Range of environmental temperature
conditions in the United Kingdom
For: Department of Transport
Date: 17 May 2011
Authors: Matthew Perry and Nicola Golding
temperature_range_report_merged_v2.doc
© Crown copyright 2008
-1–
Document History
Version
Purpose
Date
1.1
Final results
28 April 2011
1.2
Final results with minor modifications
17 May 2011
Prepared by: Matthew Perry (Scientific Consultant) and Nicola
Golding (Adaptation Scientist)
Reviewed by: Simon Brown (Climate Extremes Research Manager),
Catrina Johnson (Manager, Scientific Consultancy) and Hazel
Thornton (Manager, Climate Adaptation Team)
Authorised for issue by: Lindsey Smith (Account Manager)
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Contents
Introduction..................................................................................................................... 3
Part 1: Range of Conditions Experienced..................................................................... 3
1.1. Identifying the area of interest ............................................................................... 3
1.2. Analysis methods ................................................................................................... 5
1.2.1 Data extraction and quality control....................................................................... 5
1.2.1.1 Temperature ................................................................................................. 5
1.2.1.2 Solar radiation............................................................................................... 6
1.2.2 Extreme Value Analysis....................................................................................... 7
1.3. Maximum temperature results ............................................................................... 8
1.4. Minimum temperature results ...............................................................................11
1.5. Mean temperature results .....................................................................................14
1.5.1 24 hour mean temperature .................................................................................14
1.5.2 Eight hour mean temperature .............................................................................19
1.6. Solar radiation results ...........................................................................................24
1.6.1 24 hour average insolation .................................................................................24
1.6.2 Eight hour average insolation .............................................................................26
1.6.3 Insolation on a vertical surface ...........................................................................26
1.7 Discussion of results..............................................................................................28
Part 2: Climate Change Information .............................................................................32
2.1 Introduction.............................................................................................................32
2.1.1 UKCP09 Projections...........................................................................................32
2.2 Climate change temperature information..............................................................37
2.2.1 Discussion of variables .......................................................................................37
2.2.2 Results..................................................................................................................38
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2.2.3 Discussion of results...........................................................................................45
2.2.3.1 Annual mean of daily mean temperatures .......................................................45
2.2.3.2 Summer mean of daily mean temperatures .....................................................45
2.2.3.3 Winter mean of daily mean temperatures ........................................................46
2.3 Climate change solar insolation information ........................................................47
2.3.1 Discussion of variables .......................................................................................47
2.3.2 Results..................................................................................................................48
2.3.3 Discussion of results...........................................................................................49
2.4 Wider context ..........................................................................................................50
2.4.1 Limitations of this methodology ..........................................................................52
Conclusions ...................................................................................................................53
References .....................................................................................................................54
Appendices ....................................................................................................................58
Appendix 1: Consideration of the uncertainty due to emissions scenarios....................58
Appendix 2: Maximum temperature for other seasons (not JJA):..................................59
Appendix 3: Minimum temperature for other seasons (not DJF): ..................................60
Appendix 4: Change in mean daily maximum total downward surface shortwave flux for
other seasons (not JJA) ...............................................................................................62
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Introduction
The transport of radioactive materials is tightly regulated and controlled in order to
maintain very high levels of safety. Packages are required to be designed to account for
the effects of temperature and insolation in accordance with the European Agreements for
the transportation of dangerous goods via road rail and inland waterways (ADR, RID,
AND), which in turn apply the International Atomic Energy Agency (IAEA) Regulations for
the Safe Transport of Radioactive Material TS-R-1. However, for packages travelling
solely within the UK, different temperatures and insolation values can be assumed, which
may permit a reduced cost in design (DfT Technical Specification, 2010).
In recognition of this, and in response to the impact of climate change, the Department for
Transport (DfT) has identified a requirement to study the range of temperature and
insolation values likely to be experienced in the UK. The first part of this report provides
information on the current range of temperature and solar irradiance values experienced
around the UK, based on historical observed data. The second part provides an initial
assessment of model projections of these two variables over the next few decades, and
considers the changes possible in response to climate change.
Part 1: Range of Conditions Experienced
1.1. Identifying the area of interest
The area of interest specified is all major public road and rail routes in the UK.
Movements of radioactive material may cover substantial distances across the UK, so the
whole of the UK is considered as one geographical area.
Geographical Information
System capabilities were used to identify climate stations representative of these routes.
A buffer zone was created of 1 km either side of all main (‘A’) roads, motorways and
railways. These areas were merged together, and all climate stations within this zone
were identified. Figure 1 shows the merged transport route buffer zone together with all
selected climate stations within this zone.
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Figure 1: Buffer zone (1 km) of UK transport routes together with selected climate stations
within this zone.
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Any climate station which recorded air temperature at any time between 1961 and 2010
was included in the search, a total of 1506 stations of which 912 (61%) were within the
buffer zone. Three of the selected stations were excluded: Snowdon Summit, which was
only selected due to the railway going up Snowdon; Cairnwell which, at an altitude of
928m above sea level, is over 250m above the height of the A93 road, making it
unrepresentative of the route; and Glen Ogle which at 564m above sea level is over 250m
above the height of the A85 road, again making it unrepresentative of the route. Stations
in the Channel Islands were also excluded. The remaining selected stations provide an
excellent coverage of main UK transport routes. The highest station was Holme Moss at
520m above sea level, which is adjacent to the A6024 road in Yorkshire.
Most of the selected stations have not operated for the whole of the 50 year period from
1961 to 2010. Of the 909 stations, between 200 and 400 stations had daily temperature
data available on any one day, with an average of 344 stations. Climate stations all
record the 24 hour maximum and minimum air temperature daily at 0900. Some of these
stations additionally record hourly air temperature, and a smaller subset record hourly
solar radiation. For solar radiation, 100 of 144 stations were selected as being within the
transport routes zone.
1.2. Analysis methods
1.2.1 Data extraction and quality control
1.2.1.1 Temperature
24 hour maximum and minimum temperatures were extracted from the Met Office
database for the period 1961 to 2010, for all selected stations. This 50 year period was
chosen to provide a long enough period to produce robust statistics, starting in 1961
because there is much less digitised data available prior to this date. For each day, the
highest maximum temperature and the lowest minimum temperature recorded at any
station was found. In addition, the average maximum and minimum temperatures across
all stations on each day were calculated. Daily mean temperatures were calculated for
each station every day by taking an average of the maximum and minimum temperatures
for that day. Although this method is an approximation, it has the advantage of having
many more stations available compared to using hourly data. Studies have shown that
the root mean square error of this method compared to calculating an average from 24
hourly values is between 0.6°C to 1°C (Dall’Amico a nd Hornsteiner, 2006; Weiss and
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Hays, 2005). The highest and lowest mean temperature recorded at any station on each
day was found, and the average mean temperature across all stations also calculated.
Although the data in the database have already been quality controlled, further quality
checks were applied by checking the differences between the highest (or lowest) and
second highest (or second lowest) station temperatures on each day.
For maximum
temperature, if the warmest station was at least 4°C warmer than the second warmest
station on any day, then the warmest station was marked as suspect and excluded.
Given the density of the station network, it is unlikely that local variations of maximum
temperature of more than 4°C will occur between sta tions, so such differences are likely to
be caused by incorrectly reported values or poorly exposed or maintained recording
equipment. For minimum temperature, localised variations can be greater due to the
formation of cold air pools in hollows. For this reason, a slightly higher threshold of 5°C
was allowed between the coldest and second coldest station on each day. For daily mean
temperature, differences of 3°C and 4°C were allowe d respectively between the warmest
and second warmest stations and the coldest and second coldest stations on each day.
For eight hour temperatures, the average temperatures of the hottest and coldest eight
hour periods of the day were calculated for each station on each day. The hottest and
coldest periods were chosen by calculating the long term average over all selected
stations by hour and month. As few stations had hourly data available prior to 1971, the
data period analysed was 1971 to 2010. From 1971 at least 22 stations were available
each day, increasing to 50 by 1988 and 100 by 2009. Similar quality control was carried
out as for the 24 hour mean temperatures.
1.2.1.2 Solar radiation
Solar irradiance or insolation is a measure of the power or rate of energy per unit area.
Solar radiation is the total energy received per unit area over a period of time. The Met
Office database contains hourly values of total global solar radiation received on a
horizontal surface in KJ / m2. This is recorded using pyranometers at meteorological
stations which are inspected regularly to ensure that they meet the required standard.
The term ‘global’ means the total solar energy reaching the surface, i.e. the sum of direct
and diffuse radiation.
Diffuse radiation is radiation that has been scattered by the
atmosphere or reflected from the ground.
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Solar radiation has only been widely recorded across the UK in more recent years, so the
data period chosen was from 1993 to 2010, a length of 18 years. Of the 100 stations
selected, between 19 and 72 stations were available on each day. From 2000 onwards
there were at least 50 stations available.
Daily totals of global solar radiation on a horizontal surface were extracted from the Met
Office database for these stations, and were converted into average daily irradiance rates
in W / m2. The averages over 24 hours (midnight to midnight) will be much less than the
peak rate during the middle of the day, as the averaging period includes the night time
when there is no radiation received at the surface. Also extracted were radiation totals
over an eight hour period when the irradiance rate would be highest on a cloudless day.
Quality control was carried out on the data to ensure that erroneous values were
removed.
This was done by calculating the number of standard deviations that the
maximum and minimum station values were away from the mean on each day, and
checking any values more than 5 standard deviations away. In addition, maximum values
for each month were checked by inspecting hourly values and comparing values with
neighbouring stations.
1.2.2 Extreme Value Analysis
For each day in the 50 year data series (or 40 years for the eight hour mean), data from
all available stations representative of major UK transport routes was pooled to find the
site with the most extreme (hot or cold) temperature on that day.
These data were
analysed using extreme value analysis techniques in order to estimate the shape of the
tail of the distribution of these statistics. This enables estimates to be made of the values
of each statistic expected to occur over a range of return periods up to 1:10,000 years.
The method chosen was a peaks-over-threshold approach using the Generalised Pareto
Distribution, because this method makes better use of all available data compared to the
annual maximum approach (Coles, 2001).
This method assumes that threshold
exceedances are independent, and this is not the case for daily temperature data because
hot and cold temperatures tend to occur in spells of several days. One method for dealing
with this is declustering, and Coles describes a method of achieving this called ‘runs
declustering’. In this method, a cluster is considered active until r consecutive values fall
below the threshold u. When this happens the cluster is terminated, a new cluster being
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initiated on the next exceedance of u. The choices of u and r are a balance between bias
and variance, together with sensitivity checks of the results to the choices made. There
are also diagnostic plots available to help with the choice of the threshold value and to
assess the quality of fit of the model to the data.
The computations described in Coles (2001) are available as a set of functions in the R
statistical computing language (R Development Core Team, 2007), and these have been
developed into a graphical interface called ‘extRemes’, as described in Gilleland and Katz
(2006), which includes a tool for runs declustering. This tool was used to carry out the
extreme value analysis, the results of which are described in sections 1.3, 1.4 and 1.5.
95% confidence limits for the return levels were estimated using the profile likelihood
method (Coles, 2001). The level of uncertainty associated with the return level estimates
increases as you extrapolate to probabilities beyond the range of the data, leading to
wider confidence limits, especially for the 1:10,000 year event.
Extreme value analysis is not appropriate for the solar radiation data for two reasons.
Firstly, the 18 years length of data is not long enough to obtain reliable results. Secondly,
the physical limits on solar radiation mean that values very close to the maximum possible
will already have been recorded in the period of data studied.
This will occur on a
cloudless day close to the time of year (or month) when the day is longest and the sun is
highest in the sky.
1.3. Maximum temperature results
The highest air temperature recorded at any of the selected stations during the period
1961 to 2010 was 38.5°C, at Faversham in Kent on 10 th August 2003. This is also the
record highest temperature ever recorded in the UK. Table 1 shows monthly statistics for
the series of temperatures from the warmest station on each day. For example, on an
average July day the temperature would reach 25.2°C in the hottest part of the UK
representative of a major transport route.
Extreme value analysis was carried out on the series of values for each month, as well as
for the year as a whole. The series were declustered into hot spell maxima using a runs
separation r of 3 days. The threshold was chosen so that between one and two events
per year occurred for the monthly models, and approximately three events per year for the
whole year model. The results were found to be insensitive to small changes in the
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threshold within a sensible range. Inspection of diagnostic plots showed that the fitted
models were generally a good fit to the observed data, and the plots for the annual model
are shown in Figure 2. The quantile plot shows a good fit, although the four hottest spells
tend away from the fitted model. It is assumed that these events, particularly the hottest
event from August 2003, are particularly unusual events which have occurred in this time
period. In practise the whole year analysis takes in hot spells above the threshold of 29°C
from across the summer months rather than modelling the annual cycle of the whole year.
Figure 2: Model diagnostic plots for the daily maximum temperature (hottest station) over the year
as a whole. Probability and Quantile plots give an indication of how well the observations (circles)
fit to the fitted model (blue line). The Quantile Plot shows temperature in °C. The Return Level plot
shows the return level temperature (°C) against the return period in years, with the black line
showing the fitted model, the blue lines showing an indication of upper and lower 95% confidence
limits, and the circles again being the observations. The Density plot shows a histogram of the
observations against the fitted model (blue line).
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Average
Hottest
Day
Day
Low
Best
High
Low
Best
High
Jan
11.3
18.3
14.3
14.4
14.5
16.2
16.6
17.3
Feb
11.5
19.6
14.5
14.7
14.9
16.9
17.5
18.4
Mar
13.7
25.6
17.2
17.4
17.5
21.2
22.1
23.5
Apr
16.7
27.2
21.1
21.1
21.1
24.5
25.2
26.2
May
20.4
31.9
25.3
25.3
25.4
29.3
29.7
30.5
Jun
23.4
35.6
27.9
28.1
28.4
31.7
32.6
33.8
Jul
25.2
36.5
28.5
29.6
29.8
33.5
34.2
35.1
Aug
24.8
38.5
28.4
28.6
28.8
32.8
33.9
35.8
Sep
21.9
31.6
25.8
25.9
25.9
29.1
29.7
30.7
Oct
17.9
29.5
21.1
21.1
21.2
25.1
26.1
27.3
Nov
14.0
20.8
17.2
17.3
17.4
19.7
19.9
20.3
Dec
12.0
18.1
15.1
15.1
15.4
17.2
17.4
17.8
All
17.8
38.5
31.0
31.3
31.7
34.5
35.2
36.4
Month
1 year return value
10 year return value
Table 1: Monthly and whole year statistics from daily maximum temperatures (°C) for 1961 to 2010,
at the hottest station representative of UK transport routes: average and hottest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.13
17.5
18.3
20.2
18.6
20.4
23.3
Feb
-0.15
18.6
19.6
22.0
19.8
22.0
25.7
Mar
-0.19
23.8
25.2
29.3
25.7
28.5
34.2
Apr
-0.35
26.1
27.1
30.0
17.1
28.3
32.3
May
-0.39
30.9
31.4
33.2
31.9
32.4
35.8
Jun
-0.23
34.0
35.2
38.8
35.5
37.6
42.3
Jul
-0.37
35.2
36.2
38.5
36.9
37.4
41.2
Aug
-0.13
35.9
37.9
42.9
38.6
43.0
50.6
Sep
-0.30
30.8
31.6
35.1
32.0
33.0
37.5
Oct
-0.27
27.2
28.7
32.6
29.5
30.9
35.9
Nov
-0.50
20.4
20.8
21.7
20.8
21.1
23.0
Dec
-0.45
18.0
18.2
19.1
18.3
18.5
20.3
All
-0.18
36.6
37.8
40.5
38.5
40.6
44.2
Table 2: Monthly and whole year statistics from daily maximum temperatures for 1961 to 2010, at
the hottest station representative of UK transport routes: shape parameter of the fitted extreme
value model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
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The estimated return values for annual probabilities of 1 (1 year event), 0.1 (10 year
event), 0.01 (100 year event) and 0.0001 (10,000 year event) are shown in Tables 1 and 2
together with the lower and upper bounds of 95% confidence limits.
The shape parameter is consistently negative for all of the monthly models. The important
effect of the negative shape parameter is shown in the return level plot in Figure 2, with
the fitted model curving towards the horizontal upper limit as the return period increases.
This is expected for temperature variables which have physical limits. However, the range
of values of the shape parameter from -0.13 to -0.50 has no physical explanation. The
shape parameters for the chosen models for each month are also shown in Table 2. This
monthly variation can lead in some cases to implausible results for the low probability
events (1:100 and 1:10,000 year return levels). For example, the month of August has a
less negative shape parameter which leads to higher estimated return levels for the
1:10,000 year return period than for the year as a whole. At these probabilities, the whole
year analysis is the most reliable.
1.4. Minimum temperature results
The lowest temperature recorded at any of the selected stations during the 1961-2010
period was -27.2°C on 10 th January 1982 at Braemar, which is located on the A93 road in
Aberdeenshire, Scotland.
It also equals the lowest temperature recorded in the UK.
During this cold spell, both Braemar and Grantown-on-Spey had five consecutive days
with minimum temperature below -22°C. Table 3 show s monthly statistics for the series of
daily minimum temperatures at the coldest station representative of UK transport routes.
Extreme value analysis was carried out on the series of values for each month, as well as
for the year as a whole, in the same way as for maximum temperature. The fitted models
were generally found to be a good fit to the observed data. Diagnostic plots for the whole
year model are shown in Figure 3, with the quantile plot showing a good fit. The whole
year model takes cold spells from across the winter months below the threshold of -12°C
The shape parameter is again consistently negative for all of the monthly models (see
Table 4). There is a clear seasonal distinction, with the summer months from April to
September having more strongly negative shape parameters, leading to fitted models
which curve more quickly to an upper bound as the return period increases. The winter
months generally have less negative shape such as that portrayed by the return level plot
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for the whole year model in Figure 3. The exception to this is February which has a more
negative shape parameter leading to 1:10,000 year return level estimates which are
warmer than those for either March or November. This is mainly due to the lack of
observed very cold extremes in this month, but there is not physical explanation for this
month-to-month variation, so the estimates should be interpreted with caution.
Figure 3: Model diagnostic plots for the daily minimum temperature (coldest station) over the year
as a whole.
See Figure 2 for details.
Note that temperatures are negated so that negative
temperatures are shown as positive.
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Average
Coldest
Day
Day
Low
Best
High
Low
Best
High
Jan
-5.6
-27.2
-11.7
-11.5
-11.2
-24.5
-21.1
-19.2
Feb
-5.6
-22.0
-11.5
-11.3
-11.3
-20.9
-19.3
-18.5
Mar
-3.8
-21.7
-8.3
-8.2
-8.1
-19.6
-16.5
-14.7
Apr
-2.4
-11.1
-6.9
-6.7
-6.5
-10.8
-9.9
-9.4
May
0.1
-7.7
-4.1
-3.9
-3.9
-7.3
-6.7
-6.5
Jun
3.0
-4.8
-1.3
-1.0
-0.9
-4.3
-3.7
-3.2
Jul
5.0
-2.8
0.8
0.8
0.7
-2.0
-1.5
-1.2
Aug
4.6
-4.4
-0.2
0.0
0.2
-3.2
-2.6
-2.2
Sep
2.4
-6.1
-2.7
-2.3
-2.2
-5.9
-5.2
-4.7
Oct
-0.1
-9.9
-4.9
-4.6
-4.5
-8.9
-8.2
-7.9
Nov
-3.2
-20.9
-8.6
-8.0
-7.6
-16.8
-14.6
-13.3
Dec
-5.5
-27.0
-11.7
-11.1
-10.6
-22.5
-19.7
-18.0
All
-0.9
-27.2
-15.6
-16.3
-17.1
-22.0
-23.5
-26.2
Month
1 year return value
10 year return value
Table 3: Monthly and whole year statistics from daily minimum temperatures (°C) for 1961 to 2010,
at the coldest station representative of UK transport routes: average and coldest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.16
-38.3
-27.9
-24.3
-51.3
-35.9
-23.6
Feb
-0.47
-27.6
-22.0
-20.9
-30.5
-23.2
-22.0
Mar
-0.14
-32.3
-22.5
-19.3
-33.9
-30.0
-18.2
Apr
-0.34
-14.3
-11.4
-10.7
-15.8
-12.4
-11.1
May
-0.42
-9.4
-7.7
-7.5
-10.8
-8.3
-8.0
Jun
-0.35
-6.7
-4.9
-4.3
-8.1
-5.7
-4.9
Jul
-0.33
-3.7
-2.5
-2.1
-5.2
-3.2
-2.8
Aug
-0.28
-5.4
-4.0
-3.4
-7.3
-5.1
-4.4
Sep
-0.43
-8.1
-6.3
-5.6
-9.6
-6.9
-6.1
Oct
-0.41
-11.1
-9.6
-9.2
-13.0
-10.4
-9.9
Nov
-0.14
-24.8
-19.4
-17.2
-33.2
-25.4
-20.7
Dec
-0.13
-33.5
-26.0
-23.0
-45.1
-34.1
-27.3
All
-0.14
-25.7
-28.7
-34.6
-28.6
-35.3
-43.6
Table 4: Monthly and whole year statistics from daily minimum temperatures for 1961 to 2010, at
the coldest station representative of UK transport routes: shape parameter of the fitted extreme
value model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
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© Crown copyright 2011
1.5. Mean temperature results
For mean temperature, both the hottest and coldest stations representative of major UK
transport routes were considered.
For both of these, results for the daily mean
temperature over both 24 hours and 8 hours will be shown. For the hottest station, the
warmest 8 hour period for the time of year is used, while for the coldest station the coldest
8 hour period for the time of year is used.
1.5.1 24 hour mean temperature
The highest 24 hour mean temperature recorded at any of the selected stations during the
1961-2010 period was 30.7°C at London Weather Centr e on 10th August 2003, the same
day as the highest maximum temperature was recorded nearby at Faversham. Table 5
shows average and maximum values of 24 hour mean temperature at the hottest station,
by month and for the year as a whole.
Extreme value analysis was carried out on the series of values for each month, as well as
for the year as a whole, in the same way as for maximum and minimum temperature. The
fitted models were generally found to be a good fit to the observed data. Diagnostic plots
for the whole year model are shown in Figure 4, with the quantile plot showing a good fit.
The August 2003 event stands out as being an unusual event within the data period
analysed.
The shape parameter is again consistently negative across all months. February and
August have the least negative shape parameters, leading to higher estimated 1:10,000
year return levels than would be expected, the August estimate being slightly higher than
the estimate from the whole year model.
14
© Crown copyright 2011
Average
Hottest
Day
Day
Low
Best
High
Low
Best
High
Jan
8.6
15.1
11.3
11.4
11.5
13.0
13.4
14.0
Feb
8.4
15.3
11.1
11.2
11.3
12.9
13.4
14.2
Mar
9.7
17.5
12.5
12.7
12.9
15.1
15.7
16.5
Apr
11.6
19.3
15.2
15.2
15.2
18.0
18.2
18.7
May
14.8
24.4
18.6
18.6
18.8
22.2
22.4
23.2
Jun
17.7
28.1
21.3
21.3
21.5
24.6
25.6
26.6
Jul
19.6
27.9
22.9
23.1
23.4
25.9
26.4
27.5
Aug
19.4
30.7
22.0
22.3
22.6
25.8
26.6
28.1
Sep
17.2
24.2
19.6
19.8
20.1
22.2
22.7
23.7
Oct
14.3
22.4
16.8
17.0
17.3
19.3
20.2
21.0
Nov
11.1
17.1
14.2
14.2
14.3
15.9
16.2
16.7
Dec
9.4
15.7
12.3
12.3
12.4
14.4
14.5
15.0
All
13.5
30.7
24.1
24.4
24.7
27.0
27.6
28.7
Month
1 year return value
10 year return value
Table 5: Monthly and whole year statistics from 24 hour mean temperatures (°C) for 1961 to 2010,
at the hottest station representative of UK transport routes: average and hottest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.15
14.1
14.8
16.5
15.1
16.5
18.9
Feb
-0.05
14.3
15.3
17.5
15.7
18.6
22.4
Mar
-0.22
16.6
17.5
20.0
17.6
19.2
22.5
Apr
-0.49
18.9
19.2
20.4
19.3
19.6
21.8
May
-0.40
23.8
24.1
26.2
24.7
25.0
28.2
Jun
-0.27
26.4
27.8
31.2
27.9
29.6
34.0
Jul
-0.27
27.4
28.3
31.2
28.1
29.8
33.4
Aug
-0.13
28.3
29.8
33.5
30.6
33.9
39.2
Sep
-0.23
23.6
24.5
27.2
23.8
26.1
29.6
Oct
-0.25
20.7
21.9
24.2
21.9
23.5
26.4
Nov
-0.37
16.6
17.1
18.5
17.0
17.6
19.5
Dec
-0.36
15.3
15.5
16.5
15.9
16.1
17.9
All
-0.13
28.8
30.0
32.5
30.6
33.1
36.8
Table 6: Monthly and whole year statistics from 24 hour mean temperatures for 1961 to 2010, at
the hottest station representative of UK transport routes: shape parameter of the fitted extreme
value model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
15
© Crown copyright 2011
Figure 4: Model diagnostic plots for high extremes of 24 hour mean temperature at the hottest
station, over the year as a whole. See Figure 2 for details.
The lowest 24 hour mean temperature recorded at any of the selected stations is -18.8°C
on 29th December 1985 at Fyvie Castle, near to the A947 road in Aberdeenshire. The
station recorded a minimum temperature of -21.7°C a nd a maximum of -15.9°C on this
day. This latter value is the lowest daily maximum temperature on record for the UK.
Table 7 provides statistics of 24 hour mean temperature at the coldest of the selected
stations by month and for the year as a whole.
16
© Crown copyright 2011
Average
Coldest
Day
Day
Low
Best
High
Low
Best
High
Jan
-1.0
-18.7
-4.7
-4.9
-5.3
-11.0
-12.6
-15.5
Feb
-0.7
-14.8
-3.7
-4.0
-4.4
-9.4
-10.7
-12.9
Mar
1.3
-10.7
-1.5
-1.9
-2.4
-6.3
-7.4
-9.5
Apr
3.4
-4.7
0.5
0.5
0.1
-2.9
-3.1
-3.9
May
6.1
-0.6
3.3
3.5
3.1
0.5
1.1
-0.5
Jun
8.8
2.3
6.0
5.9
5.9
4.1
3.7
3.0
Jul
10.6
5.6
8.1
8.1
8.0
6.7
6.4
6.1
Aug
10.5
5.1
7.7
7.7
7.5
5.9
5.8
5.4
Sep
8.4
0.0
5.5
5.3
5.1
2.9
2.3
1.4
Oct
5.5
-2.0
2.4
2.2
1.7
-0.9
-1.1
-1.8
Nov
1.7
-12.8
-1.8
-2.2
-2.7
-6.9
-8.1
-10.0
Dec
-0.7
-18.8
-4.4
-4.8
-5.4
-10.4
-12.0
-14.8
All
4.5
-18.8
-7.9
-8.5
-9.2
-13.5
-14.8
-17.1
Month
1 year return value
10 year return value
Table 7: Monthly and whole year statistics from 24 hour mean temperatures (°C) for 1961 to 2010,
at the coldest station representative of UK transport routes: average and coldest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.11
-15.4
-18.6
-26.6
-19.0
-27.0
-39.6
Feb
-0.18
-12.9
-15.1
-21.6
-15.0
-20.0
-29.1
Mar
-0.10
-9.4
-11.9
-16.9
-12.0
-18.2
-25.9
Apr
-0.41
-4.3
-4.6
-6.4
-5.0
-5.3
-8.2
May
-0.18
-0.3
-1.3
-4.1
-0.9
-3.3
-7.1
Jun
-0.19
2.9
2.3
0.2
2.2
0.7
-2.2
Jul
-0.36
5.9
5.7
4.9
5.5
5.2
3.8
Aug
-0.43
5.2
5.1
4.0
4.9
4.7
3.1
Sep
-0.19
1.3
0.4
-2.1
0.1
-1.7
-5.1
Oct
-0.49
-1.9
-2.2
-3.8
-2.3
-2.6
-5.6
Nov
-0.20
-10.3
-12.3
-17.0
-13.1
-17.3
-23.9
Dec
-0.04
-15.0
-18.5
-25.4
-19.8
-29.9
-41.8
All
-0.09
-17.1
-19.9
-24.5
-20.8
-27.6
-34.4
Table 8: Monthly and whole statistics from 24 hour mean temperatures for 1961 to 2010, at the
coldest station representative of UK transport routes: shape parameter of the fitted extreme value
model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
17
© Crown copyright 2011
Return values for return periods from 1 year to 10,000 years are shown in Tables 7 and 8.
Diagnostic plots for the whole year model of low extremes of mean temperature are
shown in Figure 5. The shape parameter is less negative than for hot extremes, and the
return level plot shows the effect of this, with only a slight curve in the fitted model.
December has the least negative shape parameter of the monthly models, leading to
estimated 1:10,000 year return levels which are colder than those for the whole year
model. Again there is greater confidence in the whole year model.
Figure 5: Model diagnostic plots for cold extremes of 24 hour mean temperature at the coldest
station, over the year as a whole. See Figure 2 for details. Note that temperatures are negated so
that negative temperatures are shown as positive.
18
© Crown copyright 2011
1.5.2 Eight hour mean temperature
Eight hour mean temperatures were calculated over two periods, one representing the
warmest part of the day and the other the coldest period.
For the warm (day time) period, mean temperatures were calculated over the period from
11 to 18 GMT except for the months from September to November, for which the period
from 10 to 17 GMT was found to be the warmest on average. The highest eight hour
mean temperature was 36.3°C at Wisley on 10 th August 2003. Several other stations in
south-east England also had eight hour mean temperatures of over 35°C on this day,
which is the same day as the highest 24 hour maximum and mean temperatures were
recorded.
Statistics were calculated for the daily series of values at the hottest station representative
of major UK transport routes, and these are shown in Table 9. As expected, the values
are higher than those for the 24 hour mean temperature but lower than those for the 24
hour maximum temperature.
Extreme value analysis was carried out as for 24 hour mean temperature for each month
and for the year as a whole. The models generally fitted well to the data, and diagnostic
plots for the whole year model are shown in Figure 6. This shows that the August 2003
hot spell was an unusual event as it stands well above the next hottest event. However,
the model for November had a very low shape parameter, and the results for this month
are not considered robust.
19
© Crown copyright 2011
Average
Hottest
Day
Day
Low
Best
High
Low
Best
High
Jan
9.0
15.7
12.2
12.3
12.4
13.9
14.3
14.9
Feb
9.1
17.1
12.1
12.2
12.3
14.0
15.2
16.2
Mar
10.9
19.8
15.2
15.2
15.3
17.8
18.3
19.2
Apr
13.7
24.9
18.4
18.5
18.7
22.4
22.8
23.7
May
17.3
30.5
22.4
22.4
22.6
27.3
27.8
29.0
Jun
20.2
33.5
25.6
25.7
26.0
29.4
30.6
32.1
Jul
22.4
33.6
27.1
27.2
27.6
31.0
31.7
32.8
Aug
22.1
36.3
26.0
26.2
26.5
30.3
32.1
34.0
Sep
18.8
27.6
22.5
22.6
23.1
26.2
26.5
27.5
Oct
15.0
25.9
17.8
18.2
18.6
21.8
22.8
24.3
Nov
11.4
17.4
15.1
15.1
15.1
16.8
16.9
18.2
Dec
9.7
16.6
13.1
13.1
13.2
14.9
15.4
16.1
All
15.0
36.6
28.7
29.1
29.6
32.3
33.1
34.5
Month
1 year return value
10 year return value
Table 9: Monthly and whole year statistics from 8 hour mean temperatures (°C) for 1961 to 2010, at
the hottest station representative of UK transport routes: average and hottest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.25
14.9
15.4
17.2
15.7
16.5
18.7
Feb
-0.21
15.2
17.0
20.0
16.4
18.9
22.9
Mar
-0.32
19.1
19.8
22.8
20.0
20.9
24.5
Apr
-0.39
24.0
24.5
26.8
24.9
25.5
29.3
May
-0.39
29.3
30.0
32.7
30.5
31.3
35.9
Jun
-0.28
31.5
33.2
37.5
33.7
35.3
40.7
Jul
-0.39
32.5
33.4
36.4
33.4
34.5
38.5
Aug
-0.21
32.9
35.7
41.1
35.5
39.3
46.5
Sep
-0.46
27.5
27.9
30.7
28.1
28.5
32.5
Oct
-0.22
24.2
25.5
29.6
25.8
28.2
33.6
Nov
-0.56
17.2
17.4
17.5
17.5
17.6
19.3
Dec
-0.28
16.0
16.6
18.8
16.8
17.6
20.3
All
-0.17
34.4
35.7
38.7
36.3
38.8
42.7
Table 10: Monthly and whole year statistics from 8 hour mean temperatures for 1961 to 2010, at
the hottest station representative of UK transport routes: shape parameter of the fitted extreme
value model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
20
© Crown copyright 2011
Figure 6: Model diagnostic plots for 8 hour mean temperature (11 - 18 GMT) at the hottest station,
over the year as a whole. See Figure 2 for details.
For the cold period, mean temperatures were calculated over the period from 02 to 09
GMT for January, 01 to 08 GMT for November, December and February, 00 to 07 GMT
for March, April, September and October and 23 to 06 GMT for May to August. The
lowest eight hour mean temperature was -21°C at Alt naharra, on the A836 road in
northern Scotland on 8th January 2010. Another notably cold night occurred on the 3rd
March 2001 when an 8 hour mean temperature of -19.5°C was recorded at Kinbrace, also
in northern Scotland. And on 30th December 1995 there was an 8 hour mean temperature
of -20.3°C at Aviemore.
Statistics were calculated for the daily series of values at the coldest station
representative of major UK transport routes, and these are shown in Table 11.
21
© Crown copyright 2011
Average
Coldest
Day
Day
Low
Best
High
Low
Best
High
Jan
-0.7
-21.0
-6.6
-6.6
-6.7
-13.7
-15.7
-19.5
Feb
-1.0
-18.2
-5.4
-5.9
-6.5
-11.7
-13.4
-16.5
Mar
0.1
-19.5
-4.3
-4.7
-5.3
-10.0
-11.9
-16.0
Apr
1.3
-6.4
-2.6
-2.7
-2.9
-5.0
-5.3
-5.8
May
4.1
-3.8
-0.1
-0.2
-0.2
-3.0
-3.1
-5.1
Jun
7.1
-1.7
3.5
3.4
3.1
0.7
0.2
-0.8
Jul
9.1
1.6
5.6
5.6
5.4
3.3
2.9
2.1
Aug
8.8
0.1
4.6
4.5
4.4
2.0
1.5
0.7
Sep
6.2
-3.3
0.8
0.8
0.7
-1.4
-1.8
-2.6
Oct
4.0
-7.1
-1.4
-1.7
-1.9
-4.3
-5.0
-6.2
Nov
1.3
-17.9
-4.1
-4.4
-4.9
-8.9
-10.5
-13.8
Dec
-0.6
-20.3
-6.9
-7.3
-7.8
-13.5
-15.5
-19.7
All
3.3
-21.0
-10.7
-11.5
-12.4
-17.1
-18.9
-22.6
Month
1 year return value
10 year return value
Table 11: Monthly and whole year statistics from 8 hour mean temperatures (°C) for 1961 to 2010,
at the coldest station representative of UK transport routes: average and coldest day and 1 and 10
year return values (best estimate and lower and upper limits of 95% confidence intervals).
Month
Shape
100 year return value
10,000 year return value
parameter
Low
Best
High
Low
Best
High
Jan
-0.17
-18.4
-21.8
-33.1
-21.3
-28.8
-45.1
Feb
-0.11
-16.0
-19.2
-26.8
-19.3
-27.1
-38.7
Mar
0.05
-15.0
-20.0
-28.8
-21.1
-39.3
-58.3
Apr
-0.44
-5.9
-6.2
-7.5
-6.4
-6.7
-8.9
May
-0.62
-3.6
-3.8
-6.2
-3.8
-4.0
-4.1
Jun
-0.29
-0.7
-1.5
-4.2
-1.8
-2.8
-6.2
Jul
-0.35
2.2
1.7
-0.7
1.5
0.9
-1.9
Aug
-0.38
0.8
0.2
-2.1
0.2
-0.6
-3.6
Sep
-0.32
-2.4
-3.1
-5.3
-3.2
-4.0
-6.7
Oct
-0.21
-6.0
-7.1
-10.4
-6.4
-9.2
-13.6
Nov
0.07
-13.3
-17.7
-24.9
-20.0
-36.3
-52.6
Dec
-0.05
-18.4
-22.8
-33.1
-21.9
-34.9
-52.7
All
-0.09
-20.7
-24.8
-32.2
-23.3
-33.5
-44.5
Table 12: Monthly and whole year statistics from 8 hour mean temperatures for 1961 to 2010, at
the coldest station representative of UK transport routes: shape parameter of the fitted extreme
value model and 100 and 10,000 year return values (best estimate and 95% confidence interval).
22
© Crown copyright 2011
Extreme value analysis was carried out as for 24 hour mean temperature for each month
and for the year as a whole. Diagnostic plots for the whole year model are shown in
Figure 7. The shape parameters for the monthly models vary considerably, partly due to
seasonality and partly to random effects. This makes the monthly return values for 100
and 10,000 year events inconsistent and should be interpreted with caution. In particular,
the months of March and November had unusually cold events within the observed
record, and this contributes to them having positive shape parameters and more
extremely cold return levels for low probability events than expected. May has a very
negative shape parameter which also leads to unrealistic estimates of the 1:100 and
1:10,000 year return levels. The whole year model gives the most reliable estimation of
the severity of low probability cold spells.
Figure 7: Model diagnostic plots for 8 hour mean temperature (00 - 07, 01 - 08 or 02 - 09 GMT) at
the coldest station, over the year as a whole. See Figure 2 for details. Note that temperatures are
negated so that negative temperatures are shown as positive.
23
© Crown copyright 2011
1.6. Solar radiation results
1.6.1 24 hour average insolation
Table 13 shows the maximum daily average global solar insolation on a horizontal surface
recorded at any of the selected stations representative of UK transport routes during each
month of the year. Also provided are the 95th and 99th percentile values, which give an
indication, based on the 1993 to 2010 period, of the highest values to be expected on
major UK transport routes on one in 20 and one in 100 days respectively.
These insolation rates will be much lower than the peak insolation during the middle of the
day because they are averaged over a full 24 hours (midnight to midnight) including the
night time.
Table 13 shows the locations and dates of the maximum recoded daily
radiation total in each month, and Figure 8 plots out the hourly insolation rates for these
events for four of the months. The smooth profile of these days indicate, as expected,
that these are days with very little or no cloud.
Only in the April event is there an
indication of a small amount of cloud in the afternoon. The maximum hourly average
insolation rate on the day with the highest total radiation (at Liverpool on 27th June 2005)
was 976 W / m2 for the hour ending 13:00 GMT. Within this hour the maximum insolation
would have been slightly higher. Locations in the south of the UK are more likely to
receive the highest radiation amount in the winter.
In summer the longer days
experienced in the north means that northern locations are able to receive similar or even
higher daily radiation amounts compared to locations in the south.
24
© Crown copyright 2011
Month
Station with greatest radiation
th
th
Maximum value
Average
95 %ile
99 %ile
Max
Location
Date
Jan
50
78
85
90
Cardinham Bodmin
29 January 2006
Feb
90
132
144
158
Cardinham Bodmin
29 February 2004
Mar
158
213
231
249
Hawarden (Chester)
30 March 2000
Apr
239
288
301
312
Camborne
30 April 2001
May
301
353
366
374
Stornoway
26 May 2008
Jun
321
364
373
392
Liverpool Museum
27 June 2005
Jul
297
349
358
363
Stornoway
5 July 2008
Aug
252
306
318
330
Dunstaffnage (Oban)
5 August 1995
Sep
183
236
246
257
Liverpool Museum
2 September 2005
Oct
112
157
167
184
Cardinham Bodmin
8 October 1998
Nov
61
90
100
110
Southsea
1 November 2003
Dec
41
59
72
79
Brooms Barn, Suffolk
31 December 2001
All
176
341
359
392
Liverpool Museum
27 June 2005
2
Table 13: Monthly and whole year statistics of daily (00 to 00) global solar irradiance (W / m ) at the
location with the highest value on each day, from 1993 to 2010. Also shown is the location and
date of the maximum value.
1000
June
Solar irradiance (W / sq m)
900
April
800
October
700
December
600
500
400
300
200
100
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
Time of day (GMT)
2
Figure 8: Diurnal cycle of hourly average irradiance values (W / m ) for the days and locations with
maximum total radiation in June, April, October and December (see Table 9 for details of locations
and dates).
25
© Crown copyright 2011
1.6.2 Eight hour average insolation
The profiles in Figure 8 also show that the maximum radiation for an 8 hour period occurs
between 08 and 16 GMT for all months. Statistics of the average insolation rates over this
period at the station with the maximum value on each day are shown in Table 14. As
expected, the values are greater than those averaged over 24 hours, especially for the
winter when the 8 hour period includes almost all of the radiation received. The location
and date of the maximum values for each month are generally the same except that the
Scottish locations no longer feature as their summer diurnal cycles are spread over a
slightly longer period of daylight, but with a lower peak insolation.
Month
Station with greatest radiation
th
Maximum value
Average
95 %ile
99 %ile
th
Max
Location
Date
Jan
146
229
252
261
Cardinham Bodmin
29 January 2006
Feb
256
369
408
435
Cardinham Bodmin
29 February 2004
Mar
424
565
595
638
Hawarden (Chester)
30 March 2000
Apr
595
702
729
751
Camborne
30 April 2001
May
699
803
823
836
Liverpool Museum
29 May 2005
Jun
726
817
836
861
Liverpool Museum
27 June 2005
Jul
684
787
808
812
Liverpool Museum
13 July 2004
Aug
612
716
749
766
Southsea
2 August 2003
Sep
480
604
631
649
Hawarden (Chester)
10 September 1999
Oct
316
431
461
515
Cardinham Bodmin
8 October 1998
Nov
179
267
293
321
Southsea
1 November 2003
Dec
123
179
215
238
Brooms Barn, Suffolk
31 December 2001
All
437
773
811
861
Liverpool Museum
27 June 2005
2
Table 14: Monthly and whole year statistics of 8 hour (08 to 16) global solar irradiance (W / m ) at
the location with the highest value on each day, from 1993 to 2010. Also shown is the location and
date of the maximum value.
1.6.3 Insolation on a vertical surface
Solar radiation is usually only recorded on a horizontal surface, but the amount of
radiation received on inclined planes is often of interest. The amount of radiation received
on inclined planes varies according to the position of the sun in the sky and how this
relates to the plane of interest. The position of the sun in the sky varies with the time of
day and the time of year, as well as the latitude of the location.
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In this case, the plane of interest is a vertical surface, because of the rectangular shape of
packages. The orientation of the plane will vary at different times during transportation,
but the most exposed orientation is considered, this being south-facing. Various models
are available to estimate the solar radiation received on inclined surfaces from that
recorded on a horizontal surface. These models require both global (total) radiation and
diffuse radiation as inputs (e.g. Badescu, 2008; SolarTool, 2008). Hourly diffuse radiation
is only available from two stations in the UK; Camborne and Lerwick, and only up to 1998.
Camborne is located in Cornwall and is a location that can receive close to the maximum
possible solar radiation in the UK at most times of the year. Lerwick is located on the
Shetland Islands, and has the longest days in the UK during the summer.
The series of equations from Solar Tool (2008) have been used to estimate the insolation
on a south-facing vertical surface at Camborne and Lerwick from total and diffuse
insolation values.
This has been done for the hour from 12 to 13 GMT on the days of
each month of the year when the radiation for this hour was the greatest (and diffuse
radiation data was available).
These hours generally would have had clear sky
conditions, but due to the restricted time period of data available some of the hours may
have had some cloud, which will affect the results. The results are shown in Table 15.
Month
Camborne
Lerwick
Horizontal
Vertical
Ratio
Horizontal
Vertical
Ratio
January
369
791
2.15
167
644
3.86
February
551
853
1.55
364
668
1.83
March
756
827
1.09
583
866
1.48
April
870
723
0.83
756
824
1.09
May
946
606
0.64
842
763
0.91
June
954
576
0.60
868
699
0.81
July
926
569
0.61
847
710
0.82
August
870
622
0.72
732
731
1.00
September
783
746
0.95
615
799
1.30
October
614
823
1.34
431
661
1.53
November
393
786
2.00
217
490
2.26
December
287
825
2.88
115
343
2.98
2
Table 15: Horizontal and estimated vertical insolation (W / m ) for the hour from 12 to 13 GMT at
Camborne and Lerwick, on the days of each month when radiation was greatest for that hour. The
ratio of vertical to horizontal insolation is also shown.
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The results show a clear annual cycle of the ratio between vertical and horizontal
insolation, with the greatest ratios occurring in the winter, and the lowest in the summer.
The ratios are higher at Lerwick than at Camborne. This means that the annual cycle of
insolation on a vertical surface varies much less than that for a horizontal surface.
Maximum values of insolation on a vertical surface tend to occur in the spring and autumn
months.
1.7 Discussion of results
A range of temperature and insolation statistics have been presented. The approach
taken has been to identify areas of the UK which are representative of major transport
routes, and to consider the most extreme conditions (worst case) recorded at any stations
within that area on each day. This is in line with the stated requirement to consider the
whole of the UK that is representative of major transport routes as one area, because a
journey involving the transport of a package may cover a substantial distance across any
part of the UK. Another possible approach would be to take the spatial average across all
stations within the representative zone on each day. This is not considered likely to be of
use because this would not reveal the extreme conditions which are of interest.
For high and low temperature extremes, three different statistics have been considered
based on different durations of exposure. The peak temperature at any time of day, the
temperature averaged over the hottest eight-hours of the day, and the 24 hour average
temperature (the average of the maximum and minimum temperatures). The choice of
statistics may be based on how quickly packages respond to changing temperature, and
how this affects the design of packages. The impact of the choice of statistic on the
results is illustrated in Figure 9, using the 1:10 year return levels as an example. The
uncertainty of these estimates is also shown on these charts in the form of 95%
confidence limits.
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-12
Temperature (deg C)
Temperature (deg C)
38
36
34
Max
32
8-hour
30
24-hour
28
-16
24-hour
-20
8-hour
Min
-24
-28
26
Figure 9: 1:100 year return levels of a) hot temperature extremes and b) cold temperature
extremes, showing the impact of time averaging duration.
Upper and lower limits of 95%
confidence intervals are shown as solid lines in the same colour as the best estimate (dashed line).
For each temperature statistic representing different duration of exposure, there are
ranges of statistics presented to represent different probabilities of occurrence. As well as
the average and most extreme day in the observed record, extreme value analysis has
been carried out in order to estimate the values associated with probabilities of
occurrence ranging from 1:1 year to 1:10,000 years. Here, the choice of statistic is likely
to be based on an acceptable level of risk or a balance between the cost of design and
the cost of times when packages could not be transported due to the weather conditions.
For example, if designing to a 1:10 year return level, you would expect on average one
spell every 10 years to be more extreme than the temperature designed to. Although the
hot or cold spell may last for several days, the return level designed to is only likely to be
exceeded on the hottest or coldest day of the spell. The impact of the choice of statistic
on the results is illustrated in Figure 10, using the example of the eight hour temperatures,
and showing the range of probabilities associated with this statistic.
This figure also
shows how uncertainty increases when extrapolating to lower probability events.
Although the confidence intervals do overlap in some cases, in reality the order of the
values would not change.
For the hot extreme models, there is a tendency for the hottest events (especially the
August 2003 event) to be hotter than predicted by the fitted model. It is assumed that
these events belong to the same population but are particularly unusual events which
have occurred in this time period. However there could be a different population for very
hot events with a less negative shape parameter. This could come about through a
division of hot days into those where evaporative cooling takes place and those where it
does not because it is too dry (Brabson et al, 2005).
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Temperature (deg C)
44
40
1:1
1:10
36
1:100
1:10,000
32
28
-10
Temperature (deg C)
-15
-20
1:1
-25
1:10
-30
1:100
1:10,000
-35
-40
-45
Figure 10: Eight hour mean temperatures for a range of annual probabilities of occurrence: a) hot
extremes and b) cold extremes. Upper and lower limits of 95% confidence intervals are shown as
solid lines in the same colour as the best estimate (dashed line).
The temperature results are summarised in Tables 16 (high extremes) and 17 (low
extremes), showing the best estimate values for a range of return periods and durations of
exposure.
Statistic
1 year
10 years
100 years
10,000 years
Maximum
31.3
35.2
37.8
40.6
8 hour mean
29.1
33.1
35.7
38.8
24 hour mean
24.4
27.6
30.0
33.1
Table 16: Summary of results for high extremes of temperature, showing the best estimate
temperatures (°C) for a range of return periods and durations of exposure.
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Statistic
1 year
10 years
100 years
10,000 years
Minimum
-16.3
-23.5
-28.7
-35.3
8 hour mean
-11.5
-18.9
-24.8
-33.5
24 hour mean
-8.5
-14.8
-19.9
-27.6
Table 17: Summary of results for low extremes of temperature, showing the best estimate
temperatures (°C) for a range of return periods and durations of exposure.
As well as the whole year results used so far in this section, results have been provided
for each month of the year. These results may be useful where movements of packages
are to be restricted to particular times of year. There is less confidence in the results for
individual months compared to the whole year results, so that results for probabilities
extrapolated beyond the range of the data (1:100 and 1:10,000 year probabilities) should
be used with caution.
Different statistics have been provided for solar insolation because extreme value analysis
is not appropriate for these data. Averages over both 24 hours (00 to 00) and eight hours
(08 to 16) have been provided. The eight hour averages are higher and likely to be most
useful, because the 24 hour average values include the hours of darkness when no solar
radiation is received. As for temperature, the average and maximum value across all
days in each month have been provided (again based on the series of values from the
station with the highest value on each day).
The maximum values will be close to the
physical limit on insolation. This will occur on a cloudless day near to the time of the
month or year and the location when and where the combination of the position of the sun
in the sky and the length of day leads to a maximum potential insolation. Instead of return
values for probabilities down to 1:10,000 years, 95 and 99 percentile values are provided.
The 99 percentile values are those which occur once every 100 days on average, which is
approximately equivalent to once every three years for a monthly value or three times per
year for the whole year value. This gives an indication of the expected frequency of
occurrence of such days with high insolation levels.
The results presented in Part 1 of this report have been based on analysis of historical
observed data over the last 50 years. The analysis has not taken temporal trends into
account, but Part 2 of this report provides further information on the impact of climate
change on temperature and insolation values.
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Part 2: Climate Change Information
2.1 Introduction
Over the last century the Earth’s climate has changed (Houghton et al., 2001; Levitus et
al., 2001). The mean global temperature has risen by approximately 0.74 ˚C, and in
addition there have been reductions in snow cover and sea ice extent, and observations
show the sea level and the heat held by the oceans have increased (Zwiers, 2002). All
model projections indicate that this warming is likely to continue into the future, regardless
of the ‘emissions scenario’ used. This warming will also affect other climate variables.
Part 1 of this report has provided information on the current range of temperature and
solar radiation values experienced around the UK. This information has been given for
areas within 1km both sides of all main roads and railway lines across the UK, and has
identified the locations of extreme values of temperature and solar insolation. Because
the results in Part 1 have considered UK transport routes as a single area, they cannot be
directly compared to the results in Part 2. The results presented in Part 2 will, however,
complement those from Part 1 by showing how the range of conditions may change in the
future.
This report will provide an initial assessment of model projections of the two variables over
the next few decades, and will consider the changes possible in response to climate
change. It will use the UKCP09 projections to establish maximum, mean and minimum
temperatures for the 2020s and 2030s and the uncertainty in these projections at a 25km
grid resolution. These will be graphically presented, showing the relevant percentiles of
future distributions (10th, 50th and 90th percentiles) to represent the uncertainty in the
projections. Change in total short wave radiation flux (equivalent to ‘solar radiation’, and
relative to the 1961-1990 average) will also be presented alongside the relevant
uncertainty ranges. Future absolute radiation flux projections are not available; however
this information can be used to give an indication of future radiation strengths through
comparison with the observed radiation properties presented in the first report.
2.1.1 UKCP09 Projections
The UK Climate Projections (UKCP09) are the most recent set of climate change
scenarios released by the UK Climate Impacts Programme. UKCP09 describes how the
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UK climate might change during the 21st Century using leading climate science developed
at the Met Office Hadley Centre (for more detail see Jenkins et al. 2009).
The main difference from its predecessor (UKCIP02, Hulme et al. 2002)) is the systematic
incorporation of some of the uncertainties associated with the climate system (see Figures
11 and 12).
From UKCIP02 only a
single representation of
change was available
With IPCC AR4, multiple
models allowed
quantification of
structural uncertainty
In UKCP09, a statistical
framework accounting
for known uncertainties
allowed probabilistic
projections.
Figure 11: An illustration of the shift in methodology from the single model projections of UKCIP02
to the probabilistic projections of UKCP09.
The Intergovernmental Panel on Climate Change
Fourth Assessment Report (IPCC AR4) used models from the many different modelling centres
around the world.
Modern, complex climate models are an essential tool for simulating and understanding
the present-day and future climate.
Confidence in model estimates of future climate
evolution has been enhanced significantly in the last few years since the IPCC Fourth
Assessment Report (AR4).
Climate models have been demonstrated to reproduce
observed features of recent climate and past climate changes and there is considerable
confidence that Atmosphere-Ocean General Circulation Models (AOGCMs) provide
credible quantitative estimates of future climate change, particularly at continental and
larger scales (Randall et al., 2007). However, due to resolution constraints, caused by
limitations to computing power, and uncertainties inherent in the modelling process,
simulations cannot be expected to perfectly replicate reality. The quantification of these
uncertainties and their conversion into probabilistic projections formed the basis of the
UKCP09 findings (Figure 12). Multiple simulations of the Met Office Hadley Centre’s
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global climate model were created using perturbations of uncertain model parameters and
switches. These simulations covered the present-day and the future. Using a Bayesian
statistical framework, these were combined with output from several other international
global climate models, observations and further high resolution (25km) regional climate
simulations to generate the final projections. (Murphy, et al., 2009).
UKCIP02
Single
projection
UKCP09
Central
estimate
(50%)
Very unlikely
to be more
than (90%)
Summer Rainfall 2080’s
Very unlikely
to be less than
(10%)
Figure 12: Moving from uncertainty to probability, an example of the difference between results for
UKCIP02 and UKCP09 for summer mean rainfall across the UK for the 2080s for the high emission
scenario (Hulme et al., 2002; Jenkins et al., 2009).
The probabilistic projections derived from this framework ‘represent the relative degree to
which each climate outcome is supported by the evidence currently available, taking into
account our understanding of climate science and observations, and using expert
judgement’ (Murphy, et al., 2009). The ‘lower 10%’ estimate gives changes which are
very likely to be exceeded whilst the ‘upper 90%’ estimate gives changes very unlikely to
be exceeded. Providing information in this probabilistic way is useful for planning and
policy development as users can start to assess expected losses, costs and benefits that
might occur over a range of likely outcomes (for further discussion see Zwiers, 2002).
UKCP09 handles uncertainty arising from differing greenhouse gas emissions by
considering separate pathway scenarios used by the IPCC (Meehl et al., 2007). These
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scenarios are not intended to act as a comprehensive set of possible emission futures and
currently no scenario is viewed as more likely than any other. Projections are given for
three scenarios; low, medium and high, referred to as ‘B1’, A1B’ and ‘A1FI' (Nakicenovic
and Swart, 2000) by the IPCC. The low emissions scenario follows a pathway of clean
technologies replacing fossil fuel use. The medium is based on a mixture of future fossil
fuel and non-fossil fuel whilst the upper is reflective of a pathway predominantly fossil fuel
based (see Figure 13). The A1B scenario replaces the two medium emissions scenarios
used in UKCIP02 (medium-high and medium-low) that corresponded to the IPCC SRES
B2 and A2 so that only one medium emissions scenario is given in UKCP09.
Figure 13: The three SRES emissions scenarios used in UKCP09 are A1FI (high), A1B (medium)
and B1 (low).
Where an analysis is concerned with only the first half of the 21st century it is usual to
consider only the one emissions scenario, as until the 2050s there is little divergence in
the modelled climate variables between scenarios.
This is largely because a good
proportion of the change in the next few decades will result from the climate (in particular
the ocean) adjusting to the change in the atmosphere that have already occurred (Zwiers,
2002). For this report we will therefore focus on results from the medium emissions
scenario (A1B) for the 2020s and 2030s. We will, however, present some results from the
latter part of the century (2080s) for illustrative purposes (see for instance Appendix 1).
The UKCP09 projections are provided for seven overlapping thirty year time-periods with
reference to the 1961-1990 baseline. Each future time period is referred to by its central
decade, so the 2020s refers to 2010-2039 and the 2030s refers to 2020-2049 and so on.
This report will use UKCP09 probabilistic projections for the decades 2020s and 2030s,
where the decade is represented by the 30 year period centred on that decade.
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The spatial resolution of the model has improved since UKCIP02, and grid boxes are now
25km rather than 50km allowing better representation of finer scale patterns. Results are
aggregated for 16 administrative regions (Figure 14), 23 river basins, and 9 marine
regions.
Figure 14: An illustration of the resolution of the UKCP09 climate projections showing the 25km
grid (left) and the 16 administrative regions (right).
This report provides a summary of the projected UKCP09 changes across the UK in mean
daily temperature, daily temperature extremes, and maximum solar insolation values.
Temperature and insolation changes are presented for annual mean, and also for
seasonal mean changes where relevant.
A summary considering the usefulness of projecting the changes presented on climate
change timescales is also given, as well as an assessment of additional methods that may
provide further robustness to this initial analysis.
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2.2 Climate change temperature information
2.2.1 Discussion of variables
Future mean daily maximum, mean and minimum temperatures for each decade are
available at a 25 km grid resolution across the UK.
It is important to note that the
temperature information given is a temporal mean of the variable, for example the
maximum temperature data available is the mean of daily maximum temperatures across
the 30 year period at a location, and therefore individual days with extreme maximum
temperatures will not be captured using this information. Different sources of information
are available to consider how future temperatures could change, but in this assessment
UKCP09 probabilistic information is used. For a full assessment which may capture more
of the individual peaks a combination of information would be necessary. In particular to
explore extreme events regional climate model data would be more appropriate but is
outside the scope of this project. However, a more complete representation of the known
uncertainties is captured by the probabilistic information given here than use of the
regional climate model data alone would give and therefore the projections are still of
value to help understand future temperature extremes.
Given that extremes of temperature are of greatest interest here we have selected
information about the annual mean, minimum and maximum across the 2020s and 2030s.
We have then considered the mean daily temperature and the mean daily maximum
temperature for the summer months (June, July, August) for both decades (see Appendix
2 for maximum temperatures in other months), and also mean daily temperature and the
mean daily minimum temperature for the winter months (December, January, February)
for both decades (see Appendix 3 for minimum temperatures in other months).
Further to this, plots are included of the change in maximum temperature of the warmest
day in summer (this can be thought of as the change in the extreme value of the season,
although strictly we have used the 99th percentile of the daily distribution of maximum
temperature over the 30 year period) and the change in the minimum temperature of the
coolest day in winter (again the change in the extreme value of the season, or the 99th
percentile of the daily distribution of minimum temperature over the 30 year period). This
should allow for the seasonal variations to be captured and will give an indication of the
range of temperatures we might expect in the future.
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Please note the upper and lower estimates (90th and 10th percentile) given throughout are
not representative of the entire range. We indicate here that only 10% of results fall
above the value of the 90th percentile, and 10% fall below the value of the 10th percentile.
These estimates should therefore not be used as an absolute limit on temperatures and
solar flux in any way, but as an indication of the range considered very likely to be within
based on the uncertainties included in the UKCP09 analysis.
2.2.2 Results
Annual mean daily mean temperature, daily maximum temperature and daily
minimum temperature
th
th
th
Figure 15a) 2020s Annual mean daily mean temperature – 10 , 50 , 90 percentile.
th
th
th
Figure 15b) 2020s Annual mean daily minimum temperature – 10 , 50 , 90 percentile.
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th
th
th
Figure 15c) 2020s Annual mean daily maximum temperature – 10 , 50 , 90 percentile.
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th
th
th
Figure 15d) 2030s Annual mean daily mean temperature – 10 , 50 , 90 percentile.
th
th
th
th
th
th
Figure 15e) 2030s Annual mean daily minimum temperature – 10 , 50 , 90 percentile.
Figure 15f) 2030s Annual mean daily maximum temperature – 10 , 50 , 90 percentile.
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Summer (JJA) mean daily mean temperature, daily maximum temperature and
warmest day maximum temperature.
th
th
th
Figure 16a) 2020s Summer (JJA) mean daily mean temperature – 10 , 50 , 90 percentile.
th
th
th
Figure 16b) 2020s Summer(JJA) mean daily maximum temperature – 10 , 50 , 90 percentile.
th
Figure 16c) 2020s Change in the maximum temperature of the warmest day in Summer – 10 ,
th
th
50 , 90 percentile.
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th
th
th
Figure 16d) 2030s Summer (JJA) mean daily mean temperature – 10 , 50 , 90 percentile.
th
th
th
Figure 16e) 2030s Summer(JJA) mean daily maximum temperature – 10 , 50 , 90 percentile.
th
Figure 16f) 2030s Change in the maximum temperature of the warmest day in Summer – 10 ,
th
th
50 , 90 percentile.
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Winter (DJF) mean daily mean temperature, daily minimum temperature and coolest
day temperature.
th
th
th
Figure 17a) 2020s Winter (DJF) mean daily mean temperature – 10 , 50 , 90 percentile
th
th
th
Figure 17b) 2020s Winter (DJF) mean daily minimum temperature – 10 , 50 , 90 percentile.
th
Figure 17c) 2020s Change in the minimum temperature of the coolest day in Winter – 10 ,
th
th
50 , 90 percentile.
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th
th
th
Figure 17d) 2030s Winter (DJF) mean daily mean temperature – 10 , 50 , 90 percentile
th
th
th
Figure 17e) 2030s Winter (DJF) mean daily minimum temperature – 10 , 50 , 90 percentile.
th
Figure 17f) 2030s Change in the minimum temperature of the coolest day in Winter – 10 ,
th
th
50 , 90 percentile.
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2.2.3 Discussion of results
2.2.3.1 Annual mean of daily mean temperatures
The projections for annual mean of all daily temperature, daily maximum temperature and
daily minimum temperature for the years 2010-2039 (2020s) and 2020-2049 (2030s)
retain the current spatial characteristics of the UK climate, with temperatures still highest
in the south of England and lowest in central Scotland (Figures 15a-f). For the majority of
the UK projections of annual mean daily temperatures are between 6°C and 12°C for the
2020s and between 6°C and 15°C for the 2030s (Figur es 15a and 15d). Projections of
mean temperatures are fairly consistent across the UKCP09 analysis range.
Projections of annual mean daily minimum temperatures (Figures 15b and 15e) suggest
similar results to the mean with the majority of the UK in the 2020s projected temperatures
of 3°C to 9°C (for the 2030s the figure is also 3°C
to 9°C). Exceptions to this include
central Scotland (range of 0°C to 6°C for mean dail y mean temperature and minimum
temperature) and coastal England and Wales (range of 3°C to 12°C for mean daily
minimum temperature).
Projections of annual mean daily maximum temperature (Figures 15c and 15f) display a
lower degree of confidence (greater difference across the UKCP09 analysis range), with
temperatures across the majority of the UK ranging from 9°C to 18°C for the 2020s (for
the 2030s the figure is also 6°C to 18°C). This ra nge of temperatures is due to both
uncertainty in the UKCP09 analysis range and spatial variation across the UK.
The
greatest maximum temperatures are projected for southeast England (12°C to 18°C).
2.2.3.2 Summer mean of daily mean temperatures
The highest values of summer mean daily mean temperature and daily maximum
temperature are projected for the southeast of England, with daily mean of 15°C to 21°C,
and daily maximum 18°C to 27°C for the 2020s (Figur es 16a and 16b). Results for the
2030s are similar (Figures 16d and 16e), although a greater proportion of the UK is
projected to experience the higher temperatures of up to 27°C (in the 90 th percentile
projections).
Projections of the change in maximum temperature of the warmest day in summer in the
2020s show much greater variation across the UKCP09 analysis range, with change in
temperature of the warmest day ranging from -4°C to +6°C (Figure 16c).
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The 10 th
percentile runs project a negative change across the whole of the UK, whereas the 50th
and 90th percentiles project positive changes to temperatures of up to 2°C and 6°C
respectively. These changes are spatially consistent across the UK.
Projections for the 2030s are less spatially coherent (Figure 16f), and project further
increases in temperature of up to an 8°C increase i n maximum temperature of the
warmest day in summer for northern England, southern Scotland and parts of central
southern England in the 90th percentile runs. 10th percentile estimates are still negative
across the UK (a possible decrease in temperature of up to 2°C).
2.2.3.3 Winter mean of daily mean temperatures
Across the majority of England and Wales winter mean daily temperature and daily
minimum temperature are projected to be between 0°C to 9°C and 0°C to 6°C
respectively (Figures 17a, b, d and e). The lowest daily temperatures are reached in
central Scotland with mean temperatures ranging from -3°C to +6°C and minimum
temperatures from -3°C to +3°C. These projections change very little between the 2020s
and 2030s with most of the range of results coming from spatial variation across the UK
and differences across the UKCP09 analysis range.
Projections of the change in minimum temperature of the coolest day in winter for the
2020s (Figure 17c) show spatial consistency across the UK, but with large variation
across the UKCP09 analysis range (projections for the UK as a whole range from -2°C to
+4°C. Negative changes are projected across the UK by the 10th percentile, and the 50th
and 90th percentiles project increases in the temperature of the coolest day right across
the UK. Again there is no discernible change from these results by the 2030s (Figure
17f).
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2.3 Climate change solar insolation information
2.3.1 Discussion of variables
Total downward surface shortwave flux is a measure of the amount of shortwave radiation
received by a unit area per unit time at the Earth’s surface. The total downward surface
shortwave flux is equivalent to global irradiance or insolation. The UKCP09 projections
provide information on the 30-year average of monthly, seasonal and annual total
downward surface shortwave flux. It is important to note that there is significantly less
scientific understanding of solar radiation than future temperatures, due to uncertainties
associated with future cloud distributions and amounts (see for e.g. Wu et al., 2004).
Future absolute flux projections are not available, and therefore we provide plots of
change in total short wave radiation flux (relative to the 1961-1990 average) which can be
used to give an indication of future irradiance strengths through comparison with the
observed insolation properties presented in Part 1 of this report.
Again, as extremes of solar insolation are of greatest interest here we have selected
information about change in maximum total short wave radiation on an annual basis
across the 2020s and 2030s. We have then considered the change in insolation values
for the summer months (June, July, August) for both decades as this is when maximum
solar insolation occurs (see Appendix 4a-c for other months).
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2.3.2 Results
Change in annual mean daily maximum total downward surface shortwave flux
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Figure 18a) 2020s Annual mean daily maximum flux – 10 , 50 , 90 percentile.
Figure 18b) 2030s Annual mean daily maximum flux – 10 , 50 , 90 percentile.
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Change in Summer mean daily maximum total downward surface shortwave flux
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Figure 18c) 2020s Summer (JJA) mean daily maximum flux – 10 , 50 , 90 %ile
Figure 18d) 2030s Summer (JJA) mean daily maximum flux – 10 , 50 , 90 %ile
2.3.3 Discussion of results
The projections of change in annual mean daily maximum solar flux (Figures 18a and
18b) indicate that changes may be small, and there is little to distinguish between
changes expected in one decade and the next (again the emission scenario chosen has
little bearing on the results this early in the century and therefore only the medium
scenario is presented here). Projections at the 10th percentile for both the 2020s and
2030s show a small decrease in solar radiation (of up to -10 Wm-2), whilst 50th and 90th
percentiles project a small increase (up to 10 Wm-2). The changes projected are spatially
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consistent across the UK. The changes projected are negligible in the scale of changes
experienced seasonally at present (Murphy et al., 2009).
More significant changes are projected in the change in summer mean daily maximum
solar flux (Figures 18c and 18d), particularly in the upper end projections. At the 90th
percentile for the 2020s an increase in solar radiation of up to 30 Wm-2 is projected for the
southwest of England and parts of Wales. For the 2030s there is a slight expansion in
areas projected to receive between 10 Wm-2 and 20 Wm-2 in the 50th percentile projections
and areas receiving up to 30 Wm-2 in the 90th percentile now cover the whole of the
southwest region and Wales.
The projections here highlight an increase in the expected maximum solar flux on an
average summer’s day. These changes are expected due to a reduction in average
cloudiness across the summer months (Murphy et al., 2009). However, the highest solar
flux values at any point during the summer would occur in clear sky conditions, for
instance during a heat wave. Given that the total incoming shortwave radiation from the
sun will not change on these timescales, days with clear skies (and therefore maximum
solar flux) in the future will experience levels of solar flux no higher than the present
maximum (although results shown here and in the wider literature (see section 2.4)
suggest that clear sky days may occur more frequently).
It will be particularly important to consider the joint occurrence of maximum temperatures
and maximum solar flux when using the data presented here. Greatest increases in solar
flux associated with decreases in cloud cover are unlikely to occur during times of
projected maximum temperature, when cloud cover is already low. Therefore it is not
appropriate to combine the highest projected values of temperature from section 2.2 of
this report with the greatest changes in solar flux projected in section 2.3.
2.4 Wider context
The projections shown here give results broadly consistent with those used in the wider
peer-reviewed literature. The incidence of heat waves in Europe in summer increased
during the 20th century (Schär et al., 2004; Beniston and Stephenson, 2004; McGregor et
al., 2005). Regional climate model studies conclude that this trend is likely to continue
through the next century, and extreme temperature events such as the European heat
wave in 2003 may become more common (e.g. Zwiers and Kharin, 1998; Huth et al.,
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2000; Kharin and Zwiers, 2000; Miehl et al., 2000; Stott et al., 2004; Meehl et al., 2007).
However the IPCC note the need to improve the accuracy and regional detail of
projections as the current data and analyses are not sufficient to draw robust conclusions.
The wider literature, for instance, do not discuss in detail the incidence of warm periods
outside the summer seasons as we have done here.
Some authors make suggestions of climatic zones moving northward (one estimate gives
a shift of 400-500km by the end of the twenty first century), potentially giving the UK an
occurrence of days with extreme high temperatures (above 30°C) similar to what parts of
southern France may experience at present (Beniston et al., 2007). Gosling et al. (2009)
used the Hadley Centre model HadCM3 to consider the impacts of changing temperatures
on heat-related mortality in cities across the world including London and found that higher
mortality in future projections was attributed both to increases in the mean temperatures
but also in increased variability of temperatures.
This has implications for the
interpretation of projections shown here and elsewhere.
Further studies looking at regional scale models over the UK consider the uncertainty of
projections. Rowell (2006) notes that whilst the regional scale model ‘adds significant
value to the user, it inevitably contributes a further source of uncertainty’. The author finds
that the uncertainty in UK mean climate projections tends to be greatest in the summer
season, and of a similar magnitude to large-scale natural climate variations, however
confidence is greater for temperature predictions than for precipitation. The dominant
source of uncertainty is found to arise from the structure and physics of the GCMs used
for downscaling rather than the RCMs themselves.
Rivington et al. (2008) also considered the skill of the Hadley Centre regional climate
model HadRM3 in representing weather characteristics across the UK. It was found that
the model performs very well over some regions and variables, but poorly for others. In
particular maximum temperature estimates were generally represented well, however cold
extremes and minimum temperatures were overestimated. Solar radiation was also found
to be overestimated in general, but good results were produced for some regions.
Several other authors have used UKCIP analysis for a range of applications. Semenov
(2011) for instance uses the UKCP02 analysis to consider the impacts of extreme
temperatures among other variables on the production of wheat in England and Wales.
Their results suggest that predicted increases in maximum temperatures by 2050 with a
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high emissions scenario are between 2°C and 4°C wit h highest values reached in August.
Using UKCIP02-based projections for the UK, Semenov (2007) demonstrated that by the
end of the 21st century the frequency of heat waves across the UK could increase
substantially (by an order of magnitude), and also the length and severity would increase,
with higher peak temperatures reached.
A recently published review of the UKCP09 methods and results concluded that the data
used in this analysis represent a large step beyond UKCIP02. However the review did
caution careful use of the data and awareness of assumptions made. They also point to
the importance of the skill of the global climate model and state that errors in this overlying
model ‘cannot be compensated by any downscaling (as used in UKCP09) and will be
reflected in uncertainties on all scales’ (UKCP09 Steering Group, 2011).
Ongoing
research is considering structural uncertainties of GCMs, such as the resolution of the
stratosphere and the influence this may have on mid-latitude storm systems (Scaife et al.,
in press) such as those experienced in the UK. Whilst this has been shown to have a
potentially significant impact on projections of winter winds, rainfall and flooding, further
research is being done to establish whether this may influence other variables such as
temperature extremes. Scaife et al. conclude that making these types of changes to the
underlying structure of the GCM could potentially ‘represent a first-order correction to
climate projections for the mid-latitudes’. There is research ongoing to identify precisely
how much influence this would have on projections such as those analysed in this report.
2.4.1 Limitations of this methodology
Despite the benefits of the probabilistic information presented here, this method does
have some limitations. This assessment gives a change across a 30 year averaged
period rather than the maximum changes seen during those 30 years. It may, for instance
be useful to consider not just the 1 in 1 year event as described here (warmest day in
summer and coolest day in winter figures), but also more extreme values such as the 1 in
10 or 100 year event as done in the first part of this report. In addition it may be useful to
consider changes to the frequency of extreme insolation values, in addition to the change
in maximum values as we have described.
To better capture the temperature and solar flux extremes a more detailed analysis could
be undertaken. This analysis would make use of the Met Office Hadley Centre regional
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climate model projections for the UK, combined with statistical analysis to better quantify
the changes in UK temperature and solar insolation into the future.
Conclusions
Part 1 of this report has shown the range of temperatures which has been observed close
to major transport routes in the UK. As well as the maximum and minimum temperatures,
results have been presented for the mean temperature over eight hour and 24 hour
periods. The observed data has been analysed in order to estimate temperatures which
are expected to occur with annual probabilities of 1, 0.1, 0.01 and 0.0001. In addition,
eight hour and 24 hour average insolation values have been provided, and the maximum
observed values have been shown to be close to the maximum possible.
The results presented in Part 2 of this report concur with the wider literature and find that
annual and seasonal mean and maximum temperatures across the UK can be expected
to rise under scenarios of continued climate change.
The majority of the UKCP09
analysis range also projects a UK-wide increase in the mean temperature of the warmest
summer day, which will have implications for the transportation of temperature-sensitive
materials. Minimum temperatures across the UK in winter and annually, as well as the
winter mean temperature of the coolest day are also very likely to rise, thereby reducing
cold extremes.
The UKCP09 analysis range presented here does however note the
possibility of a reduction in coolest day temperatures and in warmest day temperatures.
As the scientific understanding improves through time updated estimates of mean
temperature changes will be available.
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Appendices
Appendix 1: Consideration of the uncertainty due to emissions scenarios.
1a) 2030s Annual mean daily mean temperature – Low, medium and high emissions scenarios
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(50 percentile only)
1b) 2080s Annual mean daily mean temperature – Low, medium and high emissions scenarios
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(50 percentile only)
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Appendix 2: Maximum temperature for other seasons (not JJA):
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2a) 2020s Winter (DJF) daily maximum temperature – 10 , 50 , 90 percentile.
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2b) 2020s Spring(MAM) daily maximum temperature – 10 , 50 , 90 percentile.
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2c) 2020s Autumn (SON) daily maximum temperature – 10 , 50 , 90 percentile.
Appendix 3: Minimum temperature for other seasons (not DJF):
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3a) 2020s Spring (MAM) daily minimum temperature – 10 , 50 , 90 percentile.
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3b) 2020s Summer(JJA) daily minimum temperature – 10 , 50 , 90 percentile.
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3c) 2020s Autumn (SON) daily minimum temperature – 10 , 50 , 90 percentile.
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Appendix 4: Change in mean daily maximum total downward surface shortwave
flux for other seasons (not JJA)
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4a) 2020s Winter (DJF) mean daily maximum flux – 10 , 50 , 90 percentile.
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4b) 2020s Spring(MAM) mean daily maximum flux – 10 , 50 , 90 percentile.
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4c) 2020s Autumn (SON) mean daily maximum flux – 10 , 50 , 90 percentile.
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