CGE TRAINING MATERIALS FOR VULNERABILITY AND ADAPTATION ASSESSMENT

Chapter 4: Climate Change Scenarios

CONTENTS

CONTENTS.......................................................................................................I

4.1 INTRODUCTION....................................................................................1

4.1.1 Why Do We Use Climate Change Scenarios?.................................2

4.2 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

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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-scenarios-vulnerability-and-adaptation-assessme>.

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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 with what is known about regional climatology, is also required. Experts such as climate

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.

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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 Table 4- and Table 4- and are referred to regularly through the different modules of the training materials.

3 <http://www.ipcc.ch/>.

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Table 4-: 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-: 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- 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.

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Table 4-: 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 mid-20th 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)

Intensity of Substantial increases are found Intensity of precipitation events Very likely in

4 Further detail on sea level rise is provided in chapter 5: Coastal Resources.

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peak precipitation

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

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

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 mid-continental 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.

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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,

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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. 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:

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;

5 <http://ipcc-wg2.gov/SREX/>.

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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-. 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- in particular Lu (2006) and Puma and Gold (2011).

Table 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

<http://ncsp.undp.org/browsedocs/163/785?doctitle=Climate+Scenarios>Lu, 2006

Applying Climate Information for Adaptation Decision Making: A Guidance Resource Document

<http://www.undp.org/environment/docs/lecrds/applying_climate_information.pdf>

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 >

Mearns et al., 2003

General Guidelines on the Use of Scenario Data for Climate Impacts and Adaptation Assessment, Version 2

www.ipcc- data .org/ guidelines /TGICA_ guidance _sdciaa_v2_final.pdf Carter, 2007

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Other

Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections

http://www.ipcc-wg2.gov/meetings/EMs/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf

Knutti et al., 2010

A Framework for Assessing Uncertainties in Climate Change Impacts: Low-Flow Scenarios for the River Thames

Wilby et al., 2006

Formulating Climate Change Scenarios to Inform Climate-Resilient Development Strategies: A Guidebook for Practitioners

http://content.undp.org/go/cms-service/download/publication/?version=live&id=3259633

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 CLIMATE-SCENARIO 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- with further discussion points useful for

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addressing each of these questions (and capturing the data provided through the analysis) provided in Puma and Gold (2011).

Figure 4-: 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 non-scientific aspects. Therefore, the framework provides a platform that should foster clear and frequent dialogue between team members.

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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

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very important for the National Communication Steering Committee (or other institutions managing the national communication project) to ensure institutional arrangements are 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–Ocean GCMs (AOGCMs) control simulation runs over multiple centuries, and hence can 6 <http://www.rothamsted.bbsrc.ac.uk/masmodels/larswg.php>.

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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:

Analogues are built by identifying recorded climate regimes that may resemble the future climatic conditions for a given region;

The recorded regime could be from the past (temporal analogues) or from a different region in the present (spatial analogues).

7 <www. ipcc - data .org/ >.

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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;

6) Use the “ensemble” to characterize consensus not only about the projected mean but also about the range and other aspects of variability.

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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- to provide support for choosing an approach appropriate for a particular context.

Table 4-: Advantages and disadvantages of climate change scenario options (modified from: Lu, 2006)

Options Sub-Options Advantages Disadvantages

Arbitrary changes in climate variables

Easy to create and apply

Can represent broad range of potential changes in climate

Combinations may be physically inconsistent

Could represent very unlikely changes in regional climate

Analogue scenarios

Instrumental record

High spatial and temporal resolution

Captures climate variability

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

Climate models

Global climate models (GCMs)

Simulate global response to Increased GHG concentrations

Internally consistent

Low spatial resolution

Models have different starting conditions and parameterizations, which makes comparison of results challenging

Regional climate models (RCMs)

Substantially higher spatial resolution

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 climate-scenario 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.

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Figure 4 - : Relationship between the constraints, complexity of analysis and spatial-temporal 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-Error: 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 specific model/tool can be found through the web links in Table 4-Error: Reference source not found.

8 <http://unfccc.int/adaptation/nairobi_work_programme/knowledge_resources_and_publications/items/2674.php>.

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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 );

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.

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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.

Projections are available for:

10 climate variables

Three emission scenarios: B1 (low), A1B (medium), A2 (high)

Three time periods including 2030, 2055 and 2090

Up to 18 global climate models

The tool can be accessed from: <http://www.pacificclimatefutures.net/>.

Cook Islands

Federated States of Micronesia

Fiji

Kiribati

Marshall Islands

Nauru

Niue

Palau

Papua New Guinea

Samoa

Solomon Islands

Timor Leste

Tonga

Tuvalu

Vanuatu

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Table 4-: 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)

Training workshops available

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.gov.uk/precis/>

MAGICC SCENGEN

US National Center for Atmospheric Research

Regional (2.5° by 2.5° grid)

Low (Windows OS)

Training workshops available

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.edu/cas/wigley/magicc/>

SDSM (Statistical DownScaling Model)

Dr Robert Wilby and Chris Dawson (UK)

Regional Low (Windows 98, XP)

Training seminars available

SDSM is a software tool designed to implement statistical downscaling methods to produce high-res monthly climate information from GCM simulation

<http://co-public.lboro.ac.uk/cocwd/SDSM/>

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SimCLIM CLIMsystems

Regional and Global

Low/Med (Windows OS: XP, Vista, 7)

Training available

SimCLIM is a commercial software package that links data and models in order to simulate the impacts of climatic variations and change

<http://www.climsystems.com/simclim/>

ClimateWizard The Nature Conservancy

Regional and Global (50 km grid)

Very Low (Internet connection only)

No training required

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.climatewizard.org/>

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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-) and data from global and regional climate model runs (Table 4-). 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-.

Table 4-: 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.ipcc-data.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.columbia.edu/docfind/databrief/cat-atmos.html>

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.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html>

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.columbia.edu/SOURCES/.NOAA/.NCDC/.GDCN/>

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

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.

<http://www.cru.uea.ac.uk/cru/data/tmc.htm>

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, frost-day frequency, daily minimum temperature, daily mean temperature, daily maximum temperature,

<http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html>

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precipitation, wet-day frequency and vapour pressure

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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.worldclim.org

APHRODITE daily precipitation (0.5 and 0.25 degree) for portions of Asia

http://www.chikyu.ac.jp/precip/cgi-bin/aphrodite/script/aphrodite_cgi.cgi/register.

Table 4-: 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.meteo.unican.es/en/projects/CORDEX>

CMIP3 CMIP3 (Coupled Model Intercomparison Project 3) contains global model output data on which the IPCC Fourth Assessment Report (AR4) was based

<http://cmip-pcmdi.llnl.gov/cmip3_overview.html>

CMIP5 CMIP5 contains global model output data that will be the foundation of the IPCC Fifth Assessment Report (AR5)

<http://cmip-pcmdi.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://ensemblesrt3.dmi.dk/>

PRUDENCE 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://prudence.dmi.dk/>

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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-. 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-). 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.

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Figure 4-: The new ‘parallel process’ for modelling climate change scenarios, impacts and socio-economic factors through representative concentration pathways (RCPs) (Moss et al., 2010)

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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

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Maskell (eds.). Cambridge University Press New York: Cambridge University Press.

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.

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