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INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Global Climate Prediction: Between
Weather Forecasting and Climate-
Change Projections
F. J. Doblas-Reyes
ICREA & IC3, Barcelona, Spain
in collaboration with V. Guémas, J. García-Serrano (IC3), Ch.
Cassou (CERFACS), A. Weisheimer (ECMWF)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Prediction on climate time scales
Progression from initial-value problems with weather
forecasting at one end and multi-decadal to century
projections as a forced boundary condition problem at the
other, with climate prediction (sub-seasonal, seasonal and
decadal) in the middle. Prediction involves initialization
and systematic simultaneous comparison with a reference.
Meehl et al. (2009)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Seamless prediction
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Illustration of the application of seamless climate and
weather information. Example from the IRI-Red Cross
collaboration.
Courtesy IRI
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Sources of climate predictability
• Both internal and external
o
o
o
o
o
o
o
o
o
o
o
ENSO
- large single signal
Other tropical ocean SST
- difficult
Remote tropical atmospheric teleconnections
Climate change
- largest for temperature
Local land surface conditions
- soil moisture, snow
Atmospheric composition
- difficult
Volcanic eruptions
- important for large events
Mid-latitude ocean temperatures
- longer time scales
Remote soil moisture/snow cover
- not well established
Sea-ice anomalies
- at least local effects
Stratospheric influences
- various possibilities
• Unknown or Unexpected
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Methods for climate forecasting
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
• Empirical/statistical forecasting
o Use past observational record and statistical methods
o Works with reality instead of error-prone numerical models
o Limited number of past cases
o A non-stationary climate is problematic
o Can be used as a benchmark
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• GCM forecasts
o Include comprehensive range of sources of predictability
o Predict joint evolution of ocean and atmosphere flow
o Includes a large range of physical processes
o Includes uncertainty sources, important for prob. Forecasts
o Systematic model error is an issue!
NLOA2012: Global Climate Prediction
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Madrid, 5 July 2012
To produce dynamical forecasts
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
• Build a coupled model of the climate system
• Prepare an ensemble of initial conditions to initial
represent uncertainty
o The aim is to start the system close to reality. Accurate SST is
particularly important, plus ocean sub-surface. Usually, worry
about “imbalances” a posteriori.
• Run an ensemble forecast
o Run an ensemble on the e.g. 1st of a given month (the start date)
for several weeks to years.
• Run a set of re-forecasts to deal with systematic error.
• Produce probability forecasts from the ensemble
• Apply calibration and combination if significant
improvement is found
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Ensemble climate forecast systems
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Assume an ensemble forecast system with coupled initialized GCMs
Lead time = 3
May 87
Aug 87
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Nov 87
Feb 88
May 88
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Data assimilation for initial conditions
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
• Example for the ocean.
Equatorial Atlantic: Taux anomalies
ERA15/OPS
• Large uncertainty in
wind products lead to
large uncertainty in
ocean subsurface.
• Possibility to use
additional information
from ocean data
ERA40
Equatorial Atlantic upper heat content anomalies. No assimilation
• Assimilation of ocean
data:
 constrain the ocean
state
Equatorial Atlantic upper heat content anomalies.
Assimilation
 improve the ocean
estimate
 improve the seasonal
forecasts
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Why running several forecasts
A farmer is
planning to
spray a crop
tomorrow
NO
Dry
Consensus
forecast:
dry  YES
Rainy
Should
he/she?
Let p denote
the probability
of rain from
the forecast
?
NO
The farmer loses L if insecticide washes out.
• He/she has fixed (eg staff) costs
• If he/she doesn’t go ahead, there may be a
penalty for late completion of the job.
• By delaying completion of the job, he/she
will miss out on other jobs.
• These cost C
Is Lp>C? If p > C/L don’t spray!
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Weather regimes and climate anomalies
Mean frequencies:
NAO+ : +62%, AR:+16%,
NAO- : -75%, BL:-3%
Observed anomalies for NDJFM 2007-8
Air Temp
Precip
Courtesy Ch. Cassou (CERFACS)
NLOA2012: Global Climate Prediction
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Weather regimes and the MJO
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Interaction between the Madden-Julian oscillation and the
North Atlantic weather regimes
OLR/STRF300
Cassou (2008)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Predicting the NAO and the MJO
NAO ensemble-mean correlation from monthly forecasts
with the Canadian forecast system (using observed SSTs).
Lin et al. (2010)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
ENSO: a seasonal prediction forcing
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Collins et al. (2010)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Extra-tropical links of ENSO
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
ROC area of the (left column) statistically downscaled and (right column)
original DEMETER seasonal predictions for several events where the years
verified are segregated depending on the ENSO phase. Only values
statistically significant with 90% confidence level are shown.
Frías et al. (2010)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Sources of predictability: snow cover
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Correlation of System 3 MAM temperature in 1981-2005 wrt
to GHCN temperature (adapted from Shongwe et al., 2007).
ROC area for anomalies
above the upper quartile
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ROC area for anomalies
below the lower quartile
Madrid, 5 July 2012
How good are the seasonal forecasts?
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Correlation of System 3 seasonal forecasts of temperature
(top) and precipitation (bottom) wrt GHCN and GPCC over
1981-2005. Only values significant with 80% conf. plotted.
JJA
DJF
T2m
T2m
JJA
DJF
Prec
Prec
NLOA2012: Global Climate Prediction
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Decadal predictions
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
EC-Earth 2-5 year near-surface air temperature ensemble-mean
predictions started in November 2011. Anomalies are computed with
respect to 1971-2000.
Initialised
Uninitialised
Difference
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CMIP5 decadal predictions
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
(Top) Near-surface temperature multi-model ensemble-mean correlation
from CMIP5 decadal initialised predictions (1960-2005); (bottom)
correlation difference with the uninitialised predictions of 2-5 year (left)
and 6-9 year (right) wrt ERSST and GHCN.
Init ensemblemean
correlation
Init minus
NoInit
ensemblemean
correlation
difference
Doblas-Reyes et al. (2012)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
But there are systematic errors
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
• Model drift is typically comparable to signal
 Both SST and atmosphere fields
• Forecasts are made relative to past model integrations
 Model climate estimated from e.g. 25 years of forecasts (1981-2005), all
of which use a 11 member ensemble. Thus the climate has 275
members.
 Model climate has both a mean and a distribution, allowing us to estimate
eg tercile boundaries.
 Model climate is a function of start date and forecast lead time.
• Implicit assumption of linearity
 We implicitly assume that a shift in the model forecast relative to the
model climate corresponds to the expected shift in a true forecast relative
to the true climate, despite differences between model and true climate.
 Most of the time, the assumption seems to work pretty well. But not
always.
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Model drift: West African Monsoon
Averaged precipitation over 10ºW-10ºE for the period 1982-2008 for
GPCP (climatology) and ECMWF System 4 (systematic error) with start
dates of November (6-month lead time), February (3) and May (0).
(FEB)
(NOV)
GPCP climatology
(MAY)
ECMWF S4 - GPCP (NOV)
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ECMWF S4 - GPCP (FEB)
ECMWF S4 - GPCP (MAY)
Madrid, 5 July 2012
Multi-model benefits: Reliability
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Reliability for T2m>0, 1-month lead, May start, 1980-2001
System 1
System 2
System 3
System 5
System 6
System 7
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System 4
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Error reduction: stochastic physics
Precipitation bias (DJF, 1-month lead, 1991-2001, CY29R2)
CASBS reduces the tropical and blocking frequency biases
Blocking frequency
control
CASBS
ERA40
Control-GPCP
Stochastic physics-GPCP
Berner et al. (2008)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
Skill improvement: model inadequacy
System with the best skill score for one-month lead seasonal probability
predictions (Brier skill score). The predictions have been formulated with
a multi-model (MME), a system with stochastic physics (SPE) and a
system with parameter perturbations (PPE) over 1991-2005.
Weisheimer et al. (2011)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Increase of model resolution
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
SST (with North Atlantic Current path) and North Atlantic zonal wind bias,
plus winter blocking frequency for HadGEM3 using the (left) standard
(1º) and (right) high-resolution (0.25º) ocean components.
Scaife et al. (2011)
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
Summary
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
● Substantial systematic error, including lack of reliability, is
still a fundamental problem in dynamical forecasting and
forces a posteriori corrections to obtain useful predictions.
Don’t take model probabilities as true probabilities.
• Initial conditions are still a very important issue.
• Estimating robust forecast quality is difficult, but there are
windows of opportunity for reliable skilful predictions, and
there is always the anthropogenic warming.
• There is potential in methods that deal with model
inadequacy (multi-model ensembles, stochastic physics).
• Model development is a must. And many more processes
to be included: sea ice, anthropogenic aerosols, chemistry,
…
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012
INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA
NLOA2012: Global Climate Prediction
Madrid, 5 July 2012