Research Module F: Cooperative Model Framework for the NWA and CAG Module Lead: Jinyu Sheng, Katja Fennel, Randall Martin Dalhousie University

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1 Research Module F: Cooperative Model Framework for the NWA and CAG Module Lead: Jinyu Sheng, Katja Fennel, Randall Martin Dalhousie University Collaborating Researchers (with direct participations): Youyu Lu Senior Scientist at BIO of DFO, Adjunct at DAL Will Perrie Entcho Demirov Michael Li Eric Oliver Senior Scientist at BIO of DFO, Adjunct at DAL Associate Professor, MUN Senior Scientist at GSC of NRCan Emerging Researcher at DAL Other Collaborating Researchers: Richard Greatbatch (GEOMAR), Adreas Oschlies (GEOMAR), Jon Grant (DAL) Yongsheng Wu (BIO), Oylind Brevik (ECMWF), Fabrice Arhuin (IFREMER) John Warner (WHOI), Dave Whitehouse (Lloyd s Register) (November 2, 2016)

2 Overarching Objectives of Module F: a) Develop and calibrate an integrative, multi-scale, physicalbiogeochemical model framework for the NWA-CAG b) Simulate physical and biogeochemical conditions and extreme events in the past and future using the model framework (linked strongly to modules A-D, G, I, K, N-Q) c) Assimilate observations into the models (linked strongly to modules K, O-Q d) Investigate the effect of climate change on shifting ecosystem dynamics (linked strongly to modules K, O,-Q e) Help to develop regional-scale understanding of ocean changes in the NWA and CAG based on model results (linked strongly to modules under Ocean Frontier Changes

3 Components of the Proposed Cooperative Model Framework for the NWA and CAG An incremental approach will be taken in developing a coupled currents-ice-wave-sediment-biogeochemical model for the region. We will first develop and calibrate 7 components and then couple them either internally or through an external coupler (such as OpenPALM).

4 Level 1 Level 2 Level 3 Level 4 Focal area requiring a resolution of about 1 km or finer Topography of the northwest Atlantic Ocean

5 F1: Development and applications of a large-scale ocean circulation (parent) model Principal Investigator: Youyu Lu (BIO/DFO) Collaborators: Richard Greatbatch, Jinyu Sheng, Entcho Demirov, Will Perrie, Katja Fennel, Randall Martin Major tasks: Develop and improve a large-scale ocean circulation (parent) model for the NWA, including the Canadian Arctic Archipelago (CAG). Conduct multi-decadal hindcasts, sub-seasonal and/or seasonal prediction tests, and evaluation. Provide open boundary conditions to a fine-resolution ocean circulation (child) model to be developed by F2 (lead: J. Sheng). Provide large-scale circulation model for implementation of a biogeochemical model in F3 (lead: K. Fennel) Implement data assimilation to be developed by F6 (lead: E. Demirov).

6 Configuration of the large-scale ocean circulation model: based on NEMO (Nucleus for European Modelling of the Ocean) and multicategory ice model CICE. Domain cover 3 Oceans around Canada Two versions of horizontal resolution: ¼-deg in lat/lon (~25 km) for testing and debugging, 1/12-deg (~8 km) for final product. 75 vertical z-levels Forcing includes: tides & full set of atmospheric forcing Proposed work: Hindcast 1992-present ocean circulation using selected atmospheric reanalysis product Sub-seasonal & seasonal prediction experiments through collaboration with ECCC Model evaluation with a large variety of in situ and satellite remote sensing observations, Test and improve model s sub-grid mixing parameterization and new numerical schemes (e.g., for advection)

7 Some preliminary model results: (a) Simulated upper ocean currents (b) Simulated ice thickness (c) Inflow through Bering Strait

8 F2: Development and applications of fine-resolution ocean circulation (child) models Principal Investigator: Jinyu Sheng (DAL) Collaborators: Richard Greatbatch, Youyu Lu, Entcho Demirov, Katja Fennel Will Perrie, Michael Li, Eric Oliver Major tasks: Develop a relocatable, fine-resolution current-ice (child) model for coastal and shelf waters of the NWA-CAG. Nesting this child model inside a large-scale parent model to be developed by F1 (lead: Y. Lu). Implement biogeochemical model components in the high-resolution ocean circulation model in collaboration with F3 (lead: K. Fennel) Implement a data assimilation scheme into the child model in collaboration with F6 (lead: E. Demirov) Develop an integrated and coupled currents-waves-ice-sediment modelling system in collaboration with F1, F3-6. Simulate physical conditions and extreme events in the past and future

9 Examples of Model Results (Aug 31-Sep 15, 2012) (a) sea surface temperature ~7 km (b) sea surface salinity ~2 km Results over the region marked by the black box are produced by the fine-resolution (2 km) sub-model. Outside this region, the model results are produced by the coarse-resolution (7 km) sub-model. 9

10 F3: Development and applications of a coupled physicalbiogeochemical model Principal Investigator: Katja Fennel (DAL) Collaborators: Youyu Lu, Jinyu Sheng, Andreas Oschlies, Entcho Demirov, Randall Martin Main tasks: Implement biogeochemical model components in the large-scale circulation model for the NWA and CAA from F1 (lead: Y. Lu) Assimilate physical and biogeochemical observations into models (incl. SST, SLA and color from satellites, Argo and bioargo floats) Collaboration with F6 (lead: E. Demirov) and links with Module B (lead: D. Wallace) Implement biogeochemical model components in the high-resolution limitedarea ocean circulation model from F2 (lead: J. Sheng) where nesting information is supplied from large-scale physical-biogeochemical model Apply coupled biogeochemical models in studies of climate change impacts on primary production, oxygen and inorganic carbon in order to assess the effects of productivity changes, deoxygenation and acidification on biogeochemical cycling and marine species.

11 Biogeochemical variables of interest include: phytoplankton and zooplankton (which form the basis of the marine food web supporting higher trophic level species of commercial importance) inorganic carbon (which is relevant for quantifying the oceanic uptake of atmospheric CO 2 and ocean acidification) oxygen (declining oxygen trends in coastal regions of the NWA have already been shown to have negative effects on some species). Approach: Excretion and sloppy feeding Uptake Small phytoplankton NO 3 Nitrification Uptake and NH 4 exudation Large phytoplankton Zooplankton Uptake and exudation Sloppy feeding Semilabile DON Aggregation Large N detritus Small N detritus Remineralization Refractory DON Solubilization Sinking A representative cross-section of state-ofthe-art biogeochemical models will be implemented for coastal and deep-water sites, optimized and evaluated. Optimal model will be implemented in largescale and high-resolution models. Model skill will be assessed. Sinking N 2 Resuspension W State-of-the-art data assimilation technique Denitrification PON (deterministic Ensemble Kalman Filter with Burial localization) will be implemented in largescale Example of biogeochemical model structure model.

12 Constraining biogeochemical ocean dynamics remains a scientific challenge. (2) (1) Satellite observation of ocean color (1) are not sufficient. Profiling bioargo floats (2) provide vertical information and expand the suite of observable parameters (chl, POC, nitrate, oxygen, ph) potential for breakthrough. Ensemble-based assimilation has proven to be robust for satellite and profile observations. We have shown that physical and biogeochemical model state has to be updated, see (4), otherwise bgc state does not improve (3) or can be degraded. Nitrate profiles (3) Updates to bgc variables only True state Free run Assimilation (4) Updates to physics & bgc vars

13 F4: Development and applications of a circulation-wave model Principal Investigator: Will Perrie (BIO/DFO) Collaborators: Jinyu Sheng, Michael Li, Youyu Lu, Oylind Brevik, Fabrice Ardhuin, John Warner, Randall Martin a. Satellite image Major tasks: Implement a wave-current coupling component in the large-scale model for NWA-CAG from F1 (lead: Y. Lu) Implement a wave-current coupling component in the high-resolution ocean circulation model for selected areas with F2 (lead: J. Sheng) Assess and update model performance, with better physics, adapt models to allow sea ice, sediment in F5 (lead: M. Li), air-sea fluxes and lower atmosphere with links to F7 and Module A (lead R. Martin). Apply coupled wave-current model systems in climate change studies for impacts on upper ocean circulation, waves, TS, mixed layer depths, vertical mixing, air-sea fluxes with links to F3 (lead K. Fennel). b. Gulf Stream via SST (a) Low backscatter on (b) Gulf Stream warm side (b) at (70W, 38N) due to wave-current interactions linked to nonlinear Ekman divergence

14 Some major ocean surface processes: Wave breaking and Langmuir turbulence can significantly increase mixing and deepen the mixed layer depth in the upper ocean Coriolis-Stokes force plays a important role in setting up Ekman spiral Radiation stress or the equivalent form of a vortex force plus the Bernoulli head gradient that couple waves and the upper ocean Water-side stress as it depends on waves The air-ice-ocean system. From our 2015 field experiment in the Beaufort Sea. Approach: State-of-the-art community coupled model set up for selected coastal waters, tested and optimized. Modern updated physics will be put in for large-scale and highresolution coastal models. Model skill will be assessed. Up to 40% Impacts of currents on wave heights, Hs

15 Impacts of Langmuir turbulence on deepening mixed layer Percentage increase in mixed layer depth with wave forcing relative to no wave forcing when Langmuir turbulence is parameterized into a 1-D mixed layer model 180 days after a near-summer solstice initial profile. From D'Asaro et al. (2014, GRL)

16 F5: Development and applications of a coupled circulation-wave-sediment model Significance: Principal Investigator: Michael Li (GSC/NRCan) Collaborators: Jinyu Sheng, Will Perrie, Yongsheng Wu, John Warner kg/m/s Knowledge of seabed forcing, suspended sediment concentration and sediment transport for a shelf region is very important because of important applications such as: Evaluation of seabed stability for offshore engineering and energy projects Assessment of relative scales of natural disturbance versus impacts from developments Specification of ecological regions and sensitive habitats Facilitation of evaluation and approval of energy/engineering development plans and support ecosystem-based management of coasts and oceans Sediment transport flux at peak flood in Bay of Fundy

17 State of sediment transport modelling and issues: Depth-averaged currents used, overestimated bottom currents Currents and waves from separate models, not from coupled circulation-wave models No capacity to predict morphological changes and sediment erosion/accumulation patterns Approach and objectives: Evaluate, adapt, and implement a highresolution sediment transport model over 1-2 shelf regions within the large-scale NWA and CAG domain currents and wave data from the regional coupled circulation-wave models (from F2 and F4); Sediment model likely be coupled with the circulation model for feedbacks Currents will be 3D and focused on the nearbed currents Based on combined wave-current bottom boundary layer theories for enhanced bed stress and sediment transport Sediment transport on Grand Banks at the peak of the Feb 1982 storm kg/m/s Ultimate goals: Comprehensive sediment transport model will be developed and applied to predict bottom shear stress, suspended sediment concentration, sediment transport, seabed morphology, and sediment erosion and accumulation patterns on selected coastal-shelf regions

18 Sub-component F6: Development and applications of a computationally efficient data assimilation Major tasks: Principal Investigator: Entcho Demirov (MUN) Collaborators: Katja Fennel, Youyu Lu, Jinyu Sheng, Will Perrie Implement a data assimilation method in the large-scale model for the NWA and CAA from F1 (lead: Y. Lu) coupled with biogeochemical model from F3 (lead: Katja Fennel) Implement a data assimilation scheme in a high-resolution limitedarea ocean circulation model from F2 (lead: J. Sheng) coupled with biogeochemical model from F3 (lead Katja Fennel). Test and validate the assimilation scheme in hindcast simulations with the large-scale and coastal models. Assimilate physical and biogeochemical observations into the model (incl. SST, SLA and color from satellites, Argo and bioargo floats) Collaboration with F2 (lead: K. Fennel) and links with Module B (lead: D. Wallace)

19 Data assimilation: Main goal is to reduce uncertainty in ocean model simulations through assimilation of observations. The data assimilation scheme is based on the reduced order Ensemble Kalman filter (SEEK). The error statistics is represented in multivariate reduced space. The error is initialized by using EOFs and then is propagated forward by using the Kalman Filter equations. The SEEK method has been proved to be efficient approach, suitable for long term ocean and climate studies Approach: The model error statistics will be estimated based on ensemble model simulations for the large scale and coastal ocean models. SEEK algorithm will be implemented in the two models. The data assimilation method will be validated in hindcastsimulations

20 Pathfinder SST observations Assimilation of ocean observations Satellite observations of sea surface temperature, sea level anomaly and ARGO temperature and salinity profiles will be assimilated in the physical model. The satellite observations provide information for the surface. The data assimilation method projects observations into the water column in the dynamically consistent way. The SEEK data assimilation method can be easily extended towards assimilation of observations in coupled physical and bio-chemical models. AVISO SLA anomaly Previously SEEK data assimilation scheme has been successfully implemented and validated in assimilation of SSH and SST in a North Atlantic ocean model. The data assimilation has proved to be an efficient tool for dynamically consistent interpolation and interpretation of scattered in time and space observations by using ocean models. SSH Ocean simulation with data assimilation SST

21 F6: Development and Applications of Estimates of Atmospheric Nutrient Delivery (e.g. N, Fe) to the Surface Ocean Principal Investigator: Randall Martin (DAL) Collaborators: Katja Fennel, Youyu Lu Example: Satellite-Model Based Estimate of Changes in Deposition of Reactive Nitrogen (NO y ) over (Geddes and Martin, in prep) Geddes and Martin, in prep

22 Module Linkage 1) Research Module A: Marine Atmospheric Composition and Visibility (R. Martin) 2) Research Module B: NWA Carbon Sink (D. Wallace). 3) Research Module C: Microbial Community Structure (J. LaRoche) 4) Research Module D: Impact of Warming on Valued Atlantic Groundfish (S. Iverson) 5) Research Module E: Indicators of Ecosystem Change (P. Snelgrove) 6) Research Module G: Future-Proofing Marine Protected Area (MPA) Networks (B. Worm) 7) Research Module I: Informing Governance Responses in a Changing Ocean (R. Chuenpagdee)). 8) Research Module K: Novel Sensors for Farmed Fish Health and Welfare (J. Grant). 9) Research Module N: Marine Transportation Policy and Risk Reduction (R. Martin) 10) Research Module O: Transforming Ocean Observations (D. Wallace) 11) Research Module Q: Integrative Ocean Data Tools and Analytics (S. Matwin)