Developing Climate Scenarios for V&A Assessments

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Developing Climate Scenarios for V&A Assessments

Consultative Group of Experts on National Communications from Parties not Included in Annex I to the UNFCCC (CGE)

Hands-on Training Workshop on Vulnerability and Adaptation for Asia and Pacific Countries

20~24 March 2006

Jakarta, Indonesia

Xianfu Lu

National Communications Support Programme (NCSP), UNDP-UNEP-GEF

Xianfu.lu@undp.org

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In the next hour or so…

• Introduction: what are climate scenarios and why do we need them?

• Review of climate scenario approaches: how to develop climate scenarios?

• Treatment of uncertainties: What scenarios tell us and what they do not?

• Guidance documents, methods, tools and data sources for developing climate scenarios

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What are climate scenarios?

A scenario is: “a coherent, internally consistent and plausible description of a possible future state of the

world” (Parry and Carter, 1998)

Not a forecast or a prediction, but alternative views of what the world could look like in the future

Hence, a climate scenario is:

“…. a plausible future climate that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change”. (IPCC, 2001)

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What are climate scenarios?

• Climate scenarios versus climate projections

• Climate scenarios versus climate change scenarios

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Why do we need climate scenarios?

• To provide data for impact/adaptation assessment studies;o Any attempt to evaluate future climate change impacts,

adaptation and vulnerability requires some assumptions about how climate would change in the future;

o However, there are formidable uncertainties associated with the socio-economic drivers and GHG & aerosol emissions, and the responses of global and regional climate to the radiative forcing of GHG & aerosol emissions;

o Therefore, precise forecasts of future climate trends are not possible. An alternative approach is to construct Climate Scenarios.

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Why do we need climate scenarios?

• To provide data for impact/adaptation assessment studies;

• To act as an awareness-raising device;

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Decarbonised and adaptive world

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Why do we need climate scenarios?

• To provide data for impact/adaptation assessment studies;

• To act as an awareness-raising device;

• To aid in strategic planning and/or policy formation;

• To structure our knowledge (or ignorance) of the future;

• To explore the implications of decisions

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Review of climate scenario approaches

Three general types of climate scenarios representing three approaches to scenario development:

• Synthetic/incremental scenarios;

• Analogue scenarios; and

• Model-based scenarios

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4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies

Advantages – easy to construct and apply, allows sensitivity of sectors/models to be explored

Disadvantages – arbitrary (and unrealistic) changes, not related to wider scenario frameworks

Review of climate scenario approaches: Incremental scenarios

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4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies

Review of climate scenario approaches: Spatial analogue scenarios

UK c l imate HadCM2 2020s med ium-h igh s c enario to 1961-90 Globa l

U.K. climate HadCM2 2050s medium-high scenario matched to 1961-90 GlobalAnalogue scenarios for UKCIP98 2050sMedium-high scenario

[source: David Rogers, Oxford]

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4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies

Review of climate scenario approaches: Analogue scenarios

Palmer Drought Severity Index (PSDI) for the US Corn Belt, 1030~80.

[Source: Roserzweig et al., 1993]

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4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies

Review of climate scenario approaches: Analogue scenarios

• Valuable in testing and validating impact

models;

• But, it is not usually recommended that

they be adopted to represent the future

climate in quantitative impact

assessments.

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4.3.1 Incremental (arbitrary) Scenarios for Sensitivity studies

Review of climate scenario approaches: Model-based scenarios

• Simple climate models;

• Full General Circulation Models (GCMs);

• Regionalization Models/techniques

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Climate models Simulating the response of the global climate system to increasing greenhouse gas concentrations

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The MAGICC/SCENGEN Climate Scenario Generator

[source: Wigley, Raper, Hulme]

An example of Simple Climate Model

Simple climate model-derived scenarios

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Advantages

Multiple simulations being conducted rapidly;

Enabling an exploration of the climatic effects of alternative scenarios of radiative forcing, climate sensitivity and other parametrization uncertainties.

Disadvantages

Hardly able to represent the non-linearities of some processes that are captured by more complex models.

Simple climate model-derived scenarios

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General Climate Models (GCMs)

Conceptual structure of a coupled atmosphere-ocean circulation model

[Source: Viner and Hulme, 1997]

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Advantages – easily accessible, numerous model runs, global in scale, numerous variables

Disadvantages – coarse resolution (300km), daily extremes poorly represented

General Climate Models (GCMs)

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Regionalization (downscaling) models/techniques

• Statistical Downscaling

o Regression-based methods;

o Synoptic weather typing;

o Stochastic weather generator

• Dynamic downscaling

o Higher resolution GCMs;

o Varying-resolution GCMs;

o Regional climate models (RCMs)

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Statistical Downscaling

Advantages – site or locality specific scenarios, long and multiple daily weather sequences produced

Disadvantages – requires a lot of historic data to calibrate, based on empirical relationships which may change, climate model data not always available

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Dynamic downscaling (RCMs)

Disadvantages – few runs available, can be time-consuming to run, not good for representing uncertainties in risk assessment

Advantages – higher resolution (50km), local geography well represented, daily weather extremes more realistic

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Why do we have such a hard time in using climate model outputs for scenario development?

A typicalyear, 2050s

Ideally, we would like to have …

… daily weather, for a place, for now and for a future year

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Why do we have such a hard time in using climate model outputs for scenario development?

However, in reality, the following problems often prevent us from achieving this in most cases...

• Climate models are NOT accurate;

• Different climate models give different results;

• It is expensive to run many (global/regional) climate model experiments for many future emissions;

• Climate models give us results at the “wrong” spatial scale

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Climate models are NOT accurate…

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Climate models are NOT accurate…

… So we cannot use model outputs directly in I, A &V

assessment. Rather, we derive the climate change

scenarios from climate model simulations and then

combine it with observed data to create climate

scenarios for a future time horizon.

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Different models give different results…

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Different models give different results…

… So we have difficulty knowing which

model(s) to use, and opted for using a range of

model(s).

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Different models give different results…

The range of DJF precipitation changes for 2071~2100 simulated by different GCMs under SRES B2 for the region covering Bhutan (25~30N, 85 ~ 95E)

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Different models give different results…

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It is expensive to run many (global/regional) climate model experiments for many future emissions…

… So we often have to make choices about

which emissions scenarios from which we build

our climate scenarios. And, techniques such as

pattern-scaling have been developed to

expand the range of uncertainties we could

explore in climate scenarios.

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It is expensive to run many (global/regional) climate model experiments for many future emissions…

(http://

www.aiaccpro

ject.org/

resources/

GCM/

ARTICLES/

PATTERN_.PD

F)

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Climate models give us results at the “wrong” scales…

… So we have to develop and apply

one or more downscaling methods.

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Uncertainties associated with model-derived climate scenarios

Scenario development is largely an exercise of handling uncertainty.

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Representing uncertainties associated with model-derived climate scenarios

• Pattern-scaling;

• Defining climate signals;

• Scenario annotation;

• Probabilistic scenarios

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Patten-scaling

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Patten-scaling

Two fundamental assumptions:

• The defined GCM response patterns adequately depict the climate “signal” under anthropogenic forcing;

• These response patterns are representative across a wide range of possible anthropogenic forcings.

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Defining climate signals

If the objective is to specify the impacts of the anthropogenic cliamte signal alone:

• To maximise the signal and minimise the noiseo using long (30-year or more) averaging periods;

o using results from multi-member ensemble simulations; o Comparing the responses of single realisations from experiments

completed using different models.

• To supply impact assessments with cliamte scenarios contraining both elemetns and also companion descriptions of future cliamte that contain only noise, thus allowing impact assessors to generate their own impact signal-to-noise ratios.o defining noise from observed climate data;

o defining noise from model-simulated natural climate variability

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Scenario annotation

To document or explicitly treat the uncertainties in climate scenarios: make a list of caveats, along with some assessment as to their implications for the scenario user.

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Probabilistic scenarios

Cumulative probabilities of temperature and precipitation change for each season(DJF, JJA) for South Asia

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Guidance documents on climate scenario development

1. Guidelines on the use of scenario data for climate impact

and adaptation assessment (http://ipcc-ddc.cru.uea.ac.uk/guidelines/ggm_no1_v1_12-1999.pdf)

2. Guidelines for use of scenario data developed from regional climate model experiments (http://ipcc-ddc.cru.uea.ac.uk/guidelines/dgm_no1_v1_10-2003.pdf)

3. Guidelines for use of scenario data developed from statistical downscaling methods (http://ipcc-ddc.cru.uea.ac.uk/guidelines/dgm_no2_v1_09_2004.pdf)

4. Using a climate scenario generator for vulnerability and adaptation assessment (http://ncsp.undp.org/site_documents/magicc_scengen_workbook1.pdf)

5. Using SDSM Version 3.1 — A decision support tool for the assessment of regional climate change impacts (http://www.sdsm.org.uk)

6. Creating high resolution climate scenarios using PRECIS (http://www.undp.org/cc/pdf/publications%20and%20flyers/RCM_draft.pdf)

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Models & tools for climate scenario development

Simple climate models• MAGICC/SCENGEN 4.1 (http://

www.cgd.ucar.edu/cas/wigley/magicc/index.html)

• COSMIC (can be obtained free of charge by registering with Dr. Larry Williams of the Electric Power Research Institute LJWILLIA@epri.com)

Downscaling Tools• Statistical Downscaling Model (SDSM) (

http://www.sdsm.org.uk)

• LARS-WG (http://www.rothamsted.bbsrc.ac.uk/mas-models/larswg.php)

• PRECIS (http://www.metoffice.com/research/hadleycentre/models/PRECIS.html)

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Data sources for climate scenario development

1. Climatic Research Unit (

http://www.cru.uea.ac.uk/cru/data/)

2. IPCC Data Distribution Centre (http://ipcc-ddc.cru.uea.ac.uk)

• Observed climate

• Climate scenarios

• GCM archive

3. Program for Climate Model Diagnosis and Intercomparison (http://www-pcmdi.llnl.gov/)

4. Future climate in world regions – an intercomparison of model-based projections for the new IPCC emissions scenarios (http://ipcc-ddc.cru.uea.ac.uk/sres/scatter_plots/scatterplots_home.html)

5. National Centre for Environment Predictions (NCEP) re-analysis data (http://www.cdc.noaa.gov/)

6. The Canadian Climate Impacts Scenarios Group (http://www.cics.uvic.ca/scenarios/index.cgi?Scenarios)

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Finally, a few points worth remembering… • Before embarking on the “shopping expedition” for models/tools and data, spending time to clearly define what climate scenarios (variables, temporal and spatial scales, time horizon etc.) are NEEDED for your V&A study;

• Pay much attention to the quality of your observed data (for case studies, model evaluation, reference climate to perturb);

• If you can keep things simple, keep them simple;

• Whenever time and resource permit, try to use a range of approaches/methods, and model outputs for scenario development;

• Don’t take on an RCM if you’re not in the ‘game’ already and certainly not unless you have a ‘friend’