Past extreme windstorms, such as Daria, Herta, Vivian, and Wiebke in 1990, each caused significant losses in Europe. In contrast, the individual storms of 2013–2014 caused relatively low levels of loss. While not extreme on a single-event basis, the accumulated activity and loss across the season was notable, primarily due to the specific characteristics of the jet stream.

A stronger-than-usual jet stream off the U.S. Eastern Seaboard was caused by very cold polar air over Canada and warmer-than-normal sea-surface temperatures in the sub-tropical West Atlantic and Caribbean Sea. Subsequently, this jet stream significantly weakened over the East Atlantic.

Therefore, the majority of systems were mature and wet when they reached Europe. These storms, while associated with steep pressure gradients, brought only moderate peak gust wind speeds onshore, mainly to the U.K. and Ireland. In contrast, the storms that hit Europe in 1990 were mostly still in their development phase under a strong jet stream as they passed over the continent.

The 2013––2014 storms were also very wet, and many parts of the U.K. experienced record-breaking rainfall resulting in significant inland flooding. Again, individual storms were not uniquely severe, but the impact was cumulative, especially as the soil progressively saturated.

Not all events this winter season weakened before impact. Windstorms Christian and Xaver were exceptions, only becoming mature storms after crossing the British Isles into the North Sea and were more damaging.

Christian impacted Germany, Denmark, and Sweden with strong winds. RMS engineers visited the region and observed that the majority of building damage was dominated by the usual tile uplift along the edges of the buildings. Fallen trees were observed, but in most cases, there was sufficient clearance to prevent them from causing building damage.

Xaver brought a significant storm surge to northern Europe, although coastal defenses mostly withstood the storm. Xaver, as well as some of this year’s other events, demonstrated the importance of understanding tides when assessing surge hazard as many events coincided with some of the highest tides of the year. The size of a storm-induced surge is much smaller than the local tidal range; consequently, if these events had occurred a few days earlier or later, the astronomical tide would have been reduced, significantly reducing the high water level.


Wind, flood, and coastal surge are three components of this variable peril that can make the difference between unsettled and extreme weather. This highlights the importance of modeling the complete life cycle of windstorms, the background climate, and antecedent conditions to fully understand the potential hazard.

This season has also raised questions about the variability of windstorm activity in Europe, how much we understand this variability, and what we can do to better understand it in the future. While this winter season was active, we have been in a lull of storm activity for about 20 years.

Given the uncertainty that surrounds our ability to predict the future of this damaging peril, perhaps for now we are best positioned to learn lessons from the past. This past winter provided a unique opportunity, compared to the more extreme events that have dominated the recent historical record.

RMS has prepared a detailed report on the 2013–2014 Europe windstorm season, which analyzes the events that occurred and their insurance and modeling considerations. To access the full report, visit RMS publications.

When Did Windstorms Become So Wet?

Looking back to the start of the European windstorm season, my colleague Brian Owens pondered whether the insurance industry would experience a windfall or windy fall? Well, a week into February, I think all observers would agree that this has been a very active season.

As the industry continues to count the cost of the succession of systems that have assaulted our shores, it is apparent that the accumulated losses over the season will make this a year from which much can be learned.

The storms impacting northern Europe have frequently brought damaging winds to coastal areas, occasionally exceeding 90mph in the most exposed areas.

However, the driving jet stream has typically been very strong to the west but tapered off in the northeast Atlantic. This has caused systems to explosively deepen and mature before they reached the U.K. and Ireland, but then decay as they approached these shores. Consequently, the long storm paths have prompted higher waves and storm surges, but the latter decay, even for extremely deep cyclones, has meant less damaging winds. This has thus far spared Atlantic-facing countries from extreme wind losses.

But as the season has developed, the main story hasn’t been storm gusts. Anyone living in or visiting the U.K. this winter can testify that it has been exceedingly wet. Not just from excessive rainfall, but from repeated coastal inundations from storm surges combined with high tides as well. Consequently inland and coastal flooding has been significant, dominating our attention.

UK precipitation compared to long-term average; dark blue > 200% of average.

U.K. precipitation compared to long-term average; dark blue > 200% of average

The persistent rainfall since December has caused river catchments such as the River Severn and Somerset Levels to swell, particularly across southern England and Wales. Groundwater reservoirs and soils are also saturated, leading to pluvial and groundwater flooding.

However, perhaps most interesting this season has been the surge-driven coastal flooding. Storm surges occur when strong winds force the underlying water toward the coast. As the surge develops, water levels are influenced by the shape of the coastline and tidal interactions, both of which can act to amplify surge heights and resulting coastal flooding.

While property damage has not yet reached the scale of prior major flood incidents in the U.K., this series of events highlights the importance of evaluating the complete flood cycle, from the initiating precipitation and antecedent conditions to the final mode of flooding, as seen during the 2012 U.K. flooding.

With tidal ranges as large as 15 m in the U.K., the timing of the surge is vital for determining the scale of the hazard. Surges that impact a region at high (spring) tide pose the most risk for flooding. The storms impacting northern Europe this winter have consistently coincided with some of the highest tides of the year.

Level of surge (green), relative to actual (blue) and predicted (red) storm-tide

Level of surge (green), relative to actual (blue) and predicted (red) storm-tide

Beginning with Windstorm Xaver in December, the U.K. east coast and coastal locations in Germany were given their sternest test since the devastating 1953 and 1962 events. Fortunately coastal defenses have been improved since those historical floods and the subsequent flooding was not significant.

Numerous systems have continued to arrive through January, with southeast England and Wales, Ireland and northern France particularly affected. As recently as last week, Windstorms Petra and Ruth brought yet more coastal damage and flooding, and the risk of more flooding remains high this week.

As with the wind and inland flood impacts of each individual storm, the coastal damage may not be viewed significant in isolation. Consequently specific storms from this season may not stick in the memory, like 87J has. But the accumulating damage and cost of this continuous series of events has made this a season to remember.

It has also posed a question around how we as an industry evaluate our wind and flood risk. Do we evaluate these perils in isolation or do we consider the correlation these perils have in winter months. A question that may become more prominent as the future of flood insurance in the U.K. evolves.