Transcript No Slide Title

Deriving observational
constraints on climate model
predictions
Gabriele Hegerl, GeoSciences, University
of Edinburgh
01-12-2000
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The Problem
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Climate model predictions are uncertain,
and quantifying these uncertainties is
essential for useful predictions
Only observations can really constrain
predictions – so attempts to arrive at
probabilistic predictions make use of
observations in some form
There are a number of ways to do that,
depending on the problem, timescale,
information available, climate variable….
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Prediction uncertainty
Internal variability uncertainty: weather/climate
variability not/not entirely predictable beyond days
=> uncertainty, can be estimated
Forcing uncertainty: Future emissions unknown –
scenarios; volcanoes? Sun?
Model uncertainty: uncertainty due to unknown
physics and unknown parameters in models,
structural errors, missing processes…
3 is mainly what we try to estimate, although some
recent work also tries to predict 1
1.
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Forcing uncertainty, model uncertainty and internal
climate variability also vary with timescale
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From Hawkins and Sutton, 2009 BAMS: fraction of uncertainty
due to climate variability, model uncertainty, forcing
uncertainty and model uncertainty
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How predictons are constrained depends
on timescale
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Nearterm:
initial
conditions
MAY matter
Intermediate
transient
warming
Longterm:
Equilibrium
climate
sensitivity
From IPCC AR4,
CH10 (Meehl et al.)
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Why? A very simple Energy Balance
Model: Held et al., 2010
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cF
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H
cD
dT
CF
  bT  H  G
dt
dTD
CD
H
dt
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Equation for surface ocean with
heat capacity CF
H   (T  TD )
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Total radiative forcing G
Surface Temperature change T
Change in outgoing radiation bT
Exchange of heat into deep ocean
H
Equation for deep ocean with heat
capacity CD
There are two distinct timescales
Fast response: deep ocean has not yet
significantly taken up heat (TD)=0
  C F /(b   )
Response time:
Dominated by transient climate response
cF
H
cD
timescale for the deep ocean is much slower.
Equilibrium climate sensitivity reached
once ocean takes up no more heat
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This works well for the GFDL model, with
transient near term warming almost completely
dominated by the first case
Lets start with the
transient climate change
in the 21rst century
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‘naïve approach’: models that
do well over the 20th century
will do well over the future
But: external forcings driving
this are uncertain…
Greenhouse gas forcing is
quite well known, but sulfate
aerosol forcing, other
anthropogenic forcing (BC
etc) poorly, solar not well
know either
models can agree with data
because they are correct, but
they could also agree because
of cancelling errors
We may also wind up rejecting
models that are correct but
their forcing was
wrong…(CMIP5)
What we need to do is:
Identify what is the response to
individual forcings that influenced 20th
century climate
Project forward the components that
are predictable or projectable (eg
greenhouse gas increases)
Fingerprints can separate the
contribution by different external
drivers because of different physics of
forcing
- Eg: solar warms entire atmosphere
- Aerosols have different spatial pattern
and temporal evolution than
greenhouse gases
- Volcanoes have pronounced shortterm
impact
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El Chichón, 1982
Pinatubo, 1991
Ingredients for detection and
attribution
o
o
o
Observation y
Climate change signals (“fingerprints”) X=(xi),i=1..n
typically from model simulation: one for each
fingerprint, or for a small number of combinations
Noise: data for internal climate variability, usually
from a long model control simulation
y  bX  u   bi xi  u
o
If X contains realizations of climate variability, a
total least square fit can be used (Allen and Stott)
y  b ( X  v)  u
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Observed amplitude estimate
Signal amplitude
bi i1,n  ( X
T
1
 X) X  y
T
scalar product can reduce noise if using inverse
noise covariance X  y  XC 1 y
C   T
Uncertainty in b determined by superimposing samples of
climate variability onto fingerprint (bit more complicated for tls)
Observational uncertainty:
-Use model data only where observations exist – like with like
-Use samples of observations
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Attribution results yields range of scaling factors that
are consistent with observed change
• Scaling factors b
that show which
range of up-or
downscaling of model
response is
consistent with
observations
• Warming due to
greenhouse gases
Greenhouse gases; other
anthropogenic, natural
(solar+volcanic)
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Fig. 9.9c, Hegerl et al., 2007
This translates directly to transient climate response:
• Estimated warming at the time of CO2 doubling in
response to a 1% per year increase in CO2
• Constrained by observed 20th century warming via
the estimated greenhouse gas signal
Fig 9.21 after Stott et al.
Observational
constraints suggest
…
• very likely >1°C
• very unlikely >3.5°C
• supports the overall
assessment that TCR
very unlikely >3°C
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Probabilistic prediction
of global mean change:
one of the pdfs is based
on TCR, others on other
obs. constraints
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Scalability: the
pattern does not
change much with
signal strength
Other approaches: select models based
on observational feedbacks
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Hall et al., 2006: spring
albedo against
temperature change vs
seasonal cycle
Using information from
climate model ‘quality’
ie ability to simulate
mean climate and short
term variations used
particularly for regional
scales – needs to be
demonstrated that it is
relevant for predictions
Use of observations:
compare like with like;
sample as
observations do, not
models do; bring
models to
observations
Can we get closer with initial conditions
for the near term:
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Can climate change be
predicted based on initial
condition just like weather? =>
initial value problem
Initialization is not easy
Large ensemble of such runs
done right now for nearterm
Problem: evidence for useful
predictability beyond a year or
few is weak, particularly for
things that matter (regional
climate)
Smith et al., 2006, science
Top: 1yr, middle 9 yr, bottom ave 1-9 yrs, 595% ranges
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EQUIP: End to End Quantification of Impacts Prediction
Heatwaves:
 Summer
maximum daily
temperature –
predictions
capture not only
trend, but some
of the structure
(is this just plain
lucky?)
Hanlon and Hegerl,
in prep.
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EQUIP lead by
Andy Challinor,
Leeds
Other work: Crop
predictions,
Water deficit,
fisheries
How to
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First: Bias
correct the model
Needs to be done
differently for hot
extremes than
mean
Is there any
added value from
initial
conditions? – not
clear based on
correlation
• Use skill score based on
Murphy 1988
MSSS(Y ,W , X )  1  AY / AW
• Forecast system Y vs W
(eg climatology,
noninitialized)
• MSSS can be
composed into
correlation, conditional
bias and mean bias
• Updated to compare
against NoAssim (no
ICs) rather than
persistence (Goddard et
al., in prep)
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Decadal predictions raise many questions
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Is there skill that is statistically
significant
How can the prediction be quantified –
arrive at uncertainty ranges
How long does skill last
So far: predictability largely for fish…
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Approach for long term: Estimating
climate system properties
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Equilibrium climate sensitivity
First: example single line of evidence
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1. What can we learn about climate
sensitivity from the last millennium?
Decadal NH 30-90N land temperature; Hegerl et al., J Climate, 2007
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CH-blend reconstruction
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Weighted average of decadal records, many
treering data (RSS)
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Calibration: Total least square scales
communality between instrumental and
proxy data to same size
Method tested with climate model data to
assess if uncertainties estimated properly
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Climate forcing over the last millennium
Northern Hemispheric 30-90N
mean radiative forcing
(decadally smoothed) from
Crowley
Uncertainties:
~ 40% in amplitude of volcanic
forcing
Large in amplitude and shape
of solar forcing
And in aerosol forcing
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1. Estimating equilibrium climate
sensitivity
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Simulate observed climate change not with a single
best fit, but a large ensemble of model simulations
with different sensitivities
Determine probability of models in agreement with
data, given: internal variability, uncertainty in data,
uncertainty in model
Miss
uncertainties: too
narrow
Use information
incompletely: too
wide
p
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ECS [K]
Estimating ECS
Run EBM with > 1000 model simulations, varying ECS, effective ocean
diffusivity, and aerosol forcing
Residuals between reconstruction and range of EBM simulations with
different climate sensitivity
Var(Res-resmin ) ~ F(k,l)
(after Forest et al., 2001)
Uncertainties included:
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Calibration uncertainty of
reconstruction
Noise and internal variability
Uncertainty in magnitude of past
solar and volcanic forcing
Uncertainties:
Simple representation of efficacy
Systematic biases in reconstructions
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Estimated PDF for climate sensitivity
Response
small ~
climate
variability
Larger amplitude
Smaller forcing
Nonlinear relationship
sensitivity – volcanic
cooling
Result for different reconstructions,
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13th century to 1850
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Results vary between reconstructions
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2. Multiple lines of evidence
Bayesian update,
using a prior pdf
based on late 20th
century (Frame et
al)
Hegerl et al. 06, nature
Multiple lines of evidence reduce probability of high
sensitivity, and of very small sensitivity
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Estimates from
many different
sources
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Difficult
question:
can these
estimates
strengthen
each other?
How?
Knutti and Hegerl, 2008
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Towards improving use of observations
for constraints on predictions
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Make sure that the model is brought as
closely to the observational product as
possible: synthetic satellite data;
synthetic palaeo data
Use uncertainty estimates; models can
be used to test processing uncertainty
Be wary of spatial and temporal
autococrrelation
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If using observations to provide
constraints on predictions
Relevance of observed evidence for
prediction needs to be established
 Uncertainty in observations needs to be
included in estimate
 Approach will vary with timescale
Many questions remain:
 Decadal predictions with reasonable
uncertainty
 How to predict regional changes?
 How to combine information from different
sources into an overall estimate of
uncertainty of ECS
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Future change uncertainty ranges reflect
uncertainty in transient response
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SPM Fig. 5
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Likely
(>66%)
range
Are based, among
other things, on
observational
constraints
This is a significant
advance