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)
Climate model Had CM2 2050
Temperature change
Precipitation change
(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/catatmos.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 highresolution 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 environments
c) Extreme events
d) Exotic predictands
e) Transient change/ensembles Is not appropriate for data-poor regions
f) 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 than
the 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
RCM
GCM
Temperature
Precipitation
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
1C 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
• Peru
• Barbados
• Phillipines
• China
• Samoa
• Cook Islands
• Solomon Islands
• Fiji
• Tonga
• Indonesia
• Trinidad and Tobago
• Maldives
• Tuvalu
• Marshall Islands
• Vanuatu
• Mongolia
• Vietnam
• Nauru
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.
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 tools
b) Method:
1. Define your region of interest
2. Choose your climate parameter
3. Choose tool or tools or models outputs
4. Assess anomalies for current climate conditions
5. Consider emission scenarios or concentration pathways
6. Develop future climate scenarios, projections at different time
horizon (2030, 2050 and 2100)
7. Examine issues of:
1) GCM, RCM or statistical methods
2) Stationnarity
3) 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?