ECCO2: High resolution global ocean and sea ice data synthesis

Dimitris Menemenlis^1^, Jean-Michel Campin^2^, Patrick Heimbach^2^,

Chris Hill^2^, Tong Lee^1^, An Nguyen^1^, Michael Schodlok^1^, and Hong
Zhang^1^

^1^*Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, USA*

^2^*Massachusetts Institute of Technology, Cambridge, USA*

**Abstract**

The Estimating the Circulation and Climate of the Ocean (ECCO) project
was established in 1998 as part of the World Ocean Circulation
Experiment (WOCE) with the goal of combining a general circulation model
(GCM) with diverse observations in order to produce a quantitative
depiction of the time-evolving global ocean state. Such combinations,
also known as data assimilation, are important because available
remotely sensed and in-situ observations are sparse and incomplete
compared to the scales and properties of ocean circulation. These
combinations also provide rigorous consistency tests for models and for
data. In contrast to numerical weather prediction that also combines
models and data, ECCO estimates are physically consistent; in
particular, ECCO estimates do not contain discontinuities when and where
data are ingested. First generation ECCO solutions are available and
widely used for numerous science applications but the coarse horizontal
grid spacing and the lack of Arctic Ocean and of sea ice of these
first-generation solutions limits their ability to describe the real
ocean. To address these shortcomings, the follow-on ECCO, Phase II
(ECCO2) project aims to produce a best-possible, global, time-evolving
synthesis of most available ocean and sea-ice data at a resolution that
admits ocean eddies. A first ECCO2 synthesis for the period 1992--2007
has been obtained using a Green\'s Function approach (Menemenlis, et
al., 2005a) to estimate initial temperature and salinity conditions,
surface boundary conditions, and several empirical ocean and sea ice
model parameters. Data constraints include altimetry, gravity, drifter,
hydrography, and observations of sea-ice. A large complement of
high-frequency and high-resolution diagnostics has been saved; these
diagnostics are made available to the scientific community via ftp and
OPeNDAP servers at [http://ecco2.org](http://ecco2.org/). This Note
provides a brief overview of this first ECCO2 synthesis and of some
early science applications.

**Introduction**

Physically consistent estimates of ocean circulation constrained by in
situ and remotely sensed observations, as produced by the ECCO project,
have now become routinely available and are being applied to myriad
scientific applications (Wunsch and Heimbach, 2007). The coarse
horizontal grid spacing of current-generation ECCO solutions, however,
is a severe limitation on their ability to describe the real ocean, for
example, mesoscale eddies, flow over narrow sills, boundary currents,
and regions of deep convection and of restratification. Despite the very
great progress made toward parameterizing sub-grid scale processes, some
problems remain intractable through this route. First, eddy
parameterizations are not based upon completely fundamental principles
and they fail to adequately account for known anisotropies in their
fluxes. Everything we know about eddies suggests that their property
fluxes can accumulate in the ocean, changing it measurably and
importantly from what it would be if eddies were absent. That is, the
long-wavelength, low-frequency features characterizing climate are
controlled in part by eddy fluxes. Second, studies have shown that
horizontal grid-spacing of order 2 km is required to resolve
restratification processes, in which stratified fluid in the periphery
of the convection patch is drawn over the surface, allowing the
convected fluid to be \`swallowed\' by the ocean. If restratification is
not represented adequately, then the water-mass properties of the
modeled ocean deteriorate over time, there are model drifts, and the
attendant air-sea fluxes become compromised and have to be
\`corrected\'. Restratification of mixed layers is a ubiquitous feature
of the ocean but is particularly important in strong frontal regions and
in regions of deep-water formation. Third, scalar property transports
(heat, fresh water, carbon, oxygen, etc.) are of central interest for
climate studies and in the ocean, narrow western and eastern boundary
currents make major contributions; these boundary currents are not
parameterizable and, until they are resolved, there will always be
doubts that the ocean model is carrying their property transports
realistically. Ultimately, water mass properties in the ocean are
important to climate and climate change. In the abyssal ocean, the
inability to resolve major topographic features, e.g., fracture zones
and sills, leads to systematic errors in the movement of deep-water
masses with consequences, for example, on the accuracy of computation of
carbon uptake.

Another limitation of current-generation ECCO solutions is that they
exclude the Arctic Ocean and that they lack an interactive sea-ice
model. This restricts the use of satellite data over ice-covered regions
and the usefulness of current-generation solutions for describing and
studying high-latitude processes. Coupled ocean and sea ice state
estimation is an integral component of the ECCO2 project. The inclusion
of an interactive sea-ice model provides for more realistic surface
boundary conditions in Polar Regions and allows the model to be
constrained by satellite observations over ice-covered oceans. The
sea-ice model also provides the ability to estimate the time-evolving
sea-ice thickness distribution and to quantify the role of sea-ice in
the global ocean circulation. Improved representation of high-latitude
processes will enhance hindcasting and forecasting capability, both of
which are needed for climate-change studies, for the design of in-situ
measurement campaigns, and for operational purposes, e.g., navigation,
drilling activity, wildlife behavior, and dispersion of pollutants.
Improved representation of high-latitude processes is also a crucial
requirement for reducing uncertainty in predictions of the climate
response to prescribed increases in greenhouse gases and in predictions
of how the oceanic sink for greenhouse gases might change in the future
in response to a changing climate.

**Model description**

ECCO2 data syntheses are obtained by least squares fit of a global
full-depth-ocean and sea-ice configuration of the Massachusetts
Institute of Technology general circulation model (MITgcm; Marshall et
al., 1997) to the available satellite and in-situ data. The
computational demands of rigorous ocean state estimation, aka data
assimilation, are enormous. Depending on the method and on the
approximations that are used, the computational cost of state estimation
is several dozen to several thousand times more expensive than
integrating a model without state estimation. This has limited the
resolution of first-generation ECCO solutions to horizontal grid spacing
of order 1 degree (except at the Equator where meridional grid spacing
is 1/3-degree in one of the solutions). First-generation solutions also
exclude the Arctic Ocean and lack an interactive sea-ice model, which
restricts the utilization of satellite data over Polar Regions.
Therefore, a necessary condition for a next generation synthesis is an
efficient truly global model and significant computational resources.

A first, global ECCO2 solution was obtained on the MITgcm model
configuration of Menemenlis et al. (2005b) and depicted on Figure 1. A
cube-sphere grid projection is employed, which permits relatively even
grid spacing throughout the domain and which avoids polar singularities
(Adcroft et al., 2004). Each face of the cube comprises 510 by 510 grid
cells for a mean horizontal grid spacing of 18 km; this is inadequate
for fully resolving the processes discussed in the introduction but it
is the best that can be achieved at the moment with available
computational resources. The model has 50 vertical levels ranging in
thickness from 10 m near the surface to approximately 450 m at a maximum
model depth of 6150 m. Bathymetry is from the S2004 (W. Smith,
unpublished) blend of Smith and Sandwell (1997) and of General
Bathymetric Charts of the Oceans (GEBCO) one arc-minute bathymetric
grid. The partial-cell formulation of Adcroft et al. (1997), which
permits accurate representation of the bathymetry, is used. The model is
integrated in a volume-conserving configuration using a finite volume
discretization with C-grid staggering of the prognostic variables. The
ocean model is coupled to a sea-ice model that computes ice thickness,
ice concentration, and snow cover as per Zhang et al. (1998) and that
simulates a viscous-plastic rheology using an efficient parallel
implementation of the Zhang and Hibler (1997) solver. Inclusion of
sea-ice provides for more realistic surface boundary conditions in Polar
Regions and allows the system to be constrained by polar satellite
observations. The sea-ice model also permits estimation of the
time-evolving sea-ice thickness distribution.

**Estimation approaches**

The first high-resolution global-ocean and sea-ice data synthesis was
obtained for the period 1992-2002 by calibrating a small number of
control variables using a Green's function approach (Menemenlis, et al.,
2005a). The control parameters include initial temperature and salinity
conditions, atmospheric surface boundary conditions, background vertical
diffusivity, critical Richardson numbers for the Large et al. (1994) KPP
scheme, air-ocean, ice-ocean, air-ice drag coefficients, ice/ocean/snow
albedo coefficients, bottom drag, and vertical viscosity. Data
constraints include sea level anomaly from altimeter data, time-mean sea
level from Maximenko and Niiler (2005), sea surface temperature from
GHRSST-PP, temperature and salinity profiles from WOCE, TAO, ARGO, XBT,
etc., sea ice concentration from passive microwave data, sea ice motion
from radiometers, QuikSCAT, and RGPS, and sea ice thickness from ULS.

In parallel with the Green's function optimization, work is underway
towards adjoint-method optimization on the same grid. One objective of
the MITgcm ocean and sea-ice model development effort has been to
provide the capability for automatic generation of the adjoint model
from up-to-date versions of the MITgcm, which is invaluable for ocean
state estimation. A major milestone was reached recently with the
completion of an adjoint of the full-fledged dynamic/thermodynamic
MITgcm sea-ice component. The coupled ocean/sea-ice adjoint now yields
stable and physically meaningful adjoint sensitivities or Lagrange
multipliers. By way of example, Figure 2 depicts sensitivity maps of
sea-ice export through Fram Strait during December 1995 to changes in
ice thickness at any point in the domain 1, 2, 3, and 4 years prior
(Heimbach, 2008). The dominant pattern reflects the advective pathways
of sea-ice through the Arctic. In general terms, points furthest away
from Fram Strait are connected to Fram Strait export by faster advective
time scales. Time-varying Lagrange multipliers for many other variables,
including sea-ice (concentration, thickness, salinity, and velocity),
snow (thickness and velocity), ocean (temperature, salinity, and
velocity), atmospheric forcing (surface air temperature, specific
humidity, precipitation, and wind velocity), and internal model
parameters (vertical diffusivity, and bottom drag) are available, and
are being analyzed to understand the main causes of sea-ice variability.

Mazloff (2008) demonstrated the feasibility of high-resolution
adjoint-based state estimation on a regional scale. At this point a
preliminary solution (based on 22 iterations) of a 1/6-degree Southern
Ocean State Estimate (SOSE) covering the years 2005 and 2006 is
available and is being analyzed both in-house and by other research
groups. Almost all of the data employed in the first-generation ECCO
efforts have been utilized with major emphasis on the satellite
altimetry, the Argo profiles, and satellite Sea Surface Temperature
(SST). Given the success of this regional, eddy resolving, adjoint-based
state estimation effort and of the sea-ice sensitivity experiments, we
are currently attempting an adjoint-method optimization on the global,
cubed-sphere, ocean and sea-ice model configuration as a way to increase
the number of control variables relative to the existing Green's
function optimization.

In related work, the ECCO2 project is contributing to the development of
an open-source Automatic Differentiation (AD) tool called OpenAD. Effort
during this past year has gone towards the generation of efficient
adjoint code for the MITgcm. After demonstrating that OpenAD can handle
a simplified configuration of the MITgcm we are now in a position to
adjoint a global, coarse-resolution MITgcm configuration (including the
GM/Redi eddy parameterization scheme), which has been a workhorse setup
for various adjoint sensitivity studies. It is noteworthy to report that
the availability of an adjoint model based on a different AD tool has
enabled us to trace a bug in TAF
([http://fastopt.de](http://fastopt.de/)), which is the current
\"production\" AD tool for ECCO2. This is just one aspect of the
advantages of having an independent AD tool at our disposal. A major
breakthrough is the implementation of a hierarchical checkpointing
algorithm, without which adjoint integrations would have been limited to
a few days. Using OpenAD, we have conducted a 100-year adjoint
integration to compute transient sensitivities of Atlantic meridional
heat transport.

**A first ECCO2 synthesis**

The specific objective of the first ECCO2 optimization was to reduce
large-scale biases and drifts of the model relative to observations.
Figure 3 displays some of the large-scale biases that were present in
the baseline integration and the equivalent fields from the optimized
solution, demonstrating significant improvements of the optimized
solution relative to the baseline integration. Specifically, the top
left panel compares the 1992--2002, 0--750-m time-mean temperature of
the ECCO2 baseline and optimized solutions to the World Ocean Atlas 2005
(WOA05) climatology. The baseline simulation exhibits a global warm bias
of up to 3° C, which is not present in the optimized solution. The top
right panel shows baseline (top) and optimized (bottom) Antarctic sea
ice distribution on September 1992 (left) and September 2002 (right).
Simulated sea is shown in blue. The thin white line is passive
radiometer observations of sea-ice extent (15% concentration). The
problematic open-water winter polynyas in the Ross and Weddell Seas,
which appear in the baseline integration, are no longer present in the
optimized solution. The bottom left panel shows Weddell Sea T/S
properties of the WGCH climatology (top), of the baseline integration
(middle), and of the optimized solution (bottom). The optimized Weddell
Sea T/S properties are considerably closer to the observations compared
to the baseline integration. The bottom right panel shows Drake Passage
transport in the baseline and optimized solutions, which can be compared
to the estimate of Sloyan and Rintoul (2001). The Drake Passage
transport in the baseline integration is 35 Sv too strong compared to
the Sloyan and Rintoul (2001) estimate while that of the optimized
solution is much more realistic due, in part, to a much-improved
Southern Ocean hydrography.

Despite significant large-scale improvements in this preliminary ECCO2
solution relative to the baseline integration, there remain many
problematic aspects, for example, the representation of Mode Water
formation processes, boundary current separation, Arctic sea ice
distribution and hydrography, and ice-shelf-ocean interactions, which
are the subject of ongoing work. These shortcomings are being addressed
using improved model physics, for example, of deep water formation over
continental shelves (Campin and Goose, 1999), of salt plumes rejected by
sea ice during freezing (Nguyen et al., 2008), of ice shelf-ocean
interactions (Losch, 2008), and of small-scale ice mechanics and
thermodynamics (Kwok et al., 2008). As discussed in the previous
Section, we are also seeking improved estimates of initial and boundary
conditions using the adjoint method, which permits a much larger number
of control variables than does the Green's function approach.

**Available ECCO2 products**

ECCO2 modeling and estimation tools and results are freely available to
scientific community. Model configurations and parameterizations are
available at [http://mitgcm.org](http://mitgcm.org/). Automatic
differentiation tools are available at
<http://www-unix.mcs.anl.gov/OpenAD>. Finally, modeling and estimation
results are available at [http://ecco2.org](http://ecco2.org/). The
ECCO2 results include 1992--2002 quasi-global simulations at 1/4, 1/8,
and 1/16-degree horizontal grid spacing (Hill et al., 2007), a
2005--2006 adjoint-method Southern Ocean State Estimate at 1/6-degree
horizontal grid spacing (Mazloff, 2008), and a 1992--present
global-ocean and sea-ice Green's function optimization on a cubed-sphere
grid with 18 km horizontal grid spacing (Menemenlis et al., 2005b). For
the global cubed-sphere grid configuration, in addition to the optimized
solution, upwards of 80 forward model sensitivity experiments are
available. These experiments, which were used for the Green's function
optimization, explore the model's response to different surface boundary
conditions, initial conditions, horizontal and vertical mixing
parameters, sea-ice model parameters, and to the addition of various
sub-grid scale parameterizations. There is also a longer, 1979--present,
forward integration carried out using the optimized model parameters.
For all of these solutions a large complement of high-frequency and
high-resolution model diagnostics has been saved. The diagnostics
include surface fluxes, sea surface height, bottom pressure, mixed and
mixing layer depths, sea-ice thickness, concentration, salinity, and
velocity, ocean temperature, salinity, density, and velocity, and eddy
transports of mass, temperature, and salt. A large portion of these
diagnostics (\~100 TB) is readily available online via ftp, http, and
OPeNDAP servers. The complete diagnostics are stored on tapes at the
NASA Advanced Supercomputing (NAS) and are made available upon request.

**Early science applications**

This section lists some early science applications of the ECCO2
products. A first set of applications concerns improved error estimates
and eddy parameterizations for coarse-resolution ocean simulations and
estimations. Forget and Wunsch (2007) used hydrographic data and an
early ECCO2 simulation to estimate global hydrographic variability and
data weights in oceanic state estimates. Ponte et al. (2007) used
altimeter data and an early ECCO2 simulation for spatial mapping of
time-variable errors in Jason-1 and TOPEX/POSEIDON sea surface height
measurements. ECCO2 high-resolution simulations have also been used to
inform the model parameterization of sub-grid scale processes
(Fox-Kemper and Menemenlis, 2008; Danabasoglu et al., 2008).

A second set of early science applications concerns the impact of
mesoscale eddies on large-scale ocean circulation and its variability.
Fu (2006) used correlation between successive maps of sea-surface height
to estimate eddy propagation characteristics. For example, Figure 4
shows the zonally averaged zonal component of the eddy propagation
velocity as a function of latitude. The excellent agreement between the
model simulation and altimeter observations demonstrates the model's
skill in producing realistic eddy propagation characteristics.
Similarities and differences between results from observed and simulated
sea-surface height variability improve understanding of model and data
errors and of the underlying physical processes. Volkov and Fu (2008)
studied the dynamics of the Zapiola Anticyclone, which is situated in a
highly energetic area of the ocean, by analyzing the vorticity balance
of the anticyclone. This helped to understand the main physical
mechanisms that drive the variability of the anticyclone. ECCO2
solutions are also being used to study the impact of eddies on mode
water formation. For example, Maze et al. (2008) and Forget et al.
(2008) investigated North Atlantic subtropical mode water formation
while Davis (2008) studied the formation, evolution, and dispersal of
Subtropical Mode Water (STMW) in the North Pacific Ocean.

A third set of early science applications concerns the study of Polar
Oceans. Condron et al. (2008) studied the response of the Arctic
freshwater budget to extreme North Atlantic Oscillation (NAO) forcing.
Kwok et al. (2006) used ECCO2 estimates of Arctic sea surface height
variability to estimate contributions of the oceanographic circulation
signal to Ice, Cloud, and land Elevation Satellite (ICESat) retrievals
in order to help interpret ICESat altimetric and reflectivity profiles.
Kwok et al. (2008) compared sea ice results from one of the ECCO2
solutions and from other coupled-ice-ocean models to observations
obtained by the RADARSAT Geophysical Processor System (RGPS). Nguyen et
al. (2008) investigated the reasons why coupled ocean and sea ice models
in the Arctic tend to misrepresent the upper ocean stratification.
Specifically, results from the Arctic Ocean Model Intercomparison
Project (AOMIP) showed that participating ocean models consistently
failed to either produce and/or maintain the cold halocline layer at the
50--200-m depth. Without a cold halocline, excess heat flux from the
warm Atlantic water source at greater depths can inhibit production of
realistic sea ice extent and thickness. To address this problem, a new
sub-grid-scale parameterization of salt plumes was developed, resulting
in a considerably more realistic representation of the cold halocline in
the Arctic Ocean (Figure 5).

Finally, ECCO2 results are being used to supply boundary conditions for
regional studies and to drive biogeochemical, geodetic, acoustic, and
electromagnetic models. For example, Manizza et al. (2008) used ECCO2
results to examine the fate of riverine fluxes of Dissolved Organic
Carbon (DOC) in the Arctic Basin. Dushaw et al. (2007) used ECCO2
results to design an acoustic array for observing gyre-scale acoustic
variability in the North Atlantic. Glazman and Golubev (2005) used an
early ECCO2 simulation to investigate the spatial and temporal
variability of the Earth\'s magnetic field component induced by ocean
circulation.

**Conclusion**

The focus of ocean state estimation during the past ten years has been
to demonstrate the feasibility and utility of physically-consistent,
global, sustained estimates, with considerable success for upper ocean
and for equatorial processes. But many pressing scientific questions,
for example, quantifying and monitoring ocean sources and sinks in the
global carbon cycle, understanding the recent evolution and variability
of the Polar Oceans, and quantifying the time-evolving term balances
within and between different components of the Earth System, require
much improved accuracy in the estimation of water mass formation and
transformation rates, eddy-mixed layer interactions, and high-latitude
processes. The accurate monitoring of these processes in turn requires
developing state estimation systems, of the sort we have described in
this Note, that can fully capitalize on continuing advances in
computational and observational technologies.

**Acknowledgements**

ECCO2 is a contribution to the NASA Modeling, Analysis, and Prediction
(MAP) program. We gratefully acknowledge computational resources and
support from the NASA Advanced Supercomputing (NAS) Division and from
the Jet Propulsion Laboratory (JPL) Supercomputing and Visualization
Facility (SVF).

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