Storm Eleanor was named by the Irish National Meteorological Service, with Storm Friederike named by the Berlin Institut für Meteorologie.
- Several major storms pounded Western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analyzed here in the context of climate change.
- Strong winds associated with such storms are observed to have become less frequent in the past four decades.
- Models show a different signal, with no significant change in their frequency until now and a slight increase in the future.
- By analysing a number of climate simulations, we conclude that human-induced climate change has had so far no significant influence on storms like the two studied. However, all simulations indicate that global warming could lead to a marginal increase (0-20%) of the probability of extreme hourly winds until the middle of the century. These trends do not account for the other factors, such as roughness, aerosols, and decadal variability, that contributed to the observed reduction in probability.
Storm Friederike led to at least eleven casualties and caused major disruption in the Netherlands and parts of Germany. In advance of Storm Friederike, warnings were issued in both the Netherlands and Germany for severe wind gusts. On January 17 in the Netherlands, a code yellow warning was issued and, subsequently, raised to a code orange. By the morning of January 18, when the worst winds were experienced, a code red was issued for a number of provinces at 09:16 CET. The timing of the strongest winds was around 9:00–11:00 a.m., just after the peak of the morning commute, with many people already on the road and, in some cases, caught unaware by the strong winds. In Germany, the German Meteorological Office (DWD) also issued warnings, asking people to stay indoors, and many schools were closed. In addition to the wind hazard, snow created icy road conditions, and eight people were killed by falling trees or in car accidents caused by dangerous road conditions. Storm Friederike is estimated to have caused around €900 million worth of damage to residential houses and office buildings, and a further €100 million in damage to cars, according to Germany’s Federation of Private Insurers, the GDV. They estimate this was the second most expensive storm to strike Germany in the past 20 years.
In the Netherlands, three people were killed during the storm. For the first time in history, train traffic was completely shut down across the country. Amsterdam Airport Schiphol was closed and more than 300 flights were canceled. Numerous roads were blocked by fallen trees and overturned trucks. Due to their height, trucks were susceptible to being blown off the roads, which caused disruptions and accidents.
The other major storm, Storm Eleanor, led to major disruptions in France during the ski holiday season and is estimated to have cost the insurance and reinsurance industry as much as EUR 1.6 billion. Ski resorts were closed for one or two days in the Alps, with significant economic consequences. Wind gusts of more than 130 km/hr and nearing 150 km/h were reported over several flat regions in France and in Switzerland. Large waves at the Atlantic coasts of Spain and France killed two people. Storm Eleanor was well forecasted by Météo-France, with an orange alert set the day before the event. Over France, according to the severity index developed by Météo-France, Eléanor was the sixth most severe storm since 1995.
More storms than these two were reported during the month. For instance, Storm Carmen, which preceded Storm Eleanor by two days, crossed Southern France with wind gusts exceeding 130 km/h. On January 17, another storm, Fionn, passed over parts of the Mediterranean region and broke wind speed records, including at Cap Corse at the northern tip of Corsica (225 km/h). From a monthly view and in terms of number of events, January 2018 is the most stormy month in France since 1998.
The storm activity was due to a strong westerly flow that persisted throughout the month (as shown in Figure 2, first row) and was enhanced by the jet stream extension eastward of its normal position. The persistence of the flow is also characterized by the frequency of occurrence of the so-called “zonal weather regime” (ZO), as defined by Michelangeli et al. (1995) using cluster analysis on SLP data from the NCAR/NCEP reanalysis. Approximately 45% of the January days were classified in this cluster (Figure 2, remaining panels), which is characterised by mild and wet winter weather. The average frequency of the ZO weather regime is close to 25%. Although not exceptional, this high frequency is significantly higher than normal.
Storm Friederike was the result of rapidly developing cyclogenesis and the area with highest wind speeds, located south of the trough center, moved fast from west to east. It crossed the Netherlands and mid-Germany in about half a day. In this analysis, the salient event characteristics will be represented by an indicator defined on the basis of daily maximum wind speed, derived from observations available from the Integrated Surface Database (“Lite” version, ISD-Lite). The database contains hourly global weather data for eight variables. Many of these observations are made at airports. However, many stations only contain three hourly data for the earlier part of the record. Also, when analyzing model output from some of the models contributing to EURO-CORDEX, the daily maximum near-surface wind speed was obtained on the basis of three-hourly wind speeds. For these reasons, we only sampled observations every three hours and the daily maximum wind speed was calculated only if at least four of the eight sampled observations were available.
In Figure 3a, we plot the values of the daily maximum wind observed over Northwestern Europe on the days of the storms. The track of Storm Friederike can be seen in the box [2-15E; 50-53N] where wind speeds are largest. We, therefore, selected as the event indicator the seasonal maximum value of this land area average of daily maximum wind speed (see also Figure 2).
This area contains 68 stations observing wind speed. The area average cannot be exactly calculated using the stations because the distribution of the stations is not even or dense enough, but we take the station average as a reasonable approximation. Using this indicator, Storm Friederike is the eleventh strongest storm in the area since 1 January 1976, with an indicator value of 16.0 ms-1 max daily wind. The 2017-2018 winter season (DJF) becomes the seventh in terms of strongest winter winds over this station set (some seasons had multiple stronger storms). We also considered the daily mean wind for models that did not store higher-frequency data. In terms of that indicator, Friederike was not remarkable with 8.7 m/s-1, as it was a very short duration storm.
For models, the area average is calculated over land grid points, which slightly lowers the indicator value (see comparisons in Table 1 for model evaluation). In order to calculate seasonal return periods, we take the maximum value of the indicator over the winter season (DJF).
The structure of Storm Eleanor was very different. Eleanor was embedded in a large-scale, deep low pressure system. Its strong winds affected a much broader area than Storm Friederike: from Ireland and the U.K. via western France, to Switzerland and the Riviera coast. Its high wind speeds, unusual in the inland part of Western Europe, constituted its most striking aspect. We, therefore, construct the same indicators as for Friederike, which are daily maximum and mean of wind speed, but averaged over a much wider area, from 0 to 10°E and 42°N to 52°N (see Figure 1b and 3b). The value of the indicator is 12.3 m/s for maximum winds and 8.3 m/s for daily mean winds.
Observations, model ensembles and evaluation
For the observational part of the attribution analysis, we used two sources of station data. Unfortunately, the available quantities were slightly different in the different datasets. The analysis is mainly based on the ISD-lite database described above, in which we used the daily maximum of three-hourly instantaneous wind speed. Additional results are based on the Royal Netherlands Meteorological Institute (KNMI) climatological service database, which provides the daily maximum of the hourly averaged wind speed. The KNMI data have also been converted to potential winds, i.e., the wind speed at 10 m assuming a roughness length of 3 cm over land and 2 mm over water, and assuming neutral stability (Weber and Groen, 2009). This corrects to first order for changes in the elevation of the wind anemometer and changes in roughness surrounding the station, which are deduced from the high-frequency variability of the wind (taking into account the response time of the recorder, Weber and Groen 2009). The highest hourly wind of the year series were visually quality controlled. For three series, early data was discarded for obvious inhomogeneities supported by the metadata (Leeuwarden <1990, De Bilt <2002, Lichteiland Goeree <1995). Most series start in 1981, but they are notably more variable and possibly unreliable before circa 1990.
We used four climate model simulation ensembles. The first ensemble is the RACMO regional climate model ensemble downscaling 16 initial-condition realizations of the EC-EARTH 2.3 coupled climate model in the CMIP5 RCP8.5 scenario (Lenderink et al., 2014, Aalbers et al., 2017). The RACMO model uses a 0.11° (12 km) resolution and the daily maximum of near-surface wind speed is analysed. In RACMO, the near-surface wind speed is diagnosed from the model wind and stability vertical profile as the wind speed at 10 m, applying a roughness length of at most 3 cm for land grid points, and a Charnock-type relation for sea grid points. This ensemble was previously used to estimate the change in the odds of wind stagnations in Northwestern Europe (Vautard et al., 2017) and was found to simulate monthly wintertime wind speeds over Western Europe in a satisfactory manner. RACMO simulations are available for the 1950-2100 period. As in previous analyses (see e.g., Philip et al., 2018), we use a 20th century early 30-year period [1951-1980] to estimate odds in the past climate, and the 2001-2030 period to estimate odds in the current climate. We also use two future periods, a period called “near future” [2021-2050] and a period called “mid century” [1941-1970]. As a cross-check, we fitted a time-dependent generalized extreme value (GEV) function to the whole period 1971-2070, as described in van der Wiel et al (2018).
The second model ensemble is the HadGEM3A ensemble (Christidis et al., 2013; Vautard et al., 2018), which includes a set of 15 realizations of atmospheric simulations using observed SSTs (reflecting the actual world) and a set using SSTs where the estimated patterns of anthropogenic heat contribution are removed to reflect the ocean response to a pre-industrial atmospheric composition (as the natural/counterfactual world). The latter runs also use pre-industrial greenhouse gas and aerosol concentrations. Land use and, hence, roughness is put to 1850 values in the HistoricalNat ensemble. For this model, the wind speed daily maximum was not available and the daily mean wind was used instead. No future simulations were available.
The third ensemble is the multi-model EURO-CORDEX ensemble (Jacob et al., 2014), using a 0.11° resolution over Europe. For this ensemble, only 11 simulations were used and bias correction was applied (Vautard et al., in preparation) using the Cumulative Distribution Function transform (CDFt, Vrac et al., 2016). These simulations have been evaluated in the context of the CLIM4ENERGY Copernicus Climate Change Service project. The reference data used for bias correction is the Watch Forcing Data ERA-Interim (WFDEI, Weedon et al., 2014). For wind speed, it is essentially an interpolation of ERA-Interim over a 0.5°×0.5° grid. This data set has a relatively low resolution, so extreme winds are not expected to be accurately represented. This weakness is, therefore, probably propagated to the EURO-CORDEX ensemble. The ensemble is pooled, which is formally possible because the bias correction method corrects data making it homogeneous across the multi-model distribution. However, caution must be taken when interpreting changes using such a pooled ensemble, as changes in the tails of the distribution may be different for each model, leading to potential heterogeneity in extremes for periods different than the reference period.
The fourth ensemble is obtained from simulations using the distributed computing framework known as weather@home (Massey et al. 2015). We used four different large ensembles of December-February wind speeds using the Met Office Hadley Centre for Climate Science and Services regional climate model HadRM3P at 25 km resolution over Europe embedded in the atmosphere-only global circulation model HadAM3P at N96 resolution. The first set of ensembles represents possible winter weather under current climate conditions. This ensemble is called the “all forcings” scenario and includes human-caused climate change. The second set of ensembles represents possible winter weather in a world as it might have been without anthropogenic climate drivers, using different estimates of pre-industrial SST deduced from the CMIP5 ensemble and pre-industrial greenhouse gas and aerosol concentrations. Land-use in both ensembles is identical. This ensemble is called the “natural” or “counterfactual” scenario (Schaller et al., 2016). The third set of ensembles represents a future scenario in which the global mean surface temperature is 1.5°C higher than pre-industrial global temperatures. The fourth scenario is the same as the third, but for 2°C of future global mean temperature. To simulate the third and fourth scenario, we use atmospheric forcings derived from RCP2.6 and 4.5 and sea surface temperatures that match the atmospheric forcing obtained from CMIP5 simulations (Mitchell et al., 2017).
The evaluation of the models’ ability to simulate the indicator is made using the ISD-Lite observations, which are available in near-real time. In order to evaluate the capacity of the models to simulate the winds, we extracted wind speed daily maxima at the locations of ISD-Lite stations and averaged these values over all stations in the area. Then, we compared the simulated mean, 95th centile and 99th centile, with the observed equivalent for each model ensemble. For HadGEM3-A and weather@home, as daily maxima were not available, we used daily averages of the wind speeds.
For RACMO, HadGEM3-A and weather@home, model values are pooled together to compute the distribution statistics. For EURO-CORDEX, we calculated both individual model and pooled statistics. Results are presented in Table 1 for the average over all grid points closest to the 68 ISD-lite stations, together with equivalent statistics when the average is made over all land grid points, instead of the positions of the stations. Results show that the models reproduce the indicator with success along the distribution. Comparisons to station data indicate a general underestimation of models within a 10% range. EURO-CORDEX simulations are bias corrected, so the bias is essentially reflecting the WFDEI (ERA-Interim based) bias. The fact that statistics do not differ from one model to the other supports pooling the models’ simulations together in a common distribution. This bias is consistent with models not simulating observational noise due to remaining turbulence. For weather@home, we only have daily values for mean wind speed, so we calculate the maximum mean daily wind speed in a winter season. The simulated values are higher than the observed values for this quantity, especially for the mean, while the 95th and 99th quantile are comparable to observations in particular for the Storm Friederike.
Grid point averages reach lower values than station averages, which is a probable consequence of the higher density of stations near the North Sea coast where winds are stronger which is reflected in the observed area average.