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Decadal Climate Prediction
Jochem Marotzke
Max Planck Institute for Meteorology (MPI-M)
Centre for Marine and Atmospheric Sciences
Hamburg, Germany
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Outline
A curious apparent paradox… Seamless prediction of weather and climate Examples of decadal climate prediction Ocean observations and decadal prediction
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A curious apparent paradox…
We confidently predict weather one week into the future…
We confidently state that by 2100, anthropogenic global warming will be easily recognisable against natural climate variability…(cf., IPCC simulations)
Yet we make no statements about the climate of the year 2015
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Two types of predictions
Edward N. Lorenz (1917–2008)
Predictions of the 1st kind Initial-value problem Weather forecasting Lorenz: Weather forecasting
fundamentally limited to about 2 weeks
Predictions of the 2nd kind Boundary-value problem IPCC climate projections
(century-timescale) No statements about
individual weather events Initial values considered
unimportant; not defined from observed climate state
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Can we merge the two types of prediction?
John von Neumann wrote in 1955: “The approach is to try first short-range forecasts, then long-range forecasts of those properties of the circulation that can perpetuate themselves over arbitrarily long periods of time....and only finally to attempt forecasts for medium-long time periods.”
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Seamless prediction of weather and climate
“It is now possible for WCRP to address the seamless prediction of the climate system from weekly weather to seasonal, interannual, decadal and centennial climate variations and anthropogenic climate change.” (WCRP 2005)
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Seamless prediction of weather and climate
Combination of predictions of first and second kind – start from observed climate state; include change in concentrations of greenhouse gases and aerosols
Already practiced in seasonal climate prediction (El Niño forecasts)
In decadal prediction, anthropogenic climate change and natural variability expected to be equally important
Atmosphere loses its “memory” after two weeks – any predictability beyond two weeks residing in initial values must arise from slow components of climate system – ocean, cryosphere, soil moisture…
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Seamless prediction of weather and climate
Data assimilation & initialisation techniques (developed in weather & seasonal climate prediction) must be applied to ocean, cryosphere, soil moisture
Also “imported” from seasonal climate prediction: building of confidence (“validation”) of prediction system, by hindcast experiments (retroactive predictions using only the information that would have been available at the time the prediction would have been made)
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Policy relevance of decadal climate prediction
“Long-term” planning in industry, business & public sector overwhelmingly occurs on the decadal timescale
Adaptation planning to climate change overwhelmingly occurs on the decadal timescale
Clear that, in addition to the multi-decadal mitigation planning & very-long term perspective, decadal timescale is crucial
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Examples of decadal climate prediction
Differences arise from models used, but mainly (?) from the method by which the ocean component of coupled model is initialised:
1. “Optimal interpolation” (Hadley Centre, European Centre for Medium-Range Weather Forecasts)
2. Forcing of sea surface temperature (SST) in coupled model toward observations (IFM-GEOMAR & MPI-M)
3. Using 4-dimensional ocean synthesis (ECCO) to initialise ocean component (MPI-M & UniHH)
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D. M. Smith et al., Science 10 August 2007
Hadley Cntr. prediction, global-mean surface temp.
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IFM-GEOMAR & MPI-M decadal prediction
Keenlyside
et al. (Nature 2008)
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Decadal-mean global-mean surface temp.
Keenlyside et al. (Nature 2008)
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Assimilation
HadISST
Hind- & Forecasts
Free model
MPI-M & UniHH prediction: N-Atl. SST
Annual
Pentadal
Decadal
Pohlmann et al. (2008)
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Assimilation
HadISST
Hind- & Forecasts
Free model
MPI-M & UniHH prediction: Global SST
Annual
Pentadal
Decadal
Pohlmann et al. (2008)
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MPI-M & UniHH prediction: N-Atl. SST
Pohlmann et al. (2008)
HadISST
Forecasts
Free model
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Organisation of decadal prediction (WCRP)
Decadal prediction is a vibrant effort if one considers the focus on Ocean initialisation Atlantic
We need to develop broader scope concerning Areas other than the Atlantic Roles in initialization of:
Cryosphere Soil moisture Stratosphere
The science of coupled data assimilation & initialisation has not been developed yet
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Ocean observations and decadal prediction
Initialisation of ocean component of coupled models is the most advanced initialisation aspect of decadal prediction
Yet, methodological uncertainties are huge Example: Meridional Overturning Circulation
(MOC) in the Atlantic Take-home message: Comprehensive and
long-term in-situ and remotely-sensed observations are crucial
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North Atlantic Meridional Overturning Circulation
Quadfasel (2005)
(a.k.a. Thermohaline Circulation)
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Bryden et al. (2005)
ECMWF
MOC at 25N in ocean syntheses (GSOP)
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Monitoring the Atlantic MOC at 26.5°N (Marotzke, Cunningham, Bryden, Kanzow, Hirschi, Johns, Baringer, Meinen, Beal)
Data recovery :
April, May, Oct. 2005; March, Mai, Oct., Dec. 2006, March, Oct 2007, March 2008
Church (SCIENCE, 17. August 2007)
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Monitoring the Atlantic MOC at 26.5°N (Marotzke, Cunningham, Bryden, Kanzow, Hirschi, Johns, Baringer, Meinen, Beal)
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Monitoring the Atlantic MOC at 26.5°N (Marotzke, Cunningham, Bryden, Kanzow, Hirschi, Johns, Baringer, Meinen, Beal)
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S. A. Cunningham et al., Science (17 August 2007)
First observed MOC time series, 26.5N Atlantic
MOC
Florida Current
Ekman
Geostro-phic
upper mid-
ocean
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Modelled vs. observed MOC variability at 26.5N
ObservationsECCO (Ocean Synthesis)ECHAM5/MPI-OM
RMS variability
Correlation
Baehr et al. (2008)
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Update – 2.5 years of MOC time series at 26.5 N
Kanzow et al. (2008, in preparation)
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Outlook – MOC monitoring at 26.5N
Dec. 2007: NERC will continue the funding for MOC monitoring until 2014
Transformation into operational array must take place during that period
Data need to enter data assimilation system, to be used in initialising global coupled climate models
Symbiosis of sustained observations and climate prediction (analogy to atmospheric observations and weather prediction)
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Conclusions and outlook (1)
Climate prediction up to a decade in advance is possible, as shown by predictive skill of early, relatively crude efforts
Desirable: multi-year seasonal averages, several years in advance, on regional scale
Sustained (operational-style) observations crucial
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Conclusions and outlook (2)
Large potential for methodological improvement: Initialisation beyond ocean-atmosphere
(cryosphere, soil moisture) Development of coupled data assimilation
(challenge: disparate timescales) Provision of uncertainty estimate by ensemble
prediction – challenge: Construct ensemble spanning range of uncertainty in
initial values; Poorly known which processes dominate error growth
on decadal timescale) Increase in model resolution for regional aspects Vast increase in computer power required
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Thank you for your attention