**Freakonometrics - Tag - R-english**, and kindly contributed to R-bloggers)

In the paper on the heat wave in Paris (mentioned here)

I discussed changes in the distribution of temperature (and

autocorrelation of the time series).

During the workshop on *Statistical Methods for Meteorology and
Climate Change* today (here) I

observed that it was still an important question: is climate change

affecting only averages, or does it have an impact on extremes ? And

since I’ve seen nice slides to illustrate that question, I decided to

play again with my dataset to see what could be said about temperature

in Paris.

Recall that data can be downloaded here (daily

temperature of the XXth century).

tmaxparis=read.table("/temperature/TX_SOUID100124.txt",

skip=20,sep=",",header=TRUE)

Dmaxparis=as.Date(as.character(tmaxparis$DATE),"%Y%m%d")

Tmaxparis=as.numeric(tmaxparis$TX)/10

tminparis=read.table("/temperature/TN_SOUID100123.txt",

skip=20,sep=",",header=TRUE)

Dminparis=as.Date(as.character(tminparis$DATE),"%Y%m%d")

Tminparis=as.numeric(tminparis$TN)/10

Tminparis[Tminparis==-999.9]=NA

Tmaxparis[Tmaxparis==-999.9]=NA

annee=trunc(tminparis$DATE/10000)

MIN=tapply(Tminparis,annee,min)

plot(unique(annee),MIN,col="blue",ylim=c(-15,40),xlim=c(1900,2000))

abline(lm(MIN~unique(annee)),col="blue")

abline(lm(Tminparis~unique(Dminparis)),col="blue",lty=2)

annee=trunc(tmaxparis$DATE/10000)

MAX=tapply(Tmaxparis,annee,max)

points(unique(annee),MAX,col="red")

abline(lm(MAX~unique(annee)),col="red")

abline(lm(Tmaxparis~unique(Dmaxparis)),col="red",lty=2)

On the plot below, the dots in red are the annual

maximum temperatures, while the dots in blue are

the annual minimum temperature. The plain line is the regression line

(based on the annual max/min), and the dotted lines represent the

average maximum/minimum daily temperature (to illustrate the global tendency),

It is also possible to look at annual boxplot, and to focus either on

minimas, or on maximas.

annee=trunc(tminparis$DATE/10000)

boxplot(Tminparis~as.factor(annee),ylim=c(-15,10),

xlab="Year",ylab="Temperature",col="blue")

x=boxplot(Tminparis~as.factor(annee),plot=FALSE)

xx=1:length(unique(annee))

points(xx,x$stats[1,],pch=19,col="blue")

abline(lm(x$stats[1,]~xx),col="blue")

annee=trunc(tmaxparis$DATE/10000)

boxplot(Tmaxparis~as.factor(annee),ylim=c(15,40),

xlab="Year",ylab="Temperature",col="red")

x=boxplot(Tmaxparis~as.factor(annee),plot=FALSE)

xx=1:length(unique(annee))

points(xx,x$stats[5,],pch=19,col="red")

abline(lm(x$stats[5,]~xx),col="red")

Plain dots are average temperature below the 5% quantile for minima, or

over the 95% quantile for maxima (again with the regression line),

We can observe an increasing trend on the minimas, but not on the

maximas !

Finally, an alternative is to remember that we focus on annual maximas

and minimas. Thus, Fisher and Tippett theory (mentioned here)

can be used. Here, we fit a GEV distribution on a blog of 10

consecutive years. Recall that the GEV distribution is

install.packages("evir")

library(evir)

Pmin=Dmin=Pmax=Dmax=matrix(NA,10,3)

for(s in 1:10){

X=MIN[1:10+(s-1)*10]

FIT=gev(-X)

Pmin[s,]=FIT$par.ests

Dmin[s,]=FIT$par.ses

X=MAX[1:10+(s-1)*10]

FIT=gev(X)

Pmax[s,]=FIT$par.ests

Dmax[s,]=FIT$par.ses

}

The location parameter is

the following, with on the left the minimas and on the right the

maximas,

while the scale parameter is

and finally the shape parameter is

On those graphs, it is very difficult to say anything regarding changes

in temperature extremes… And I guess this is a reason why there is still active research on that area…

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