West Coast Modeling Igor Shulman, NRLSSC ePOPf WC Modeling Workshop, Portland, Oregon, September 20-22, 2010

West Coast Models (NCOM CCS) 41 hybrid vertical levels (19 sigma layers, 21 –z- layers) 9km - 115W to135W ; 30N to 49N 3.5km -115W to 135W ; 30N to 52N atmospheric forcing - COAMPS TM (Doyle et al., 2009). open boundary conditions - the NRL 1/8° Global NCOM (Rhodes et al., 2002, Barron et al., 2004). River inflows from monthly climatology 9km model assimilate satellite SST and SSH through MODAS (Fox et al., 2002). 3.5km model uses the NCODA (Cummings, 2006,2010) for assimilation of satellite altimeter observations, satellite and in-situ sea surface temperature, vertical temperature and salinity profiles from gliders, ships, moored buoys Includes ecosystem model based on the formulation of Chai et al. (2002) and Fujii et al. (2007). In real-time mode, models run experimentally in support of the real-time field programs and requests of research communities along the US West Coast. ~9km ~3.5km

NRL Coupled Ocean Data Assimilation system (NCODA) Model forecast fields and prediction errors are used in the QC of newly received ocean observations Ocean Model Ocean QC 3D MVOI Ocean Obs Innovations Increments Forecast Fields Prediction Errors First Guess Data Assimilation Utilize Data Assimilation systems which are being transitioned to NAVO Cummings, 2006, 2010 MCSST GOES SST LAC SST Altim. SSHA Moorings CTDS Ship CTDs Gliders CTDs SSM/I Sea Ice 3D VAR

Nitrate [NO 3 ] Advection & Mixing NO 3 Uptake Micro- Zooplankton [Z1] Grazing Ammonium [NH 4 ] Excretion NH 4 Uptake Detritus-N [DN] Fecal Pellet Sinking Silicate [Si(OH) 4 ] Small Phytoplankton [P1] Diatoms [P2] Si Uptake N-Uptake Meso- Zooplankton [Z2] Sinking Detritus-Si [DSi] Grazing Fecal Pellet Sinking Predation Lost Carbon Dioxide [TCO 2 ] Oxygen [O 2 ] Phosphate [PO 4 ] Alkalinity [ALK] Air-Sea exchange Respiration Photosynthesis Physical Model CoSINE, Fei Chai et al.

Monterey Bay models 1-4km, 30 sigma layers km 50 hybrid (sigma-z) vertical layers atmospheric forcing - the Navy Coupled Ocean and Atmospheric Mesoscale Prediction System (COAMPS TM ) (Doyle et al., 2009). open boundary conditions - the NCOM CCS models River inflows from monthly climatology The Navy Coupled Ocean Data Assimilation (NCODA. Cummings, 2006, 2010) is used for assimilation of satellite altimeter observations, satellite and in-situ sea surface temperature as well as available in-situ temperature and salinity profiles from gliders, ships, moored buoys. includes ecosystem model based on the formulation of Chai et al. (2002) and Fujii et al. (2007). In real-time mode, the model was run in support of the real-time experiments.

AOSN (Autonomous Ocean Sampling Network) Platforms, sensors and integrative models 2000, 2003, 2006

Impact of glider data assimilation during AOSN II (2003) and ASAP (2006) experiments. (2003) (2006) The Monterey Bay model domain (bounded by black lines), areas sampled by Slocum (blue) and Spray (green) gliders in August of 2003 (AOSN II) and area of the ASAP experiment (red) in 2006 Observed SSH anomalies at tide gauge stations at Monterey Bay (top) and San Diego (bottom) for July through August of 2003 (red) and 2006 (blue) over climatological mean (black) and standard deviation (shaded area). I. Shulman, S. Anderson, C. Rowley, S. DeRada, J. Doyle, S. Ramp, JGR (Oceans), 2010

Remote forcing during the ASAP. Hovmoeller diagram of the HYCOM global model SSH anomaly along the West Coast for May-September of Figure shows coastal Kelvin waves propagating along the coast. HYCOM predictions show good agreement with observed tide gauge measurements at Monterey Bay and San-Diego stations. Observations are black lines, HYCOM predictions are blue lines. Monterey Bay station location San Diego station location

Anomalous Conditions during MB2006 Currents at M1 AOSN II, 2003 MB2006 Northward flow during relaxation Northward flow during upwelling Southward flow during upwelling Southward flow during relaxation upwelling relaxation

No Assimilation With assimilation glider data TemperatureSalinity Impact of glider data assimilation. AOSN II, Aug. 2 – Sept. 3, 2003 Observations M2 With assimilation glider, ship, aircraft data M2 I. Shulman, C. Rowley, S. Anderson, S. DeRada, J. Kindle, P. Martin, J. Doyle, S. Ramp, F. Chavez, D. Fratantoni, R. Davis and J. Cummings, DSRII, 2009

M1 No Assimilation With assimilation glider data Temperature Salinity MB2006, Aug. 1 – Aug. 17, 2006 Impact of glider data assimilation. gliders With assimilation glider, ship, aircraft data Observations M1 During MB2006 glider tracks were to the north of moorings, for this reason glider data assimilation has minimal impact on model predictions at mooring locations. In opposite to ASON |Model predictions with only glider data assimilated are different from model predictions when other observations were also assimilated.

Comparisons of SSH anomalies from the global HYCOM and regional NCOM CCS models. Monterey Bay station location San Diego station location The HYCOM matches very well observations. The NCOM CCS underestimates observed SSH anomalies. There are distinct differences at the open boundary of the Monterey Bay model (~35.5N). Black – observations Blue - Global HYCOM Red - regional NCOM CCS Open Boundary

Nesting in NCOM and HYCOM REGIONAL NCOM CCS GLOBAL HYCOM GLOBAL NCOM NCOM ICON

Evaluation of the NCOM Monterey Bay model currents predictions on the shelf at ADCP1 and ADCP2 sites Remote forcing at open boundary of the NCOM ICON model is important for predictions of currents on the shelf (at ADCP1 and ADCP2). The NCOM ICON model run nested in the Global HYCOM (with good representation of the remote forcing) shows good agreement with observed currents on the shelf. Table. Complex correlations and angular displacements (in accord to Kundu, 1976) between model-simulated and observed currents at ADCP 1 and ADCP 2. The NCOM ICON runs are nested in Regional NCOM California Currents system (NCOM CCS) or global HYCOM.

Objectives: Approach: Use nested, coupled physical-bio-optical models of the coastal region Combine bio-optical and physical in-situ and remotely sensed observations with the model predictions via data assimilation. Inherent Optical Properties (IOPs) based approach for underwater light predictions Leverage multi-institutional collaborations and field programs in the Monterey Bay Science Issues: Global Regional Coastal models BIOSPACE Bio-optical Studies of Predictability and Assimilation in the Coastal Environment (FY08-FY12) Improve our understanding of: The variability and predictability of the underwater light and bio-optical, physical properties on time scales of 1 to 5 days. Coupled bio-optical and physical processes in the coastal zone. How predictable are the optical properties in coastal ocean on 1-5 days time scales (TS)? How bio-optical properties are impacted by changes in physical regimes? Upwelling/relaxation, fronts, upwelling shadow area How complex a bio-optical modeling is required to represent variability of optical properties on 1-5 TS? What are model uncertainties and sensitivities on 1-5 TS? Metrics for assessing skill of coupled models. What are effective coupled bio-optical physical data assimilation schemes? What are optimal and adaptive strategies in sampling variability of bio-optical, physical properties in coastal ocean.

Ensemble of the Monterey Bay model runs are being created by varying: Initial and boundary conditions: nesting inside different realizations of regional NCOM CCS model; nesting inside GODAE products: HYCOM Global, NCOM Global; nesting in climatology Atmospheric forcing: COAMPS 3km vs 9km nests. Modifications to SWR (PAR) and bulk heat formulations Smoothed bathymetry (sigma coordinate) and unsmoothed bathymetry (hybrid, sigma-z coordinates); Physical and Bio-chemical model parameters and light propagation schemes Assimilation of different observational assets (data denial experiments: no assimilation, assimilation of only gliders, assimilation of only cruise data etc) Different horizontal resolution of the Monterey Bay models The ensemble are being used to: assess model uncertainty and errors; estimate coupled error covariances; order reduction of the data assimilation problem What are model uncertainties and errors?

Combining biochemical, physical and bioluminescence intensity models Water-leaving radiance at the surface due to stimulation of the modeled BL intensity at different depths (right panels). The modeled BL intensity for different depths of stimulations (middle panels), and a sum of a (absorption) and b b (backscattering) averaged from the depth of BL stimulation to the surface (left panels).

Research Topics (projects) Modeling dynamics of bio-optical layers in coastal ocean Developing Ensemble Methods to Estimate Uncertainties in Remotely-Sensed Optical Properties Resolving bio-optical feedbacks to ocean/atmosphere dynamics

Data Assimilation (combining model predictions and observations) NRL Coupled Ocean Data Assimilation system (NCODA) Multivariate bio-optical, physical data assimilation (Poster by S. Frolov) Variational Data Assimilation

2010 Field Program (Oct. 9 th - 22 th ) Study of formation, dispersal, and decay of phytoplankton blooms in the northern Monterey Bay. Platforms (in progress…): NRLSSC : 4 Slocum gliders, Pt. Sur bio-optical physical surveys, Scanfish, SEPTRs (BOPPER), imagery (MODIS, MERIS, HICO) NRLDC : hyperspectral aircraft surveys MBARI, CenCOOS : Western Flyer surveys (end of September), support of Pt. Sur surveys, AUV Dorado, drifters, LRAUVs, moorings, ESP USCS : John Martin surveys, hyperspectral overflights Stanford, Berkley, UH : mooring array, REMUS AUV, acrobat towed vechile, dye releases. CalPoly: REMUS AUVs (2), REMUS 600 Rutgers : 2 Gliders NPS : glider

Global Assimilative Ocean Modeling at the Naval Research Laboratory Slides from Charlie N. Barron Patrick J. Hogan Naval Research Laboratory, Stennis Space Center, MS

Changes in operational versions require a validation test report (VTR) and OPTEST. 22 Comparing observed and predicted surface drifter trajectories in GOFS 2.0. MLD (Kara): |GOFS 2.5 mean error| - |GOFS 2.6 mean error| Comparing error in predicting mixed layer (MLD) depth between GOFS 2.5 and 2.6. Comparing forecast skill in predicting acoustically- relevant properties among GOFS alternatives

Global SSH on 23 Mar 2007 Gray areas are ice covered SSH and independent IR frontal analysis: 10 March 2008 Frontal analysis < 4 days old = white, analysis ≥ 4 days old = black Running in real time, soon to be declared operational Global 1/12° HYCOM with NCODA Data Assimilation Horizontal grid: 1/12° equatorial resolution 4500 x 3298 grid points, ~6.5 km spacing on average, ~3.5 km at pole Mercator 79°S to 47°N, then Arctic dipole patch Vertical coordinate surfaces: 32 for σ 2 * KPP mixed layer model Thermodynamic (energy loan) sea-ice model Surface forcing: FNMOC NOGAPS 0.5° wind stress, wind speed, thermal forcing, and NOGAPS 1.0° precipitation Monthly river runoff (986 rivers)

1/12º Global HYCOM SSH and surface drifters in the Kuroshio Region Unassimilated drifters are independent evaluation