Download - UNDP Climate Change Adaptation

Providing High-Resolution Regional
Climates for Vulnerability Assessment
and Adaptation Planning
Joseph Intsiful, African Adaptation Programme
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Climate scenarios
• Climate scenario
“A scenario is a coherent, internally consistent and
plausible description of a possible future state of the
world. It is not a forecast; rather, each scenario is one
alternative image of how the future can unfold.”
Key point 1: Internal consistency
Socio-Economic scenario → Emissions scenario → Climate scenario
Key point 2: Scenarios are NOT the same as
‘predictions’: we can have many plausible scenarios.
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Types of climate scenarios
• Incremental scenarios for sensitivity studies
• Analogue scenarios
• Scenarios based on outputs from Climate Models
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1. Incremental scenarios
• Particular climatic elements are changed incrementally
by plausible though arbitrary amounts.
• Use for testing system sensitivity
• Use for identifying critical thresholds or discontinuities
in climate
• Potentially leads to unrealistic scenarios
• Not related to anthropogenic emissions
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2. Analogue scenarios
• Identify recorded climate regimes which may resemble the
future climate in a given region.
• Spatial analogues
• Temporal analogues
• Palaeoclimatic
• Instrumental
• Not related to anthropogenic emissions
• Often physically implausible
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3. Scenarios based on outputs from
Climate Models
• Coupled Atmosphere-Ocean Global Climate Models (AOGCMs)
• Coarse resolution, and often have large biases
• Based on physics
• Internally consistent
• Dynamically downscaled AOGCMs
• High resolution GCMS (e.g. PRECIS)
• Require large computer resources
• Can inherit biases from AOGCM
• Statistically downscaled AOGCMs
• Statistical methods are based on current climate and trained on short-term
variability
• Difficult to develop internally consistent climate variables
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Predicting impacts
IPCC
National research
centres
CORDEX
You!
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Downscaling
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… from a global climate model (GCM) grid
to the point of interest.
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Winter precipitation over Britain
300km
Global
Model
25km
Regional
Model
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50km
Regional
Model
Observed
10km
Regional climate models (RCMs)
simulate high resolution weather
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RCMs simulate extreme events
e.g. tropical cyclones
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Represent smaller islands
Projected changes in summer surface air temperature between
present day and the end of the 21st century.
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What is a Regional Climate Model?
• Mathematical model of the atmosphere and land surface (and
sometimes the ocean)
• ‘High’ resolution: Produces data in
grid cells < 50km in size
• Spans a limited area (region) of the globe
• Contains representations of many of the important physical
processes within the climate system
• Cloud
• Radiation
• Rainfall
• Atmospheric aerosols
• Soil hydrology
• etc.
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Boundary conditions
• Limited area regional models require
meteorological information at their edges
(lateral boundaries)
• These data provide the interface between
the regional model’s domain and the rest
of the world
• The climate of a region is always
strongly influenced by the global
situation
• These data are necessarily provided by
global general circulation models (GCMs)
• or from observed datasets with global
coverage
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More adverse than beneficial impacts on
ecological and socioeconomic systems
are projected
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Impacts Assessment
• Evaluation of the detrimental and beneficial
consequences of climate change on natural and
human systems.
• Impacts models require climate scenarios as inputs.
• The impact of the climate change is determined by
contrasting the effect of the observed/baseline
climate with that of the future climate (scenario) on
the exposure unit
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Using climate model
scenarios with impact models
• To assess an impact a baseline needs to be established, i.e. the
impacts model needs to be run with baseline climate information
• This baseline climate information can either be taken from a climate
model simulation of present-day conditions or directly from
observations
• If the baseline climate information is taken from a model simulation of
current climate then the future climate scenario can be derived directly
from a future climate simulation
• If the baseline climate is taken from observations then climate change
scenario information from a climate projection needs to be combined
with this baseline to provide the future climate scenario
• The impacts model is then run either of these future climate scenarios
and the results compared with the impact baseline
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One approach to combining climate
observations and simulations
• If, as a result of systematic biases in the GCM/RCM simulations, the
impact baseline is unrealistic then a simple approach is to apply the
model change factor rather than the model output directly
• Model change factor = Model future – Model baseline: or
• Model change factor =(model future / model baseline) *100
• We can then add the change factor to an observed record to get a
future scenario with the bias seen in the baseline removed
• Future climate scenario = Observed + model change factor: or
• Future climate scenario = Observed * model change factor (%)
• This approach may provide impact results which are more reasonable
but the simple change factor applied does not account for changes in
variability and may result in inconsistent future climates
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Example: Modelling Impacts of
climate change on agriculture
Use of PRECIS and the crop model CERES to simulate yield changes per
hectare of three grain crops (rice, wheat, maize) in China when applying
one future climate scenario and a representation of CO2 fertilization
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Xiong et al, 2007, Climate change and critical thresholds in China’s food
security, Climatic Change, 81:205-221
Example: modelling climate
change impacts on Hydrology
2020s
• Change in water stress in the
Ganges-Brahmaputra-Meghna
Basin derived using the Global
Water Availability Assessment
(GWAVA) model
2050s
(CLASIC project – work with
CEGIS)
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Example: Modelling Storm
Surge under climate scenarios
Simulated tropical cyclone and resulting storm surge.
Produced using PRECIS and POLCOM storm surge model
SLR projections from GCM
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An impacts case-study
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Climate change scenarios from a
recent climate model: estimating
change in runoff in southern Africa
• Nigel Arnell
• (Dept. of Geography, University of Southampton, U.K.)
• Debbie Hudson and Richard Jones
• (Hadley Centre for Climate Prediction and Research)
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Methods
• Runoff: calculated from water balance
• runoff = precipitation – evaporation – absorption by
soil
• Two sets of models - a climate model and a runoff model
• Baseline climate → Run-off model → Baseline Run-off
• Future climate → Run-off model → Future Run-off
• Compares different methods of constructing future
climate scenario
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Mean temperature and rainfall
Average annual rainfall is systematically overestimated by the model
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Rainfall variability is accurately represented
by the model
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Some different methods for CCS
construction
Constructing the baseline and future timeseries of
data required by the runoff model. For example:
BASELINE
FUTURE CLIMATE
SCENARIO
Mean
Variance
Mean
Variance
Observed Observed Observed + Observed
model
difference
Simulated Simulated Simulated Simulated
…these are just some of the possibilities
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The best method CCS
construction in this case?
BASELINE
Mean
Observed
Simulated
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FUTURE CLIMATE
SCENARIO
Variance
Mean
Variance
Observed Observed + Observed
model
difference
Simulated Simulated Simulated
Summary
• There are several techniques for producing future
climate information
• Only climate model based climate change predictions
can be used for providing climate scenarios which are
plausible and self consistent
• Even when using a single climate model (or family of
models) there are many different ways to provide
climate change information for impacts studies
• The method of climate scenario construction adds a
further uncertainty in assessing impacts of climate
change
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Questions and answers
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