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ECCO2: HighResolution GlobalOcean and SeaIce Data Synthesis

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Comparison of WOA01 potential temperature in top 750 m with time ... MITgcm assimilation efforts (Cornuelle, SIO) Adjoint assimilation efforts (Edwards, UCSC ) ... – PowerPoint PPT presentation

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Title: ECCO2: HighResolution GlobalOcean and SeaIce Data Synthesis


1
ECCO2 High-Resolution Global-Ocean and Sea-Ice
Data Synthesis
MIT Marshall, Heimbach, Hill Wunsch JPL Fu, Kwok,
Lee Menemenlis Zlotnicki GSFC Rienecker
Suarez ARC Henze, Taft HARVARD Tziperman GFDL Adcr
oft ARGONNE Hovland, Utke
Velocity (m/s) At 15 m depth
Objective synthesis of global-ocean and sea-ice
data that covers the full ocean depth and that
permits eddies. Motivation improved estimates
and models of ocean carbon cycle, understand
recent evolution of polar oceans, monitor
time-evolving term balances within and between
different components of Earth system, etc.
2
Comparison of WOA01 potential temperature in top
750 m with time-mean estimates contributed to
first CLIVAR/GODAE Meeting on Ocean Synthesis
Evaluation, August 2006, Reading, UK.
3
Greens Function Estimation Approach (Stammer and
Wunsch 1996 Menemenlis and Wunsch 1997
Menemenlis, Fukumori, and Lee 2005)
GCM
x(ti1) M(x(ti),?)
Data yo
H(x) ? G(?) ?
Cost function J
?TQ-1? ?TR-1?
Linearization G(?)
G(0) G? G is an np matrix, where n is the
number of observations in vector yo and p is the
number of parameters in vector ?. Each column of
matrix G can be determined by perturbing one
element of ?, that is, by carrying out one GCM
sensitivity experiment.
GCM-data residual yd yo
G(0) G? ? Solution
?a
PGTR-1yd Uncertainty covariance P
( Q-1 GTR-1G )1 The solution satisfies the
GCMs prognostic equations exactly and hence it
can be used for budget computations, tracer
problems, etc.
4
Evaluation of first ECCO2 Greens function
optimization
Winter ice extent compared to passive microwave
data
Drake Passage Transport (Schodlok)
Weddell Sea T/S diagram (Schodlok)
5
22-23 January 2007 ECCO2 meeting Early User
Applications
Subtropical mode water (Maze, MIT) Eddy
propagation velocity (Fu, JPL) GRACE data
constraints (Zlotnicki, JPL) Errors estimates
(Forget, MIT) Eddy parameterizations (Ferreira,
MIT) Arctic freshwater budget (Condron,
WHOI) Arctic sea ice budget (Kwok, JPL) Sea ice
data/model comparison (Nguyen, JPL) Carbon cycle
modeling (Manizza, MIT) Eddy variability in
Indian Ocean (Lee, JPL) Darwin project (Hill,
MIT) Southern Ocean (Schodlok, JPL) MITgcm
assimilation efforts (Cornuelle, SIO) Adjoint
assimilation efforts (Edwards, UCSC )
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