1. Ocean Prediction Systems: Advanced Concepts and Research Issues Allan R. Robinson Harvard University Division of Engineering and Applied Sciences Department of Earth and Planetary Sciences

2. Interdisciplinary System Concept Harvard Ocean Prediction System Research Multi-Scale Examples Wind-Induced Upwelling –Episodic – Mass. Bay/GOM –Sustained – Monterey Bay Conclusions Harvard University Patrick J. Haley, Jr., Pierre F.J. Lermusiaux, Wayne G. Leslie, X. San Liang, Oleg Logutov, Patricia Moreno Avijit Gangopadhyay (Umass.-Dartmouth)

3. Interdisciplinary Ocean Science Today Research underway on coupled physical, biological, chemical, sedimentological, acoustical, optical processes Ocean prediction for science and operational applications has now been initiated on basin and regional scales Interdisciplinary processes are now known to occur on multiple interactive scales in space and time with bi-directional feedbacks

4. System Concept The concept of Ocean Observing and Prediction Systems for field and parameter estimations has crystallized with three major components  An observational network: a suite of platforms and sensors for specific tasks  A suite of interdisciplinary dynamical models  Data assimilation schemes Systems are modular, based on distributed information providing shareable, scalable, flexible and efficient workflow and management

5. Interdisciplinary Data Assimilation Data assimilation can contribute powerfully to understanding and modeling physical-acoustical-biological processes and is essential for ocean field prediction and parameter estimation Model-model, data-data and data-model compatibilities are essential and dedicated interdisciplinary research is needed

6. Physics - Density Biology – Fluorescence (Phytoplankton) Acoustics – Backscatter (Zooplankton) Almeira-Oran front in Mediterranean Sea Fielding et al, JMS, 2001 Griffiths et al, Vol 12, THE SEA Interdisciplinary Processes - Biological-Physical-Acoustical Interactions

7. Distribution of zooplankton is influenced by both animal behavior (diel vertical migration) and the physical environment. Fluorescence coincident with subducted surface waters indicates that phytoplankton were drawn down and along isopycnals, by cross-front ageostrophic motion, to depths of 200 m. Sound-scattering layers (SSL) show a layer of zooplankton coincident with the drawn-down phytoplankton. Layer persists during and despite diel vertical migration. Periodic vertical velocities of ~20 m/day, associated with the propagation of wave-like meanders along the front, have a significant effect on the vertical distribution of zooplankton across the front despite their ability to migrate at greater speeds. Biological-Physical-Acoustical Interactions

8. P AA P AO P AB P = P OA P OO P OB P BA P BO P BB Coupled Interdisciplinary Data Assimilation Physics: x O = [T, S, U, V, W] Biology: x B = [N i, P i, Z i, B i, D i, C i ] Acoustics: x A = [Pressure (p), Phase (  )] x = [x A x O x B ] P =   (x – x t ) ( x – x t ) T  ˆˆ Coupled error covariance with off-diagonal terms Unified interdisciplinary state vector

9. Data Assimilation in Advanced Ocean Prediction Systems

10. THREE PHASES OF (ITERATIVE) COUPLED MODELS SKILL ASSESSMENT VALIDATION Structures of models generally capable of representation of relevant dynamical processes Adequacy and compatibility of physical/biological data for process identification Dynamical models with data assimilation robustly represent events and identify processes CALIBRATION Tuning of models’ parameters: dynamical rates, subgridscale processes, computational protocols Dynamical models without data assimilation represent events that are validated by data previously assimilated VERIFICATION Real-time prediction verified by skill assessment quantities within a priori specified bounds Hindcasts and simulations with quantitative statistical dynamical behavior

11. HOPS/ESSE Long-Term Research Goal To develop, validate, and demonstrate an advanced relocatable regional ocean prediction system for real-time ensemble forecasting and simulation of interdisciplinary multiscale oceanic fields and their associated errors and uncertainties, which incorporates both autonomous adaptive modeling and autonomous adaptive optimal sampling

12. HOPS/ESSE System Error Subspace Statistical Estimation Harvard Ocean Prediction System

13. Harvard Generalized Adaptable Biological Model (R.C. Tian, P.F.J. Lermusiaux, J.J. McCarthy and A.R. Robinson, HU, 2004)

14. To achieve regional field estimates as realistic and valid as possible: Approach every effort is made to acquire and assimilate both remotely sensed and in situ synoptic multiscale data from a variety of sensors and platforms in real time or for the simulation period, and a combination of historical synoptic data and feature models are used for system initialization “fine-tune” the model to the region, processes and variabilities: examine model output, modify set-up (e.g. grids, etc.) and alter structure and values of parameters (e.g. SGS, boundary conditions, etc.) continuously evaluate and iterate tuning as necessary

15. Ongoing Research Objectives To extend the HOPS-ESSE assimilation, real-time forecast and simulation capabilities to a single interdisciplinary state vector of ocean physical- acoustical-biological fields. To continue to develop and to demonstrate the capability of multiscale simulations and forecasts for shorter space and time scales via multiple space-time nests (Mini-HOPS), and for longer scales via the nesting of HOPS into other basin scale models. To achieve a multi-model ensemble forecast capability.

16. Mini-HOPS – Corsican Channel 2003 Modeling Domains Designed to locally solve the problem of accurate representation of sub-mesoscale synopticity, including inertial motions Involves rapid real-time assimilation of high-resolution data in a high-resolution model domain nested in a regional model Produces locally more accurate oceanographic field estimates and short-term forecasts and improves the impact of local field high-resolution data assimilation Dynamically interpolated and extrapolated high-resolution fields are assimilated through 2-way nesting into large domain models In collaboration with Dr. Emanuel Coelho (NATO Undersea Research Centre)

17. Mini-HOPS for MREA-03 During experiment: –Daily runs of regional and super mini at Harvard –Daily transmission of updated IC/BC fields for mini-HOPS domains –Mini-HOPS successfully run aboard NRV Alliance Prior to experiment, several configurations were tested leading to selection of 2-way nesting with super-mini at Harvard Mini-HOPS simulation run aboard NRV Alliance in Central mini-HOPS domain (surface temperature and velocity)

18. Results of MREA03 Re-analysis and Model Tuning Real-time Model/Data ComparisonRe-analysis Model/Data Comparison Model Temp. Observed Temp. Bias residue <.25 o C Tuned parameters for stability and agreement with profiles (especially vertical mixing) Improved vertical resolution in surface and thermocline Corrected input net heat flux Improved initialization and synoptic assimilation in dynamically tuned model

19. Error Analyses and Optimal (Multi) Model Estimates Real-Time Forecast Training via Maximum-Likelihood Correction Model Temp. Observed Temp. UncorrectedTraining: Full Data Set Training: 1 ProfileTraining: 3 Profiles

20. Nesting HOPS in a Coarse-Resolution Climate PCM A.R. Robinson, P.J. Haley, Jr., W.G. Leslie Nest a high-resolution hydrodynamic model within a coarse-resolution Global Circulation Model (GCM) to provide high-resolution forecasts of mesoscale features in the Gulf of Maine Results applicable to cod and lobster temperature- dependent behavior change, including recruitment Parallel Climate Model (PCM) outputs for 2000 and 2085 for coarse circulation (1 degree) Gulf of Maine Feature Model provides higher-resolution synoptic circulation (5km) Harvard Ocean Prediction System (HOPS) dynamically adjusts Feature Model output Setup Concept

21. Gulf of Maine Feature Model Parallel Climate Model (PCM) Ocean Fields Prevalent circulation features are identified Synoptic water-mass structures are characterized and parameterized to develop T-S feature models Temperature and Salinity feature model profiles are placed on a regional circulation template Gangopadhyay, A., A.R. Robinson, et al., 2002. Feature-oriented regional modeling and simulations (FORMS) in the Gulf of Maine and Georges Bank, Continental Shelf Research, 23 (3-4), 317-353

22. OA “synoptic” climatology OA slope water climatology Add Maine Coastal Current Resulting synoptic estimate Dynamically adjusted fields for September 2000 Synoptic Estimate of Surface Salinity

23. Nesting High Resolution HOPS in a Climate Model A.R. Robinson, P.J. Haley, Jr., W.G. Leslie In order to arrive at future state, calculate the difference between the global model fields in 2000 and 2085 on the shelf and in deep water Perturb present-day observed fields by the “climate change” profile to achieve the 2085 fields Deep Shelf

24. Dynamically adjusted fields for September 2000 Compare September 2000 and September 2085 Dynamically adjusted fields for September 2085 Temperature Salinity Temperature increases from 2000-2085 Salinity increases from 2000-2085 Details under study Fields provided to Union of Concerned Scientists (UCS) for studies on regional climate change effects http://oceans.deas.harvard.edu/UCS

25. Wind-Induced Upwelling Massachusetts Bay Episodic upwelling Monterey Bay Sustained Upwelling Red = Wind, Blue = Upwelling

26. Dominant circulation and bio- physical dynamics for trophic enrichment and accumulation Patterns are not present at all times Most common patterns (solid), less common (dashed) Patterns drawn correspond to main currents in the upper layers of the pycnocline where the buoyancy driven component of the horizontal flow is often the largest

27. ASCOT-01 (6-26 June 2001): Positions of data collected and fed into models

28. ASCOT-01: Sample Real-Time Forecast Products Massachusetts BayGulf of Maine 2m Temp.10m Temp.3m Temp. 25m Temp. 5m Chlorophyll15m Nitrate

29. Moderate southerly winds lead to upwelling on the western side of Cape Cod Bay Near the surface temperature decreases from 17 o C to 12 o C Near the surface chlorophyll increases from 1.4 mg Chl/m 3 to 2.3 One-half day later, chlorophyll –continues to increase near the surface –decreases between 5-10m Between 3-10m there is maximum primary production Advective effects are stronger, bringing the newly produced chlorophyll closer to the surface Primary production during the upwelling event is mainly due to ammonium uptake Nitrate acts as a passive tracer Validation: Upwelling event in Massachusetts Bay Upwelling signature in T (top) and chlorophyll (bottom) P.A. Moreno

30. Calibration: Nutrient uptake parameters 20 sets of nutrient uptake parameters tried followed by 3 model runs. Nutrient uptake data is used to determine the photosynthesis and nutrient limitation parameters. Guided by MWRA primary productivity data and literature survey. Red - data Black - model

31. Zooplankton grazing parameters Choose zooplankton grazing parameters such that the nutrient uptake is approximately balanced by grazing. Data: Nutrient uptake rate in the euphotic zone for June 12-13, 2001 Phytoplankton equation: Photosynthesis Nutrient limitation Grazing

32. Winds Observed at Mass. Bay Buoy Feature Wind Event Replacing Observations Simulation begins 6 June 2001 and lasts 20 days. Feature event starts four days into simulation. Historical wind data (May-June 1985-2005) indicates strongest wind events of 1-2 days and maximum wind stress 2-4 dyn/cm 2 Design two feature wind events: duration 2 days, 2 and 4 dyn/cm 2 Prediction - towards verification Episodic upwelling events in Massachusetts Bay

33. Prediction: For strong winds the chlorophyll is upwelled and advected away from the coast; work in progress Chlorophyll June 2001 Wind

34. Integrated Ocean Observing and Prediction Systems Platforms, sensors and integrative models: HOPS- ROMS real-time forecasting and re-analyses AOSN II

35. Coastal upwelling system: sustained upwelling – relaxation – re-establishment 6 Aug 30m Temperature: 6 August – 3 September (4 day intervals) Descriptive oceanography of re-analysis fields and and real-time error fields initiated at the mesoscale. Description includes: Upwelling and relaxation stages and transitions, Cyclonic circulation in Monterey Bay, Diurnal scales, Topography-induced small scales, etc. 10 Aug 14 Aug 18 Aug 22 Aug26 Aug30 Aug3 Sep

36. HOPS AOSN-II Re-Analysis Ano Nuevo Monterey Bay Point Sur 18 August 22 August

37. Which sampling on Aug 26 optimally reduces uncertainties on Aug 27? 4 candidate tracks, overlaid on surface T fcst for Aug 26 ESSE fcsts after DA of each track Aug 24 Aug 26 Aug 27 2-day ESSE fct ESSE for Track 4 ESSE for Track 3 ESSE for Track 2 ESSE for Track 1 DA 1 DA 2 DA 3 DA 4 IC(nowcast) DA Best predicted relative error reduction: track 1 Based on nonlinear error covariance evolution For every choice of adaptive strategy, an ensemble is computed

38. Strategies For Multi-Model Adaptive Forecasting Error Analyses and Optimal (Multi) Model Estimates Error Analyses: Learn individual model forecast errors in an on-line fashion through developed formalism of multi-model error parameter estimation Model Fusion: Combine models via Maximum-Likelihood based on the current estimates of their forecast errors 3-steps strategy, using model-data misfits and error parameter estimation 1.Select forecast error covariance and bias parameterization 2.Adaptively determine forecast error parameters from model-data misfits based on the Maximum-Likelihood principle: 3.Combine model forecasts via Maximum-Likelihood based on the current estimates of error parameters (Bayesian Model Fusion) O. Logutov Where is the observational data

39. Bayesian Adaptive Multi-Model Forecasting ROMS and HOPS SST forecasts for August 28, 2003 with track of validating NPS aircraft SST data taken on August 29, 2003 Model-data misfits is the source of information that is utilized to estimate the uncertainty parameters in models via Maximum-Likelihood. The models are then combined based on the uncertainty parameters, as

40. Bayesian Adaptive Multi-Model Forecasting ROMS and HOPS individual SST forecasts and the NPS aircraft SST data are combined based on their estimated uncertainties to form the central forecast A new batch of model-data misfits and priors on uncertainty parameters determine via the Bayesian principle uncertainty parameter values that are employed to combine the forecasts. The Bayesian model fusion technique that we advocate treats forecast errors from different models as uncorrelated in order to gain its capability to work with a small sample of past validating events, however, accounts for spatial structure in forecast error covariances.

41. Multi-Scale Energy and Vorticity Analysis

42. MS-EVA is a new methodology utilizing multiple scale window decomposition in space and time for the investigation of processes which are: multi-scale interactive nonlinear intermittent in space episodic in time Through exploring: pattern generation and energy and enstrophy - transfers - transports, and - conversions MS-EVA helps unravel the intricate relationships between events on different scales and locations in phase and physical space. Dr. X. San Liang

43. Multi-Scale Energy and Vorticity Analysis Window-Window Interactions: MS-EVA-based Localized Instability Theory Perfect transfer: A process that exchanges energy among distinct scale windows which does not create nor destroy energy as a whole. In the MS-EVA framework, the perfect transfers are represented as field-like variables. They are of particular use for real ocean processes which in nature are non-linear and intermittent in space and time. Localized instability theory: BC: Total perfect transfer of APE from large-scale window to meso-scale window. BT: Total perfect transfer of KE from large-scale window to meso-scale window. BT + BC > 0 => system locally unstable; otherwise stable If BT + BC > 0, and BC  0 => barotropic instability; BT  0 => baroclinic instability; BT > 0 and BC > 0 => mixed instability

44. Wavelet Spectra Surface Temperature Surface Velocity Monterey Bay Pt. AN Pt. Sur

45. Multi-Scale Energy and Vorticity Analysis Multi-Scale Window Decomposition in AOSN-II Reanalysis Time windows Large scale: > 8 days Meso-scale: 0.5-8 days Sub-mesoscale: < 0.5 day The reconstructed large- scale and meso-scale fields are filtered in the horizontal with features < 5km removed. Question: How does the large-scale flow lose stability to generate the meso-scale structures?

46. Both APE and KE decrease during the relaxation period Transfer from large-scale window to mesoscale window occurs to account for decrease in large-scale energies (as confirmed by transfer and mesoscale terms) Large-scale Available Potential Energy (APE) Large-scale Kinetic Energy (KE) Windows: Large-scale (>= 8days; > 30km), mesoscale (0.5-8 days), and sub-mesoscale (< 0.5 days) Dr. X. San Liang Decomposition in space and time (wavelet-based) of energy/vorticity eqns. Multi-Scale Energy and Vorticity Analysis

47. MS-EVA Analysis: 11-27 August 2003 Transfer of APE from large-scale to meso-scale Transfer of KE from large-scale to meso-scale

48. Multi-Scale Energy and Vorticity Analysis Process Schematic

49. Multi-Scale Energy and Vorticity Analysis Multi-Scale Dynamics Two distinct centers of instability: both of mixed type but different in cause. Center west of Pt. Sur: winds destabilize the ocean directly during upwelling. Center near the Bay: winds enter the balance on the large-scale window and release energy to the mesoscale window during relaxation. Monterey Bay is source region of perturbation and when the wind is relaxed, the generated mesoscale structures propagate northward along the coastline in a surface-intensified free mode of coastal trapped waves. Sub-mesoscale processes and their role in the overall large, mesoscale, sub- mesoscale dynamics are under study. Energy transfer from meso-scale window to sub-mesoscale window.

50. Monterey Bay August 2006 Adaptive Sampling and Prediction (ASAP) Assessing the Effects of Submesoscale Ocean Parameterizations (AESOP) Undersea Persistent Surveillance (UPS) Layered Organization in the Coastal Ocean (LOCO)

51. Entering a new era of fully interdisciplinary ocean science: physical-biological-acoustical- biogeochemical Advanced ocean prediction systems for science, operations and management: interdisciplinary, multi- scale, multi-model ensembles Interdisciplinary estimation of state variables and error fields via multivariate physical-biological- acoustical data assimilation CONCLUSIONS http://www.deas.harvard.edu/~robinson