SUNSHINE, CLOUD COVER AND AIR TEMPERATURES IN EUROPE
I have completed a preliminary analysis using stations selected from the European Climate Assessment
(ECA) data set (acknowledgement as requested to Klein Tank, A.M.G. and Coauthors, 2002. Daily
dataset of 20th-century surface air temperature and precipitation series for the European Climate
Assessment. Int. J. of Climatol., 22, 1441-1453.) This post summarizes the results of the analysis but
draws no conclusions. Readers will hopefully will be able to add some.
Figure 1 shows the stations used. Locations are constrained by data availability and by the difficulty of
selecting good stations from the massive ECA data set. Further discussion of the ECA data and data
treatment is provided at the end of this post.
http://oi42.tinypic.com/2hi0cax.jpg
The recent “UK Temperatures since 1956” post presented the results of an analysis of the impacts of
sunshine hours on UK temperatures using stations selected from the UKMO data base. The key result
was summarized on the graph below. It shows a good correlation between sunshine hours and Tmax in
the UK for five-year means:
http://www.euanmearns.com/wp-content/uploads/2013/11/sunshine_Tmax_5y_23stations.png
The equivalent result for the European stations is shown in Figure 2. There is a good correlation
between annual mean sunshine hours and surface air temperature (SAT) in Europe after 1985 but not
before (SAT was defined as the average of Tmax and Tmin and the data were normalized by dividing
by two standard deviations):
http://oi43.tinypic.com/2uze16a.jpg
Repeating the analysis for SAT and cloud cover gave generally good short-term peak-trough matches
(Figure 3; the cloud cover scale is inverted so that it moves in the same sense as SAT). Over the longterm, however, SAT and cloud cover are only weakly correlated:
http://oi40.tinypic.com/1z2lgns.jpg
As shown in Figure 4, cloud cover is also only weakly correlated overall with sunshine hours (the cloud
cover scale is again inverted):
http://oi41.tinypic.com/fdc01t.jpg
Figure 5 is a map showing the number of months by which sunshine hours lead SAT at individual
stations. The lead time was defined by lagging sunshine hour monthly means relative to SAT monthly
means and picking the month with the highest correlation coefficient (value shown in brackets).
http://oi39.tinypic.com/23r9sw2.jpg
Figure 6 shows the results for SAT versus cloud cover. Cloud cover and SAT are either uncorrelated or
show a weak positive correlation (i.e. more clouds, higher temperatures) at the stations designated “U”.
http://oi44.tinypic.com/2pt86kn.jpg
A review of data from ten stations in and around the Alps shows that the impacts of sunshine and cloud
cover on SAT decrease with increasing elevation. As shown in the first graph in Figure 7 the correlation
coefficient between SAT and sunshine hours decreases by a factor of almost two between sea level and
3,200m while the correlation between SAT and cloud cover decreases to zero at 2,500m and becomes
positive above 2,500m. The second graph shows that re-plotting the data with snow cover instead of
elevation on the X-axis gives substantially the same results. (The ten stations are 1=Wien, 2=Basel,
3=Geneva, 4=Hohenpeissenberg, 5=Feldberg, 6=Davos, 7=Wendelstein, 8=Saentis, 9=Zugspitze and
10=Sonnblick):
http://oi39.tinypic.com/2dj53eb.jpg
Some brief notes on data and data treatment:
The ECA data set is available at http://eca.knmi.nl/indicesextremes/customquerytimeseriesplots.php
It contains multiple variables for over 8,000 stations between Greenland and Kyrgyzstan and Finland
and the Canary Islands, although the majority are in Germany. It stores monthly means for different
variables in different files, so obtaining all the data for one station requires a number of separate
downloads.
I downloaded monthly means of maximum temperature, minimum temperature, sunshine hours, cloud
cover and snow depth (for the Alpine stations only). I calculated mean temperature by averaging Tmax
and Tmin and also calculated Tmax-Tmin. This gave six variables (sun, clouds, Tmax, Tmin, Tavg and
Tmax-Tmin) that could be compared 15 different ways, and I compared sunshine hours and clouds with
Tavg partly for simplicity and partly because the (alleged) impacts of increasing CO2 are quantified in
relation to average rather than maximum or minimum temperatures.
Tmax tends to be most closely correlated with sunshine hours and clouds, but not greatly more so than
Tavg. The table below summarizes the means and standard deviations of correlation coefficients
measured at 21 stations, with no allowance for leads or lags:
CORRELATION COEFFICIENTS, 21 EUROPEAN STATIONS
Variable
Versus Variable
Mean R Value
Standard Deviation
Sunshine Hours
Average Temperature
0.73
0.15
Maximum Temperature
0.76
0.15
Minimum Temperature
0.68
0.14
Max-Min Temperature
0.66
0.39
Average Temperature
-0.29
0.41
Maximum Temperature
-0.34
0.41
Minimum Temperature
-0.24
0.40
Max-Min Temperature
-0.59
0.23
Cloud Cover
-0.62
0.26
Cloud Cover
Sunshine Hours
The analyses are based on unadjusted monthly means except for the the annual means shown in Figures
2, 3 and 4, which are arithmetic averages of the monthly means. I discarded suspect data, such as
Liepaja temperatures after 1999 and the the Bjornoya cloud cover data. I downloaded data back to
1901 but there are too few stations to estimate reliable annual means before 1950.