Towards a High-Resolution Global-

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Towards a High-Resolution Global- Ocean and
Sea-Ice Data Synthesis
Dimitris Menemenlis Jet Propulsion
Laboratory [phone] menemenlis_at_jpl.nasa.gov
http//ecco2.org Collaboration Chris Hill and
Patrick Heimbach at MIT, Chris Henze at NAS, and
many more ...

• OUTLINE
• Background and motivation
• ECCO2 project description
• Some early science results

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ECCO Estimating the Circulation and Climate of
the Ocean (SIO Stammer, et al., MIT Marshall,
Wunsch, et al., JPL Fu, Fukumori, et al.)
Mean hf corrections.
Std hf corrections.
Baseline cost function.
W/m2
Mean fw corrections.
Std fw corrections.
m/yr
Optimized cost funtion.

• Some 100 publications have originated from ECCO
  (http//www.ecco-group.org/publications.html) on
  ocean circulation (Gebbie et al. 2004), ocean
  biogeochemical/carbon cycle (McKinley et al.
  2004), air-sea fluxes (Stammer et al. 2004),
  subgridscale parameterizations (Ferreira et al.
  2004), earth rotation and polar motion (Gross et
  al. 2004), etc.

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DJF hindcast for March initial conditions
Initialization of Climate Forecasts
(Mechoso, Boezio, Spahr)
ECCO
baseline
persistence
Anomaly correlation
ECCO
baseline
ESMF superstructure
persistence
Boezio, et al. Impact of ECCO Ocean State
Estimates on the Initialization of Seasonal
Climate Forecasts. J. Climate, submitted.
Standard error
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Ocean Carbon Cycle (Gruber, Fletcher, Krakauer)
Gruber, et al. Oceanic sources and sinks of
atmospheric CO2. Global Biogeochem. Cycles,
submitted.Krakauer, et al. Carbon isotope
evidence for the latitudinal distribution and
wind speed dependence of the air-sea gas transfer
velocity. Tellus, in press. Fletcher, et al.
Inverse estimates of the oceanic sources and
sinks of natural CO2 and their implied oceanic
transport. Global Biogeochem. Cycles ,
revised.Fletcher, et al. (2006). Inverse
estimates of anthropogenic CO2 uptake, transport,
and storage by the ocean. Global Biogeochem.
Cycles, 20, GB2002.
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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.
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Preliminary optimization using Greens functions
Perturbation experiments are being computed for
different initial conditions, surface forcing
fields, and empirical model parameters. Figure
shows impact of changing sea-ice albedo.
Formally combining the different perturbations in
order to minimize the overall difference between
model and observations generates state estimates
that are consistent with both the underlying
model physics and the available observations
(Menemenlis, Fukumori, and Lee, 2005).
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Towards eddy permitting, adjoint-based estimation
Nested, eddy-permitting state estimation in the
North Atlantic Subduction region. (Gebbie, et al.)
Gebbie, Heimbach, and Wunsch. Strategies for
Nested and Eddy-Resolving State Estimation. J.
Geophys. Res., in press.
Eddy-permitting state estimation in the Southern
Ocean. (Mazloff, et al.)
Iteration 0
Iteration 15
State estimation with a coupled ocean and sea-ice
model. (Heimbach, et al.)
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Investigating global ocean model solution
convergence
1/16
1/4
1/8
1/4
1/8
1/16
Hill, Menemenlis, Ciotti, and Henze.
Investigating solution convergence in a global
ocean model using a 2048-processor cluster of
distributed shared memory machines. J.
Scientific Programming, submitted.
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Petabit Ocean Modeling
Boston, MA, August 2006
Chris Hill, MIT
Bryan Green and Chris Henze, ARC
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Depth of ocean surface turbulent mixing layer
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East-west wind stress
Surface fresh-water flux
Net short-wave radiation
Surface heat flux
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Example early applications of ECCO2
products Observing and Modeling Ocean Eddies
(Fu, JPL) Estimating representation errors for
non-eddying models (Ponte, AER) Estimating
global hydrographic variability (Forget,
MIT) Diagnosing eddy diffusivity (Marshall,
MIT) Informing parameterizations in climate
models (Ferrari, MIT) Interpreting altimetric
and reflectivity measurements over sea ice (Kwok,
JPL) Synthesis of the Arctic System Carbon Cycle
(Follows, MIT) Mode water formation (Maze, MIT)
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Observing and Modeling Ocean Eddies Containing
90 of the kinetic energy in the ocean, ocean
eddies (storm of currents) with scales from
10-100 km, are difficult to observe and simulate.
Using data from TOPEX/Poseidon, Jason, and ERS
radar altimeters, the energy level and
propagation velocity of eddies were estimated and
compared to a high-resolution simulation from the
MIT-JPL ECCO Model. Shown below is an example in
the Argentine Basin.
Eddy sea surface variability and propagation
velocity from altimetry
ECCO2 simulation
Coastal tides not included in the model
cm
cm
10 km/day
L-L. Fu D. Menemenlis
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Estimating representation errors for non-eddying
models Ponte (AER), et al. Objective is to
estimate altimeter instrument and representation
errors, which are needed for fitting ocean models
to altimeter sea surface height data. For
non-eddying models, representation errors are
dominated by mesoscale variability.
Estimate of standard deviation of the eddy
representation error obtained by subtracting sea
surface height variance of a 1o ECCO integration
from sea surface height variance of a 1/8o ECCO2
model integration.
Ponte, Wunsch, and Stammer. Spatial mapping of
time-variable errors in Jason-1 and
TOPEX/POSEIDON seasurface height measurements.
J. Atmos. Oceanic Tech., in press.
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Estimating global hydrographic variability
(Forget and Wunsch, MIT) Objective is to estimate
the three-dimensional global oceanic temperature
and salinity variability, omitting the seasonal
cycle, both as a major descriptive element of the
ocean circulation and for use in the error
estimates of state estimation.
Variance of T at 200m (left) and 400m (right) in
(oC)2 as estimated from the data (upper) and as
simulated by an ECCO2 simulation (lower).
Forget and Wunsch. Estimated global hydrographic
variability. J. Phys. Oceanogr., in press.
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Eddy diffusivity diagnosed from model (Marshall,
et al., MIT)
K (m2/s)
Ganachaud and Wunch (2000) ECCO2 estimates
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Informing parameterizations in climate models
(Ferrari, MIT)
0m 100m 200m 300m 400m 500m
Heat transport vT across 47S
Standard GM tapering (non-eddying CCSM)
GM FM (transition zone smooth tapering)
Diagnosed from 1/8o ECCO2 simulation
ECCO2 simulation shows much better agreement with
inferences from floats, etc. It serves as
benchmark/ground-truth for the GMFM
parameterization.

-5 0 5 10 15 20 25
TW/m
Ferrari and McWilliams. Parameterization of eddy
fluxes at the ocean boundaries, Ocean Modeling,
submitted. Danabasoglu, Ferrari, and McWilliams.
Sensitivity of an ocean general circulation model
to a parameterization of near-surface eddy
fluxes, J. Climate, submitted.
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Interpretation of ICESat altimetric and
reflectivity profiles Kwok (JPL), et
al. Objective is to provide an assessment of the
ICESat altimeter for studying the Arctic Ocean
and to examine the magnitude of the large- and
small-scale expressions of geophysical processes
embedded in the elevation profiles.
Kwok, Cunningham, Zwally, Yi (2006). ICESat over
Arctic sea ice Interpretation of altimetric and
reflectivity profiles. J. Geophys. Res., vol.
111, doi10,1029/2005JC003175.
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Synthesis of the Arctic System Carbon Cycle
McGuire (Fairbanks), Follows (MIT), et al.
Objective is to study Arctic region carbon cycle,
including (a) exchanges between marine and
terrestrial carbon pools and (b) possible
exchanges between these large carbon reservoirs
and the atmosphere. ECCO2 solutions will be used
to drive offline carbon/biogeochemistry models
leading to improved estimates and models of
air-sea-land-ice exchanges of CO2 in the presence
of realistic eddying flows and with active
biological and chemical processes.
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Mode water formation
(Marshall, Maze, and Hill, MIT) Objective is to
study the dynamics of eighteen-degree mode water
formation in the North Atlantic through
analytical, numerical and observational
approaches.
Marshall (2005). CLIMODE a mode water dynamics
experiment in support of CLIVAR. Clivar
Variations, vol.3, pp. 8-14.
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Mode water formation White streaks are wind
stress. Green contours are mixed layer
depth. Purple contours are 16oC contours at
200m. Red and blue shading indicate heat flux.
(Henze, ARC)
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http//ecco2.org
Questions?