Heatwave in northern Europe, summer 2018

The summer of 2018 has been remarkable in northern Europe. A very persistent high-pressure anomaly over Scandinavia caused high temperature anomalies and drought there from May to (at least) July.

Southern Europe was unusually wet, with damaging thunderstorms in France in the first half of June. In this analysis we investigate the connection between one aspect, the highest temperatures so far in Northern Europe, and climate change.

Aspects other than temperature are much less straightforward to analyse but may be considered in subsequent studies. It is important to note that, compared to other attribution analyses of European summers, attributing a heatwave early in the season with the whole of August still to come will only give a preliminary result of the 2018 Northern hemisphere heatwave season.

Here we present an attribution study of the ongoing heat wave made in near real time using well assessed methodologies. It is not peer-reviewed and was written quickly. We used thoroughly tested methods to do the analysis, evaluation of models and checked the observations for errors. The return times are partly based on forecasts and therefore have additional uncertainties. However, the changes in probability are based on past observations and model results, and the authors are confident that these results are robust. We are very grateful to Peter Thorne and Peter Thejll for making the Irish (from Met Eiréann) and Danish (from DMI) temperature observations available to us.

Key findings

  • The heat (based on observations and forecast) is very extreme near the Arctic circle, but less extreme further south: return periods are about 10 years in southern Scandinavia and Ireland, five years in the Netherlands
  • From past observations and models we find that the probability of such a heatwave to occur has increased everywhere in this region due to anthropogenic climate change, although in Scandinavia this increase was not visible in observations until now due to the very variable summer weather.
  • We estimate that the probability to have such a heat or higher is generally more than two times higher today than if human activities had not altered climate.
  • Due to the underlying warming trend even record breaking events can be not very extreme but have relatively low return times in the current climate.
  • With global mean temperatures continuing to increase heat waves like this will become even less exceptional

Here we present an attribution study of the ongoing heat wave made in near real time using well assessed methodologies. It is not peer-reviewed and was written quickly. We used thoroughly tested methods to do the analysis, evaluation of models and checked the observations for errors. The return times are partly based on forecasts and therefore have additional uncertainties. However, the changes in probability are based on past observations and model results and the authors are confident that these results are robust. We are very grateful to Peter Thorne and Peter Thejll for making the Irish (from Met Eiréann) and Danish (from DMI) temperature observations available to us.

Introduction

The summer of 2018 has been remarkable in northern Europe. A very persistent high-pressure anomaly over Scandinavia (Figure 1a) caused high temperature anomalies and drought there from May to (at least) July (Figures 1b,c). These reached  as far southwest as Ireland. Southern Europe was unusually wet, with damaging thunderstorms in France in the first half of June. In this analysis we investigate the connection between one aspect, the highest temperatures so far in Northern Europe, and climate change. Aspects other than temperature are much less straightforward to analyse but may be considered in subsequent studies. It is important to note that, compared to other attribution analyses of European summers, attributing a heat wave early in the season with the whole of August still to come will only give a preliminary result of the 2018 Northern hemisphere heatwave season.

May-July average temperatures 2018
Figure 1: May-July averages of a) Z500 anomaly showing the anomalous circulation, b) temperature anomalies and c) relative precipitation anomalies. a,b: ECMWF analyses and forecasts compared to ERA-interim, c: CPC analysis (up to 23 July).

The analysis is based on observations from the 1st of May up to 24 July plus 5-day forecasts from the ECMWF deterministic model. In fact, the hottest days of the summer so far are occurring as we do the analysis, so most results are based on one- to three day forecasts. Experience has shown these to be accurate enough. For relatively common events, the change in probability that we compute is not very sensitive to the exact temperature of the event. In this analysis that applies to all areas except the most northern one, where we take the uncertainty due to the forecast into account.

Event definition

To define the event, we analyse the three-day maximum temperature average (TX3x) at individual locations. In most of the locations the three-day heat waves were actually not extremely hot in the current climate, so the return time of the event at each place is small and the event we look at is not very extreme. The persistence of the heat is probably the more exceptional factor in this summer’s heat waves, but changing the definition from three to seven does not change the return time much. Looking at even longer events would probably lead to a definition of a rarer event, however long temporal averages would also lead to much less data to analyse in the observations and thus to higher uncertainties. Therefore we chose to use the 3-day maximum average, which also facilitates comparison with previous analyses, even though longer time scales would show a stronger connection to global warming.

Figure 2 shows the anomaly of the 2018 summer in this measure, i.e., the highest value for the summer so far compared to the normal highest value of the summer. It shows that the highest anomalies were in northern Scandinavia and in western Ireland, with heat waves already more than five degrees warmer than the average hottest three days of the year in 1981-2010.  The Netherlands are also experiencing a heat wave that is forecast to be about three degrees warmer than normal in the 3-day running mean. Note that we expect this map to show more red areas after the summer, because there could well be hotter periods in August than the ones shown.

The hottest 3-day average of Tmax in 2018
Fig. 2 The hottest 3-day average of Tmax in 2018 (ECMWF analyses up to 24 July, forecasts up to 31 July) compared to the highest 3-day maximum temperature in the period 1981-2010 that is currently the “normal” period (ERA-interim). Along coasts there are artefacts from comparing the high-resolution analyses with the lower-resolution ERA-interim reanalysis.

Based on this map, we are focusing on Northern Europe and analyse the following stations with long, homogeneous records and preferably not too close to coasts in order to enable comparisons with climate models:

  • Phoenix Park (Dublin, Ireland, 53.36N, -6.32E, 49.0m),
  • De Bilt (Netherlands, 52.10N, 5.18E, 1.9m),
  • Landbohøjskolen (Copenhagen, Denmark, 55.7N; 12.5E, 9m),
  • Oslo Blindern (Norway, 59.94N, 10.72E, 94.0m),
  • Linköping (Sweden, 58.40N, 15.53E, 93.0m),
  • Sodankyla (Finland, 67.37N, 26.63E, 179.0m) and
  • Jokioinen (Finland, 60.81N, 23.50E, 104.0m).

Methods

In this article we do not analyse large area averages or country averages as in previous analyses of high temperatures but focus instead on a number of individual locations in Northern Europe where long records of observed data are available.

We firstly analyze observed temperatures and estimate how rare the current heat wave is, measured as three-day maximum temperatures, and whether or not there is a trend toward increasing temperature. As appropriate for our event definition, we fit a Generalized Extreme Value Distribution (GEV), described by three parameters: the position parameter μ, the scale parameter σ and the shape parameter ξ. In this statistical approach, global warming is factored in by allowing the fit to the distribution to be a function of the (low-pass filtered) global mean surface temperature (GMST), where GMST is taken from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Science (GISS) surface temperature analysis (GISTEMP, Hansen et al., 2010). We assume that the scale parameter σ shifts with the position parameter μ, thus the PDF is shifted up or down with GMST but does not change shape. In this way, it results in a distribution that varies continuously with GMST. This distribution can be evaluated for a GMST in the past (e.g., 1950 or 1900) and for the current GMST. A 1000-member non-parametric bootstrap procedure is used to estimate confidence intervals for the fit.

We can then assess the probability of occurrence of the observed event in the present climate, p1, and past climate, p0. These probabilities are communicated as return periods of the event in the present and past: 1/p1 and 1/p0 respectively. The risk ratio is evaluated as the ratio of p1 to p0. If the 95% confidence interval for risk ratio does not encompass unity, we say that the risk ratio is significantly larger (or smaller) than one and there is a detectable positive (or negative) trend in the observational data. This approach has been used before, e.g., Philip et al. (2017) for drought, Schaller et al. (2014) and van der Wiel et al. (2017) for heavy precipitation, Uhe et al. (2016) and van Oldenborgh et al. (2015) for temperature, and Vautard et al. (2017) for wind stagnation.

Secondly, to assess the role of climate change, we compare observations with results from climate models that are available and suitable for the temperatures in these locations. This answers the question whether