met office - range of environmental temperature conditions in the

temperature_range_report_merged_v2.doc - 1 – © Crown copyright 2008

Range of environmental temperature conditions in the United Kingdom For: Department of Transport Date: 17 May 2011 Authors: Matthew Perry and Nicola Golding

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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© Crown copyright 2011

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

2


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.

5


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

9


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).

10 © Crown copyright 2011

1 year return value 10 year return value

Month Average

Day

Hottest

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

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).

100 year return value 10,000 year return value Month

Shape

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).


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.


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


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.



Month Average

Day

Coldest


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

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



Shape


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



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.


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.



Month Average

Day

Hottest


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

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


100 year return value 10,000 year return value

Month Shape


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




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.



Month Average

Day

Coldest


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





Shape


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).


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.


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.


1 year return value 10 year return value Month

Average

Day

Hottest


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

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



Month Shape


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





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.


1 year return value 10 year return value Month

Average

Day

Coldest


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 Shape


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


the coldest station representative of UK transport routes: shape parameter of the fitted extreme



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.


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.


Station with greatest radiation Maximum value

Month Average 95th %ile 99th %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

Table 13: Monthly and whole year statistics of daily (00 to 00) global solar irradiance (W / m2) 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.

0

100

200

300

400

500

600

700

800

900

1000

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)

Sol

ar ir

radi

ance

(W /

sq m

)

June

April

October

December

Figure 8: Diurnal cycle of hourly average irradiance values (W / m2) for the days and locations with

maximum total radiation in June, April, October and December (see Table 9 for details of locations

and dates).


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.

Station with greatest radiation Maximum value

Month Average 95th %ile 99th %ile 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

Table 14: Monthly and whole year statistics of 8 hour (08 to 16) global solar irradiance (W / m2) 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.


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.

Camborne Lerwick Month

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

Table 15: Horizontal and estimated vertical insolation (W / m2) 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.


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.


26

28

30

32

34

36

38T

empe

ratu

re (

deg

C)

Max

8-hour

24-hour

-28

-24

-20

-16

-12

Tem

pera

ture

(de

g C

)

24-hour

8-hour

Min

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).


28

32

36

40

44

Tem

pera

ture

(de

g C

)

1:1

1:10

1:100

1:10,000

-45

-40

-35

-30

-25

-20

-15

-10

Tem

per

atu

re (

deg

C)

1:1

1:10

1:100

1:10,000

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.


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.


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


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).

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

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.


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).

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

UKCIP02 Single projection

Very unlikely to be less than (10%)

UKCP09 Central estimate (50%)

Very unlikely to be more than (90%)

Sum

mer

Rai

nfal

l 208

0’s


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.


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.


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.


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 t emperature and daily

minimum temperature

Figure 15a) 2020s Annual mean daily mean temperature – 10th, 50th, 90th percentile.

Figure 15b) 2020s Annual mean daily minimum temperature – 10th, 50th, 90th percentile.


Figure 15c) 2020s Annual mean daily maximum temperature – 10th, 50th, 90th percentile.


Figure 15d) 2030s Annual mean daily mean temperature – 10th, 50th, 90th percentile.

Figure 15e) 2030s Annual mean daily minimum temperature – 10th, 50th, 90th percentile.

Figure 15f) 2030s Annual mean daily maximum temperature – 10th, 50th, 90th percentile.


Summer (JJA) mean daily mean temperature, daily max imum temperature and

warmest day maximum temperature.

Figure 16a) 2020s Summer (JJA) mean daily mean temperature – 10th, 50th, 90th percentile.

Figure 16b) 2020s Summer(JJA) mean daily maximum temperature – 10th, 50th, 90th percentile.

Figure 16c) 2020s Change in the maximum temperature of the warmest day in Summer – 10th, 50th, 90th percentile.


Figure 16d) 2030s Summer (JJA) mean daily mean temperature – 10th, 50th, 90th percentile.

Figure 16e) 2030s Summer(JJA) mean daily maximum temperature – 10th, 50th, 90th percentile.

Figure 16f) 2030s Change in the maximum temperature of the warmest day in Summer – 10th, 50th, 90th percentile.


Winter (DJF) mean daily mean temperature, daily min imum temperature and coolest

day temperature.

Figure 17a) 2020s Winter (DJF) mean daily mean temperature – 10th, 50th, 90th percentile

Figure 17b) 2020s Winter (DJF) mean daily minimum temperature – 10th, 50th, 90th percentile.

Figure 17c) 2020s Change in the minimum temperature of the coolest day in Winter – 10th, 50th, 90th percentile.


Figure 17d) 2030s Winter (DJF) mean daily mean temperature – 10th, 50th, 90th percentile

Figure 17e) 2030s Winter (DJF) mean daily minimum temperature – 10th, 50th, 90th percentile.

Figure 17f) 2030s Change in the minimum temperature of the coolest day in Winter – 10th, 50th, 90th percentile.


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). 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).


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).


2.3.2 Results

Change in annual mean daily maximum total downward surface shortwave flux

Figure 18a) 2020s Annual mean daily maximum flux – 10th, 50th, 90th percentile.

Figure 18b) 2030s Annual mean daily maximum flux – 10th, 50th, 90th percentile.


Change in Summer mean daily maximum total downward surface shortwave flux

Figure 18c) 2020s Summer (JJA) mean daily maximum flux – 10th, 50th, 90th %ile

Figure 18d) 2030s Summer (JJA) mean daily maximum flux – 10th, 50th, 90th %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


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.,


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


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


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.


References

Badescu V (2008). Modeling solar radiation at the earth surface. Springer.

Beniston, M (2004) The 2003 heat wave in Europe: a shape of things to come? Geophys

Res Lett 31:L02202

Beniston, M. Stephenson, D.B., Christensen, O.B., Ferro, C.A.T., Frei, C., Goyette, S.,

Halsnaes, K., Holt, T., Jylhä, K., Koffi, B., Palutikof, J., Schöll, R., Semmler, T., Woth, K.

(2007). Future extreme events in European climate: an exploration of regional climate

model projections. Climatic Change 81:71-95

Brabson BB, Lister DH, Jones PD, Palutikof JP (2005). Soil moisture and predicted spells

of extreme temperatures in Britain. Journal of Geophysical Research Atmospheres 110.

Coles, SG (2001). An introduction to statistical modelling of extreme values. Springer.

Dall’Amico M, Hornsteiner M (2006). A simple method for estimating daily and monthly

mean temperatures from daily minima and maxima. International Journal of Climatology

26: 1929 – 1936.

Gilleland E and Katz RW (2006). Analyzing seasonal to interannual extreme weather and

climate variability with the extremes toolkit (extRemes), 18th Conference on Climate

Variability and Change, 86th American Meteorological Society Annual Meeting, Atlanta.

Gosling, S.N., McGregor, G.R., Lowe, J.A. (2009) Climate change and heat-related

mortality in six cities Part 2: climate model evaluation and projected impacts from changes

in the mean and variability of temperature with climate change. Int J Biometeorol 53:31-51

Houghton, J.T. et al. (eds) (2001), Climate Change 2001: The Scientific Basis (Cambridge

University Press, 2001).

Hulme M, Jenkins GJ, Lu X, Turnpenny JR, Mitchell TD, Jones RG, Lowe J, Murphy JM,

Hassell D, Boorman P, McDonald R and Hill S (2002) Climate Change Scenarios for the

United Kingdom: The UKCIP02 Scientific Report, Tyndall Centre for Climate Change

Research, School of Environmental Sciences, University of East Anglia, Norwich, UK.


Huth, R., Kysely, J., Pokorna, I. (2000) A GCM simulation of heat waves, dry spells, and

their relationships to circulation. Clim Change 46: 29–60

Jenkins, G.J., Murphy, J.M., Sexton, D.S., Lowe, J.A., Jones, P. and Kilsby, C.G. (2009).

UK Climate Projections: Briefing report. Met Office Hadley Centre, Exeter, UK.

Kharin, v.v., Zwiers, F.W. (2000) Changes in the extremes in an ensemble of transient

climate simulations with a coupled atmosphere-ocean GCM. J. Climate 13: 3760-3788

Levitus, S. et al. (2001), Anthropogenic warming of the Earth’s climate system, Science

292: 267-270.

McGregor, G.R., Ferro, C.A.T., Stephenson, D.B. (2005) Projected changes in extreme

weather and climate events in Europe. In: Kirch W, Menne B, Bertollini R (eds) Extreme

weather and climate events and public health responses vol. 13–23 Dresden. Springer,

Berlin Heidelberg New York, p 303

Meehl GA, Zwiers F, Evans J, Knutson T, Mearns L, Whetton P (2000) Trends in extreme

weather and climate events: issues related to modelling extremes in projections of future

climate change. Bull Am Meteorol Soc 81: 427–436

Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A.

Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver and Z.-

C. Zhao, (2007) Global Climate Projections. In: Climate Change 2007: The Physical

Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen,

M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press,

Cambridge, United Kingdom and New York, NY, USA.

Murphy, J.M., Sexton, D.M.H., Jenkins, G.J., Booth, B.B.B., Brown, C.C., Clark, R.T.,

Collins, M., Harris, G.R., Kendon, E.J., Betts, R.A., Brown, S.J., Humphrey, K.A.,

McCarthy, M.P., McDonald, R.E., Stephens, A., Wallace, C., Warren, R., Wilby, R., Wood,

R.A. (2009), UK Climate Projections Science Report: Climate change projections. Met

Office Hadley Centre, Exeter, UK.

Nakicenovic, N. & Swart, R. (Eds.) (2000). Special Report on Emissions Scenarios. A

Special Report of Working Group III of the Intergovernmental Panel on Climate Change.

Cambridge University Press, Cambridge, UK and New York, NY, USA, 599pp


R Development Core Team (2007). R: A language and environment for statistical

computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0,

URL http://www.R-project.org.

Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman,

J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, (2007) Cilmate Models

and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution

of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on

Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt,

M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom

and New York, NY, USA.

Rivington, M., Miller, D., Matthews, K.B., Russell, G., Bellocchi, G., Buchan, K. (2008)

Evaluating regional climate model estimates against site-specific observed data in the UK.

Climatic Change 88: 157-185.

Rowell, D. (2006) A demonstration of the uncertainty in projections of UK climate change

resulting from regional model formation. Climatic Change 79: 243-257

Semenov, M.A., (2011) Impacts of climate change on wheat in England and Wales.

J.R.Soc. Interface 6: 343-350.

Semenov, M.A. (2007) Development of high resolution UKCP01-based climate change

scenarios in the UK. Agric. Forest Meteorology 144: 127-138

Schär, C., Vidale, P.L., Lüthi, D., Frei, C., Häberli, C., Liniger, M., Appenzeller, C. (2004)

The role of increasing temperature variability in European summer heatwaves. Nature

427: 332–336

SolarTool (2008). Generating solar radiation at inclined planes. EMD International,

Aalborg, Denmark.

Stott, P., Stone, D.A., Allen, M.R., (2004) Human contribution to the European heatwave

of 2003. Nature 432: 610-614

UKCP09 Steering Group, (2011) Responses to the UKCP09 Science Peer Review March

2011 http://ukclimateprojections.defra.gov.uk/content/view/1140/664/


Weiss W, Hays CJ (2005). Calculating daily mean air temperatures by different methods:

implications from a non-linear algorithm. Agricultural and Forest Meteorology 128: 57-65.

Wu, T., Wang, Z., Liu, Y., Yu, R. and Wu, G. (2004) An evaluation of the effects of cloud

parameterization in the R42L9 GCM. Adv. Atmospheric Sciences 21: 153-162.

Zwiers, F.W., Kharin, V.V. (1998) Changes in the extremes of the climate simulated by

CCC GCM2 under CO2 doubling. J Climate 11: 2200–2222

Zweirs, F. (2002), The 20-year forecast, Nature 416: 690-691.


Appendices

Appendix 1: Consideration of the uncertainty due to emissions scenarios.

1a) 2030s Annual mean daily mean temperature – Low, medium and high emissions scenarios (50th percentile only)

1b) 2080s Annual mean daily mean temperature – Low, medium and high emissions scenarios (50th percentile only)


Appendix 2: Maximum temperature for other seasons ( not JJA):

2a) 2020s Winter (DJF) daily maximum temperature – 10th, 50th, 90th percentile.

2b) 2020s Spring(MAM) daily maximum temperature – 10th, 50th, 90th percentile.


2c) 2020s Autumn (SON) daily maximum temperature – 10th, 50th, 90th percentile.

Appendix 3: Minimum temperature for other seasons ( not DJF): 3a) 2020s Spring (MAM) daily minimum temperature – 10th, 50th, 90th percentile.


3b) 2020s Summer(JJA) daily minimum temperature – 10th, 50th, 90th percentile.

3c) 2020s Autumn (SON) daily minimum temperature – 10th, 50th, 90th percentile.


Appendix 4: Change in mean daily maximum total down ward surface shortwave

flux for other seasons (not JJA)

4a) 2020s Winter (DJF) mean daily maximum flux – 10th, 50th, 90th percentile.

4b) 2020s Spring(MAM) mean daily maximum flux – 10th, 50th, 90th percentile.


4c) 2020s Autumn (SON) mean daily maximum flux – 10th, 50th, 90th percentile.

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