What’s the Outlook for Europe Windstorm Activity This Winter?

With annual windstorm losses in Europe ranging from a couple of hundred million to tens of billions of Euros, it is no wonder the insurance industry is interested in forecasting winter storminess. However, we cannot let the potential of good returns distract from a full understanding of what winter forecasts really say about future wind losses.

Over the past few years, RMS have been distilling the vast amount of research in this field into key insights for the insurance industry, with a series of annual blogs on the outlook backed up by a more detailed research paper (available to RMS licensing clients). Before we discuss the forecast for this year, we look back to last year’s forecast.

How Good was the Forecast Last Year?

In last year’s blog, we noted how the climate predictors indicated a stormier winter than in recent times, and the 2017/18 season turned out to be probably the costliest windstorm season since 2006/07, with about three billion Euros (US$3.45 billion) of insured wind losses in Europe. While this headline is very encouraging for research efforts, a detailed assessment is more sobering:

  • The forecast contained no spatial information, e.g. on how Germany would bear the brunt of the losses.
  • The forecasted stormier conditions in the later part of the 2017/18 winter did not validate, instead freeze and snow were the top perils.
  • Luck plays a big part when the outcome is close to the expected value of a wide distribution of possibilities.

In brief, the 2017/18 forecast was successful, but provided little information on regional loss, and had some good luck.

Forecast for the 2018/19 Winter Season

The forecast summary is:

Meteorological indicators point to a less stormy North Atlantic this winter compared to last

Two major caveats are attached to this forecast. First, forecasts contain a wide distribution of possible outcomes, therefore this winter could be very different from the expected value.  Second, windstorm losses in Europe have a weak relation to the forecasted meteorological variables.

How Do We Arrive at This Forecast?

This year, we will conduct a more detailed analysis of some aspects of the science underlying the forecast, and emphasize the main messages below in bold font. We describe the status of the main indicators of North Atlantic storminess, namely the El Niño-Southern Oscillation (ENSO), the Quasi-Biennial Oscillation (QBO), solar flux, North Atlantic sea-surface temperature (SST) and Arctic sea-ice; and how they collectively point to a less stormy winter.

1. An El Niño is forecast to occur this winter and this tends to favor negative phases of the North Atlantic Oscillation (NAO), meaning fewer storms in the North Atlantic and northern Europe. The left plot in Figure 1 below shows the ENSO impacts on the NAO Index. The spread of possible outcomes is large, due to different patterns of sea surface temperature (SST) anomalies in the equatorial Pacific, a variety of other modulating factors as well as internal noise in the system.

Additionally, researchers found the opposite signal in early winter: the El Niño favors more Atlantic cyclones in the Nov-Dec period.

Of special note this year are larger anomalies in the central than eastern Pacific, and the right panel of Figure 1 illustrates how these central Pacific El Niños tend to produce cooler January-March periods (fewer expected cyclones) in northern Europe.

Figure 1: Left: histogram of January-March NAO index anomalies for ENSO phases beyond one standard deviation between 1706 and 2000; Right: Temperature at Uppsala in January–March as a function of September-to-February averages of NINO3.4, for central Pacific (right) ENSO type, using data from 1870 to 1995. (From Figures 7 and 13 of Brönnimann, respectively).

2. The current easterly phase of the QBO in the stratosphere also favors weaker westerly winds over Europe or fewer storms in winter. As with ENSO, the signal is of a much smaller amplitude than the spread. It is notable how the current winds in the equatorial upper stratosphere have westerly anomalies and these have recently been linked to stronger westerly winds over northern Europe in December.

3. The solar driver indicates more westerly winds over the North Atlantic in early winter and cold, non-stormy easterly anomalies in later winter. Gray et al. did a controlled study of the effects of the eleven-year solar cycle on North Atlantic winter weather using long observational datasets and described how the winter-mean impact hides significant monthly variability. Next winter is the fifth since the peak in early 2014 of Solar Cycle 24, and Figure 2 below shows the observed monthly anomalies in mean sea-level pressure in such a solar cycle phase, based on data from the past fourteen solar cycles. The pressure anomalies point to weak anomalous westerlies over northern Europe in December, which flips to quite strong anomalous easterlies (much less storminess) in February.

Figure 2: the anomalies in monthly mean sea-level pressure (hPa) in the fifth winter after a solar cycle peak, based on observed data from 1870 to 2010. From Figure 5 of Gray et al. (2016).

Labitzke and Kunze analyzed the non-linear interactions between ENSO, QBO, and solar flux on the late winter at high latitudes. Their Figure 4 contains four different Februaries (1966, ’73, ’77 and ‘87) similar to current conditions — easterly QBO, low solar flux and El Niño — and all four had a warm polar vortex in February indicating a less stormy late winter, and consistent with the signals from each driver in isolation. However, the significant storms on April 2, 1973, and March 27, 1987, are reminders of the limited influence of the polar vortex state on European storm losses.

4. The North Atlantic SST anomalies for September contain a horseshoe pattern that is related to more westerly airflow over northern Europe (or more storms) in the following November to January. This is quite consistent with the early winter signal from the previous three drivers, though not consistent in January.

5. Arctic sea-ice is the last factor we consider for this winter’s outlook. There are two aspects here, and we begin with the second-order effect as it is clearer, then discuss major issues concerning the big picture of sea-ice decline.

Sea-ice extents in September from NSIDC show more sea-ice on the Canadian side (especially Beaufort Sea) and less off the Russian coastline (especially Laptev Sea) compared to last year. This change favors a negative NAO, though its impact is small, and much smaller again when converting NAO to loss impacts in Europe.

The big picture is one of long-term decline of Arctic sea-ice, and researchers have shown how declines, especially in the Kara and Barents Seas, are strongly linked to negative NAO, e.g. Honda et al., Yang and Christensen and many others. However, observations contain a puzzle: sea-ice extent in September in the Kara Sea over the past 15 years is about one third of the value from 1980 to 1999, yet the winter NAO over the past 15 years has been on average slightly positive. Where is the sea-ice forcing of NAO?

A plausible explanation is that the winter NAO responds to transient, year-to-year change in sea-ice, and this fits with some results (e.g. the Wang et al. model de-trend all time-series, and Honda et al. do seasonal experiments).

The central question for insurance is whether wind losses are following the total sea-ice extent, or those interannual transients captured by the NAO. Recent December to February periods with significant positive NAO and low sea-ice (2011/12, 2013/14, 2014/15, 2015/16, 2016/17) have produced Europe-wide wind losses less than the median of the Dec-Feb loss distribution. This indicates long-term losses are more influenced by the total sea-ice extent rather than NAO in recent times. Of course, the low losses in those positive-NAO winters could occur by chance from a small sample, however, there is a compelling argument to indicate this is a signal:

  • Windstorm losses are caused by the tiny fraction of most severe storms
  • And the severities of the most extreme storms are limited by the heat contrast between the sub-polar and sub-tropical air masses
  • And the sub-polar air mass is warming much faster due to Arctic Amplification
  • Hence sea-ice loss has more impact on those extreme storms driving loss, compared to the everyday cyclones reflected in the winter NAO

The evidence suggests the new, lower Arctic sea-ice climate regime could be shifting the balance towards extreme storms becoming rarer, and the NAO is not reflecting this longer-term change. If true, then this winter could be in a low quantile of the long-term climate of winter losses. Besides being an untested hypothesis of current risk, the near-term outlooks for sea-ice extent and sub-tropical temperatures are uncertain, and more research is needed before changing views of windstorm risk.

Summary

This year, the climate drivers point to more cyclones earlier in the season, and fewer after the New Year. Given the relative importance of the Jan-Mar period, we expect a less stormy North Atlantic sector this winter compared to last. The usual caveats apply: the forecast contains a wide distribution of possible outcomes hence winter could be very different from the expected value, and windstorm losses in Europe have a weak relation to the forecasted variables.

Director, Model Development

Stephen is a hazard specialist who leads the development of climate hazard models from our London office. After joining RMS in 2009, most of Stephen’s focus has been on developing the Europe Windstorm (EUWS) hazard module; working with station data to calibrate RiskLink 11 EUWS event set hazard, then various hazard improvements to the RiskLink 15 EUWS version, and the RiskLink 16 EUWS clustering model. Stephen also spent 15 months leading the recalibration of the U.S. and Canada Severe Convective Storm model, released in January 2014. Before RMS, Stephen worked in various research and development posts over a period of 13 years at the U.K. Meteorological Office, including the development of short-range weather forecasting; designing and building new seasonal and decadal climate prediction systems; and the development of radiation and cloud physics parametrizations.

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