L. Padman, S. Erofeeva, G. Egbert Presentation at - PowerPoint PPT Presentation

1 / 14
About This Presentation
Title:

L. Padman, S. Erofeeva, G. Egbert Presentation at

Description:

AGU Ocean Sciences Meeting, Portland OR, January 2004. 1. A Barotropic Inverse Tidal Model ... Archipelago. 9/16/09. L. Padman, S. Erofeeva, G. Egbert; Presentation at ... – PowerPoint PPT presentation

Number of Views:36
Avg rating:3.0/5.0
Slides: 15
Provided by: coas80
Category:

less

Transcript and Presenter's Notes

Title: L. Padman, S. Erofeeva, G. Egbert Presentation at


1
A Barotropic Inverse Tidal Modelfor the Arctic
Ocean
  • S. Erofeeva 1, L. Padman 2, G. Egbert 1
  • 1 Oregon State University, Corvallis, OR
  • 2 Earth Space Research, Seattle, WA
  • Sponsored by NSF Polar Programs (Arctic)

2
Why study Arctic tides?
  • Tides as noise
  • Remove ocean tide and load tide on solid earth
    from satellite gravity records (e.g., GRACE).
  • Remove tidal currents from vessel ADCP records.
  • Tides as signal
  • Tidal currents contribute to ocean mixing,
    rectified mean flows.
  • Periodic divergence of tidal currents affects ice
    roughness, mean open water fraction, mean ice
    formation rates.

3
Model domain and data sites
  • Tide gauge data () prov-ided by A. Proshutinsky
    and G. Kivman
  • ERS and TOPEX/Poseidon radar altimetry (lilac and
    yellow dots, respectively) provided by R. Ray and
    B. Beckley
  • 7 subdomains.

Water depth (m)
4
Steps to obtain an inverse model
  • Get a prior solution
  • Define bathymetryIBCAO
  • Set dynamic equations....linearized SWE
  • Choose open boundary conditionsTPXO6.2.
  • Assimilate data (EBF 1994, EE 2002)
  • Set linearized dynamic equations
  • Assign errors in dynamics and data
  • Minimize quadratic penalty functional sum of
    dynamics and data misfits weighted with error
    covariances.

5
Prior model main features
  • 8 constituents M2,S2,N2,K2,K1,O1,P1,Q1
  • High resolution (5 km) Bathymetry from IBCAO
  • Dynamics is based on shallow water equations
    (SWE) solved by direct matrix factorization.
    Simplifications to the SWE include
  • tidal loading and self attraction computed from a
    global model (TPXO6.2)
  • linear benthic friction, F(r/H)U, where r is the
    friction velocity, H is the water depth, and U is
    the depth-integrated transport (r0.5 m s-1 for
    semi-diurnal constituents and 2 m s-1 for
    diurnals)
  • No sea ice.
  • Open boundary conditions taken from the latest
    global tidal model TPXO6.2, 1/4º resolution.

6
Tide height fields Semidiurnal (M2)
Diurnal (K1)
(From 5-km inverse solution)
7
Elevation comparison of the prior and KP94
semi-diurnals
M2 Amp(Prior)-Amp(KP94) (cm)
White Sea
Canadian Arctic Archipelago
8
Elevation comparison of the prior and KP94
diurnals
K1 Amp(Prior)-Amp(KP94) (cm)
Baffin Bay Archipelago
9
Inverse model main features
  • Corrects 4 most energetic constituents M2, S2,
    K1, O1
  • Assimilates 364 cycles of T/P data (11178 sites),
    108 cycles of ERS data (18224 sites), 250-310
    tide gauges per constituent
  • Dynamics covariance as in EBF 1994, decorrelation
    length scale 50 km
  • Data error set for TG 0.05 cm, estimates for
    satellite 3 cm (up to 20 cm)
  • Use effective data assimilation scheme
    implemented in OTIS (OSU Tidal Inversion
    Software) http//www.oce.orst.edu/po/research/tide
    /index.html.

10
Elevation comparison of prior and inverse
semi-diurnals
M2 Amp(Inverse)-Amp(Prior) (cm)
White Sea
11
Elevation comparison of prior and inverse
diurnals
K1 Amp(Inverse)-Amp(Prior) (cm)
Baffin Bay Archipelago
12
Model comparisons to tide gauge data
Semidiurnal M2 Diurnal . K1
13
Mean tidal current speed
  • The calculation is based on simulating 14 days of
    hourly total tidal speed from the inverse
    solution
  • Spring tide maximum speed
  • umax?2u

u (cm s-1) logarithmic color scale
14
Conclusions
  • Averaged over the entire model domain, the M2,
    S2, K1 and O1 tides account for 79, 10, 5 and
    1 of the total (8 constituent) tidal potential
    energy, respectively. Tide height variability is
    overwhelmingly dominated by M2.
  • Inverse Arctic tide model is most consistent with
    available tide gauge data and satellite
    altimetry. Long term goal to develop
    dynamics-only models with comparable accuracy.
  • Further improvements are likely through increased
    resolution, addition of ice-ocean interactions,
    and more sophisticated dissipation
    parameterizations, including benthic friction and
    the conversion of barotropic tidal energy to
    internal tides .
  • The inverse model is available from
    http//www.oce.orst.edu/po/research/tide/Arc.html
    (Fortran-based) and http//www.esr.org/arctic_tide
    s_index.html (Matlab-based).
Write a Comment
User Comments (0)
About PowerShow.com