CGE TRAINING MATERIALS - VULNERABILITY AND ADAPTATION ASSESSMENT

CHAPTER 8

Climate Change Scenarios

Objectives and expectations

• Having read this presentation, in conjunction with the related handbook, the reader should:

a) Be familiar with key terms, concepts and an overview of climate change scenarios;

b) Have a general understanding on the approaches for construction climate scenarios for impact assessment

c) Be familiar with the concept of General Circulation Models (GCM) and Regional Climate Models (RCMs) and their advantages and limitations;

d) Have a general understanding of available methods, tools and data sources necessary for generating climate scenarios.

Outline

• What are climate change scenarios?

• Why we use scenarios?

• Climate change overview

• Approach to scenario development

• Methods, tools and data sources

• Future directions in scenario development

Why Use Climate Change Scenarios?

• We are unsure exactly how regional climate will change

• Scenarios are plausible combinations of variables, consistent with what we know about human-induced climate change

• Think of them as the prediction of a model, contingent upon the greenhouse gas (GHG) emissions scenarios

• Estimates of regional change by models differ substantially, consequently, individual model estimates should be treated more as a scenario

• Scenarios help us to understand climate change impacts and determine key vulnerabilities

• They can also be used to evaluate and identify adaptation strategies

Brief Primer on Regional Climate Change

• Temperatures over most land areas are likely to rise:

a) Other factors, e.g., land-use change, may also be important

b) Warmer temperatures mean increases in heat waves and evaporation

• Global-mean sea level rise: 0.18 to 0.59m by 2100 is based on IPCC 2007 findings:

a) Modified by local subsidence/uplift

• Precipitation will change (increase) globally:

a) Local changes uncertain: critical uncertainty

b) Increase in storm intensity in some regions.

Evolution of climate models

GCM grid

Projected Global Surface Warming

Multi-model global averages of surface warming relative to 1980-1999

(Source: IPCC, 2007 WG I)

Climate and IPCC Socio-economic Scenarios from Special Report on Emission Scenarios (SRES)

Precipitation change

Temperature changeClimate model Had CM2 2050

(Source: Nakicenovic and Swart, 2000.)

Projected Patterns of Precipitation Changes

Relative changes in precipitation for the period 2090-2099, relative to 1980-1999 based on multi-model averages for A1B SRES scenario

(Source: IPCC, 2007 WG I)

What are Climate change scenarios?

• Climate change scenarios are tools to:

a) Help envision how regional climates may change with increased greenhouse gas (GHG) concentrations

b) To understand and evaluate how sensitive systems may be affected by human-induced climate change in the hope for policy-relevant information about expected changes and guidance for appropriate mitigation and adaptation measures

• It is critical to keep in mind that climate change scenarios are not a prediction nor forecast of future climate change.

• The use of regional climate change scenarios in a V&A assessment means:

a) They must provide information on the climate variables needed for V&A at a spatial and temporal scale needed for analysis will require daily or even sub-daily spatial data

What Are Reasonable Scenarios?

• Scenarios should be:

a) Consistent with our understanding of the anthropogenic effects on climate

b) Internally consistent:

• e.g. Clouds, temperature, precipitation

• Scenarios are a communication tool about what is known and not known about climate change:

a) They should reflect plausible range for key variables.

Approach to Climate Change Scenario Development

1. Evaluate and determine the needs for climate scenario development

2. Specify the baseline climate

3. Develop climate change scenarios:

• Arbitrary scenarios, analogue scenarios, GCMs, regional climate models (RCMs), downscaling techniques etc.

• There is a range of existing guides to support scenario development process, which are available through UNDP/GEF:

Lu (2007) http://www.undp.org/environment/docs/lecrds/applying_climate_information.pdf

Puma & Gold (2011) http://content.undp.org/go/cms-service/download/publication/?version=live&id=3259633

Approach to Climate Change Scenario Development

A range of existing guides to support scenario development process available through UNDP/GEF

Evaluation and Determination of Needs for Climate-Scenario Development

• 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

• 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 national communication.

Lu, 2006

Evaluation and Determination of Needs for Climate Scenario Development

(Source: Puma and Gold, 2011 adapted from Lu, 2006)

Identification of User Needs Using the UNDP Framework)

• The Framework provides the following:

a) Helps decision makers identify their constraints (e.g. financial, computing, workforce, scientific, etc.) and understand the interplay among them, to better approach climate-scenario development, in particular with respect to resource allocation.

b) 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

c) A platform that fosters clear and frequent dialogue between team members:

a) Scientific experts in charge of scenario development, are not fully aware of the managers needs and the non-scientific aspects of projects.

Specification of the Baseline Climate

• Baseline climate is important to identify key characteristics of the current climate regime.

• 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)

• There are several questions that need to be answered to define the baseline climate:

a) Which climate scenarios data are needed? Scale variability

b) Which baseline period should be selected? WMO 30-year

c) What data sources are available?

Identification of Key data Sources

• Baseline climate is important to identify key characteristics of the current climate regime

• Key data sources available to define baseline climate include:

a) National meteorological agencies archives

b) Weather generators

c) Climate model outputs

d) Reanalysis data

e) Outputs from GCM control simulations.

Some Climate Data Sources

• IPCC data distribution center http://www.ipcc-data.org/

• International Research Institute for Climate Prediction (IRI) http://iridl.ldeo.columbia.edu/docfind/databrief/cat-atmos.html

• Tyndall Centre for Climate Change Research http://www.cru.uea.ac.uk/cru/data/tmc.htm

• US National Oceanic and Atmospheric Administration (NOAA) http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

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

IPCC Data Distribution Centre

• The IPCC Data Distribution Centre is probably the best site for public-access climate model data

• Observed climate data 1901-1990

a) Gridded to 0.5 x 0.5°

b) 10 and 30 year means

(Source:http://ipcc-ddc.cru.uea.ac.uk/http://sedac.ciesin.columbia.edu/ddc/)

IPCC Data Distribution Center (continued)

• GCM data from

a) CCC (Canada)

b) CSIRO (Australia)

c) ECHAM5 (Germany)

d) GFDL-R30 (U.S.)

e) HadCM3 (UK)

f) NIES (Japan)

g) IPSL (France)

• Can obtain actual (not scaled) GCM output

IPCC Data Distribution Centre (continued)

• Contains monthly-mean data from GCMs on:

a) Mean temperature (°C)

b) Maximum temperature (°C)

c) Minimum temperature (°C)

d) Precipitation (mm/day)

e) Vapour pressure (hPa)

f) Cloud cover (%)

g) Wind speed (m/s)

h) Soil moisture.

Observational Record

• National meteorological offices

• CMORPH (precip, satellite)

• Climate research Unit (CRU) of the University of East Anglia

• Réanalysis (ERA INT, NCEP)

• GPCC (gauge data)

• ISCCP (cloudiness, satellite)

• TRMM (precip, satellite)

• GPCP (precip, satellite and gauge)

Development of Climate Change Scenarios

• Climate change scenarios need to be at a scale necessary for analysis:

a) Spatial:

• e.g. to watershed or farm level

b) Temporal:

• Monthly

• Daily

• Sub-daily.

Options for Climate Change Scenario Development

• Past climates: analogues

• Spatial analogues

• Arbitrary changes; incremental

• Climate models.

Past Climates

• Options:

a) Instrumental record

b) Paleoclimate reconstructions.

• Instrumental record:

a) Pros:

• Can provide daily data

• Includes past extreme events

b) Cons:

• Range of change in past climate is limited

• Data can be limited.

Past Climates (continued)

• Paleoclimate reconstructions:

a) From tree rings, boreholes, ice cores, etc.

b) Can give annual, sometimes seasonal, climate

c) Can go back hundreds of years

• Pro:

a) Wider range of climates

• Cons:

a) Incomplete data

b) Uncertainties about values.

Past Climates: Reconstruction of N. Hemisphere Temperatures

(Source: Mann et al., 1998 )

Spatial Analogues

Source: NAST, 2000.

Spatial Analogues (continued)

• Pro:

a) Communication tool: perhaps easier to understand

• Con:

a) A model result must be used to choose the spatial analogue region

b) Does not capture changes in variability.

Arbitrary/Incremental Scenarios

• Assumes uniform annual or seasonal changes across a region for example:

a) +2°C or +4°C for temperature

b) +/-10% or 20% change in precipitation

• Can also make assumptions about changes in variability and extremes.

Arbitrary/Incremental Scenarios (continued)

• Pros:

a) Easy to use

b) Can simulate a wide range of conditions

• Cons:

a) It assumes a uniform change over the year or across a region and may fail to capture important seasonal or spatial details

b) The combinations of changes in climate for different variables can be physically implausible.

Climate Models

• Models are mathematical representations of the climate system.

• A model that incorporates the principles of physics, chemistry and biology into a mathematical model of climate, e.g. GCM or RCM (limited area model).

• Such a model has to answer what happens to temperature, precipitation, humidity, wind speed and direction, clouds, ice and other variables all around the globe over time

• They can be run with different forcings, e.g., higher GHG concentrations.

• Models are the only way to capture the complexities of increased GHG concentrations.

General Circulation Models

• Pros

a) Can represent the spatial details of future climate conditions for all variables

b) Can maintain internal consistency

• Cons

a) Relatively low spatial resolution

b) May not accurately represent climate parameters

Example Data from DDC – Temperature

Example of GCM Output

Example Data – Precipitation

Program For Climate Model Diagnosis and Intercomparison (PCMDI) Has GCM Output

Downscaling from GCMs

• Downscaling is a way to obtain higher spatial resolution output based on GCMs

• Options include:

a) Combine low-resolution monthly GCM output with high-resolution observations

b) Use statistical downscaling:

• Easier to apply

• Assumes fixed relationships across spatial scales

c) Use regional climate models (RCMs):

• High resolution

• Capture more complexity

• Limited applications

• Computationally very demanding.

Dynamical downscaling…From GCM to RCM

GCM are lateral boundary conditions of RCMs or RCMs nudged in GCMs.With GCM acting as boundary conditions for an RCM, is it possible to represent regional climate with good accuracy?

Combine Monthly GCM Output with Observations

• An approach that has been used in many studies.

• Typically, one adds the (low resolution) average monthly change from a GCM to an observed (high resolution) present-day “baseline” climate:

a) 30 year averages should be used, if possible e.g. 1961-1990 or 1971-2000:

• Make sure the baseline from the GCM (i.e., the period from which changes are measured) is consistent with the choice of observational baseline.

• This method can provide daily data at the resolution of weather observation stations

• Assumes uniform changes within a GCM grid box and over a month:

a) No spatial or daily/weekly variability.

How Many GCM Grid Boxes Should Be Used?

• Using the single grid box that includes the area being examined would be ideal, however:

a) There can be model noise at the scale of single grid boxes

b) Many scientists do not think single box results are reliable.

• Hewitson (2003) recommends using 9 grid boxes: the grid being examined plus the 8 surrounding grid boxes.

• Need to consider the total area covered by all those grid boxes. Does it include topography or climates not similar to the area being studied?

• Do not use an isolated single location: it is better to do the analysis with group of stations; set of regional small scale indices.

• Find out whether the output is not a singular case or influenced by small size or the data sample?

• Or is it physically plausible and significant, meaning that you can reasonably develop predictions or real time applications?

Statistical Downscaling

• Statistical downscaling is a mathematical procedure that relates changes at the large spatial scale that GCMs simulate to a much finer scale:

a) For example, a statistical relationship can be created between variables simulated by GCMs such as air, sea surface temperature, and precipitation at the GCM scale (predictors) with temperature and precipitation at a particular location (predictands).

• There is a direct statistical relationship with sea-surface temperature (SST) indices (or other physically established predictor indices)

• Statistical downscaling from numerical model output is widely used in climate change downscaling from daily GCM fields “perfect prognosis” assumption.

Downscaling principles

• Downscaling as the process of making the link between the state of some variable representing a large space (or “large scale”) and the state of some variable representing a much smaller space (or “small scale”) (Benestad, 2002)

• There are two main approaches to downscaling: dynamical and empirical–statistical

a) Dynamical downscaling makes use of limited area models (RCMs) with progressively higher spatial resolution than the global climate model (GCM).

b) Statistical downscaling is an extraction of information about statistical relationships between the large-scale climate and the local climate.

Downscaling Methods

• Do not use isolated single location.

• Find out whether the output is a singular case or influenced by small size or the data sample?

• Or is it physically plausible and significant, meaning that you can reasonably develop predictions or real time applications?

• It is better to do the analysis with:

a) A group of stations

b) Or a set of regional small scale indices

c) Or time series of a grid at high resolution.

Statistical Downscaling (continued)

• Is most appropriate for:a) Subgrid scales (small islands, point processes, etc.)b) Complex/heterogeneous environmentsc) Extreme eventsd) Exotic predictandse) Transient change/ensembles Is not appropriate for data-poor regionsf) Where relationships between predictors and predictands may change

• Statistical downscaling is much easier to apply than regional climate modelling.• In climate change studies, one important question is what implications

a global warming has for the local climate. The local climate can be regarded as the result of a combination of the local geography (physiography) and the large-scale climate (circulation)

local climate, y = f(X, l, G)where X = Regional climate;

l = local geography; G = Global climate

Statistical Downscaling (continued)

• Statistical downscaling assumes that the relationship between the predictors and the predictands remains the same.

• Those relationships could change.

• In such cases, using regional climate models may be more appropriate

• Four necessary conditions must be fulfilled in Empirical-Statistical Downscaling (ESD):

a) Strong relationship

b) Model representation

c) Description of change

d) Stationarity.

Spatial structure of precipitation from radar reflection and a typical size of an RCM grid box, showing spatial variations at scales smaller thanthe model’s spatial resolution.

Statistical Downscaling Model (SDSM)

• Currently, this is only feasible based on outputs from a few GCMs.

Global Data to Use in Downscaling with SDSM

• Canadian website with Global Data:

a) Go to scenarios, then SDSM

b) Get output for individual grid.

Regional Climate Models (RCMs)

• These are high resolution models that are “nested” within GCMs:

a) A common grid resolution is 50 km:

• Some are higher resolution

b) RCMs are run with boundary conditions from GCMs

• They give much higher resolution output than GCMs

a) Hence, much greater sensitivity to smaller scale factors such as mountains, lakes

b) Good to investigate higher order climate variability.

RCM Limitations

• Can correct for some, but not all, errors in GCMs

• Typically applied to one GCM or only a few GCMs

• In many applications, just run for a simulated decade, e.g., 2040s

• Still need to parameterize many processes

• May need further downscaling for some applications

• Needs diagnostics based on known weather and climate features

• RCM evaluation limited by (observations) data availability.

GCM vs. RCM Resolution

Temperature Precipitation

GC

MR

CM

Extreme Precipitation (JunJulAug)

Observation RCM

GCM

Extremes

• Intensity of storms sensitive to model resolution

• Higher resolution improves intensity of precipitation

• Higher resolution improves intensity location.

By Now You May Be Confused…

• So many choices, what to do?

• First, let’s remember the basics:

a) Scenarios are essentially educational tools to help:

• See ranges of potential climate change

• Provide tools for better understanding the sensitivities of affected systems.

• So, we need to select scenarios that enable us to meet these goals.

Selected Methods and Tools

• MAGICC/SCENGEN

• PRECIS

• SDSM (Statistical Downscaling Model)

• ASD (Automated Statistical Downscaling)

• ClimateWizard

• Clim.pact (R package)

• http://www.cru.uea.ac.uk/projects/ensembles/ScenariosPortal/Downscaling2.htm

• Climate Explorer

• SimCLIM

Tools for Assessing Regional Model Output

• It is useful first to compare results from a number of GCMs that might be used to drive an RCM

• Normalized GCM results allow comparison of the relative regional changes

• Can analyse the degree to which models agree about change in direction and relative magnitude:

a) A measure of GCM uncertainty.

Tools for Assessing Regional Model Output (continued)

• Agreement between GCMs does not necessarily mean that they are all correct – they may all be repeating the same mistakes.

• Still, GCMs are the primary tool for estimating the range of future possibilities.

Normalizing GCM Output

• Expresses regional change relative to an increase of 1C in mean global temperature (GMT):

a) This is a way to avoid high-sensitivity models dominating results

b) It allows us to compare GCM output based on relative regional change.

• Normalized temperature change = ΔTRGCM/ΔTGMTGCM

• Normalized precipitation change = ΔPRGCM/ΔTGMTGCM

Pattern Scaling

• Is a technique for estimating change in regional climate using normalized patterns of change and changes in GMT.

• Pattern scaled temperature change:

a) ΔTRΔGMT = (ΔTRGCM/ΔTGMTGCM) x ΔGMT

• Pattern scaled precipitation:

a) ΔPRΔGMT = (ΔPRGCM/ΔTGMTGCM) x ΔGMT

Tools to Survey GCM Results

• Finnish report: “Future climate . . .”

• MAGICC/SCENGEN

Finnish Publication

• Shows regional output on temperature and precipitation for a number of models:

a) For three time slices over 21st century

b) Uses some scaling

• Useful as a look-up to see degree of model agreement or disagreement.

• MAGICC/SCENGEN and COSMIC provide more flexibility to users.

Finnish Environment Example

Source: Ruosteenoja et al., 2003, p. 55.

MAGICC/SCENGEN

• MAGICC is a simple model of global T and sea level rise (SLR)

• Used in IPCC TAR

• SCENGEN uses pattern scaling for 17 GCMs

• Yield:

a) Model by model changes

b) Mean change

c) Intermodel standard deviation

d) Interannual variability changes

e) Current and future climate on 5 x 5 grid

(Source:http://www.cgd.ucar.edu/cas/wigley/magicc/)

Using MAGICC/SCENGEN

MAGICC: Selecting Scenarios

MAGICC: Selecting Scenarios (continued)

MAGICC: Selecting Forcings

MAGICC: Displaying Results

MAGICC: Displaying Results (continued)

Running SCENGEN

Running SCENGEN (continued)

SCENGEN: Analysis

SCENGEN: Model Selection

SCENGEN: Area of Analysis

SCENGEN: Select Variable

SCENGEN: Scenario

SCENGEN: Map Results

SCENGEN: Quantitative Results

INTER-MOD S.D. : AREA AVERAGE = 5.186 % (FOR NORMALIZED GHG DATA) INTER-MOD SNR : AREA AVERAGE = -.067 (FOR NORMALIZED GHG DATA) PROB OF INCREASE : AREA AVERAGE = .473 (FOR NORMALIZED GHG DATA) GHG ONLY : AREA AVERAGE = -.411 % (FOR SCALED DATA) AEROSOL ONLY : AREA AVERAGE = -.277 % (FOR SCALED DATA) GHG AND AEROSOL : AREA AVERAGE = -.687 % (FOR SCALED DATA)   *** SCALED AREA AVERAGE RESULTS FOR INDIVIDUAL MODELS *** (AEROSOLS INCLUDED)  MODEL = BMRCD2 : AREA AVE = 2.404 (%) MODEL = CCC1D2 : AREA AVE = -5.384 (%) MODEL = CCSRD2 : AREA AVE = 6.250 (%) MODEL = CERFD2 : AREA AVE = -2.094 (%) MODEL = CSI2D2 : AREA AVE = 6.058 (%) MODEL = CSM_D2 : AREA AVE = 1.245 (%) MODEL = ECH3D2 : AREA AVE = .151 (%) MODEL = ECH4D2 : AREA AVE = -1.133 (%) MODEL = GFDLD2 : AREA AVE = 1.298 (%) MODEL = GISSD2 : AREA AVE = -3.874 (%) MODEL = HAD2D2 : AREA AVE = -5.442 (%) MODEL = HAD3D2 : AREA AVE = -.459 (%) MODEL = IAP_D2 : AREA AVE = -.088 (%) MODEL = LMD_D2 : AREA AVE = -6.548 (%) MODEL = MRI_D2 : AREA AVE = .065 (%) MODEL = PCM_D2 : AREA AVE = -3.451 (%) MODEL = MODBAR : AREA AVE = -.687 (%)

SCENGEN: Global Analysis

SCENGEN: Error Analysis

SCENGEN Error Analysis (continued)

UNWEIGHTED STATISTICS MODEL CORREL RMSE MEAN DIFF NUM PTS mm/day mm/day BMRCTR .632 1.312 1.026 20 CCC1TR .572 1.160 -.207 20 CCSRTR .587 .989 .322 20 CERFTR .634 1.421 -1.167 20 CSI2TR .553 1.112 -.306 20 CSM_TR .801 1.044 -.785 20 ECH3TR .174 1.501 -.649 20 ECH4TR .767 1.121 -.881 20 GFDLTR .719 .954 -.553 20 GISSTR .688 .799 .123 20 HAD2TR .920 .743 -.598 20 HAD3TR .923 .974 -.883 20 IAP_TR .599 1.408 -.734 20 LMD_TR .432 2.977 -2.103 20 MRI_TR .216 2.895 -2.026 20 PCM_TR .740 1.372 -1.041 20 MODBAR .813 .879 -.654 20

PRECIS (Providing Regional Climates for Impacts Studies)

• Developed by the UK Met Office Hadley Centre

• Designed to run on PC with Linux

• Provides high resolution regional climate projections of all key climate variables with hourly, daily, monthly and yearly means

• Significant computational requirements.

• A PRECIS experiment can take several months to run on a standard level PC

PRECIS Minimum hardware requirements

• PC running Linux operating system

• Memory: 512MB minimum; 1GB+recommended

• Minimum 60GB disk space + offline storage for archiving data

• Simulation speed proportional to CPU speed

How fast does it go?

30 year integration, 100x100 grid points

• NEC (supercomputer processors): 5 days (1 node with 8 processors)

• PC (Intel Pentium 4, 3.2GHz processor): 2.5 months (1 processor)

PRECIS: Outputs

• RCMs can provide:

a) Climate scenarios for any region

b) An estimate of uncertainty due to different emissions

c) An estimate of uncertainty due to different GCMs

d) An estimate of uncertainty due to climate variability.

• Data available from RCMs:

a) Comprehensive for atmosphere and land-surface

b) Grid-scale box average quantities

c) Maximum time resolution one hour.

PRECIS: Outputs

GCM RCM: PRECIS

PRECIS: Support

• Detailed climate scenarios using the UKCIP02 methodology for the main developing country regions

• Detailed simulation for the recent climate (past 50 years) for many developing country regions

• Basic capacity-building and technology transfer enabling mitigation and adaptation activities via:

a) Scientific and technical support for applying PRECIS to scenario development and climate research

b) Ad hoc advice on using scenarios in impacts assessment, developing collaborations and research proposals.

PRECIS: Selected Applications

• PRECIS-Caribbean initiative

• Vulnerability and Adaptation in Cuba

• IIT India

• University of Cape Town, South Africa

• Uganda

• Study of Climate Change Impact in China

• Impacts, Vulnerability and adaptation to climate change in Latin America

• Climate change Assessment for Esmeraldas, Ecuador

• Georgia Second National Communication

• Climate Change Assessment for Sorsogon, Philippines

SimCLIM: What is SimCLIM?

• SimCLIM is a research product from New Zealand Climate Change Impacts Studies (CLIMPACTS)

• An integrated computer model for climate change impact assessment

• Built-in customized geographic information system (GIS) to support multi-level spatial analysis

• Based on IPCC guidelines and upgradeable with latest scientific research information.

SimCLIM: Applications

• Describe the baseline climate

• Examine current climate variability and extremes

• Assess risks – present and future

• Investigate adaptation – present and future

• Create climate change scenarios

• Conduct sensitivity analyses

• Examine sectoral impacts

• Examine uncertainties

• Facilitate integrated impacts analyses.

SimClim: Extreme Event Analyser

Using SimCLIM extreme

Event Analyser and daily

time-series data to calculate

change in extreme

temperature. This example

shows a present day return

period of 46.52 years for a

39°C event. Under the SRES

A2 scenario at 2050 the

return period for the same

temperature event becomes

13.24 years, providing an

example of how extremes will

become more frequent under

climate change for this

particular location.

SimCLIM: User Interface

SimCLIM: Where It Has Been Used

• Bahamas

• Barbados

• China

• Cook Islands

• Fiji

• Indonesia

• Maldives

• Marshall Islands

• Mongolia

• Nauru

• Peru

• Phillipines

• Samoa

• Solomon Islands

• Tonga

• Trinidad and Tobago

• Tuvalu

• Vanuatu

• Vietnam

ClimateWizard

• Developed by The Nature Conservancy, University of Washington and University of Southern Mississippi

• Useful screening tool to provide instant national-level snapshot of baseline temperature and precipitation as well as projections for SRES scenarios to 2050 and 2080. GCM downscaled to 50km Grid http://climatewizard.org/

ClimateWizard

How to Select Scenarios

• Use one or several of the methods and tools to assess the range of temperature or precipitation changes.

• Models can be selected based on:

a) How well they simulate current climate

• SCENGEN has a routine

b) How well they representing a broad range of conditions.

1A.96

How to Select Scenarios (continued)

• Use results from actual GCM data or scaled data

• Can include other sources for scenarios, e.g. arbitrary, analogue.

Selecting GCMs

• Some factors to consider in selecting GCMs

a) Age of the model run:

• More recent runs tend to be better, but there are some exceptions

b) Model resolution:

• Higher resolution tends to be better

c) Model accuracy in simulating current climate:

• MAGICC/SCENGEN has a routine.

What to Use under What Conditions?

• There is nothing wrong with using combinations of different sources for creating scenarios, e.g. models and arbitrary scenarios

• The climate models tend to be better for longer run analyses, e.g. beyond several decades (beyond 2050)

• Climate analogues tend to be better for near term, e.g. within several decades (2010-2030).

Scenarios for Extreme Events

• Difficult to obtain from any of these sources.

• Options:

a) Use long historical or paleoclimate records

b) Incrementally change historical extremes:

• Try to be consistent with transient GCMs

c) These methods are primarily useful for sensitivity studies.

Final Thoughts

• Remember that individual scenarios are not predictions of future regional climate change.

• If used properly, they can help us understand and portray:

a) What is known about how regional climates may change

b) Uncertainties about regional climate change

c) The potential consequences.

Uses

• If assessing vulnerability, scenarios ought to reflect a wide, but realistic range of climate change:

a) Serves education purpose

• If examining adaptation, it is important to reflect a wide range of climate change

• If the selected uncertainty range is too narrow, this could lead to ill-informed decisions.

Climate and IPCC (recent and coming activities)

• IPCC(2012) has published also a Special report on Managing

the Risks of Extreme Events and Disasters to Advance Climate

Change Adaptation.

• The IPCC Fifth Assessment Report (AR5) process is now

underway.

• The AR5 will consist of three working group reports and a

synthesis report to be completed in 2013/2014

Climate Change Scenarios Exercise

• Aim:a) To explore different climate change scenarios methods and toolsb) Method:

1. Define your region of interest2. Choose your climate parameter3. Choose tool or tools or models outputs4. Assess anomalies for current climate conditions5. Consider emission scenarios or concentration pathways6. Develop future climate scenarios, projections at different time

horizon (2030, 2050 and 2100)7. Examine issues of:

1) GCM, RCM or statistical methods2) Stationnarity3) Resolution (higher vs coarse)4) Trends and variability.

c) How does one address the main gaps (data, tools, …) in respect to available means and work to undertake when looking at climate change scenarios?