Modeling for Ocean Observatories: The ORION Program

1. GRAND CHALLENGES OF THE FUTURE FOR ENVIRONMENTAL MODELING Modeling for Ocean Observatories: The ORION Program Lewis M. Rothstein Graduate School of Oceanography University of Rhode Island Environmental Observatories Modeling Workshop May 16 &17 - Tucson, AZ Sponsored by the National Science Foundation

2. Outline • The ORION Program - An Overview • Scope, context, objectives, administrative structure • Role of Models in ORION • Purposes for, and types of, models; methodologies used • Recommendations from the ORION Modeling Committee • Immediate Needs/Priorities & Opportunities • Bottlenecks & investment remedies • New opportunities for modeling created by ORION • Inside and outside the discipline

3. The ORION Program:An Overview http://www.orionprogram.org

4. The ORION Program • The Ocean Research Interactive Observatory Networks (ORION) program focuses the science, technology, education and outreach of an emerging network of science driven ocean observing systems. • NSF’s Ocean Observatories Initiative (OOI) provides access to the basic infrastructure required to make sustained, long-term and adaptive measurements in the oceans. • The research-focused observatories enabled by the OOI will be networked, becoming an integral part of the proposed Integrated and Sustained Ocean Observing System (IOOS); an operationally-focused national system that will, in turn be a key U.S. contribution to the international Global Ocean Observing System (GOOS) and the Global Earth Observing System of Systems (GEOSS).

5. The ORION Program • OOI provided infrastructure for ORION includes • cables, buoys, deployment platforms, moorings • junction boxes (required for power and two-way data communication to a wide variety of sensors at the sea surface, in the water column, and at or beneath the seafloor) • unified project management • data dissemination and archiving • education and outreach activities

6. The ORION Program • Goals of a fully operational research observatory: • Continuous observations (resolving seconds-decades) • Multi-scale spatial measurements (millimeters-kilometers) • Sustained operations during storms & other severe conditions • Real-time or near-real-time data • Two-way transmission of data and remote instrument control • Power delivery to sensors between sea surface and seafloor • Standard “Plug-n-Play” sensor interface protocol • Autonomous underwater vehicle (AUV) dock for data download/battery recharge • Access to deployment and maintenance vehicles • Facilities for instrument maintenance and calibration • Data management system that makes data publicly available • Effective education and outreach program

7. Coastal Observatory

8. Regional Cabled Observatory (RCO)

9. Seafloor Geophysical Regional Observatory with Seismometers and a Variety of Geodetic Instruments

10. Forest of vertical profiling moorings and borehole observatories

11. Global Observatory

12. ORION Advisory Structure

13. Science Planning: OOI Timeline 2007-2013 2008 Phase I: Coastal and global observatories deployed Need science experiments ready to start in 2008/9 2012 Phase II: Coastal and global deployments completed 2013 Regional Cabled Observatory finish testing and commission

14. What’s next? 2006 May - Advisory committee reviews revised Conceptual Network Design based on the Design & Implementation Workshop (March, 2006) comments August - NSF Conceptual Network Design Review 2007 Spring – Final design review

15. Role of Models in ORIONTo achieve the state-of-the-art in ocean sciences EO modeling

16. Multiple Purposes • Improving the knowledge base • Testing theory (my favorite purpose!) • Designing sensors and networks (envisioned) • Are the models trustworthy enough? • Forecasting/hindcasting • The ultimate test! • Decision support • Deliverables to the clients • Public outreach • Justifying our existence!

17. Model Types • “Forward” physical ocean models: Navier-Stokes • Coupled sets of PDEs used almost exclusively • Variety of grid systems • Level vs. layer coordinate models • Telescoping vs. nested (one-way & two-way interactive) grids • Adaptive (feature-following) vs. adaptive (sampling plans) • Coupled ecological-biogeochemical-physical ocean models (again mostly PDEs) • Where Navier-Stokes meets natural selection! • One example: The NOPP PARADIGM program • Using our best physical models in combination with hierarchy of ecosystem-biogeochemical models

18. Model Methodologies • Parameter estimation/optimization • Variational adjoint methods (minimizing misfit between model solutions and observations by systematically modifying values of parameters) • Data assimilation • “Grand” challenge when considering coupled ecological-biogeochemical-physical models! • Model “In”validation! One goal is to combine models and methodologies for accomplishing the purposes just established: Observing System Simulation Experiments

19. Observing System Simulation Experiments Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

20. Observing System Simulation Experiments Our best (e.g. highest resolution) forward models - Must possess climatology, etc. as close to the real world as possible. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

21. Observing System Simulation Experiments “Nature” is sampled (with errors) as one would the real world. Information re. sampling strategies (e.g. rates, spatial scales, etc.). Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

22. Observing System Simulation Experiments “Control” and “experimental” cycles, differing in the types of data assimilated. Information about which data to collect. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

23. Observing System Simulation Experiments The model into which the data is assimilated is DIFFERENT than the “Nature” model. Avoids “identical twin” problem. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

24. Observing System Simulation Experiments Various data assimilation techniques can also be tested. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

25. Observing System Simulation Experiments Forecasts for “control” and “experimental” cycles are produced and verified against the “Nature” run. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

26. Observing System Simulation Experiments Various operational strategies can be tested for efficiency (e.g. ensemble techniques). Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

27. Observing System Simulation Experiments Calibration is performed against an actual observing system, if available. Nature Run Simulation of Observations Data Assimilation Cycles Forecasts Verification Calibration Observing System Experiment

28. The Role of OSSEs • Are OSSEs ready to help select observatory sites? • Answer depends upon the scientific questions being asked • YES for • forward models that have been designed for addressing specific science questions • single disciplinary data assimilation schemes • regions with sufficient data for structuring error models • NO for • most multi-disciplinary issues • forward models that are not configured for specific science issues • regions lacking ‘critical mass’ data base

29. ORION Modeling Committee Lewis Rothstein (U. Rhode Island) - Chair John Allen (Oregon State U.) Fei Chai (U. Maine) Shuyi Chen (U. Miami) Bruce Cornuelle (Scripps) Katja Fennel (Rutgers) Pierre Lermusiaux (Harvard) Raghu Murtugudde (U. Maryland) Yvette Spitz (Oregon State U.) Cisco Werner (U. North Carolina)

30. Terms of Reference • To help establish the role of each of a hierarchy of coupled numerical physical-biogeochemical ocean/atmosphere/geophysical models (including global, regional and coastal models) in the evolving plan for ocean observatories. • To provide advice on the current status of the different types of models required to achieve the objectives of the ORION program, and the primary issues or needs for development of those models. • To explore ways in which the ocean modeling community should interface with the atmospheric and geophysical modeling community for achieving the objectives of the ORION program.

31. General Recommendations • Foster a scientific approach where ocean observatories are fully integrated with ocean modeling programs for better understanding and predictions through: • Dynamical interpolation and synthesis of multiple data sets • Utilization of models to iteratively guide the sampling design and adaptive sampling plans (e.g. OSSEs) • Evaluation and improvement of models, including error estimates • Utilize models within ORION to study, synthesize, discover and resolve (multi)-scale interactions, physical-biogeochemical-ecological coupling and ocean/earth processes, e.g.: • Quantify interactions among coastal and global scale, mesoscale and sub-mesoscale that are not directly observable • Assess uncertain, or discover new, interdisciplinary ocean coupling • Monitor, explain and better forecast climate dynamics, ecosystem evolution, and earthquakes

32. Specific Modeling Research Challenges for ORION 1) Models need to be configured to guide the observational design and for adaptive sampling plans 2) Utilize models for dynamical interpolation of data sets 3) Evaluate, (in)validate and improve models and their parameterizations 4) Encourage ORION studies which include modeling across disciplines Details for each of these on the next 4 slides

33. Specific Modeling Research Challenges for ORION 1)Models need to be configured to guide observational design and adaptive sampling plans • Define scientific objectives for OSSEs • OSSEs should be interactive and iterative • Identify new metrics and processes for observation • Inter-disciplinary OSSEs

34. Specific Modeling Research Challenges for ORION 2) Utilize models for dynamical interpolation of data sets • Data assimilation, re-analyses (hindcasts), nowcasts and forecasts • Extract predictive understanding from the process understanding gathered from observations • Dynamic interpolation of local data into larger-scale context: forward and assimilative modeling with weak and strong constraints from observations to generate gridded products and re-analyses (physical, biogeochemical and ecological). • Hierarchy of forward/process-study models • Hypotheses testing, with reduced physics models and diagnostic tools

35. Specific Modeling Research Challenges for ORION 3) Evaluate, (in)validate and improve models • Acquire data to develop and evaluate parameterizations and their impacts on model simulated scale interactions • Develop strong & weak constraints for models, parameterizations (turbulence, mixed layer-thermocline interactions) • Target multi-scale interactions • Coastal-open ocean interactions; i.e the role of coastal upwelling in ocean general circulation • Develop improved data assimilation methods such as ensemble and particle Kalman filters, efficient error models • Identify predictable signals in the physical, biogeochemical, and ecosystem variables • Develop novel grid generation and numerical techniques for physical-ecosystem/biogeochemical coupling

36. Specific Modeling Research Challenges for ORION 4) Encourage ORION studies which include modeling across disciplines • Ocean-atmospheric-land, physical-biogeochemical-ecological, physical-acoustical-geophysical, etc. • Develop interdisciplinary data assimilation techniques for parameter estimation, identification of adequate model formulations (aggregations, behavior or scale-reductions)

37. How To Do It - General • Encourage modeling proposals as integral part of ORION observatory and/or analysis RFPs • Facilitate modeling evaluation, assessment and sharing of knowledge and skill levels between observatories • Serve resource management needs by providing prediction capability for specific regions   • Foster collaborations among different universities, institutions, government agencies and modeling centers

38. How To Do It - Operational • Planmodeling continuity through the long life of ORION • Support efficient & cooperative transition of methodologies & technologies among research & operational centers, involving universities, institutions and other agencies • Preliminary proposal: Establish ‘Modeling Centers’ for: • Maintaining hierarchy of evolving interdisciplinary models (e.g. from ‘reduced’ process-oriented models to operational forecast systems) • Model repositories with support for writing modeling manuals, etc. • Archiving and disseminating model data sets • Flexible formats for wide range of research endeavors • Linking to other IOOS activities & operational centers (e.g. NCEP) • Coordinating between the ORION research community and the ‘Modeling Centers’ through: • Visiting scientist and community postdoc programs • Student workshops

39. Ocean Model Status Coastal and Regional Models Basin-scale and Global Models

40. Coastal and Regional Models • Strengths • Multiple models available (hydrostatic to DNS equations, level to unstructured (finite element/volume) grids) • Useful data-assimilative models & nowcast/forecast systems presently developed & applied • Weaknesses • Open boundary conditions • Topographic interactions • Sub-grid-scale parameterizations • Boundary layer resolution

41. Basin Scale and Global Models • Strengths • Large-scale response to atmospheric forcing • Useful data-assimilative models & nowcast/forecast systems presently developed & applied • Community models • Weaknesses • Unreliable surface fluxes • Mixed layer/thermocline interactions • Thermocline variability • Sub-grid-scale representations/parameterizations • Topographic effects • Coastal resolution • Numerics: advection, conservations schemes

42. ORION Recommendations • Next generation model development • Multiple models used in ensemble techniques • Prediction of uncertainty, error analyses • Adaptive models: grid-, model functional-, and data- adaptive • Interdisciplinary diagnostic tools/systems to extract fundamental biogeochemical-dynamical, energy and other balances • Interactive user-interfaces and modularizations • Fully coupled (land)-ocean-atmosphere interdisciplinary models for earth system modeling • Multi-domain, 2-way 3D nested models

43. Immediate Needs/Priorities and Opportunities

44. Immediate Needs/Priorities • Bottleneck #1: Cultural Issues • Full acceptance of models (i.e. willing to share resources) from the observational community for filling the envisioned central role of observatory design and analyses. • Remedy: • Bring together teams of observationalists and modelers FROM THE BEGINNING OF THE OBSERVATORY DESIGN PROCESS so that each group can better understand the concerns of the other.

45. Immediate Needs/Priorities • Bottleneck #2: Scientific Issues • Numerous research issues ranging from better representation of unresolved physics (and ecology and biogeochemistry) for improving forward models to more efficient data assimilation schemes and comprehensive analyses techniques that are designed specifically for the science issues of a particular observatory. • Remedy: • Elevate funding for models as equal priority as the field programs. • Modeling proposals should be submitted together with observatory proposals.

46. New Opportunities • ORION will create a strong foundation for ocean modeling well into the future IF we are willing to work in teams for accomplishing common objectives. • NSF, through its Environmental Observatory Initiatives, will enable a wonderful cross-disciplinary opportunity for sharing state-of-the-art models and modeling techniques IFwe are willing to take the time to learn each other’s issues. A golden opportunity for environmental modeling!

47. GRAND CHALLENGES OF THE FUTURE FOR ENVIRONMENTAL MODELING Modeling for Ocean Observatories: The ORION Program Lewis M. Rothstein Graduate School of Oceanography University of Rhode Island Environmental Observatories Modeling Workshop May 16 &17 - Tucson, AZ Sponsored by the National Science Foundation

48. Slides Not Used

49. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2005 RFA proposals submitted Development of IMP Development of IMP RFA Panel Preparation of Preliminary PEP 2006 RFP for RCO and CI IOs RCO and CIIO Selection RFP for Global and Coastal IOs IO Selection D&I Workshop Revise PEP Prep of Prelim. PEP CDR Global and CoastalIO Selection 2007 EA / EIS Award Negotiation FDR/ PDR NSB Prep. NSB Approval PEP to NSF Revise PEP Construction Phase 2008 EA / EIS Construction Phase Coastal first stage commissioned in 2008; need science experiments ready to start

50. Imagination is the beginning of creation. You imagine what you desire, you will what you imagine and at last you create what you will. George Bernard Shaw