CGE TRAINING MATERIALS
FOR VULNERABILITY AND
ADAPTATION ASSESSMENT
Chapter 4: Climate Change
Scenarios
CONTENTS
CONTENTS ...................................................................................................... I
4.1
INTRODUCTION.................................................................................... 1
4.1.1
4.2
Why Do We Use Climate Change Scenarios?................................. 2
CLIMATE CHANGE OVERVIEW ........................................................... 3
4.2.1
Key Definitions................................................................................. 3
4.2.2
Global Climate Change ................................................................... 4
4.2.3
Regional Climate Change ................................................................ 6
4.2.4
Extreme Events ............................................................................... 8
4.3
APPROACH TO CLIMATE CHANGE SCENARIO DEVELOPMENT..... 9
4.3.1 Evaluation and Determination of Needs for Climate-Scenario
Development .............................................................................................. 10
4.3.2
SpecifICATION OF Baseline Climate ............................................ 12
4.3.3
DevelopMENT OF Climate Change Scenarios .............................. 14
4.4
METHODS, TOOLS AND DATA SOURCES ....................................... 17
4.4.1
Models and Tools .......................................................................... 17
4.4.2
Data Sources ................................................................................. 22
4.5 FUTURE DIRECTIONS IN CLIMATE CHANGE SCENARIO
DEVELOPMENT ............................................................................................ 24
4.6
REFERENCES .................................................................................... 26
Page i
Chapter 4: Climate Change Scenarios
4.1 INTRODUCTION
Climate scenarios constitute a major part of Vulnerability and Adaptation (V&A)
assessment within national communications: as direct inputs for V&A
assessments, as information serving public education about expected climate
change, and as a tool to engage stakeholders in policy dialogue, both within and
beyond the national communications process.1
Scenarios can be defined as plausible combinations of conditions that can represent
possible future situations. Scenarios are often used to assess the consequences of
possible future conditions, how organizations or individuals might respond, or how they
could be better prepared for them. For example, businesses might use scenarios of
future business conditions to decide whether some business strategies or investments
make sense now. Climate change scenarios are scenarios of plausible changes in
climate. They are used to understand what the consequences of climate change can be.
Scenarios can also be used to provide inputs for change impacts, vulnerability
assessment and identify and evaluate adaptation strategies.
This chapter provides guidance on the key steps required in climate change scenario
development and gives an overview of the key approaches, models, tools and data
sources available. Much has been written on the use of climate models in developing
climate change scenarios, and this is not replicated here. Instead this chapter aims to
provide a roadmap to the most commonly used approaches, models, tools and data
sources available for climate change scenario development and provides links to key
resources for further information. The chapter provides context on the current
understanding of climate change on both a global and regional level drawing upon the
findings from the Intergovernmental Panel on Climate Change (IPCC) Fourth
Assessment Report (AR4) Working Group I (WG I). The chapter also draws upon
existing guidelines on climate change scenario development with a focus on national
communications, namely the UNDP/UNEP/GEF Guidance on the Development of
Regional Climate Scenarios for Vulnerability and adaptation assessments (Lu, 2006) to
answer the following key questions:

What type of climate change scenario needs to be developed?

What methods and tools can be used for each scenario type?

What data are available to establish the baseline climate?

What data sources are available for climate models?
1
UNDP/UNEP/GEF Guidance on the Development of Regional Climate Scenarios for Application in Climate
Change Vulnerability and Adaptation Assessments within the Framework of National Communications from
Parties not Included in Annex I to the United Nations Framework Convention on Climate Change
<http://www.adaptationlearning.net/guidance-tools/guidance-development-regional-climate-scenariosvulnerability-and-adaptation-assessme>.
Page 1
Chapter 4: Climate Change Scenarios
4.1.1 WHY DO WE USE CLIMATE CHANGE SCENARIOS?
Climate change scenarios are developed because predictions of climate change at the
regional and local scale have a high degree of uncertainty. Regional scale can mean
scales ranging from the sub-continental scale to country level to provincial level.
Although it is likely that temperatures will rise in most regions of the world,2 changes at
the regional scale in many other key variables, such as precipitation, are uncertain for
most regions. Even if the direction of change is likely, there can be uncertainty about the
magnitude and path of change. Scenarios are tools to help envision how regional
climates may change with increased greenhouse gas (GHG) concentrations, so as to
understand how sensitive systems may be affected by climate change.
Since it is now firmly established that climate change over the past 50 years or more is
mostly due to human influence via fossil fuels burning (Solomon et al. 2007), there is a
growing concern about an increase in magnitude much larger than current observed
future climate change.
Climate change scenarios are tools to help us to envision how regional climates may
change with increased GHG concentrations to help us understand and evaluate how
sensitive systems may be affected by human-induced climate change. The information
generated from these scenarios, can be useful for relevant policies as guidance for
appropriate mitigation and adaptation measures. It is critical to keep in mind that climate
change scenarios are neither a prediction nor a forecast of future climate change.
If regional climate change scenarios are to be used in a V&A assessment, they must
provide information on the climate variables needed by V&A assessors at a spatial and
temporal scale needed for analysis. This may require daily or even sub-daily spatial data
to help detect changes at a finer scale.
Uncertainties in future scenarios stem from different sources and are conditional on
projections, meaning predictions depending on assumed socio-economic conditions.
Climate change scenarios should meet the following criteria (Mearns et al., 2001):
1. Consistent with anthropogenic influences on climate;
2. Consistent with a range of changes in considered variables: physical plausible;
3. Consistent with spatial scales (river basin, country, region …) and temporal
scales (month, season, decade …).
The best ways to ensure these conditions are met, is to work with experts on climate
change modelling to verify that scenarios are consistent with estimated changes in
global climate. Working regional specialists to verify if regional changes are consistent
2
Other anthropogenic activities, such as land-use change and emission of air pollutants, can have
significant effects on local and regional climate change relative to the influence of increased GHG
concentrations.
Page 2
Chapter 4: Climate Change Scenarios
with what is known about regional climatology, is also required. Experts such as climate
modellers, climatologists, agronomist and hydrologist could be used to have accurate
judgment of the consistencies.
If regional climate change scenarios are to be used in a V&A assessment, they must
provide information on the climate variables needed by V&A assessors at a spatial and
temporal scale needed for analysis. This may require daily or even sub-daily spatial data
to help detect changes at a finer scale.
It is critical to keep in mind that regional climate change scenarios are not a prediction of
future climate change, but rather a tool to represent what could happen as a result of
human-induced climate change (through GHG emissions) and to facilitate understanding
of how different systems could be affected by climate change.
4.2 CLIMATE CHANGE OVERVIEW
The IPCC has assessed the state of knowledge on climate change, its impacts and
mitigation of GHG emissions in its Fourth Assessment Report (AR4) published in 2007.
The reports can be found through the IPCC website.3 It is important to note that climate
change science is a rapidly evolving area. The IPCC in 2012 also published a Special
report on Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation. Work on the IPCC Fifth Assessment Report (AR5) is now underway.
The AR5 will consist of three working group reports and a synthesis report, to be
completed in 2013/2014. The various scales of climate scenarios are discussed briefly in
this section.
4.2.1 KEY DEFINITIONS
In order to maintain consistency and transparency in its climate change assessments,
the IPCC uses a set of standard terms to treat both confidence and likelihood. These
terms
are
presented
in
both
3
<http://www.ipcc.ch/>.
Page 3
Chapter 4: Climate Change Scenarios
Table 4-1 and Table 4-2 and are referred to regularly through the different modules of
the training materials.
Page 4
Chapter 4: Climate Change Scenarios
Table 4-1: Standard terms used by the IPCC to define levels of confidence (Source:
Solomon et al., 2007)
Confidence terminology
Degree of confidence in being correct
Very high confidence
At least 9 out of 10 chance
High confidence
About 8 out of 10 chance
Medium confidence
About 5 out of 10 chance
Low confidence
About 2 out of 10 chance
Very low confidence
Less than 1 out of 10 chance
Table 4-2: Standard terms used by the IPCC to define the likelihood of an outcome or result
(Source: Solomon et al., 2007)
Likelihood terminology
Likelihood of the occurrence/outcome
Virtually certain
>99% probability
Extremely likely
>95% probability
Very likely
>90% probability
Likely
>66% probability
More likely than not
>50% probability
About as likely as not
33 to 66% probability
Unlikely
<33% probability
Very unlikely
<10% probability
Extremely unlikely
<5% probability
Exceptionally unlikely
<1% probability
4.2.2 GLOBAL CLIMATE CHANGE
Global-scale historical observations and projected trends in key climate variables are
extensively discussed in the summary for policymakers in the contribution of Working
Group I (WG I) of the IPCC. Table 4-3 briefly summarizes what is known about changes
in regional climate as a result of increased atmospheric GHG concentrations, drawing
from the IPCC WG I observations.
Page 5
Chapter 4: Climate Change Scenarios
Table 4-3: State of knowledge on global climate change (adapted from IPCC, 2007)
Climate
variable
Observed trend
Direction of change to 2100
Degree of
confidence
Mean global
sea level rise4
Global mean sea level has been
rising. From 1961 to 2003, the
average rate of sea level rise
was 1.8±0.5 mm/yr. There is
high confidence that the rate of
sea level rise has increased
between the mid-19th and mid20th centuries. Significant
spatial and temporal variability
exists with sea level change
Mean sea level rise will
accelerate; IPCC projections
across all scenarios range from
0.18 m to 0.59 m
Virtually
certain
Mean surface
air
temperature
Global mean surface
temperatures have risen by
0.74°C ±0.18°C when estimated
by a linear trend over the last
100 years (1996–2005). The
rate of warming has almost
doubled in the past 50 years
All models project an increase in
global mean surface air
temperature (SAT) continuing
into the 21st century. Modelling
across all scenarios suggest an
increase of 1.8°C to 4.0°C
Likely
Temperature
extremes
Changes in temperature
extremes are also consistent
with warming of the climate. A
widespread reduction in the
number of frost days in mid
latitude regions, an increase in
the number of warm extremes
and a reduction in the number of
daily cold extremes are
observed in 70 to 75% of the
land regions where data is
available
It is very likely that heat waves
will become more intense, more
frequent and longer lasting in a
future warmer climate. Frosts
are expected to decrease almost
everywhere in middle and high
latitudes
Very likely
Precipitation
change
Precipitation has generally
increased over land north of
30°N over the period 1900 to
2005 but downward trends
dominate the tropics since the
1970s
Models simulate that global
mean precipitation increases
with global warming. However,
there are substantial spatial and
seasonal variations. Increases
are expected over the tropical
and high latitude regions with
decreases projected for many
subtropical and mid latitude
regions
Relatively
low (the
degree of
confidence in
projections
of regional
precipitation
change is
relatively
low)
4
Further detail on sea level rise is provided in chapter 5: Coastal Resources.
Page 6
Chapter 4: Climate Change Scenarios
Intensity of
peak
precipitation
Substantial increases are found
in heavy precipitation events. It
is likely that there have been
increases in the number of
heavy precipitation events (e.g.
95th percentile) within many
land regions, even in those
where there has been a
reduction in total precipitation.
Increases have also been
reported for rarer events (e.g. 1
in 50 year return period), but
only a few regions have
sufficient data to assess such
trends reliably
Intensity of precipitation events
is projected to increase,
particularly in tropical and high
latitude areas that experience
increases in mean precipitation.
Even in areas where mean
precipitation decreases (most
sub-tropical and mid latitude
regions), precipitation intensity is
projected to increase but there
would be longer periods
between rainfall events
Very likely in
many areas
Drought
Droughts have become more
common, especially in the
tropics and subtropics, since the
1970s. Observed increases in
drought in the past three
decades arise from more
intense and longer droughts
over wider area
There is a projected tendency
for the drying of the midcontinental areas during
summer, indicating a greater risk
of droughts in those areas
Likely
Tropical
cyclone wind
and peak
precipitation
rate
Globally, intense tropical
cyclone activity has increased
since about 1970
Modelling suggests that there
will be a likely increase of peak
wind intensities and notably,
where analysed, increased near
storm precipitation. Other recent
studies projected decreases in
tropical cyclone frequency,
however there is less confidence
in these projections
Likely
4.2.3 REGIONAL CLIMATE CHANGE
A summary of projected climate change at a regional level relevant to non-Annex I
Parties is presented below based on Meehl et al. (2007).
Africa
Warming is very likely to be greater than the global annual mean warming throughout the
continent and in all seasons, with drier subtropical regions warming more than the
moister tropics. Annual rainfall is likely to decrease in much of Mediterranean Africa and
the northern Sahara, with a greater likelihood of decreasing rainfall closer to the
Mediterranean coast. Rainfall in southern Africa is likely to decrease in much of the
winter rainfall region and western margins. There is likely to be an increase in annual
mean rainfall in East Africa. It is unclear how rainfall in the Sahel, the Guinean Coast and
the southern Sahara will evolve.
Page 7
Chapter 4: Climate Change Scenarios
Mediterranean region and Europe
Annual mean temperatures in Europe are likely to increase more than the global mean.
Seasonally, the largest warming is likely to be in northern Europe in winter and in the
Mediterranean area in summer. Minimum winter temperatures are likely to increase
more than the average in northern Europe. Maximum summer temperatures are likely to
increase more than the average in southern and central Europe. Annual precipitation is
very likely to increase in most of northern Europe and decrease in most of the
Mediterranean area. In central Europe, precipitation is likely to increase in winter but
decrease in summer. Extremes of daily precipitation are very likely to increase in
northern Europe. The annual number of precipitation days is very likely to decrease in
the Mediterranean area. Risk of summer drought is likely to increase in central Europe
and in the Mediterranean area. The duration of the snow season is very likely to shorten,
and snow depth is likely to decrease in most of Europe.
Asia
Warming is likely to be well above the global mean in central Asia, the Tibetan Plateau
and northern Asia, above the global mean in eastern Asia and South Asia, and similar to
the global mean in Southeast Asia. Precipitation in boreal winter is very likely to increase
in northern Asia and the Tibetan Plateau, and likely to increase in eastern Asia and the
southern parts of Southeast Asia. Precipitation in summer is likely to increase in northern
Asia, East Asia, South Asia and most of Southeast Asia, but is likely to decrease in
central Asia. It is very likely that heat waves/hot spells in summer will be of longer
duration, more intense and more frequent in East Asia. Fewer very cold days are very
likely in East Asia and South Asia. There is very likely to be an increase in the frequency
of intense precipitation events in parts of South Asia, and in East Asia. Extreme rainfall
and winds associated with tropical cyclones are likely to increase in East Asia, Southeast
Asia and South Asia.
Central and South America
The annual mean warming is likely to be similar to the global mean warming in southern
South America but larger than the global mean warming in the rest of the area. Annual
precipitation is likely to decrease in most of Central America and in the southern Andes,
although changes in atmospheric circulation may induce large local variability in
precipitation response in mountainous areas. Winter precipitation in Tierra del Fuego
and summer precipitation in south-eastern South America is likely to increase. It is
uncertain how annual and seasonal mean rainfall will change over northern South
America, including the Amazon forest. However, there is qualitative consistency among
the simulations in some areas (rainfall increasing in Ecuador and northern Peru, and
decreasing at the northern tip of the continent and in southern northeast Brazil).
Small Islands
Sea levels on average are likely to rise during the century around the small islands of the
Caribbean Sea, Indian Ocean and northern and southern Pacific Oceans. The rise will
likely not be geographically uniform but large deviations among models make regional
estimates across the Caribbean, Indian and Pacific Oceans uncertain. All Caribbean,
Indian Ocean and North and South Pacific islands are very likely to warm during this
century. The warming is likely to be somewhat smaller than the global annual mean.
Page 8
Chapter 4: Climate Change Scenarios
Summer rainfall in the Caribbean is likely to decrease in the vicinity of the Greater
Antilles but changes elsewhere and in winter are uncertain. Annual rainfall is likely to
increase in the northern Indian Ocean with increases likely in the vicinity of the
Seychelles in December, January and February, and in the vicinity of the Maldives in
June, July and August, while decreases are likely in the vicinity of Mauritius in June, July
and August. Annual rainfall is likely to increase in the equatorial Pacific, while most
models project decreases for areas just east of French Polynesia in December, January
and February.
4.2.4 EXTREME EVENTS
In February 2012, IPCC released the Special Report on Managing the Risks of Extreme
Events and Disasters to Advance Climate Change Adaptation (SREX).5 The summary
for policymakers (IPCC, 2011) highlighted the following in relation to the current
understanding of climate change and extreme events:
5

Observations since 1950 show changes in some extreme events, particularly
daily temperature extremes, and heat waves;

It is likely that the frequency of heavy precipitation will increase in the 21st
century over many regions;

It is virtually certain that increases in the frequency of warm daily temperature
extremes and decreases in cold extremes will occur throughout the 21st century
on a global scale. It is very likely – 90 per cent to 100 per cent probability – that
heat waves will increase in length, frequency and/or intensity over most land
areas;

It is likely that the average maximum wind speed of tropical cyclones (also
known as typhoons or hurricanes) will increase throughout the coming century,
although possibly not in every ocean basin. However, it is also likely – in other
words there is a 66 per cent to 100 per cent probability – that overall there will be
either a decrease or essentially no change in the number of tropical cyclones;

There is evidence, providing a basis for medium confidence that droughts will
intensify over the coming century in southern Europe and the Mediterranean
region, central Europe, central North America, Central America and Mexico,
northeast Brazil, and southern Africa. Confidence is limited because of
definitional issues regarding how to classify and measure a drought, a lack of
observational data, and the inability of models to include all the factors that
influence droughts;

It is very likely that average sea level rise will contribute to upward trends in
extreme sea levels in extreme coastal high water levels;
<http://ipcc-wg2.gov/SREX/>.
Page 9
Chapter 4: Climate Change Scenarios

Projected precipitation and temperature changes imply changes in floods,
although overall there is low confidence at the global scale regarding climate
driven changes in magnitude or frequency of river related flooding, due to limited
evidence and because the causes of regional changes are complex.
4.3 APPROACH TO CLIMATE CHANGE SCENARIO
DEVELOPMENT
The development of climate change scenarios for impact assessment has been well
documented through guidelines published by various agencies and programmes outlined
in Table 4-4. This chapter provides a broad overview on the key steps in climate change
scenario development. It is suggested that, where more detail is required, further
guidance is sought from the documents in Table 4-4 in particular Lu (2006) and Puma
and Gold (2011).
Table 4-4: Guidance documents on climate scenario development (Source: Puma and Gold,
2011)
Title
Author
UNDP-UNEP-GEF National Communications Support Programme
Guidance on the Development of Regional Climate Scenarios for Application in
Climate Change Vulnerability and Adaptation Assessment
Lu, 2006
<http://ncsp.undp.org/browsedocs/163/785?doctitle=Climate+Scenarios>
Applying Climate Information for Adaptation Decision Making: A Guidance
Resource Document
<http://www.undp.org/environment/docs/lecrds/applying_climate_information.p
df>
Lu, 2007
IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis
Guidelines for Use of Climate Scenarios Developed from Regional Model
Experiments
<www.ipcc-data.org/guidelines/dgm_no1_v1_10-2003.pdf>
General Guidelines on the Use of Scenario Data for Climate Impacts and
Adaptation Assessment, Version 2
Mearns et al.,
2003
Carter, 2007
www.ipcc-data.org/guidelines/TGICA_guidance_sdciaa_v2_final.pdf
Page 10
Chapter 4: Climate Change Scenarios
Other
Good Practice Guidance Paper on Assessing and Combining Multi Model
Climate Projections
http://www.ipccwg2.gov/meetings/EMs/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf
A Framework for Assessing Uncertainties in Climate Change Impacts: LowFlow Scenarios for the River Thames
Formulating Climate Change Scenarios to Inform Climate-Resilient
Development Strategies: A Guidebook for Practitioners
http://content.undp.org/go/cmsservice/download/publication/?version=live&id=3259633
Knutti et al.,
2010
Wilby et al.,
2006
Puma and
Gold, 2011
Before the technical considerations regarding the development of climate change
scenarios are undertaken, a thorough definition and evaluation of user needs should be
undertaken. It is one of the key steps in the approach to climate change scenario
development. The second one is specification of baseline climate for defined baseline
period. Changes in mean climate conditions and climate variability under enhanced
greenhouse gas effects, can be derived in a third step using in a given geographic area
good-quality climate data (ground or satellite observations). Experts should be cautious
in the choice of models, tools and data to consider on assessment of actual climate
change information. The last step consists of development of climate change scenarios
for plausible future changes.
4.3.1 EVALUATION AND DETERMINATION OF NEEDS FOR CLIMATESCENARIO DEVELOPMENT
In the words of Lu (2006, p. 37):
Before embarking on a “fishing expedition” for data, models and tools, it is
strongly advisable to allocate time to define clearly the scope of the climate
scenario information needed within the framework of the SNC.
The United Nations Development Programme (UNDP) Formulating Climate Change
Scenarios to Inform Climate-Resilient Development Strategies: A Guidebook for
Practitioners (Puma and Gold, 2011) clarified that approach: choosing the right method
for climate-scenario development can only be done after careful evaluation of the
available approaches against the needs (application) and constraints (e.g. financial,
computing, workforce, scientific, etc.) that project managers and their teams face.
The guidebook provides a useful set of focus questions to guide the needs analysis.
These questions are shown in Figure 4-1 with further discussion points useful for
addressing each of these questions (and capturing the data provided through the
analysis) provided in Puma and Gold (2011).
Page 11
Chapter 4: Climate Change Scenarios
Figure 4-1: Key questions to determine the purpose and needs for climate scenario
development (from: Puma and Gold, 2011, adapted from Lu, 2006)
The framework developed by UNDP for developing climate change scenarios has a
critical part that is intended to help decision-makers identify the constraints they face
including capacity, financial, technical, and others). The framework also assists in
fostering an understanding in the interplay among these decision-makers to better
approach climate-scenario development, in particular with respect to resource allocation.
The framework advices project managers to work together with a team of scientific and
technical experts to manage uncertainties, select appropriate scenario methods and
build a prospective range of scenarios. On the other side, scientific experts in charge of
scenario development are not fully aware of manager’s needs and project’s nonscientific aspects. Therefore, the framework provides a platform that should foster clear
and frequent dialogue between team members.
Page 12
Chapter 4: Climate Change Scenarios
4.3.2 SPECIFICATION OF BASELINE CLIMATE
In climate change scenario development, after establishing the needs, the first step
required is to specify a baseline climate against which future changes in climate
variables can be measured and impacts assessed. Baseline climate data helps to
identify key characteristics of the current climate regime (such as seasonality, trends and
variability, extreme events and local weather phenomena). Depending on the purpose of
the climate change scenarios, the data sources and techniques required to define the
baseline can vary widely (Lu, 2006).
Several questions need to be answered to define the baseline climate:
1) Which climate scenarios data are needed?

The baseline climate should provide sufficient information on those
present-day conditions;

From a single to comprehensive suite of variables, from local to greater
spatial scales, that will be characterized in the scenarios under a
changing climate, at the appropriate temporal and spatial scales (sub
daily, daily, seasonal, decadal to multi-decadal or even century time
scales).
2) Which baseline period should be selected?

A popular climatological baseline period is a non-overlapping, 30-year,
“normal” period (e.g., 1900-1930, 1931- 1960, 1961-1990) as defined by
the World Meteorological Organization (WMO). The current WMO normal
period is 1961-1990 (IPCC-TGCIA, 1999). However, in many places and
studies the normal period 1970-2000 is adopted to consider more recent
variations in regional climate. An alternative period could be also adopted
depending on data availability.
3) What data sources are available?
A range of data sources is available to define the climate baseline. These sources can
be categorized following Lu (2006) as described below.
National meteorological agencies and archives
National meteorological agencies maintain the day-to-day operations of weather
observations and publish weather statistics. Therefore, these agencies and their
archives are often the primary sources of observed climate data, particularly at daily or
sub-daily intervals. However, many non-Annex I Parties reported in their initial national
communications that they had difficulty in accessing quality-controlled, observed weather
data. Therefore, at the outset of the national communication project implementation, it is
very important for the National Communication Steering Committee (or other institutions
managing the national communication project) to ensure institutional arrangements are
Page 13
Chapter 4: Climate Change Scenarios
in place to grant free access to this observational climate data for the national
communication team.
Supranational and global data sets
Observations from different countries have been combined into various supranational
and global data sets, typically using funding from Annex I country governments and/or
international organizations. These data sets often include mean values for surface
variables for various periods, interpolated to a regular grid. But with improved processing
and storage capacity, there are now also a number of historical data sets providing
annual, monthly and even daily time series of gridded or site observations (see section
4.4.2).
Weather generators
Weather generators are statistical models that describe the properties of an observed
climate variable in a region using few parameters. The ability to generate a climatological
time series of unlimited length can be particularly helpful in regions with sparse observed
data. In some cases, a weather series can be generated from statistical parameters
obtained from observed data at a neighbouring site or at sites with sparse or broken
records. An example of a widely used weather generator is LARS-WG6 (Semenov and
Barrow, 1997).
Climate model outputs
Two types of data from global climate model (GCM) simulations can be used for
specifying climate baselines: reanalysis data and outputs from GCM control simulations.
Reanalysis data
To overcome the problem of sparse and irregular meteorological observations often
found in non-Annex I Countries, reanalysis data can be used for defining baseline
climatologies. These are fine resolution, gridded data which combine observations
(usually sparse and irregular in distribution) with simulated data from climate models,
through a process called data assimilation. In addition to filling gaps in conventional
observations of surface variables, the assimilation process can provide estimates of
unobserved quantities such as vertical motion, radiative fluxes and precipitation.
Therefore, reanalysis data have been very helpful for establishing statistical relationships
between locally observed surface variables and large-scale, upper-air-circulation indices.
Such relationships are needed to statistically downscale coarse-resolution GCM outputs
to create local-scale climate scenarios.
Outputs from GCM control simulations
GCM control simulations attempt to represent the dynamics of the global climate system
unforced by anthropogenic changes in atmospheric composition. Most Atmosphere–
6
<http://www.rothamsted.bbsrc.ac.uk/masmodels/larswg.php>.
Page 14
Chapter 4: Climate Change Scenarios
Ocean GCMs (AOGCMs) control simulation runs over multiple centuries, and hence can
provide data for analyses of natural variability of regional climate. Since actual
observations barely extend beyond one century in duration, model control simulations
offer an alternative source of data-enabling impact analysis for investigating the impact
of multi-decadal variations in climate. Control simulation data from a range of AOGCMs
are currently available from the IPCC Data Distribution Centre (DDC).7
How can climate baselines be applied for the V&A assessment?
Climate baseline can be used in impact assessment to characterize the sensitivity of the
exposure unit to present-day climate. Extreme events as well as trends could be also
addressed. Ultimately running biophysical models with part of the baseline climate data
will help to validate those models that could be used for assessment of future changes.
Once the baseline climate has been established, there are several possible
methodologies that can be employed to develop scenarios of future change.
4.3.3 DEVELOPMENT OF CLIMATE CHANGE SCENARIOS
Once the baseline climate has been established, there are several possible approaches
that can be employed to develop scenarios of future change. These are detailed in
Guidance on the Development of Regional Climate Scenarios for Vulnerability and
Adaptation Assessments (Lu, 2006).
There are several possible approaches that can be employed to develop scenarios of
future climate changes:
1) Assume arbitrary changes in climate variables (synthetic scenarios):

Those arbitrary adjustments at regular time periods can be used as inputs
to sectoral studies. For example:
i. Temperature changes of +1°C, +1.5°C, -1°C, -2°C, etc., could be
applied to a crop model to test the sensitivity of crop growth to
temperature change;
ii. Similarly, precipitation could be adjusted by +5%, +10%, +15%, 5%, -10%, -15%, etc., to estimate the impacts of such changes on
the same crop.
2) Use temporal and spatial analogues:

7
Analogues are built by identifying recorded climate regimes that may
resemble the future climatic conditions for a given region;
<www.ipcc-data.org/>.
Page 15
Chapter 4: Climate Change Scenarios

The recorded regime could be from the past (temporal analogues) or
from a different region in the present (spatial analogues).

Analogues are physically consistent since they are built with observed
data.
3) Develop scenarios from climate models outputs:

This is the most common approach to deriving climate change scenarios;

Three types of climate models have been developed to provide
projections of future climate changes, each with progressively higher
resolution:
i. simple climate models;
ii. GCMs;
iii. RCMs.
New efforts (CMIP5, NARCCAP, CORDEX) augment the options of climate scenarios
available. Users of climate model outputs may prefer downscaled data, that is data at
higher spatial resolution, to direct GCMs outputs. Downscaling can be statistical as per
Maurer et al., (2007) or dynamical used by Mearns et al. (2009). However guidance (see
Mote et al, 2009) is needed on how to select, treat and combine the vast amount of
climate models outputs into useful climate scenarios.
The following guidelines could be of assistance:
1) Understand to which aspects of climate the problem or decision is most sensitive
(for example, which climate variables, which statistical measures of these
variables, and at what space and time scales);
2) Determine which climate projection information is most appropriate for the
problem or decision (for example, variables, scales in space and time);
3) Understand the limitations of the method selected;
4) Obtain climate projections based on as many simulations (“ensemble”),
representing as many models and emissions scenarios, as possible;
5) Understand that regional climate projection uncertainty stems from uncertainties
about:

The drivers of change (for example, GHGs, aerosols);

The response of the climate system to those drivers;

The future trajectory of natural variability;
Page 16
Chapter 4: Climate Change Scenarios
6)
Use the “ensemble” to characterize consensus not only about the projected mean
but also about the range and other aspects of variability.
This section will not replicate the content of that guidance manual, rather the key
advantages and disadvantages of each approach is summarized in Table 4-5 to provide
support for choosing an approach appropriate for a particular context.
Table 4-5: Advantages and disadvantages of climate change scenario options (modified
from: Lu, 2006)
Options
Sub-Options
Arbitrary
changes
in climate
variables
Analogue
scenarios
Advantages
Disadvantages
Easy to create and apply
Combinations may be physically
inconsistent
Can represent broad range of
potential changes in climate
Instrumental
record
High spatial and temporal
resolution
Captures climate variability
Climate
models
Could represent very unlikely
changes in regional climate
Recent record captures only a
limited increase in GHG
concentrations
Paleoclimate
reconstructions
Can reflect a wider range of
climate conditions than
instrumental record
Forcing conditions not the same as
anthropogenic increases in GHG
concentrations
Global climate
models
(GCMs)
Simulate global response to
Increased GHG concentrations
Low spatial resolution
Regional
climate models
(RCMs)
Substantially higher spatial
resolution
Internally consistent
Models have different starting
conditions and parameterizations,
which makes comparison of results
challenging
Will not correct for mistakes of GCM
Limited applications, i.e. run with few
GCMs
Often run for limited time periods
Statistical
Downscaling
Relatively easy way to obtain
high spatial and temporal
resolution output based on GCMs
Will not capture change in
relationship between GCM variables
and climate at the local scale
In addition to the influence of scientific constraints (for example, spatio-temporal
resolution) on climate-scenario development, there are multiple non-scientific constraints
such as capacity, financial and data availability, that will also have an influence on the
development of these scenarios. Figure 4-2 presents the relationship among the
complexity of analyses (characteristics of the approach for climate-scenario
development), spatio-temporal resolution, and non-scientific constraints to climatescenario development. The figure illustrates that the potential complexity of analyses and
spatio-temporal resolution will depend on a project’s non-scientific (data, financial,
computing or work-force) constraints.
Page 17
Chapter 4: Climate Change Scenarios
Figure 4 - 2: Relationship between the constraints, complexity of analysis and spatialtemporal resolution for climate change scenario development. (Source: Puma and Gold,
2011)
4.4 METHODS, TOOLS AND DATA SOURCES
4.4.1 MODELS AND TOOLS
This section provides an overview on the models, tools and data sources for non-Annex I
Parties to develop climate change scenarios for use in national communications. Further
details on the majority of these resources can be found in the UNFCCC Compendium of
Methods and Tools to Evaluate the Impacts of, and Vulnerability and Adaptation to,
Climate Change.8
Table 4-6Error! Reference source not found. summarizes the different models and
tools that have been widely used to generate climate scenarios in national
communications. Each of these models and tools are explored in greater detail in
UNDP/UNEP/GEF Guidance on the Development of Regional Climate Scenarios for
Vulnerability and Adaptation Assessments (Lu, 2006). Further documentation for each
8
<http://unfccc.int/adaptation/nairobi_work_programme/knowledge_resources_and_publications/items/2674.php>.
Page 18
Chapter 4: Climate Change Scenarios
specific model/tool can be found through the web links in Table 4-6Error! Reference
source not found..
Importantly, there are trade-offs between the level of complexity required from the
climate modelling and the computational requirements (and associated costs and
technical capacities) to produce results. The choice of models and tools has to be
carefully considered as a result.
Key factors that should be considered in the choice of model and tools include:

Ensuring a close alignment with evaluated user needs (see section 0);

Consideration of in-country technical, financial, data and time constraints;

Opportunities to partner with neighbouring countries or regional groups to share
costs and promote the strengthening regional capacity;

Thinking about the potential to develop a scenario-development pathway that first
uses less time and resource-intensive options initially (if these approaches can
serve the purpose) with a view to using more complex models and tools in the
future.
For example, a (hypothetical) country may decide, that following a thorough evaluation of
user needs, and drawing on the models and tools shown in Table 4-6, (and following
discussions with development partners, neighbouring countries and climate experts),
opportunities exist to create an approach to the development and use of climate
scenarios that:

Uses the ClimateWizard tool for initial high-level climate change impact screening
tool – and to facilitate early stakeholder engagement on key issues;

Investigate using desktop-computer tools, such as MAGICC SCENGEN,
SimCLIM or SDSM, depending on requirements;

Exploring with neighbouring countries and regional expert groups the use of
PRECIS or the development of specific regional modelling initiatives.
This approach outlined above also reflects, in broad terms, a pathway followed by
Parties that have already undertaken a number of national communications.
Considerable expertise has been developed within non-Annex I Parties in this regard,
that is of great value to others approaching the process of choosing climate scenario
tools and models.
Page 19
Chapter 4: Climate Change Scenarios
Box 4-1: The Pacific Climate Futures Tool
The Pacific Climate Futures web tool is an initiative
of the Pacific Climate Change Science Program
developed by the Australian Government in
collaboration with nation meteorological services in
the region (see list). The web tool has been
designed to provide information and guidance on
the generation of national climate projections and
facilitate the generation of data for detailed impact
and risk assessments.
Projections are classified using two climate
variables such as rainfall and temperature, and
grouped into so-called “climate futures” (e.g.
“hotter, drier” or “slightly warmer, much wetter”).
Likelihoods are then assigned depending on the
number of climate models that fall into each
category.

Cook Islands
Projections are available for:

Federated States of Micronesia

Fiji

Kiribati

10 climate variables

Three emission scenarios: B1 (low), A1B
(medium), A2 (high)

Marshall Islands

Nauru

Three time periods including 2030, 2055
and 2090

Niue

Palau

Up to 18 global climate models

Papua New Guinea

Samoa

Solomon Islands

Timor Leste

Tonga

Tuvalu

Vanuatu
The tool can be accessed from:
<http://www.pacificclimatefutures.net/>.
Page 20
Chapter 4: Climate Change Scenarios
Table 4-6: Selected models and tools for climate change scenario development
Name
Source
Resolution
Computational
requirements and
training
Description
Link
PRECIS
(Providing
Regional
Climates for
Impacts
Studies
UK Met
Office Hadley
Centre
Regional
(25 km grid)
High: (Linux OS,
1GB RAM,
>2.0 GHz, >500 GB
available space)
PRECIS modelling provides high-resolution regional
climate projections. A typical PRECIS experiment can
take several months, hence the system is not designed
to provide instant climate scenarios. Workshops and
training are regularly conducted through institutions
around the globe in developing nations
<http://www.metoffice.g
ov.uk/precis/>
MAGICC
SCENGEN
US National
Center for
Atmospheric
Research
Regional
(2.5° by 2.5°
grid)
Low (Windows OS)
MAGICC/SCENGEN is a coupled software package that
allows users to investigate future climate change and its
uncertainties and the global mean and regional levels.
MAGICC calculates energy balances whereas
SCENGEN effectively presents the results of MAGICC
to produce spatially detailed information on future
changes in key climate variables
<http://www.cgd.ucar.ed
u/cas/wigley/magicc/>
SDSM
(Statistical
DownScaling
Model)
Dr Robert
Wilby and
Chris
Dawson (UK)
Regional
Low (Windows 98,
XP)
SDSM is a software tool designed to implement
statistical downscaling methods to produce high-res
monthly climate information from GCM simulation
<http://co-
Training workshops
available
Training workshops
available
Training seminars
available
public.lboro.ac.uk/cocwd
/SDSM/>
Page 21
Chapter 4: Climate Change Scenarios
SimCLIM
CLIMsystem
s
Regional and
Global
Low/Med (Windows
OS: XP, Vista, 7)
SimCLIM is a commercial software package that links
data and models in order to simulate the impacts of
climatic variations and change
<http://www.climsystem
s.com/simclim/>
The ClimateWizard is an online tool that provides instant
national level projections for temperature and
precipitation for a range of Special Report on Emissions
Scenarios (SRES) emission scenarios
<http://www.climatewiza
rd.org/>
Training available
ClimateWizard
The Nature
Conservancy
Regional and
Global
(50 km grid)
Very Low (Internet
connection only)
No training required
Page 22
Chapter 4: Climate Change Scenarios
4.4.2 DATA SOURCES
A range of publicly available data sources is available for use in climate change scenario
development. These data sources include meteorological data sets (Table 4-7) and data
from global and regional climate model runs (Table 4-8). The tables summarize and
provide a direct link to the key sources of data. Lu (2006) provides a comprehensive
overview on each of the data sources listed in Table 4-8.
Table 4-7: Sources of meteorological global data sets
Name
Description
Link
IPCC Data Distribution
Centre
The IPCC Data Distribution Centre provides gridded
observed data including mean climatology in a number
of formats
<http://www.ipccdata.org/>
International Research
Institute for Climate
Prediction (IRI)
The International Research Institute for Climate
Prediction hosts a range datasets of gridded observed
data for a range of spatial and temporal resolutions
<http://iridl.ldeo.colu
US National Oceanic
and Atmospheric
Administration
(NOAA) [
Reanalysis data including six-hourly observations of
daily and monthly averages for numerous climate
variables. The reanalysis extends from 1948 to present
and covers the entire globe
<http://www.esrl.noa
US National Climate
Data Centre’s Global
Daily Climatology
Network
National Oceanic and Atmospheric Administration
(NOAA) NCDC Global Daily Climatology Network:
Daily precipitation and minimum and maximum
temperature data
<http://iridl.ldeo.colu
Tyndall Centre for
Climate Change
Research
Dataset of 10-minute latitude/longitude resolution
mean monthly surface climate over global land areas,
excluding Antarctica. The climatology includes
variables such as precipitation, wet-day frequency,
temperature, diurnal temperature range, relative
humidity, sunshine hours, ground frost frequency and
wind speed
<http://www.cru.uea.
mbia.edu/docfind/dat
abrief/catatmos.html>
a.gov/psd/data/gridd
ed/data.ncep.reanaly
sis.html>
mbia.edu/SOURCES
/.NOAA/.NCDC/.GD
CN/>
ac.uk/cru/data/tmc.ht
m>
This data set also comprises over 1200 monthly grids
of observed climate for the period 1901–2002,
covering the global land surface at 0.5° resolution.
Tyndall Centre for
Climate Change
Research
Data set of country level monthly time series for 1901–
2000 which includes the 20th century climate for 289
countries and territories using nine climate variables
including cloud cover, diurnal temperature range, frostday frequency, daily minimum temperature, daily mean
temperature, daily maximum temperature,
precipitation, wet-day frequency and vapour pressure
<http://www.cru.uea.
ac.uk/~timm/cty/obs/
TYN_CY_1_1.html>
Page 23
Chapter 4: Climate Change Scenarios
WorldClim
Global 50 yr (1948-2000) dataset of meteorological
forcings derived by combining reanalysis with
observations. Available at 2.0-degree and 1.0-degree
spatial resolution and daily and 3-hourly temporal
resolution: http://hydrology.princeton.edu/data.php.
http://www.worldcli
m.org
APHRODITE
daily precipitation (0.5 and 0.25 degree) for portions of
Asia
http://www.chikyu.a
c.jp/precip/cgibin/aphrodite/script
/aphrodite_cgi.cgi/r
egister.
Table 4-8: Data archives of global and regional climate model outputs
Name
Description
Link
CORDEX
CORDEX (Coordinating Regional Climate Downscaling Experiment)
is a database of multi-dynamical and statistical downscaling models
considering multiple forcing GCMs from the Coupled Model
Intercomparison Project 5 (CMIP5) archive. The first release
available will contain 50 km gridded data for most global regions
<http://www.m
CMIP3 (Coupled Model Intercomparison Project 3) contains global
model output data on which the IPCC Fourth Assessment Report
(AR4) was based
<http://cmip-
CMIP5
CMIP5 contains global model output data that will be the foundation
of the IPCC Fifth Assessment Report (AR5)
<http://cmippcmdi.llnl.gov
/cmip5/>
ENSEMBLES
RT3
A collection of RCM output data covering Europe and Western
Africa from the EU project ENSEMBELS (2004–2009). 25km
resolution for the period 1951–2050 or 1951-2100 for the SRES A1B
scenario
<http://ensem
PRUDENCE was a European Union project during 2001–2004,
where the first coordinated archiving of regional model output was
collected, and comprises 22 fields in 50 km resolution from 11 model
runs for the period 1961–1990 and 2071–2100 for the SRES A2 and
B2 scenarios
<http://pruden
ce.dmi.dk/>
CMIP3
PRUDENCE
eteo.unican.es
/en/projects/C
ORDEX>
pcmdi.llnl.gov/
cmip3_overvie
w.html>
blesrt3.dmi.dk/
>
Page 24
Chapter 4: Climate Change Scenarios
4.5 FUTURE DIRECTIONS IN CLIMATE CHANGE
SCENARIO DEVELOPMENT
Climate change scenario development is a rapidly evolving field. Technological
advances in modelling capacity and the ever-improving scientific basis of the models
themselves ensure that technical aspects of modelling endeavours are always changing.
However, although these advances are clearly important, they are less significant than
the fundamental change in climate change modelling (including socio-economic
modelling) brought about by the shift from using IPCC SRES scenarios to representative
concentration pathways (RCPs) that “will provide a framework for modelling in the next
stages of scenario-based research” (Moss et al., 2010). The RCPs have been selected
by a broad group of experts under the auspices of the IPCC through analysis of
published literature. This analysis resulted in a consensus on the needed inputs of
emissions, concentrations and land use/cover for climate models.
Critically, the move to RCPs is leading the development of new climate change
scenarios, useful for greater integration with socio-economic scenarios (Moss et al.,
2010). Future research into the use of these integrated scenarios, will help in exploring
issues around adaptation and mitigation using set assumptions, providing insights into
the costs, benefits, and risks of different climate futures and development pathways
(Moss et al., 2010).
Importantly, the transition from SRES-based modelling to the RCP approach has begun,
with significant progress planned in 2012 and 2013, shown in Figure 4-3. While the
traditional approach is forward, starting with emissions, the new approach using RCP
starts with concentrations. Further climate parameters are then calculated on one side
and on the other side, related emission and also socio-economic scenarios.
This change in the scenario-development approach to RCPs will likely have significant
implications for non-Annex I Parties, planning to undertake climate change scenario
development work in coming years (Figure 4-3). However, it is not clear how the shift to
the RCP process will influence the development and application of climate change and
socio-economic scenarios within national communications in the short term. Non-Annex I
Parties are strongly encouraged to engage in training and capacity development
activities in this regard.
Page 25
Chapter 4: Climate Change Scenarios
Figure 4-3: The new ‘parallel process’ for modelling climate change scenarios, impacts
and socio-economic factors through representative concentration pathways (RCPs) (Moss
et al., 2010)
Page 26
Chapter 4: Climate Change Scenarios
4.6 REFERENCES
Carter, T.R. 2007. General Guidelines on the use of Scenario Data for Climate
Impact and Adaptation Assessment. Intergovernmental Panel on Climate
Change Task Group on Data and Scenario Support for Impact and Climate
Assessment (TGICA).
IPCC (Intergovernmental Panel on Climate Change). 2007: Summary for
Policymakers. 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. S Solomon, D Qin, M Manning,
Z Chen, M Marquis, KB Averyt, M Tignor and HL Miller (eds.) Cambridge and
New York: Cambridge University Press.
IPCC. 2011. Summary for Policymakers. In: Intergovernmental Panel on Climate
Change Special Report on Managing the Risks of Extreme Events and
Disasters to Advance Climate Change Adaptation. CB Field, V Barros, TF
Stocker, D Qin, D Dokken, KL Ebi, MD Mastrandrea, KJ Mach, GK Plattner,
SK Allen, M Tignor and PM Midgley (eds.). Cambridge and New York:
Cambridge University Press
Knutti R, Abramowitz G, Collins M, Erying V, Glecker PJ, Hewitson B and Mearns L.
2010. Good Practice Guidance Paper on Assessing and Combining Multi
Model Climate Projections: IPCC Expert Meeting on Assessing and
Combining Multi Model Climate Projections. Boulder, Colorado: National
Center for Atmospheric Research.
Lu X. 2006. Guidance on the Development of Regional Climate Scenarios for
Application in Climate Change Vulnerability and Adaptation Assessments,
within the Framework of National Communications from Parties not Included
in Annex I to the United Nations Framework Convention on Climate Change.
New York: National Communications Support Programme, UNDP/UNEP/GEF.
Lu, X. 2007. Applying climate information for adaptation decision-making: a guidance
resource document. New York, . National Communications Support
Programme, UNDP-UNEP-GEF
Mearns LO, Giorgi F, Whetton P, Pabon D, Hulme M, and Lal M. 2003. Guidelines
for Use of Climate Scenarios Developed from Regional Climate Model
Experiments. Intergovernmental Panel on Climate Change Task Group on
Data and Scenario Support for Impact and Climate Assessment (TGICA).
Mearns LO, Hulme M, Carter TR, Leemans R, Lal M, and Whetton P. 2001. Climate
scenario development. In: Climate Change 2001: The Scientific Basis. JT
Houghton, Y Ding, DJ Griggs, M Noguer, PJ van der Linden, D Xiaosu and K
Maskell (eds.). Cambridge University Press New York: Cambridge University
Press.
Page 27
Chapter 4: Climate Change Scenarios
Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A,
Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ and
Zhao Z-C. 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. S
Solomon, D Qin, M Manning, Z Chen, M Marquis, KB Averyt, M Tignor and
HL Miller (eds.) Cambridge and New York: Cambridge University Press.
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter
TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N,
Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP and Wilbanks TJ.
2010. The next generation of scenarios for climate change research and
assessment. Nature, 463: 747–756.
Puma MJ and Gold S. 2011. Formulating Climate Change Scenarios to Inform
Climate-Resilient Development Strategies: A Guidebook for Practitioners New
York: United Nations Development Programme (UNDP).
Semenov MA and Barrow EM. 1997. Use of a stochastic weather generator in the
development of climate change scenarios. Climatic Change, 35:397–414
Solomon S, Qin D, Manning M, Alley RB, Berntsen T, Bindoff NL, Chen Z,
Chidthaisong A, Gregory JM, Hegerl GC, Heimann M, Hewitson B, Hoskins
BJ, Joos F, Jouzel J, Kattsov V, Lohmann U, Matsuno T, Molina M, Nicholls
N, Overpeck J, Raga G, Ramaswamy V, Ren J, Rusticucci M, Somerville R,
Stocker TF, Whetton P, Wood RA and Wratt D. 2007. Technical Summary. 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. S Solomon, D Qin, M Manning, Z Chen, M Marquis, KB
Averyt, M Tignor and HL Miller (eds.). Cambridge and New York: Cambridge
University Press.
Wilby RL and Harris I. 2006. A framework for assessing uncertainties in climate
change impacts: Low-flow scenarios for the River Thames, UK. Water
Resources Research, 42, doi:10.1029/2005WR004065.
Page 28