Mapping climate change in European temperature distributions

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Mapping climate change in European temperature distributions
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Climate change poses challenges for decision makers across society, not just in preparing for the climate of the future but even when planning for the climate of the present day. When making climate sensitive decisions, policy makers and adaptation planners would benefit from information on local scales and for user-specific quantiles (e.g. the hottest/coldest 5% of days) and thresholds (e.g. days above 28 ° C), not just mean changes. Here, we translate observations of weather into observations of climate change, providing maps of the changing shape of climatic temperature distributions across Europe since 1950. The provision of such information from observations is valuable to support decisions designed to be robust in today's climate, while also providing data against which climate forecasting methods can be judged and interpreted. The general statement that the hottest summer days are warming faster than the coolest is made decision relevant by exposing how the regions of greatest warming are quantile and threshold dependent. In a band from Northern France to Denmark, where the response is greatest, the hottest days in the temperature distribution have seen changes of at least 2 ° C, over four times the global mean change over the same period. In winter the coldest nights are warming fastest, particularly in Scandinavia.

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hello my name is david together with some Chapman and Nick Watkins we've written a paper about mapping climate change the focus is on European
tempted but it could be applied to any region whether sufficient observations the idea is to create
a translation of observations or whether into observations of climate change climate
represents the distribution of expected whether we consider some particular locations such as Oxford in England we don't expect the weather on every summer day in every year to be the same whether varies from day to day and from year to year the climate represents the
distribution of weather events and can be represented by a probability distribution so we want to quantify how the
cumulative distribution function CDF is changing in time so I can take non-use of data 9 summers and form an ensemble look something like this so this is my CDF and this is temperature I'm not reflexes likelihood so for example if I pick a temperature softly up comma decimal 5 on this axis comma decimal 5 means the half the days of colder than that temperature now if I whites if I look forward in time to do the same thing again I have a different CDs and we want to find out is what's the change delta T as a function of temperature was a function of likelihood so now let's look at some actual
data so we look at the look at measurements were region around Bordeaux and look at the change so
let's look at the region around Bordeaux the red line refers to the 19 fifties in the green to the 19 nineties and we can see the CDs of shifted in time and change shape the most common and slightly warmer than average temperatures have increased more than the cooler than Everest temperatures or even the very hottest days we can also expect the change in occurrence frequency of temperatures above a given threshold here the red line shows that the probability of finding a day about 28 degrees celsius has increased by no point 1 7 so far so good
but the next challenge is to find out of that change is robust to do that we
repeat the process 10 times in each case the 2 distributions of 43 years apart but the years included in the distributions of slightly different this gives
us 10 estimates of the climate in the earlier period and 10 estimates in the later period as shown here if the smallest
change over these 10 periods is large then this can be taken as an indication of a significant change in climate about quantile threshold this is indeed the case in Bordeaux as shown by the red line
if the largest changes small this can be taken as an indication of little change this is the case in the art of region of Portugal as shown here if the
difference between the minimum change and the maximum change is large then we conclude that there is no clear signal this
plot shows an example of that case it represents the situation in Piedmont in northern Italy with this approach we can build
maps of the smallest change in our sample at different quantiles here we see maps of the
smallest change in summer temperatures at 5 quantiles across the local climate distribution the 1st thing to notice is that there
is very little change in the points to 5 and point 2 5 quantiles in most regions this is saying that the coolest summer days had not changed much in most places by contrast the upper quantiles the hottest days a changing a lot but not in a uniform fashion it is the very hottest 5 % of days which are warming most in a band from southern England and the northern France to Denmark but for the point 7 5 and pumped 5 quantile the generally hotter than average days but not the very hottest the regions of greatest warming a further south in central France and Germany in eastern Spain and Italy there's roaming in all quantiles the paper presents results for both
maximum daytime and minimum nighttime temperatures in summer and in winter across Europe it also prevents changes in the proportion of 1 nights the fall below freezing and in 7 days the go above 28 degrees C which is a threshold that
has been identified as important in building designed to avoid overheating and in labor productivity we hope this into that has been useful
and we hope you enjoy reading the paper