IPCC AR5: RCPs and CMIP5 data:
how to choose scenarios and GCMs for
climate modelling studies
Carol McSweeney, Met Office Hadley Centre
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Aims of this session
• Understand how to select driving GCM data for
RCM simulations to capture/prioritise different
sources of uncertainty
• Understand the difference between the new
‘RCPs’ and the ‘old’ SRES emissions scenarios
• Understand how we can sample the uncertainty
from a large ensemble using a smaller subset
of models.
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GCMs available for downscaling
with PRECIS
ECHAM4
HadAM3P
SRES A2
SRES B2
RCP 4.5
RCP 2.6
RCP 8.5 RCP 6.0
20+
CMIP5
GCMs
SRES A1B
HadCM3Q11
HadCM3Q0
HadCM3Q13
HadCM3Q1 HadCM3Q9HadCM3Q6
HadCM3Q10
HadCM3Q15
HadCM3Q2
HadCM3Q3 HadCM3Q7
HadCM3Q16
HadCM3Q4 HadCM3Q14
HadCM3Q8HadCM3Q12
HadCM3Q5
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Constraints on RCM
simulations
• Boundary data availability
• Computing resources
• Some large regional collaborations can draw on
resources from several large institutions to generate
large
• For most individual institutions, it is not possible to
downscale all the GCMs that are currently (or soon
to be) available, and for all emissions scenarios, so
how can we make the best choices about which
ones to use?
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Table of Contents
• Sources of uncertainty in projections of future
climate
• Emissions scenarios: SRES vs RCPs
• Ensembles: Perturbed Physics and multimodel
• Ensemble Sub-selection for Downscaling
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Main Sources of Uncertainty
Socio- Economic
Uncertainty
Natural annualdecadal
variability
(‘Internal
variability’)
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Uncertainty in
the model
representation
of physical
processes
Sources of Uncertainty in Climate
Projections
uncertaint*
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Q: Which are the most important sources
of uncertainty?
Natural variability most
important on
timescales 0-20 years,
small by 100 years
Emissions
scenario
important on
timescales 40
years +
Model
uncertainty
important at all
timescales
A: That depends on the timescale that we are looking at…
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•
Natural Variability: ‘Noise’ mainly relevant for short lead times (2-20
yrs)
•
Model uncertainty:
•
•
•
•
Variations in global climate sensitivity from model-to-model
More significantly, at regional level, determines characteristic of
changes or the Signal (e.g. will my region get wetter or drier in the
future?)
Very significant at all lead times, particularly for precipitation
Emissions scenario: determines rate at which the changes
indicated by any one model will occur
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Prioritising more models or more
emissions scenarios?
GCM1
A2
A1B
B2
X
X
X
GCM2
X
GCM3
X
GCM4
X
4 different models, 1 emissions
scenario
=> 4 patterns of change, 1 magnitude
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1 model, 3 different
emissions scenarios
=> 1 pattern of
change, 3 different
magnitudes
Emissions Scenarios
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SRES Emissions Scenarios
1. Socioeconomic
scenarios
3. Atmospheric
concentrations
2. Emissions
scenarios
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Sequential approach to developing
climate scenarios
Socioeconomic
scenarios
Emissions
scenarios
Atmospheric
concentration
s
Climate
scenarios
Impacts
• Climate modellers await results from socio-economic
modellers
• Emissions scenarios chosen early on are restrictive.. E.g.
no exploration of deliberate mitigation strategies, difficult
to explore uncertainties in carbon cycle feedbacks.
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Representative Concentration
Pathways (RCPs)
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Parallel approach to generating climate
scenarios
Atmospheric concentrations
(‘Representative Concentration Pathway’, RCPs)
Emissions scenarios
Socio-economics
Policy Intervention
(mitigation or
adaptation)
Integrated
assessment
modellers and
climate modellers
work
simultaneously
and
collaboratively
Carbon cycle and
atmospheric chemistry
Impacts
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Climate
scenarios
Choosing scenarios
• Remember that near term (10-40 years) projections are
‘emissions independent’
• For longer lead times, Emissions scenario is important
for determining rate of change, but does not determine
pattern of change
• RCPs useful for exploring ‘avoidable’ climate impacts
• SRES useful where we want to compare with previous
projections
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Model Uncertainty
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Climate Model Uncertainties
Structural Uncertainty
(IPCC CMIP3/CMIP5 multi-model ensembles)
Parameter
Uncertainty
(QUMP perturbed
physics
ensemble)
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Rainfall change: IPCC AR4
Combination
of pattern
and some
sign
differences
lead to lack
of
consensus
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Perturbed-Physics Ensembles
• An alternative route to
exploring GCM uncertainty
• Many processes in GCMs are
‘parameterised’
• Parameterisations represent
sub-gridscale processes
• Values of parameters are
unobservable and uncertain
• Explore model uncertainty by
varying the values of the
parameters in one model
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The QUMP 17-member
perturbed physics ensemble
Quantifying Uncertainty in Model Projections
• 17 members (HadCM3 Q0..Q16)
• ‘parameter space’ = range/combinations of
plausible values of parameters
• Range of plausible values of parameters
determined according to expert opinion
• 17 models sample ‘parameter space’
systematically
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Rainfall change: Hadley QUMP
• Again
significant
range of
different
projected
changes
• Similar
range and
behaviour
to IPCC
models?
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Multi-model (MME) Vs Perturbed
Physics Ensembles (PPE)
PPE Strengths:
•
•
•
Can control the experimental design – Systematic sampling of
modelling uncertainties
Wider range of physically plausible climate outcomes
Potentially include 1000s of members
•
•
Can design ensemble so that results can be interpreted
probabilistically (e.g. UKCP09)
Logistically, More straightforward logistics for distributing
boundary data due to consistent formats and MOHC ownership
(i.e. feasible to provide boundary conditions from QUMP to
PRECIS users!)
PPE Weaknesses:
• Doesn’t sample structural uncertainty (i.e. between
models from different centres)
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Approaching Model Sub-Selection
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Why sub-select?
• How do we provide ‘scientifically defendable range of
climate outcomes’?
• If we can represent the range of outcomes from the full
ensemble with a subset of 4-6…
• Save on computing resources required to run RCMs
• Save on boundary data required
• More feasible to apply outputs from fewer RCMs to impacts
models
• Why take the time to sub-select carefully?
• Because it is a waste of time to spend months running an
experiment that’s not carefully planned
• So, which ones do we use?
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Case Study #1
• Jack is considering the impacts of climate
change on international terrorism.
• He decides to use the PRECIS regional
modelling system and some members of
the QUMP ensemble to explore model
uncertainty.
Maybe I will choose
members Q1, Q7 and
Q16 to span the range of
global sensitivities?
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Case Study #2
• Beeker is exploring climate impacts on frog and pig
populations. He will apply outputs from regional models
to his species models.
• Beeker wants to use a range of projections to help him
understand the uncertainty in the future projections
climate variables that are predictors in his model.
I will choose the ‘best’ 5 models
according to their validation. I
could use the models with the
lowest RMSE for temperature
and precipitation.
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Case study #3
• Inspector Lewis is considering the impact of climate
change on flood frequency at some popular riverside
crime scenes in Oxford.
• Lewis will use the PRECIS outputs to drive a catchment
model of the Cherwell and estimate peak flows.
I might choose 4 members that
span the widest possible range
of mean precipitation change for
the GCM gridbox that Oxford
lies in.
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Case study #4
• Uncle Bulgaria is interested in the impacts of climate
change on burrow habitats on Wimbledon Common.
• He needs regional climate model data to explore the
possible changes in soil moisture and temperature in the
future.
• Uncle Bulgaria has very limited resources and can only
run one simulation.
Shall I just use Q7 to
give me a mid-range
projection?
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Can we select the models that validate the
best in our region, or eliminate/downweight
those that don’t?
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Why is selecting/eliminating
models on grounds of validation
a can of worms?
• ‘Horses for courses’
• Infinite number of potential metrics
• Circular reasoning – models calibrated
with the same data that they are
evaluated against (i.e. tuning leads to
convergence)
• ‘right for the wrong reasons’
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Some advice from Reto about
combining information from multiple
model projections
1.Metrics and criteria for evaluation must be
demonstrated to relate to projection
2.It may be less controversial to downweight or
eliminate specific projections that are clearly
unable to mimic important processes than to
agree on the best model.
3. Process understanding must complement
‘broad brush metrics’.
For more see Knutti, 2010, Climatic Change 102.
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Basis for selection:
1.) Exclude models which we have good reason to think
give unrealistic projections of the future
•
•
Avoid cherry picking best models (might be right for wrong
reasons)
But we should reject models that really don’t represent the key
large-scale processes
2.) Span the range of future outcomes in the region
•
•
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Span range of magnitudes of change (i.e. global sensitivity, and
regional sensitivity)
Span multiple variables/characteristics of change
• E.g models that are wetter or drier in different regions
• Different spatial patterns of change
• Change in key large-scale processes
Sub-selecting ensemble members for
downscaling with PRECIS – an example
from Vietnam
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Example of Model sub-selection:
Vietnam
Criteria for selection
• Validation
• Selected models should represent Asian summer monsoon
(position, timing, magnitude), and associated rainfall well,
as this is key process
• Future
• Magnitude of response: greatest/least regional/local
warming, greatest/least magnitude of change in
precipitation
• Characteristics of response
• Direction of change in wet-season precipitation
(increases and decreases)
• Spatial patterns of precipitation response over southeast Asia
• Response of the monsoon circulation
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Validation:
Monsoon Onset
•
Monsoon flow has some
systematic error – a little too
high, but timing (and position) of
features is very good.
•
All do a reasonably good job at
simulating rainfall in the region
•
Those that best represent the
characteristics of the monsoonal
flow don’t necessarily also best
represent the local rainfall…
•
No reason to eliminate any
models on grounds of
validation
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Range of Future changes
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Spatial patterns of future
changes (precip)
←Typical
Atypical →
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Recommended QUMP members for this
region
•
HadCM3Q0
– The standard model
•
•
HadCM3Q3
HadCM3Q13
– A model with low sensitivity (smaller temperature changes)
– A model with high sensitivity (larger temperature changes)
•
•
HadCM3Q10
HadCM3Q11
– A model that gives the driest projections
– A model that gives the wettest projections
•
Including Q10 and Q13 means that we also cover models which characterise the
different spatial patterns of rainfall change, and different monsoon responses.
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Summary
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Summary
•
Increasing numbers of available GCMs, as well as new emissions
scenarios, make decisions about which GCMs runs to downscale more
complicated
•
Need to balance requirement to sample a wide range of uncertainty
with minimising the number of necessary RCM runs
•
Experimental design (or choice of models/scenarios) is important – ‘adhoc’ selections may leave us with results that are very difficult to
interpret
•
GCM uncertainty is the most important source of uncertainty at the
regional level due to differences between models is patterns of rainfall
change
•
We can generate downscaled projections that represent the range of
uncertainty in a large ensemble by carefully sub-selecting models for
downscaling
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Questions and answers
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Ensemble regional prediction:
Issues and approaches
• The major uncertainties in the simulated broad-scale climate
changes come from GCMs
• Global climate models provide information which is often too coarse
for applications thus downscaling is required
• To provide the best possible information currently available we can:
• Consider a range of simulated climate changes from the global climate
models
• Downscale these to provide information relevant to applications which
accounts for this range of possible future climate changes
• Also provides a set of internally-consistent inputs for impacts models
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