The oceans, uncertainty, and climate prediction

Jochem Marotzke

Max Planck Institute for Meteorology (MPI-M)

Centre for Marine and Atmospheric Sciences

Hamburg, Germany

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Outline

1. Ocean heat capacity and climate sensitivity

2. Extreme climate states

3. Seamless prediction of weather and climate

4. Examples of decadal climate prediction

5. Ocean observations and decadal prediction

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Ocean heat capacity and climate sensitivity

Determining climate sensitivity from the instrumental records of radiative forcing, surface temperature, and ocean heat uptake (Gregory et al. 2002):

Schwartz (2007), concluding that climate sensitivity is small, by estimating very short climate response timescales (plus ocean heat capacity) drew 4 comments in one year

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Time-dependent global energy balance model (1)

C: heat capacity per unit area

T: global mean (surface) temperature in CS: total solar output (aka “solar constant”)

albedo (assumed fixed for simplicity)

A in W/m2, B in W/(m2K): linearised LW parameterisation

Steady state:

F: forcing; : climate sensitivity

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dT SC A BTdt

1 14

ST B A F

1T F B

1dTC F Tdt

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Time-dependent global energy balance model (2)

Now: Add perturbation in forcing, F:

Initial condition: – steady state

: Adjustment timescale, proportional to both heat capacity and climate sensitivity

independent of heat capacity

Short-term evolution independent of climate sensitivity

1dTC F F Tdt

0T F

0 1 2tT t T F e

C

2 0 tT t T F

2 : 0 3F

t T t T F t tC

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Time-dependent global energy balance model (3)

Short-term behaviour equivalent to only considering the first term on the rhs of (1) – change in LW radiation due to changing temperature unimportant in energy balance.

Feedback determining final equilibrium has not yet set in. Possibilities for heat capacity (→ adjustment time for

climate sensitivity of 0.75 K / (W m-2); 3 K for 2x CO2): Mixed layer (50m) → 5 years Thermocline (400m) → 40 years Whole ocean (4000m) → 400 years

Assumption: mixed as a “slab” (will return to that)

0 1 ; : 0 3dT F

C F T t T t T t T tdt C

3 : 0dT t

t C F F T t Tdt

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Time-dependent global energy balance model (4)

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Time-dependent global energy balance model (5)

Estimate effective climate sensitivity from the instrumental record:

Problem: for t<<, we had established earlier that in (1) the two first terms balance – the perturbation forcing goes entirely into storage

In (4), the denominator is the small difference of two large terms, of order t/.

If both effective heat capacity and climate sensitivity are large, there is little hope to obtain the needed accuracy from observations (cf., formal inclusions of infinite heat capacity within error bounds of Gregory et al. 2002)

Doubtful that response to volcanic eruptions gives any constraint on climate sensitivity

0 01 (4)

T T T TdTC Fdt F C dT dt

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Multiple time scales (1)

Problem with reasoning so far: there is clear evidence that ocean heat uptake exhibits multiple timescales; e.g., ECBilt-CLIO (Knutti et al. 2008)

4xCO2 for 5 years

2xCO2 for 200 years

50 years

5 years

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Multiple time scales (2) Consider 3-layer system

Mixed layer (ML, DML ~ 50 m),

Thermocline (TC, DTC ~ 400 m)

Deep ocean (DO, DD ~ 4000 m)

Coupling mixed layer – thermocline: Ekman pumping wE ~ 10-6 ms-1

Effects timescale in ML of ks-1 ~ DML/ wE ~ 1.5 y

Effects timescale in TC of ks-1 DTC/ DML ~ 13 y

Coupling thermocline – deep ocean: Meridional Overturning Circulation (MOC) wD ~ 30 x 106 m3s-1/ 3 x 1014 m2 ~ 10-7 ms-1

Effects timescale in TC of kTC-1 ~ DTC/ wD ~ 130 y

Effects timescale in DO of kTC-1 DD/ DTC ~ 1300 y

Timescale separation both in heat capacities (ML, TC, DO) and in coupling processes (Ekman, MOC in TC)

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Multiple time scales (3) Heat conservation for perturbations from equilibrium:

All initial temperature perturbations are zero. Steady state:

5S SS S TC

S S

dT TFk T T

dt C C

6TC SS S TC TC TC D

TC

dT Dk T T k T T

dt D

7TCDTC TC D

D

DdTk T T

dt D

; S TCS TC D S S TC TC

TC D

D DD D D k k k k

D D

S TC DT T T F

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Multiple time scales (4) Let us consider the first few decades; the deep ocean

has not changed yet; assume we can completely ignore it Only ML-TC interactions

ML responds quickly, by giving off heat to TC (timescale 1.5 y) and perhaps more slowly by LW radiation (timescale 2-7 years, depending on )

Combined timescale shorter than set by LW radiation

One must not interpret timescale as related to .

5S SS S TC

S S

dT TFk T T

dt C C

6TC SS S TC

TC

dT Dk T T

dt D

1S

SS S

C

C k

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Multiple time scales (5) Before TC can respond much, ML has equilibrated to

additional forcing F and heat loss to TC

While TC is still near zero, ML response is considerably smaller than final equilibrium response F

TC will then warm up, on its own timescale ML will warm alongside TC, on the TC timescale Same reasoning holds for interactions across all of ML,

TC, DO. Specifically, ML will warm as deep ocean warms, because reduced vertical T difference implies reduced vertical heat flux more upward LW radiation required for balance, implying ML warming.

5S SS S TC

S S

dT TFk T T

dt C C

1S S TC

SS S

F C k TT

C k

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ECHAM5/MPI-OM, CO2 quadrupling over 2000 y

(Chao Li, PhD thesis in progress, MPI-M)

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Ocean heat capacity and climate sensitivity

Implications for determining climate sensitivity from the instrumental records of radiative forcing, surface temperature, and ocean heat uptake (accepting 3-layer structure): If one wants to argue that the short timescale of the

surface layer is applicable for estimating what is short-term, one must take into account both lengthening of timescale by exchange with thermocline and role of mixed layer-thermocline exchange in heat budget

Alternatively, one needs to argue with thermocline heat capacity, and adjustment timescale is large

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Conceptual issues: extreme climates

PETM: Paleocene-Eocene Thermal Maximum (55 million years before present): Extreme greenhouse effect

Snowball Earth: Fully glaciated state (Neoproterozoic, 700 million years ago)

Either case poses fundamental conceptual questions, but is also an extreme test for model’s capability

Either case has exposed limitations of model formulation and/or implementation (Malte Heinemann, PhD thesis at MPI-M on PETM; Aiko Voigt, PhD thesis at MPI-M on Snowball Earth)

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Paleocene/Eocene simulation

ECHAM5/MPI-OM (Heinemann et al. 2009; PhD thesis in progress, MPI-M)

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Snowball Earth? (ECHAM5/MPI-OM, No Sun)

Marotzke and Botzet (2007)

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Snowball Earth? (ECHAM5/MPI-OM)

TSI = 0.01 %

TSI = 100 %

TSI = 100 %, CO2 = 10 x pre-industrial

TSI = 100 %, CO2 = 100 x pre-industrial

Global sea ice area [1013 m2]

Marotzke and Botzet (2007)

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Snowball Earth? (ECHAM5/MPI-OM)

Voigt and Marotzke (2009; PhD thesis in progress,

MPI-M)

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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

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)

Can the frequent verification of shorter-term forecasts help in diagnosing and eliminating model errors?

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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)

27Smith et al. (2007)

Hadley Centre: Global-mean surface temperature

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IFM-GEOMAR/MPI-M: Correlation of 10-y mean SAT

Keenlyside

et al. (2008)

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IFM-GEOMAR/MPI-M: 10-y mean global mean SAT

Keenlyside et al. (2008)

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MPI-M/UniHH SAT anomaly correlation w/ obs.

20C (“free” coupled model) Hindcasts, year 1

Hindcasts, year 10Hindcasts, year 5

Pohlmann et al. (2009)

Initialisation from GECCO 4D-Var ocean assimilation

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HadISST (Obs.)

Ocean Assimilation

Hindcasts

20C (“Free” coupled model)

MPI-M/UniHH : North Atlantic SST

Annual

Pentadal

Decadal

Pohlmann et al. (2009)

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MPI-M/UniHH : North Atlantic SST

HadISST (Obs.)

Forecasts

Scenario run (“Free” coupled model)

Pohlmann et al. (2009)

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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, November 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)

39 S. A. Cunningham et al., Science (17 August

2007)

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. (2009)

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Statistics

Gulf Stream +31.9 ± 2.8 Sv

MOC +18.8 ± 5.0 Sv

Ekman + 3.3 ± 3.5 Sv

Upper Mid-Ocean-16.3 ± 3.0 Sv

Uncertainty in 2.5 year MOC mean: 1.9 Sv; assuming 18 DOF, 1.5 Sv measurement error

Observed MOC time series, 26.5N Atlantic

Kanzow et al. (2009,

in preparatio

n)

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Observed MOC spectrum at 26.5N Atlantic

Ekman Transport dominates intra-seasonal variability Upper Mid-Ocean and Gulf Stream dominate seasonal

variability

Kanzow et al. (2009,

in preparatio

n)

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Seasonal Cycle

Gulf Stream 1.5 Sv (14 %)

MOC 4.2 Sv (37 %)

Ekman 1.4 Sv (08%)

Upper Mid-Ocean2.1 Sv (26 %)

Observed seasonal MOC variability

MOC seasonal cycle emerging, but not significant, yet (at 5 % error probability)

Kanzow et al. (2009,

in preparatio

n)

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Decomposition of mid-ocean transport

Western vs. eastern boundary contributions to mid-ocean transport (assuming time-invariant Gulf Stream and Ekman transports)

Both western and eastern boundaries contribute O(±2 Sv)

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Seasonal cycle of mid-ocean transport

Pronounced seasonality from eastern boundary (Maria Paz Chidichimo, PhD thesis MPI-M)

Seasonal cycle less well established at western boundary

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Very likely that better conceptual models are needed to estimate climate sensitivity from the instrumental record

Investigation of extreme climates is useful in developing conceptual understanding and in discovering model limitations

Climate prediction up to a decade in advance is possible, as shown by predictive skill of early, relatively crude efforts; might help reduce model errors relevant to uncertainty in climate sensitivity

Sustained (operational-style) observations crucial for climate prediction

Conclusions and outlook

Thank you for your attention