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The oceans, uncertainty, and climate prediction

The oceans, uncertainty, and climate prediction. Jochem Marotzke Max Planck Institute for Meteorology (MPI-M) Centre for Marine and Atmospheric Sciences Hamburg, Germany. Outline. Ocean heat capacity and climate sensitivity Extreme climate states Seamless prediction of weather and climate

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The oceans, uncertainty, and climate prediction

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  1. The oceans, uncertainty, and climate prediction Jochem Marotzke Max Planck Institute for Meteorology (MPI-M) Centre for Marine and Atmospheric Sciences Hamburg, Germany

  2. Outline • Ocean heat capacity and climate sensitivity • Extreme climate states • Seamless prediction of weather and climate • Examples of decadal climate prediction • Ocean observations and decadal prediction

  3. 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

  4. Time-dependent global energy balance model (1) C: heat capacity per unit area T: global mean (surface) temperature in C S: total solar output (aka “solar constant”) a: albedo (assumed fixed for simplicity) A in W/m2, B in W/(m2K): linearised LW parameterisation Steady state: F: forcing; : climate sensitivity

  5. Time-dependent global energy balance model (2) Now: Add perturbation in forcing, DF: 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

  6. 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)

  7. Time-dependent global energy balance model (4)

  8. Time-dependent global energy balance model (5) • Estimate effective climate sensitivity from the instrumental record: • Problem: for t<<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/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

  9. 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) 50 years 5 years 4xCO2 for 5 years 2xCO2 for 200 years

  10. 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)

  11. Multiple time scales (3) • Heat conservation for perturbations from equilibrium: • All initial temperature perturbations are zero. • Steady state:

  12. 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 l) • Combined timescale shorter than set by LW radiation • One must not interpret timescale as related to l.

  13. Multiple time scales (5) • Before TC can respond much, ML has equilibrated to additional forcing DF and heat loss to TC • While TC is still near zero, ML response is considerably smaller than final equilibrium response lDF • 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.

  14. ECHAM5/MPI-OM, CO2 quadrupling over 2000 y (Chao Li, PhD thesis in progress, MPI-M)

  15. 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

  16. 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)

  17. Paleocene/Eocene simulation • ECHAM5/MPI-OM (Heinemann et al. 2009; PhD thesis in progress, MPI-M)

  18. Snowball Earth? (ECHAM5/MPI-OM, No Sun) Marotzke and Botzet (2007)

  19. Snowball Earth? (ECHAM5/MPI-OM) Global sea ice area [1013 m2] TSI = 0.01 % TSI = 100 % TSI = 100 %, CO2 = 10 x pre-industrial TSI = 100 %, CO2 = 100 x pre-industrial Marotzke and Botzet (2007)

  20. Snowball Earth? (ECHAM5/MPI-OM) Voigt and Marotzke (2009; PhD thesis in progress, MPI-M)

  21. 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

  22. 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

  23. 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.”

  24. 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…

  25. 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?

  26. 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: • “Optimal interpolation” (Hadley Centre, European Centre for Medium-Range Weather Forecasts) • Forcing of sea surface temperature (SST) in coupled model toward observations (IFM-GEOMAR & MPI-M) • Using 4-dimensional ocean synthesis (ECCO) to initialise ocean component (MPI-M & UniHH)

  27. Hadley Centre: Global-mean surface temperature Smith et al. (2007)

  28. IFM-GEOMAR/MPI-M: Correlation of 10-y mean SAT Keenlyside et al. (2008)

  29. IFM-GEOMAR/MPI-M: 10-y mean global mean SAT Keenlyside et al. (2008)

  30. MPI-M/UniHH SAT anomaly correlation w/ obs. 20C (“free” coupled model) Hindcasts, year 1 Hindcasts, year 5 Hindcasts, year 10 Pohlmann et al. (2009) Initialisation from GECCO 4D-Var ocean assimilation

  31. MPI-M/UniHH : North Atlantic SST Annual HadISST (Obs.) Ocean Assimilation Hindcasts 20C (“Free” coupled model) Pentadal Decadal Pohlmann et al. (2009)

  32. MPI-M/UniHH : North Atlantic SST HadISST (Obs.) Forecasts Scenario run (“Free” coupled model) Pohlmann et al. (2009)

  33. 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

  34. 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

  35. North Atlantic Meridional Overturning Circulation (a.k.a. Thermohaline Circulation) Quadfasel (2005)

  36. Bryden et al. (2005) ECMWF MOC at 25N in ocean syntheses (GSOP)

  37. 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, Oct2007, March, November 2008 Church (SCIENCE, 17. August 2007)

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

  39. Observed MOC time series, 26.5N Atlantic Florida Current MOC Ekman Geostro-phic upper mid-ocean S. A. Cunningham et al., Science (17 August 2007)

  40. Modelled vs. observed MOC variability at 26.5N Correlation Observations ECCO (Ocean Synthesis) ECHAM5/MPI-OM RMS variability Baehr et al. (2009)

  41. Observed MOC time series, 26.5N Atlantic • 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 Kanzowet al. (2009, in preparation) Uncertainty in 2.5 year MOC mean: 1.9 Sv; assuming 18 DOF, 1.5 Sv measurement error

  42. Observed MOC spectrum at 26.5N Atlantic • Ekman Transport dominates intra-seasonal variability • Upper Mid-Ocean and Gulf Stream dominate seasonal variability Kanzowet al. (2009, in preparation)

  43. Observed seasonal MOC variability • MOC seasonal cycle emerging, but not significant, yet (at 5 % error probability) Seasonal Cycle Gulf Stream 1.5 Sv (14 %) MOC 4.2 Sv (37 %) Ekman 1.4 Sv (08%) Upper Mid-Ocean 2.1 Sv (26 %) Kanzowet al. (2009, in preparation)

  44. 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)

  45. 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

  46. Conclusions and outlook • 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 Thank you for your attention

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