COAS-CIOSS Coastal Ocean Modeling Activities Coastal Ocean Modeling Studies at COAS are focused on: Wind-driven upwelling and downwelling [Allen et al.] flow-topography effects [Gan, Kuebel Cervantes, Whitney, Kurapov et al.] nonlinear evolution of frontal instabilities [Durski et al.] ocean-atmosphere feedbacks [N. Perlin, Skyllingstad, Samelson, CIOSS] bio-physical interactions [Spitz et al.] Coastal / interior ocean interactions in the Coastal Transition Zone (CTZ) [B.-J. Choi (CIOSS), S. Springer et al.] Data assimilation [Kurapov, Allen, Egbert, Miller] Real-time ocean prediction [Erofeeva, Kurapov et al. (CIOSS)]

How does coastal ocean modeling help address CIOSS goals? Dynamical interpretation of physical features apparent in satellite data (on the shelf and in the coastal transition zone) Assimilation of satellite data, together with other data (providing dynamically based interpolation and mapping of the satellite data; filling gaps in space and time) Analysis of physical models, integrated with observations, - to improve scientific understanding of the ocean dynamics, and - to predict the ocean dynamics

Coastal Ocean Dynamics off Oregon: Movie: surface T and tracers COAST Observing System, summer 2001 HF radars (Kosro) Moorings (ADP, T, S: Levine, Kosro, Boyd) Model: space-time continuous solutions (velocity, T, S, turbulence quantities) (development of upwelling 12-22 June 2001, Princeton Ocean Model solution constrained by assimilation of COAST data)

Topographic effects [Kurapov et al., JPO, 2005]: On the mid-shelf, bottom mixed layer thickness is small at 45N, large at 44.4N in response to upwelling dB at 45N, H=98 m dB east of Stonewall Bank Turbulent KE in response to the upwelling event (day 170, 2001): Depth, 0 – 100 m At 45N At 44.4N 44.4N: As a result of bottom Ekman transport convergence, thinner surface BL, thicker bottom BL lon, W

Accurate representations of coastal upwelling processes must Coupled Ocean-Atmosphere Modeling (N. Perlin, Skyllingstad, Samelson, with support from CIOSS and ONR): Accurate representations of coastal upwelling processes must include ocean-atmosphere interactions on short temporal and horizontal scales COAMPS: wind, heat Effect of coupling on atmos. eddy visc. … cold water ROMS: upwelling response (T) Initial value problem: run from rest for 72 h heat flux… wind stress [N. Perlin et al. JPO, submitted]

[B.-J. Choi (GLOBEC-NOAA, CIOSS), S. Springer (NOPP)] Dynamical coupling of the coastal ocean and California Current System (CCS) flows through the Coastal Transition Zone (CTZ) [B.-J. Choi (GLOBEC-NOAA, CIOSS), S. Springer (NOPP)] - Unstable, separating coastal flows feed into the CCS - Mesoscale eddies (CCS) affect variability in the coastal waters Nesting: 9 km NCOM-CCS Atm. forcing: COAMPS (16-km) [J. Kindle (NRL)] 3 km ROMS-CTZ Open boundary conditions: appropriate for advective currents, coastal trapped waves, tides, Rossby waves, Columbia R. SSH (5/31/02): NCOM ROMS, NCOM Can nesting improve the prediction of coastal currents? Can data assimilation help? 0.3 0.1 -0.1 m

NCOM ROMS, NCOM SST (5/31/02): higher spatial variability in ROMS SST Surface Salinity: inclusion of Columbia R. in ROMS

Model-data comparisons: NCOM vs. alongtrack SSH altimetry Demeaned alongtrack SSH: T/P, NCOM SSH RMS diff. (NCOM – Altim.), 2002 8 (cm) 4 SSH correlation (NCOM, Altim.), 2002 1 -0.5 -130 -128 -126 -124 longitude along satellite track 40 -5 cm Even though NCOM-CCS assimilates SSH using “nudging”, the data are not fit particularly well in the CTZ Room for improvement: assimilate alongtrack SSH, together with other obs. in the CTZ domain model

Data assimilation (DA) [Kurapov, Allen, Egbert, Miller, ONR] Approach: complicated, fully-nonlinear model + simple DA simplified models + rigorous variational DA Merger of approaches: nonlinear models + variational DA Princeton OM + sequential Optimal Interpolation: assimilate HF radar (Oke et al. JGR 2002), moored velocity data (Kurapov et al. 2005abc) Assimilation of moored ADP data: improves model accuracy at a distance of 90 km in the alongshore dir. improves prediction of T, S, SSH near coast, near-bottom turbulent dissipation

Variational DA: fit the model solution to the observations over a given time interval (by correcting errors in the inputs) Minimize the penalty function: J(u) = || model error ||2 + || obs. error ||2 Obtain information on the source of model error Utilize (compute) state-dependent model error covariance Assimilate observations (incl. satellite SSH, SST, HF radar) w/out pre-processing the observations into maps Variational DA: utilizes tangent linear and adjoint models, algorithmically complicated, computationally challenging Our path: representer-based optimization (following methodology developed by Bennett, Egbert, et al.) The nonlinear optimization problem is approached as a series of linearized problems. Each linearized problem: search for the solution correction in a relatively small subspace spanned by K representer solutions, where K is the number of observations (still, no need to compute all the representers)

Tests of the representer-based method (with the shallow-water model, describing flows in the near-shore surf zone): - assimilation in presence of instabilities, intrinsic eddy interactions - correction of forcing, open boundary conditions Equilib. shear wave regime (T=60 min) More irregular regime (T=5 min) True solution (shown is vorticity) Unsteady solution in response to steady forcing (Prior = 0) DA solution: assimilate time series of z, u, v at 32 pnts correct IC, forcing Click on frame to play movie (left for 60 min, right for 5 min).

Real-time Oregon coastal simulation system (Erofeeva, Kurapov, Samelson, Egbert, CIOSS) ROMS (Dx = 2 km), forced with 3-day atmospheric NAM forecast: daily update Model data comparisons: SSH, SST (incl. monthly climatologies), HF radar data HF radar (Kosro) forecast (5/11/06) This looks way too good… somebody must be cheating… Additional QC: coordinated with the NOAA-funded OrCOOS pilot project (J. Barth, R. K. Shearman)

SUMMARY: 1. Research involving coastal ocean modeling has been focused on flow/topography, ocean/atmosphere, CCS/shelf flow interactions. 2.Variational DA has the potential of providing new versatile tools for synthesis of satellite, in-situ and land-based HF radar observations. 3. Work to advance the real-time Oregon Coastal Simulation System will be leveraged by efforts on ongoing GLOBEC, NOPP, and ONR research projects improved ROMS configuration DA (alongtrack SSH, HF radar) 4. The real-time modeling system will become an integral part of the emerging OrCOOS, facilitating interactions within COAS research community