Weather data with climate change scenarios

51 Pages·2004·3.02 MB·English

by  Parkinson, John

Weather data with
climate change scenarios

CIBSE TM34

Chartered Institution of Building Services Engineers
222 Balham High Road, London SW12 9BS
The rights of publication or translation are reserved.

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form
or by any means without prior permission.

© October 2004 The Chartered Institution of Building Services Engineers, London SW12 9BS

Registered Charity Number 278104

ISBN 1 903287 49 9

This document is based on the best knowledge available at the time of publication. However no
responsibility of any kind for any injury, death, loss, damage or delay however caused resulting from
the use of these recommendations can be accepted by the Chartered Institution of Building Services
Engineers, the authors or others involved in its publication.

In adopting these recommendations for use each adopter by doing so agrees to accept full
responsibility for any personal injury, death, loss, damage or delay arising out of or in connection with
their use by or on behalf of such adopter irrespective of the cause or reason therefore and agrees to
defend, indemnify and hold harmless the above named bodies, the authors and others involved in their
publication from any and all liability arising out of or in connection with such use as aforesaid and
irrespective of any negligence on the part of those indemnified.

Note from the publisher
This publication is primarily intended to provide guidance to those responsible for the design,
installation, commissioning, operation and maintenance of building services. It is not intended to be
exhaustive or definitive and it will be necessary for users of the guidance given to exercise their own
professional judgement when deciding whether to abide by or depart from it.
Contents
Introduction 1
Climate prediction uncertainty and the Hadley model 1
CO concentrations 2
2
Future developments in climate prediction 2
Project tasks 3
Scenarios available 4
Single example year for 2080s 5
Data sources 5
HADRM3 data 5
Variables 6
UKCIP02 information 6
Measured historical data 6
Format of the results 6
Comparisons used 7
Guide J: section 3: Weather and building services 7
3.5 Summary statistics for UK sites 7
Sunshine hours 8
Average monthly sunshine hours 8
Guide J: section 4: UK data for manual design 10
4.1 UK cold weather data 10
4.2 UK warm weather data 15
4.3 Accumulated temperature difference 27
(degree-days and degree-hours)
4.4 UK weather for an example year 34
Conclusions 38
Implications for building designers 39
References 39
Websites 39
Appendix: weather generators 40
Foreword
Climate change is one of the biggest challenges facing mankind. Buildings and their services will be
affected. CIBSE TM34: Weather data with climate change scenarios gives some data to help
engineers assess the affects.

However, there are a number of IPCC scenarios suggesting how greenhouse gases will change in the
future which are used with climate models. Hence there is a range of temperature increases from the
models. One model, from the Hadley Centre of the Met Office, has been used to generate the data
presented in this TM, and the general results of the model have been detailed by UKCIP in its recent
publications. So the engineer has to bear in mind the range when using the data presented in TM34.

I would like to thank my co-authors and the Carbon Trust for funding the work.

Geoff Levermore
Chairman, TM34 Task Group
Acknowledgement
This work was funded through a contract with the Building Research Establishment on behalf of the
Carbon Trust. The Institution gratefully acknowledges this support.
Principal authors
John Parkinson (University of Manchester)
Andrew Wright (University of Manchester)
Geoff Levermore (University of Manchester)
Tariq Muneer (Napier University)
Contributor
Mike Hulme (Tyndall Centre, University of East Anglia)
Editor
Ken Butcher
CIBSE Editorial Manager
Ken Butcher
CIBSE Publishing Manager
Jacqueline Balian
Weather data with climate change scenarios

Weather data with climate change scenarios
Introduction
Most scientists now accept that the world’s climate is changing significantly as a result of man’s
activities, principally the burning of fossil fuels. Latest predictions are for faster temperature rises
than previously thought, with a rise of up to 6 K by 2100. Research is being carried out into the
impacts of climate change on many activities. Indeed, under Article 4 of the United Nations
Framework Convention on Climate Change, individual nations are required to assess their
vulnerability to climate change. Changes in weather patterns could profoundly affect the comfort,
energy consumption and environmental impacts of current and future buildings in the UK. Designers
need weather data for the future UK climate to use in their designs for new and refurbished buildings.
This project sets out to provide suitable data for manual design, based on the output from computer
simulations of future climate for the UK.

Computer simulation is increasingly used to predict the thermal behaviour of buildings, using real
historical hourly weather data. This tends to be on larger, more complex projects, and in larger
practices. A number of projects are addressing the problem of generating synthetic future hourly
weather in a suitable format for simulation, from climate models such as HadCM3 (http://www.met-
office.gov.uk/research/hadleycentre/models/HadCM3.html).

However, the majority of building design is done using manual methods (though sometimes
implemented in software). Even when simulation is used (for example to optimise orientation, glazing
and thermal mass), manual methods are likely to be used in other areas such as plant sizing. There are
sound reasons for this — the methods are common practice across the industry, and relatively easy to
understand from first principles in most cases (unlike the ‘black box’ of simulation). In an
increasingly litigious industry, they are also a good legal defence if things go wrong.

Most manual design methods related to weather are based on CIBSE Guide A: Environmental design,
and data provided in CIBSE Guide J: Weather, solar and illuminance data(1) (Guide A contains a
subset of the Guide J data). These Guides provide weather statistics based on historical climate,
mainly from real data collected over the period 1976–1995. However, most new buildings will last
well into the 21st century, some into the 22nd century. Nearly all of them will be in use in 2025, by
which time the UK climate is expected to be significantly warmer, with more extreme events, than
now. Further change, perhaps even accelerating, is likely after that date. Designing to a climate
centred around the 1980s is likely to be inappropriate in many cases.
Climate prediction uncertainty and the Hadley model*
Predictions of future climate change depend not only on estimating the range of changes in future
greenhouse gas emissions, but also importantly on which climate model is used. Predictions of
climate over the next 40 or so years are largely insensitive to the choice of emissions scenario, but are
sensitive to the choice of model. Climate predictions for the latter part of this century depend both on
the choice of emissions scenario and on the choice of model.

The data presented in this publication all derive from climate models developed and run in the Hadley
Centre in the UK. While this is a relatively good climate model and generates climate information at
relatively high (50 km) resolution, we do not know a priori whether it is more believable than climate
models from other countries or centres. It is therefore important to be at least aware of the range of
results from other climate models. One simple measure that summarises model performance is called
the ‘climate sensitivity’. This is measure of how sensitive a particular model is to rising
concentrations of greenhouse gases, or more formally, ‘… the equilibrium rise in global surface air
temperature for a doubling of atmospheric greenhouse gas concentration’. We do not know the true
value of the climate sensitivity, but the IPCC Third Assessment Report suggested its likely range is

* This section was provided by Professor Mike Hulme, Tyndall Centre, University of East Anglia.
1
Weather data with climate change scenarios

Weather data with climate change scenarios
Introduction
Most scientists now accept that the world’s climate is changing significantly as a result of man’s
activities, principally the burning of fossil fuels. Latest predictions are for faster temperature rises
than previously thought, with a rise of up to 6 K by 2100. Research is being carried out into the
impacts of climate change on many activities. Indeed, under Article 4 of the United Nations
Framework Convention on Climate Change, individual nations are required to assess their
vulnerability to climate change. Changes in weather patterns could profoundly affect the comfort,
energy consumption and environmental impacts of current and future buildings in the UK. Designers
need weather data for the future UK climate to use in their designs for new and refurbished buildings.
This project sets out to provide suitable data for manual design, based on the output from computer
simulations of future climate for the UK.

Computer simulation is increasingly used to predict the thermal behaviour of buildings, using real
historical hourly weather data. This tends to be on larger, more complex projects, and in larger
practices. A number of projects are addressing the problem of generating synthetic future hourly
weather in a suitable format for simulation, from climate models such as HadCM3 (http://www.met-
office.gov.uk/research/hadleycentre/models/HadCM3.html).

However, the majority of building design is done using manual methods (though sometimes
implemented in software). Even when simulation is used (for example to optimise orientation, glazing
and thermal mass), manual methods are likely to be used in other areas such as plant sizing. There are
sound reasons for this — the methods are common practice across the industry, and relatively easy to
understand from first principles in most cases (unlike the ‘black box’ of simulation). In an
increasingly litigious industry, they are also a good legal defence if things go wrong.

Most manual design methods related to weather are based on CIBSE Guide A: Environmental design,
and data provided in CIBSE Guide J: Weather, solar and illuminance data(1) (Guide A contains a
subset of the Guide J data). These Guides provide weather statistics based on historical climate,
mainly from real data collected over the period 1976–1995. However, most new buildings will last
well into the 21st century, some into the 22nd century. Nearly all of them will be in use in 2025, by
which time the UK climate is expected to be significantly warmer, with more extreme events, than
now. Further change, perhaps even accelerating, is likely after that date. Designing to a climate
centred around the 1980s is likely to be inappropriate in many cases.
Climate prediction uncertainty and the Hadley model*
Predictions of future climate change depend not only on estimating the range of changes in future
greenhouse gas emissions, but also importantly on which climate model is used. Predictions of
climate over the next 40 or so years are largely insensitive to the choice of emissions scenario, but are
sensitive to the choice of model. Climate predictions for the latter part of this century depend both on
the choice of emissions scenario and on the choice of model.

The data presented in this publication all derive from climate models developed and run in the Hadley
Centre in the UK. While this is a relatively good climate model and generates climate information at
relatively high (50 km) resolution, we do not know a priori whether it is more believable than climate
models from other countries or centres. It is therefore important to be at least aware of the range of
results from other climate models. One simple measure that summarises model performance is called
the ‘climate sensitivity’. This is measure of how sensitive a particular model is to rising
concentrations of greenhouse gases, or more formally, ‘… the equilibrium rise in global surface air
temperature for a doubling of atmospheric greenhouse gas concentration’. We do not know the true
value of the climate sensitivity, but the IPCC Third Assessment Report suggested its likely range is

* This section was provided by Professor Mike Hulme, Tyndall Centre, University of East Anglia.
1
Weather data with climate change scenarios

between 1.5 K and 4.5 K. The Hadley Centre global model has a sensitivity of about 3 K, so falls
roughly in the middle of this range.

Figure 1 shows another way of presenting this information. The green dots show the climate
sensitivity of the eight leading climate models used in the last IPCC report, with the Hadley model
marked. Clearly, if this report had used a model with a higher sensitivity, larger changes in climate
would have resulted; a model with a lower sensitivity would have resulted in smaller changes.
Additionally, the blue curve shows one estimate of the probability density function (PDF) of the
climate sensitivity derived from observations alone. Again, the Hadley model falls roughly in the
middle of this distribution.

0.02
GCM-independent PDF
0.016
HadCM3
y
bilit 0.012 Other IPCC TAR GCMs
a
b
o
Pr
0.008
0.004
0
0 1 2 3 4 5 6 7 8 9 10
Climate sensitivity / K

Figure 1 Climate sensitivity of various models (source: Gregory et al. (2002)(2) and IPCC (2001)(3))

CO concentrations
2
The CO concentrations listed in Table 3 of the UKCIP02 scenarios report are the correct
2
concentrations to be used in any CIBSE design guide. Again, note that for the 2020s there is little
difference between the different emissions scenarios.

Future developments in climate prediction
Over the next few years it will become possible to develop probability-based predictions of future
climate rather than the discrete ‘deterministic’ predictions that exist at present. These probabilistic
predictions will be based on sampling and using a much large group of climate models, thus reducing
the need for making subjective or political judgements about which single model one should use.

On the other hand, the uncertainty about future emissions of greenhouse gases will remain and so
subjective choices about which emissions scenario to use will also remain. Thus by the time the next
IPCC Assessment is published in 2007, it is likely that for any chosen emissions scenario one will be
able to make statements such as: ‘For the A2 emissions scenario, the probability of the maximum
temperature in southern England by the 2020s exceeding 40 ºC in any given year is 0.03.’ This
hypothetical example is further illustrated in Figure 2 which shows the cumulative probability of the
maximum air temperature recorded in any given year in the 2020s in southern England for four
different emissions scenarios. This result is not dependent on any single model, but the probabilities
are objectively derived. In a few years time, this type of information will be available for many
different climatic variables and regions.

2
Weather data with climate change scenarios

Southern England 2020s
y 1
bilit 0.9
a 0.8
b
o 0.7
r
p 0.6

ve 0.5 SRES A1
ati 0.4 SRES B1
mul 0.3 SRES B2
u 0.2
C SRES A2
0.1
0
31 33 35 37 39 41
Maximum temperature / ºC

Figure 2 Cumulative probability of maximum air temperatures (source: Hulme(4))
Project tasks
In order to provide some indicative design data, subject to the above caveats, the tasks of the project
was to:

(1) Download weather data for periods 1960–1990 and 2070–2100 obtained by the Hadley weather
centre using the region weather model HadRM3. This uses 50 km squares for the whole of
Europe, and consists of daily data for each year. The data are available from the Climate
Impacts LINK website, maintained by the Climate Research Unit (CRU) at the University of
East Anglia, Norwich (see page 39).

(2) Extract the data for the three grid boxes which include Edinburgh, Manchester and Heathrow.
These grid boxes were chosen because they contain the principal sites used in CIBSE Guide J(1).
i.e. Edinburgh (Turnhouse), Manchester (Ringway) and London (Heathrow). Note, however,
that the observed data refers to specific sites whereas the model data refers to the whole
250 km2 of the relevant grid boxes. Hence references to modelled data for Heathrow refer to the
50 km grid box enclosing Heathrow, etc.

(3) Use the extracted data to construct tables and/or charts similar to those in CIBSE Guide J, but
for the next 100 years using two of the model scenarios.

(4) Download monthly average weather data from the UKCIP site (see page 39) in Oxford for the
same sites, and use this where appropriate to supplement the tables and charts in 3.

In relation to step 2, the following warning is given by UKCIP about use of 50 km scale data:

‘We would also strongly caution against the over-interpretation of data at the 50 km scale from
the regional climate model. Although the model has validated very well for most of the major
climate variables, this does not mean that every 50 km grid box has been individually checked
for every variable. Therefore the application of independent validation techniques, especially
for impacts models, is strongly advised.’

It was outside the scope of this project independently to validate this type of data.
3
Weather data with climate change scenarios

Southern England 2020s
y 1
bilit 0.9
a 0.8
b
o 0.7
r
p 0.6

ve 0.5 SRES A1
ati 0.4 SRES B1
mul 0.3 SRES B2
u 0.2
C SRES A2
0.1
0
31 33 35 37 39 41
Maximum temperature / ºC

Figure 2 Cumulative probability of maximum air temperatures (source: Hulme(4))
Project tasks
In order to provide some indicative design data, subject to the above caveats, the tasks of the project
was to:

(1) Download weather data for periods 1960–1990 and 2070–2100 obtained by the Hadley weather
centre using the region weather model HadRM3. This uses 50 km squares for the whole of
Europe, and consists of daily data for each year. The data are available from the Climate
Impacts LINK website, maintained by the Climate Research Unit (CRU) at the University of
East Anglia, Norwich (see page 39).

(2) Extract the data for the three grid boxes which include Edinburgh, Manchester and Heathrow.
These grid boxes were chosen because they contain the principal sites used in CIBSE Guide J(1).
i.e. Edinburgh (Turnhouse), Manchester (Ringway) and London (Heathrow). Note, however,
that the observed data refers to specific sites whereas the model data refers to the whole
250 km2 of the relevant grid boxes. Hence references to modelled data for Heathrow refer to the
50 km grid box enclosing Heathrow, etc.

(3) Use the extracted data to construct tables and/or charts similar to those in CIBSE Guide J, but
for the next 100 years using two of the model scenarios.

(4) Download monthly average weather data from the UKCIP site (see page 39) in Oxford for the
same sites, and use this where appropriate to supplement the tables and charts in 3.

In relation to step 2, the following warning is given by UKCIP about use of 50 km scale data:

‘We would also strongly caution against the over-interpretation of data at the 50 km scale from
the regional climate model. Although the model has validated very well for most of the major
climate variables, this does not mean that every 50 km grid box has been individually checked
for every variable. Therefore the application of independent validation techniques, especially
for impacts models, is strongly advised.’

It was outside the scope of this project independently to validate this type of data.
3
Weather data with climate change scenarios

Scenarios available
The main source of data was the Hadley Centre regional model for the UK, version 3 (HADRM3).
For more information on this model and the emission scenarios used (B1 Low, B2 medium-low, A2
medium high, A1 high) see elsewhere(5). It is important to emphasise that no scenario is considered to
be more likely than any other. In reality, an emission scenario will probably happen which is different
from all the model scenarios.

The HadRM3 data which are available from the CRU site consists of seven distinct runs, each of
which is for 31 years. These seven runs cover different time periods and different climate scenarios as
follows:

(1) Three runs using the historic forcing data (concentrations of carbon dioxide and other
greenhouse emission gases) for the period 1960–1970 inclusive. These are runs achgi, achgj,
achgk, and are referred to as the ‘historic ensemble’.

(2) Three runs for the period 2070–2100 using forcing data based on the predictions of the A2
scenario. This is the medium-high global warming scenario for climate change. These are runs
ackda,ackdb,ackdc, and are referred to as the ‘A2 ensemble’.

(3) A single run for the period 2070–2100 using the B2, medium-low scenario. This is run ackdd.

(4) Comparison will also be made, where appropriate, with observed data for the period 1976–
1995, given in CIBSE Guide J but see note (2) under ‘Project tasks’ (page 3).

To obtain results for intermediate time periods and/or other scenarios a method known as pattern
scaling is used. This is based on the changes which are predicted to occur between the historic and
future periods. The change from the historic (1960–1990) to the A2 scenario in the period 2070–2100
is taken as the base and changes to other periods and other scenarios are then calculated by applying
scaling factors from the Table 1, which is reproduced from Climate Change Scenarios for the United
Kingdom(5). The values in this table are based on results for large-scale general climate models (GCMs)
for which data are available for all scenarios and all time-slices.

Table 1 Scaling factors for scenarios(5)
Time-slice Low emissions Medium-low Medium-high High
(B1) (B2) (A2) (A1F1)
1970s (historic) 0 0 0 0
2020s 0.24 0.27 0.27 0.29
2050s 0.43 0.50 0.57 0.68
2080s 0.61 0.71 1.00 1.18
Note: ‘1970s’ refers to the period 1960–1990; ‘2020s’ refers to the period 2010–2040; ‘2050s’ refers to the period
2040–2070; ‘2080s’ refers to the period 2070–2100.

Results for the A2 scenario for the 2080s period will normally be presented, since for this an
ensemble of three runs is available. For the B2 scenario a single run for the 2080s is available and this
was used to determine an example year, statistically close to average in temperature terms for B2, in
order to show daily temperature and wind variations. For other tables monthly averages are needed
and these are obtained by calculating the averages over the three historic runs (the ‘historic
ensemble’) and over the three A2 runs (the ‘A2 ensemble’) and then scaling the changes in
accordance with the table.

When considering climate change, it is normal to take the difference between historic simulated and
future simulated climate, and apply this to historic climate.
4