1. Global Ocean Syntheses in support of physical and bio-geochemical studies Detlef Stammer Delmenhorst, Sep. 8, 2007 Detlef Stammer Delmenhorst, Sep. 8, 2007

2. Rational for Ocean Reanalysis A complete picture of the ocean for the purpose of (climate) research and applications will only come from a synergy between observations, modeling and data assimilation. By estimating uncertain parameters, a model can be brought into consistency with incomplete observations. If done properly, results have to be better than those obtained from the model or data alone. The output can then be used to study the ocean. A complete picture of the ocean for the purpose of (climate) research and applications will only come from a synergy between observations, modeling and data assimilation. By estimating uncertain parameters, a model can be brought into consistency with incomplete observations. If done properly, results have to be better than those obtained from the model or data alone. The output can then be used to study the ocean.

3. Approaches State estimation or “data assimilation” is just least- squares fitting of models to data. (Nudging, 4DVAR, 3DVAR, adjoint, OI, OM, Kalman filter, RTS smoother, ensemble KF, AD, Pontryagin principle, relaxation, line-searches, breeding vectors, SVD, optimals, Hessians, quelling, dual,....) The apparently different methods are variant algorithms used to find the minimum of an objective (or cost) function, the extent to which an approximation to that minimum is acceptable, and whether one seriously seeks an estimate of the error of the result. State estimation or “data assimilation” is just least- squares fitting of models to data. (Nudging, 4DVAR, 3DVAR, adjoint, OI, OM, Kalman filter, RTS smoother, ensemble KF, AD, Pontryagin principle, relaxation, line-searches, breeding vectors, SVD, optimals, Hessians, quelling, dual,....) The apparently different methods are variant algorithms used to find the minimum of an objective (or cost) function, the extent to which an approximation to that minimum is acceptable, and whether one seriously seeks an estimate of the error of the result.

4. initial conditions observations control vector The nature of the minimum, in addition to the model structure, depends directly on the weight matrices in J’. If P,Q,R are incorrect, so is the solution, no matter how wondrous the numerics. GCM Finding a minimum, subject to the model, is a numerical, not a conceptual or mainly scientific problem. Generic:

5. Challenges The spectrum of applications of ocean reanalyses for climate variability and prediction purposes spans over seasonal-to-interannual, decadal-to-centennial, and even millennial time scales. These applications pose a range of accuracy and robustness requirements on ocean reanalyses. Consequently, they necessitate somewhat different data assimilation approaches and evaluation. The spectrum of applications of ocean reanalyses for climate variability and prediction purposes spans over seasonal-to-interannual, decadal-to-centennial, and even millennial time scales. These applications pose a range of accuracy and robustness requirements on ocean reanalyses. Consequently, they necessitate somewhat different data assimilation approaches and evaluation.

6. Several global ocean data assimilation products are available today that in principle can be used for climate applications. Underlying assimilation schemes range from simple and computationally efficient (e.g., optimal interpolation) to sophisticated and computationally intensive (e.g., adjoint and Kalman filter-smoother). Intrinsically those efforts can be summarized as having three different goals, namely –climate-quality hintcasts, –high-resolution nowcasts, and –the best initialization of forecast models. Several global ocean data assimilation products are available today that in principle can be used for climate applications. Underlying assimilation schemes range from simple and computationally efficient (e.g., optimal interpolation) to sophisticated and computationally intensive (e.g., adjoint and Kalman filter-smoother). Intrinsically those efforts can be summarized as having three different goals, namely –climate-quality hintcasts, –high-resolution nowcasts, and –the best initialization of forecast models. Ongoing Synthesis

7. time Smoothed Estimate: x(t+1)=Ax(t)+Gu(t) Filtered Estimate: x(t+1)=Ax(t)+Gu(t)+  (t) x: model state, u: forcing etc,  : data increment Model Physics: A, G Data increment:  Consistency of Assimilation Data The temporal evolution of data-assimilated estimates is physically inconsistent (e.g., budgets do not close) unless the assimilation’s data increments are explicitly ascribed to physical processes (i.e., inverted).

8. 1) Description of a complex local flow field and its interaction with biology. 2) Description of the interaction of the ocean with the atmosphere and associated changes in the flow fields, ocean properties, etc. 3) Use of estimated flow field for studies on CO2 sequestering, regional impacts, regional and global sea level,... 4) Initialization of coupled forecast models (SI, decadal and and IPCC). 5) As a global framework to embed in regional/basin scale research efforts. 6) Evaluation of the observing system. Different purposes for global ocean reanalysis:

9. GECCO State Estimate Ocean synthesis, performed over the period 1952 through 2001 on a 1º global grid with 23 layers in the vertical, using the ECCO/MIT adjoint technology. Model started from Levitus and NCEP forcing and uses state of the art physics modules (GM, KPP). The models adjoint (obtained using TAF) is used to bring the model into consistency with most of the available ocean observations over the full period by adjusting control parameters. At this stage control parameters are the models initial temperature and salinity fields as well as the time varying surface forcing, leading to a dynamically self-consistent solution (next step is to include mixing). Ocean synthesis, performed over the period 1952 through 2001 on a 1º global grid with 23 layers in the vertical, using the ECCO/MIT adjoint technology. Model started from Levitus and NCEP forcing and uses state of the art physics modules (GM, KPP). The models adjoint (obtained using TAF) is used to bring the model into consistency with most of the available ocean observations over the full period by adjusting control parameters. At this stage control parameters are the models initial temperature and salinity fields as well as the time varying surface forcing, leading to a dynamically self-consistent solution (next step is to include mixing).

10. Input Data Sets and Controls

11. Flow of Information

12. 1. deg. global, 1992 – 2003; uses almost all data. RMS T residual; opt. RMS T residual; FG.

13. Application: Heat Transport and MOC The meridional overturning circulation (MOC) of the ocean caries a large amount of heat poleward. The importance of this poleward heat transport for the climate of mid and high latitudes, especially of Europe, is generally accepted. Less clear is on what space and time scales the MOC varies, what the underlying processes are, what the impact of those variations is on the European climate and if the Atlantic MOC can undergo significant fluctuations that could be responsible for major climate shifts. We can use the results from the 50-year long GECCO ocean state estimation to investigate changes of the Atlantic MOC estimated for the period 1952 through 2001. The meridional overturning circulation (MOC) of the ocean caries a large amount of heat poleward. The importance of this poleward heat transport for the climate of mid and high latitudes, especially of Europe, is generally accepted. Less clear is on what space and time scales the MOC varies, what the underlying processes are, what the impact of those variations is on the European climate and if the Atlantic MOC can undergo significant fluctuations that could be responsible for major climate shifts. We can use the results from the 50-year long GECCO ocean state estimation to investigate changes of the Atlantic MOC estimated for the period 1952 through 2001.

14. Global Ocean Heat o o o G&W o

15. Mean MOC Residual Ekman Geostrophic 25N Time mean MOC in Atlantic

16. Bryden et al. (2005) Comparison of maximum MOC at 25N

17. 25N MOC 1)Largest discrepancy to Bryden for 1957 when GECCO suggest a much lower value (could come from Florida Straight estimate). 2) In contrast to a MOC decrease, GECCO suggests an increase in MOC strength since the 60 th. 3) Initial decline due to model adjustments underlines the need for long dynamically consistent estimation approaches with improved boundary conditions and mixing parameterizations in support of MOC analyses. 1)Largest discrepancy to Bryden for 1957 when GECCO suggest a much lower value (could come from Florida Straight estimate). 2) In contrast to a MOC decrease, GECCO suggests an increase in MOC strength since the 60 th. 3) Initial decline due to model adjustments underlines the need for long dynamically consistent estimation approaches with improved boundary conditions and mixing parameterizations in support of MOC analyses.

18. Using adjoint sensitivities: Mechanisms affecting MOC variability at 25 (K öhl, 2005) 1) Local Ekman transport 2) Coastal down-welling at east coast 3) Kelvin wave propagation along the west coast 4) Baroclinically unstable long Rossby waves

19. Southward Propagation of anomalies of maximum MOC:

20. On time scales longer than 1 year, the local Ekman transport and the variability of the coastal jet off Africa to explain only a small fraction of MOC variability at 25N. Variability due to Rossby waves explains a large fraction of the MOC changes, as does the wave propagation along the west coast. The MOC signal at 48N leads the one at 25N by 3 years. Response of MOC at 48 to atmospheric forcing was described by Eden and Willebrand (2001) on time lag of 2-3 years. On time scales longer than 1 year, the local Ekman transport and the variability of the coastal jet off Africa to explain only a small fraction of MOC variability at 25N. Variability due to Rossby waves explains a large fraction of the MOC changes, as does the wave propagation along the west coast. The MOC signal at 48N leads the one at 25N by 3 years. Response of MOC at 48 to atmospheric forcing was described by Eden and Willebrand (2001) on time lag of 2-3 years.

21. Low correlation to deep convection in LS; but high correlation with LS density in 900m depth. Even higher correlation with density in Irminger Sea with about 4 year lag time: Irminger Sea is strong predictor of density changes in subpolar basin. Variability of MOC at 48N leads 25N and can be understood in terms of NAO forcing. Mechanisms involved in the change of the MOC include boundary waves propagating southward from the Labrador Sea, local Ekman transport and westward propagating Rossby waves. Low correlation to deep convection in LS; but high correlation with LS density in 900m depth. Even higher correlation with density in Irminger Sea with about 4 year lag time: Irminger Sea is strong predictor of density changes in subpolar basin. Variability of MOC at 48N leads 25N and can be understood in terms of NAO forcing. Mechanisms involved in the change of the MOC include boundary waves propagating southward from the Labrador Sea, local Ekman transport and westward propagating Rossby waves.

22. Examples of Existing Ocean Syntheses: 3D-VAR RTS smoother

23. Synthesis Evaluation Effort Is needed to determine the quality of existing global ocean analysis/synthesis products and to assess their usefulness for climate research. Identify strength and weakness of systems and explain differences among them. Define pilot set of climate-indices and diagnostic quantities to be produced on a regular basis as prototype synthesis support of global and regional CLIVAR research. Define data set required as input and identify present gabs. Is needed to determine the quality of existing global ocean analysis/synthesis products and to assess their usefulness for climate research. Identify strength and weakness of systems and explain differences among them. Define pilot set of climate-indices and diagnostic quantities to be produced on a regular basis as prototype synthesis support of global and regional CLIVAR research. Define data set required as input and identify present gabs.

24. (P. Heimbach)

25. ENSEBLES North Atlantic Heattr. Ganachaud&Wunsch(1996) (A. Koehl)

26. K-7 Max. MOC 25 o N Bryden et al. (2005)

27. K-7 Heat transport 25 o N

30. German BMBF-Consortium: The North Atlantic as part of the Earth System: The path from understanding the system to regional impact analyses.

31. Goals of Consortium: Quantitative understanding of key processes, including freshwater budget of North Atlantic and water mass formation rates. Identification of vital observing elements in key regions than can form the back bone of a North Atlantic observing and diagnosis system. Development of approaches suitable to detect seasonal to decadal climate changes, including the improvement of coupled models and the use of the consortium’s observations for initialization. Construction of pilot diagnosis and evaluation system.

32. Regional Consortium Foci: North Atlantic, including key regions of tropical and subarctic ocean Subpolar North Atlantic Subtropical Atlantic Atlantic Synthesis

33. North Atlantic Synthesis Activities (IfM Hamburg) North Atlantic ECCO adjoint model, incl. Arctic and sea ice. 1/6 degree resolution. Assimilation of all data, including CFCs. Mixing as control parameter. Nested in global GECCO results. First 1992 to present; Later 50 years.

34. Syntheses and GEOTRACCES Syntheses can be used in support of carbon and GEOTRACES studies 1.Compare estimated or hypothesized flow fields (e.g., with Schlitzer) 2.Use estimated flow fields for a tracer comparison study (off-line omip for GEOTRACES). 3.Investigation of causes for variability of tracers. 4.Investigation of source regions of tracers (sensitivities). 5.Assimilation studies. CLIVAR GSOP and Carbon program: active in defining joint pilot synthesis study Syntheses can be used in support of carbon and GEOTRACES studies 1.Compare estimated or hypothesized flow fields (e.g., with Schlitzer) 2.Use estimated flow fields for a tracer comparison study (off-line omip for GEOTRACES). 3.Investigation of causes for variability of tracers. 4.Investigation of source regions of tracers (sensitivities). 5.Assimilation studies. CLIVAR GSOP and Carbon program: active in defining joint pilot synthesis study

35. Ocean Carbon Storage Questions: (D. Wallace, GSOP Meeting) 1.How much „excess“ (≈ anthropogenic) carbon is there in the oceans and where is it in the Southern Ocean; what is the vertical depth distribution of carbon inventory 2. Can we detect changes on timescales useful for policy / carbon- management assessment and decision-making? 3.How will the uptake change in the future? Which means understanding processes underlying carbon transport divergence and storage: Where and how is CO 2 and C ant is taken up from the atmosphere? What determines the uptake rate of CO 2 and C ant ? How sensitive to change? 4. What is the magnitude and spatial-temporal variability of the air-sea CO 2 flux? This is a useful constraint for inversion modelliing of atmospheric CO2 variability (interannual / decadal / oceanic / terrestrial

36. Direct comparison of data sets doesn‘t tell you much (Most of the variability is „natural“ variability driven by biological processes coupled with circulation / eddy variability) Can we detect change in ocean carbon on policy-relevant timescales? (D. Wallace, GSOP Meeting)

37. Checking the C ant Transport Term NOAA 24.5°N section in 1998 compared with 1992 section from MacDonald, Baringer, Lee, Wallace and Wanninkhof, DSR II (2003) NOAA 24.5°N section in 1998 compared with 1992 section from MacDonald, Baringer, Lee, Wallace and Wanninkhof, DSR II (2003) 1992 C ant Transport: 0.17  0.06 PgC yr -1 1998 C ant Transport: 0.20  0.08 PgC yr -1 (D. Wallace, GSOP Meeting)

38. Heat and C ant Transports are Closely Correlated (at 24.5N) Flame model (Böning, Biastoch et al. (D. Wallace, GSOP Meeting)

39. Synthesis support for Air-Sea CO 2 Flux Studies Operational model products and reanalyses extremely useful for interpreting/extrapolating sparse surface observations; SST and Heat Flux; Heat content (upper ocean) Mixed-Layer Depth !!! Oxygen! or at least some idealised gas exchange tracer????

41. Falstaff: Seasonality in surface pCO 2 Needer are surface ocean back-trajectory analyses (incl. representation of Warming / Shoaling of mixed layer, ideally for order weeks to 1 month)

43. (Stammer et al., 2007)

45. CLIVAR/GSOP would be happy To support GEOTRACES Thank You !

46. Adjoint Sensitivities Studies Example: How is the Heat Transport and MOC change influenced by the controls? Sensitivity of mean 1993 Atlantic heat transport across 29°N (dotted line) to surface temperature on January 1, 1993,CI=20TW/K (Marotzke et. al (1999)