Decadal Climate Prediction

Jochem Marotzke

Max Planck Institute for Meteorology (MPI-M)

Centre for Marine and Atmospheric Sciences

Hamburg, Germany

Outline

A curious apparent paradox… Seamless prediction of weather and climate Examples of decadal climate prediction Ocean observations and decadal prediction

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

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

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.”

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)

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…

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)

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

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)

D. M. Smith et al., Science 10 August 2007

Hadley Cntr. prediction, global-mean surface temp.

IFM-GEOMAR & MPI-M decadal prediction

Keenlyside

et al. (Nature 2008)

Decadal-mean global-mean surface temp.

Keenlyside et al. (Nature 2008)

Assimilation

HadISST

Hind- & Forecasts

Free model

MPI-M & UniHH prediction: N-Atl. SST

Annual

Pentadal

Decadal

Pohlmann et al. (2008)

Assimilation

HadISST

Hind- & Forecasts

Free model

MPI-M & UniHH prediction: Global SST

Annual

Pentadal

Decadal

Pohlmann et al. (2008)

MPI-M & UniHH prediction: N-Atl. SST

Pohlmann et al. (2008)

HadISST

Forecasts

Free model

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

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

North Atlantic Meridional Overturning Circulation

Quadfasel (2005)

(a.k.a. Thermohaline Circulation)

Bryden et al. (2005)

ECMWF

MOC at 25N in ocean syntheses (GSOP)

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)

Monitoring the Atlantic MOC at 26.5°N (Marotzke, Cunningham, Bryden, Kanzow, Hirschi, Johns, Baringer, Meinen, Beal)

Monitoring the Atlantic MOC at 26.5°N (Marotzke, Cunningham, Bryden, Kanzow, Hirschi, Johns, Baringer, Meinen, Beal)

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

Modelled vs. observed MOC variability at 26.5N

ObservationsECCO (Ocean Synthesis)ECHAM5/MPI-OM

RMS variability

Correlation

Baehr et al. (2008)

Update – 2.5 years of MOC time series at 26.5 N

Kanzow et al. (2008, in preparation)

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)

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

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

Thank you for your attention