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United States. Type keyword s to search. Today's Top Stories. Makeover Takeover: Colonial Comeback. Treat Your Family to Homemade Cupcakes. 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 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 It is not peer-reviewed and was written quickly.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.
However, the changes in probability are based on past observations and model results and the authors are confident that these results are robust. 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. 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 Northern hemisphere heatwave season.
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.
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.
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 summer in this measure, i.
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 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. 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:. 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. 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. A 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, p 1 , and past climate, p 0. The risk ratio is evaluated as the ratio of p 1 to p 0. This approach has been used before, e. 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 and to what extent external drivers, in particular human-caused climate change, can explain the temperature trends in the observational data.
Including models allows us to give the causation of a trend. Models included in this analysis are EC-Earth 2. A more detailed description of these models can be found below. In all cases we use the approximate return time of the event as found in the observations as the event definition for analysis. For transient simulations of the changing climate, we again calculate how the probability of the event is changing over time in the model data, by fitting the temperature values to a distribution that shifts proportional to the smoothed global mean temperature.
This method assumes that global warming is the main factor affecting local temperatures since about , and that virtually all global warming is attributable to anthropogenic factors. In Europe, with very little decadal variability, the first condition is met.
For the weather home model we use the more straightforward method of directly comparing an experiment with actual conditions to another one that represents pre-industrial conditions. The change in the likelihood of the event occurring due to the change in climate the risk ratio is calculated from the fitted distributions for the current climate and a past climate. Models that show significantly different behaviour of hot events from the observations are not considered further.
To assess this behaviour, we investigate whether the fit parameters to the GEV differ significantly, allowing for an additive bias correction. In practice it means we demand that the variability of hot extremes is compatible with the observed variability, a condition which is not always met cf our analysis of the heat wave in the Mediterranean.
Finally, we synthesise the results from observations and climate models that have passed our evaluation in order to assess whether the overall change in likelihood of the event occurring is attributable to anthropogenic climate change. Instead of temperatures being slightly cooler than average, temperatures here in June may be near or slightly above average.
Areas from the Pacific Northwest into northern Texas, Arkansas and Missouri can expect temperatures to be warmer than average. Temperatures may be much above average in parts of Montana, Wyoming, Idaho and far eastern Oregon and eastern Washington. Florida, northern New England and the Upper Peninsula of Michigan into northeastern Minnesota may see near or slightly cooler-than-average temperatures. In July, near or slightly cooler-than-average temperatures are anticipated from the Southeast into eastern portions of the southern Plains and northward through the Ohio Valley into parts of the Upper Midwest.
Near or slightly warmer-than-average temperatures are likely in the Northeast, northern Plains and western areas of the central and southern Plains.
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