Transcript Probabilistic climate prediction/projection from the decadal to the

Probabilistic climate projections
from the decadal to centennial
time scale
WCRP Workshop on Regional
Climate, Lille, June 2010
© UKCIP 2006
James Murphy
Met Office Hadley Centre
© Crown copyright Met Office
Contents
• Sources of uncertainty
• Survey of alternative approaches
• More detail on a methodology developed at MOHC for UK
climate projections and ENSEMBLES
© UKCIP 2006
• Interpretation and limitations
 Internal variability
Precipitation Anomaly (mm)
 Emissions uncertainty
Temperature Change (deg C)
Uncertainty in climate projections
High Emissions
Low Emissions
E&W Precipitation
Source: UKCIP02
Modelling uncertainty

our incomplete understanding of
climate processes and inability to
model them perfectly
Change in summer precipitation (%),
2080-99 relative to 1980-99, SRES A2,
IPCC AR4 models
Multi-model ensembles (MMEs)
Key Strengths
• Each member extensively tested – credibility derived from tuning and validation
against a wide range of observables
• Constructed from a large pool of alternative components – samples different
structural assumptions
• The source of much of our knowledge of projected future changes
Some Limitations
• Not designed to sample modelling uncertainties in a systematic fashion (“ensemble
of opportunity”). No obvious “best” way of determining the distribution of
possible changes of which the MME is a sample.
• Rather small. Difficult to get robust estimates of most likely changes, or associated
uncertainties, in noisy quantities like regional changes in extreme events
Probability distributions of regional temperature
changes from multi-model ensembles
DJF
JJA
2080-99 relative to 1980-99, SRES A1B, Mediterranean Basin, derived from AR4 models.
From Tebaldi and Knutti, 2007
• Substantial assumptions needed to convert the ensemble results into probabilities
• Different methods make different assumptions and get different results.
• e.g. Can errors in each model realisation of future climate be assumed independent, and
randomly distributed about the true, unknown future ?
Errors common to all models
Systematic (black) and random (white) contributions to errors in climate mean
spatial fields of different climate variables in a multimodel ensembles of
atmosphere-mixed layer ocean models.
Collins et al (2010, in press). See also Knutti et al (2010).
© UKCIP 2006
Caveat: Part of the apparent systematic component may actually arise from
observational biases.
Probabilistic projections derived from GCM-RCM
matrix experiments
DJF
JJA
Déqué (2009), Déqué and Somot (2010)
ASK - An alternative approach
• Aim to produce probabilities which are as model-independent as possible, and
determined by uncertainties in observations of historical climate.
• Idea is to develop robust, well-understood transfer functions which link something we
want to predict with some physically related observable.
• Often termed the “ASK” approach – see, e.g., Allen et al (2000), Stott and Kettleborough
(2004), Allen and Ingram (2005), Piani et al (2005), Stott et al (2006), etc..
• The transfer function (“emergent constraint”) needs to be robust across different models.
• May be harder to find robust emergent constraints for regional variables
Observationally-constrained pdfs of the transient
climate response
Stott et al., 2006
Obtained from optimal fingerprint analysis: calculate a distribution of factors by which the
simulated historical warming attributable to greenhouse gases can be scaled up and down
while remaining consistent with observations, and assume that fractional errors in the
historical response remain unchanged in future.
Applying the observational constraint scales up the best-estimate response of the low
sensitivity model (green star), bringing it closer to the other models. The model dependence
is not totally removed, however.
Another alternative approach based on perturbed physics ensembles
• Relatively large ensembles designed to sample modelling uncertainties systematically
within a single model framework
• Executed by perturbing model parameters controlling key model processes, within
expert-specified ranges
• Key strength: Allows greater control over experimental design cf multi-model “ensembles
of opportunity”
• Key limitation: does not sample “structural modelling uncertainties”, e.g. changes in
resolution, or in the fundamental assumptions used in the model’s parameterisation
schemes – need to include results from other models to account for these.
© UKCIP 2006
• Describe an implementation based on the HadCM3 model
Ideal system for probabilistic projections based on perturbed
physics ensembles
Large perturbed physics ensemble sampling uncertainties in
time-evolving 21st century climate change at high spatial
resolution
Probabilistic
projections
Structural
modelling errors
Observational
constraints
Computational resources can’t support this yet, so the method
involves a larger set of affordable steps
© Crown copyright Met Office
Inputs to probabilistic projections for UKCP09
Ocean
Sulphate
Aerosol
Observational
Constraints
Probabilistic
Climate
Projections
Structural
Model Errors
Atmosphere
© UKCIP 2006
Regional
Climate Model
Carbon Cycle
Three stages
• Probabilistic projections of the equilibrium climate change
in response to doubled CO2 at 300km resolution
• Further steps to obtain probabilistic projections of timedependent climate change at 300km resolution
© UKCIP 2006
• Downscaling to obtain projections at 25km resolution
© UKCIP 2006
Simulations of equilibrium climate change
•
Used the atmosphere-mixed layer (“slab”) ocean configuration of the
model, HadSM3
•
Obtained expert-specified prior distributions for multiple (31) uncertain
model parameters controlling surface and atmospheric physical
processes
•
Ran an ensemble of 280 simulations (@300km horizontal resolution)
of both present day climate and the equilibrium response to doubled
CO2
•
Allowed us to sample uncertainties in processes contributing the largest
uncertainties to large-scale-regional climate changes at reasonable
expense.
..gives a large sample of possible changes (e.g. summer UK
rainfall)
Converting ensemble simulations into probabilistic
projections of equilibrium climate change
• Used a general Bayesian framework designed for making future projections of real world systems
using simulations from complex but imperfect models (Goldstein and Rougier, 2004; Rougier, 2007)
• Key ingredients included:
• An emulator, trained on the available ensemble runs and used to estimate values for historical
climate variables and the equilibrium response to doubled CO2 at points in parameter space not
sampled by a GCM simulation
• Discrepancy, an estimate of the additional uncertainties due to structural model errors which cannot
be resolved by varying poorly-constrained model parameters
• A set of observations to use in estimating the relative likelihood that different model variants (i.e.
different points in parameter space) give a true representation of the real climate system.
© UKCIP 2006
• Could then integrate over the model parameter space, weighting projections according to relative
likelihood and accounting for effects of structural errors, to obtain probabilistic projections.
Estimating discrepancy

Discrepancy represents model errors (arising from missing or structurally deficient
representations of processes) which cannot be resolved by varying uncertain
parameters

Estimated by using an international ensemble of 12 alternative slab models (AR4,
CFMIP) as set of proxies for the real system.

For each multimodel ensemble member, find a few points in the HadSM3
parameter space which give the closest historical and climate change simulations
that we can find.

The outstanding mismatches are then estimates of the effects of missing or
structurally deficient representations of processes in HadSM3.

Pool these distances over all 12 multimodel ensemble members to give an
estimated distribution for discrepancy
Global climate sensitivity
Mean impact of discrepancy
© UKCIP 2006
Discrepancy estimates do not account for errors common to all models
Simulations of time-dependent climate change using
HadCM3 coupled atmosphere-ocean ensembles
• Smaller 17 member
ensembles due to resource
limitations
Historical + A1B
forcing
• Uses a subset of the
multiple perturbation
parameter sets used in the
cheaper equilibrium
simulations
• Can then build relationships
between the equilibrium and
transient responses…
• .. and hence produce large
pseudo-ensembles of 21st
century climate realisations
by applying the scaling to
estimates of equilibrium
changes for which we have
no corresponding transient
simulation.
Observations
“Timescaling” approach to emulate large ensembles of
transient climate change projections
Normalized equilibrium response
pattern (emulated)
for a doubling in CO2 conc.
Simple Climate Model
projections for global
surface temp. anomaly
PDFs


+
Equilibrium
feedbacks
(emulated)
Correction pattern representing differences between
slab and dynamic ocean response
Sampling uncertainties in other Earth system
processes
• Further 17 member
perturbed physics
ensembles
sampling
uncertainties due
to:
• Ocean transport
processes, sulphur
cycle processes
and terrestrial
ecosystem
processes in
HadCM3
© UKCIP 2006
Dynamical downscaling to 25km scale
•
Ran an 11-member ensemble of
perturbed physics regional model
variants at 25km resolution.
•
Driven by boundary forcing from the
HadCM3 A1B transient simulations
(1950-2100).
•
Used regression relationships between
the changes simulated by the global
and regional models to convert
estimates of climate change at 300km
global model grid boxes into estimates
for 25km grid boxes, admin and riverbased regions.
Effects of downscaling on future projections
© UKCIP 2006
Winter precipitation changes for the 2080s relative to 1961-90, with
(right) and without (left) the downscaling contribution
UKCP09 probabilistic projections
Three different
emission scenarios
Seven different
timeframes
25km grid, 16
admin regions,
23 river-basins
and 9 marine
regions
UKCP09 provides probabilities which measure how strongly
different outcomes for climate change are supported by
current evidence (models, observations, understanding of
known uncertainties)
10% probability level
Very likely to be
greater than
50% probability
“Central estimate”
90% probability level
Very likely to be less
than
How important are different sources of
uncertainty?
 Varies, but typically no single source dominates.
Uncertainties in winter precipitation changes for the 2080s relative to
1961-90, at a 25km box in SE England
Testing the robustness of the results
Changes for
Wales, 2080s
relative to 196190
• Projections inevitably depend on
expert assumptions and choices
• However, sensitivities to some
key choices can be tested
Comparison of UCKP09 and ASK approaches
Coloured lines show 2.5th,
10th, 50th (thick), 90th and
97.5th percentiles of projected
past and future changes
© UKCIP 2006
Temperature changes for Northern Europe
Probabilistic projections for Europe at 300km scale
Changes in 20 year mean temperature and precip, A1B forcing.
© UKCIP 2006
•
UKCP09 methodology (minus downscaling) applied to
European regions as part of ENSEMBLES
Summary
• A number of methodologies for probabilistic projections have been developed
• The scope (types of uncertainty considered), inputs (model projections,
observations), methodologies and outputs (global, regional, univariate, multivariate,
emissions scenarios, etc) vary substantially
• All results are conditional on the input information and the assumptions made.
• The sensitivity to key assumptions should be clearly stated, and tested as far as
possible.
• Different techniques should be compared.
• Some methods are more comprehensive than others, but they are all expressions of
the spread of future projections conditioned on current models and understanding.
• So, results will change as the models and understanding improves
• Important to communicate this to users