Newsletter
Newsletter ofof thethe Climate
Climate Variability
Variability andand Predictability
Predictability Programme
Programme (CLIVAR)(CLIVAR)

Exchanges
No 44 (Volume 13, No. 1)

January 2008

Furthering the Science of Ocean Climate
Modelling
From Maltrud et al, page 5: Global Ocean Modelling in the Eddying Regime using POP

Figure 1. Sea surface height variability (cm) from a) the global 0.1o tripole, b) the global 0.1o dipole, and c) the AVISO altimeter data.
CLIVAR is an international research programme dealing with climate variability and predictability on time-scales from months
to centuries. CLIVAR is a component of the World Climate Research Programme (WCRP). WCRP is sponsored by the World
Meteorological Organization, the International Council for Science and the Intergovernmental Oceanographic Commission of
UNESCO.

CLIVAR Exchanges

Volume 13 No.1 January 2008

Editorial
I am pleased indeed to have Stephen Grifﬁes Helene Banks andAnna Pirani (ICPO) as co-editors of this issue of Exchanges on“Furthering the science of ocean climate modelling”. In part,
this edition is a follow-on from Exchanges 42 which focusedon Ocean Model Development and Assessment. The edition
also provides an article on the activities of CLIVAR’s Working
Group on Ocean Model Development (WGOMD) following
their Workshop and meeting in Bergen last August (see pages
30-32). I would particularly like to welcome Helene Banks as a
new Co-Chair of WGOMD alongside the existing Chair, Stephen
Grifﬁes. Both they and Anna provide a short introduction tothe ocean science aspects of this edition below.
Extreme events is one of the WCRP cross cutting topics which
CLIVAR, with GEWEX, has been asked to manage by the Joint
Scientiﬁc Committee (JSC) for WCRP. Long term multi-year
droughts form one aspect of these. The severe drought eventthat occurred over the Canadian Prairies from 1999-2004/05
is one focus of the GEWEX Worldwide Integrated Study ofExtremes (see the link from http://www.meteo.mcgill.ca/wise
to the “WEBS follow-on for extremes”). On pages 33-34 of
this edition of Exchanges, David Legler, David Gutzler andSieg Schubert outline current US CLIVAR efforts on drought
predictability research, with a request to the international
community for participation.
Another important CLIVAR-sponsored activity is theInternational Climate of the 20th Century Project. The article
by Jim Kinter and Chris Folland on pages 34-36 provides bothan update on progress and an indication of future directions forthe project. The latter are aimed at helping to better understandmechanistic questions relating to seasonal and decadal
predictability and forecasting. These outputs will hopefully

help in interpretation of the outcomes of both the WCRP/
CLIVAR Climate System Historical Forecast Project (www.
clivar.org/organization/wgsip/chfp/chfp.php), announced at
the WCRP/CLIVAR Seasonal Prediction Workshop in Barcelona
in June 2007, and decadal predictability activities which arespinning up under the JSC’s decadal predictability cross cut.
In terms of decadal prediction, the joint JSC/CLIVAR Working
Group on Coupled Modelling and the CLIVAR Working Groupon Seasonal to Interannual Prediction are developing plans for
coordinated experiments to study multi-decadal prediction andnear-term climate change which will draw in other CLIVAR
groups also. For example the expertise of CLIVAR’s Global
Synthesis and Observations Panel in the usefulness of ocean
syntheses will be needed for setting initial ocean conditions forsuch experiments. In addition, the involvement of the wider
international community in contributing to these experiments
and in proposing diagnostic sub-projects for their analysis will
be vital to success overall.
Finally, we also include a short account of the outcomes of
the CLIVAR Scientiﬁc Steering Group meeting in Geneva lastSeptember and the ﬁrst “GO-SHIP” meeing in November andan update on the WOCE Paciﬁc Ocean Atlas.
One additional happy event at the ICPO has been the birth of ababy boy, Alessandro, to Anna Pirani and her husband Riccardo.
Many congratuations to them. Alessandro was 7 lbs 15 oz and
21 inches at birth and is now doing ﬁne, growing and gettingused to life at home.
Howard Cattle

Editorial: Furthering the Science of Ocean Climate Modelling
S. Griffies1, Banks H2., Pirani, A.3
1
NOAA/GFDL and WGOMD co-chair, 2Met Office Hadley Centre and WGOMD co-chair, 3CLIVAR and Princeton University,
Corresponding author: anna.pirani@noc.soton.ac.uk

Ocean models are tools for use in understanding and predicting
the ocean. Indeed, models are an essential element for rationallyaddressing a wide suite of scientiﬁc problems. CLIVAR focirequiring sophisticated ocean models include global climate
projections, seasonal to decadal prediction, and ocean reanalysis.
These areas are key to three of the four CLIVAR objectives. Suchhigh-end applications require a cutting-edge level of scienceand engineering knowledge, understanding, and creativity
to be applied to ocean models to establish the integrity andreliability of the simulations. This work includes developing thealgorithms and parameterizations forming the fundamentalsof the ocean model; designing model configurations andexperiments for addressing scientiﬁc questions; efﬁciently
running the numerical experiments on a huge array of computer
platforms; and analyzing the computed output in light of anincreasing array of observational datasets.
Articles in this edition of CLIVAR Exchanges touch on issues
which live in the realm of developing the science of ocean
models and of developing experiments to enable scientiﬁcquestions to be addressed. In particular, some of the articles
provide an outline of certain methodologies used in ocean
modelling practice. Such information is crucial for the useof the models, though it is often omitted from peer-reviewed
papers. Hence, we hope that these articles assist in furthering
2

the science of ocean modelling. This goal follows from ourcharge as members of the CLIVAR Working Group for Ocean
Model Development (WGOMD), in which we aim to facilitate
the scientiﬁcally rational and robust development and use ofocean models.
More information on the work of WGOMD can be found in thisissue’s article summarising the meeting held in August 2007,
and in the detailed report that will be available on the WGOMD
web page (http://www.clivar.org/organization/wgomd/
wgomd.php). In particular, as well as holding a workshop on
Numerical Methods in Ocean Models, WGOMD discussed
the progress of Coordinated Ocean Reference Experiments(CORE), evaluation of ocean models and the future directionof WGOMD.
This edition of CLIVAR Exchanges follows on the heelsof the July 2007 edition, which focused on “Ocean Model
Development and Assessment,” with Peter Killworth as guest
editor. Indeed, many of the articles in this edition could readily
ﬁt into the July 2007 edition. As such this reﬂects the healthy
state of ocean modelling, in which a huge array of models andapplications continue to be considered with an impressive levelof realism and integrity.

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Parameterizing Submesoscale Physics in Global Climate Models
Fox-Kemper, B1., G. Danabasoglu2, R. Ferrari3, R. W. Hallberg4
1
Cooperative Institute for Research in Environmental Sciences (CIRES) and Dept. of Atmospheric and Oceanic Sciences,
University of Colorado at Boulder, 2NCAR, Boulder, USA., 3MIT., Dept. of Earth, Atmospheric and Planetary Sciences,
Massachussets, USA., 4NOAA, GFDL, Princeton, USA.
Corresponding author: Bfk@Colorado.EDU

Introduction
The ocean is vast and diverse. No computer in the foreseeable
future will be able to directly handle the range of scales present
in the ocean, yet small-scale phenomena may impact global
ocean circulation and climate. The small-scale dynamics of theocean surface mixed layer are an excellent example, because
they are not explicitly resolved by climate models even though
they mediate property exchange between the atmosphere andocean.
The majority of studies of small scales have focused on mesoscale
geostrophic eddies (typical scales of a month and 100km) or
ﬁnescale waves and turbulence (typical lengths up to hundredsof meters and inertial or faster time scales). The range of scales
in between the mesoscale and the ﬁnescale was considered to
be of only secondary importance, perhaps just the tail of themesoscale spectrum. However, recent work has shown thatthese scales, the submesoscales, have interesting dynamics
and potential climate impact through their actions near theocean surface. Limited duration ocean-only global simulations
with grids ﬁne enough to fairly represent mesoscale eddies are
becoming common, e.g., Maltrud and McClean (2005). Eddyresolving coupled climate models are expected to soon follow,
but many decades remain until the submesoscale can be wellresolved in global climate models. Oschlies (2002) demonstrates
that the near-surface model ﬁdelity is signiﬁcantly improvedin a regional ocean-only model with 2km horizontal resolution,
just brushing into the submesoscale-permitting range. Thus,
parameterization of the physics at these scales would beneﬁtmodelling for decades to come.
Submesoscale dynamics are dominated by the developmentof fronts and the ageostrophic circulations associated withthe fronts. Observations have shown that near-surface fronts
are ubiquitous at all scales larger than the local mixed layer
deformation radius, typically a few kilometres (Ferrari and
Rudnick, 2000, Hosegood et al., 2006). Recent studies ofsubmesoscale physics have addressed various aspects of frontal
dynamics: wind-front interactions (Thomas, 2005), frontogenesis(Lapeyre et al. 2006, Capet et al. 2008), and frontal instabilities(Boccaletti et al. 2007, hereafter BFF). Nice reviews of theseresults can be found in Thomas et al. (2008) and Mahadevan
and Tandon (2006). Thomas and Ferrari (2008) compare thethree effects and conclude that they are of similar magnitude
for typical oceanic conditions. In all these studies a novel viewof the upper ocean emerges, where the depth and stratiﬁcationof the surface mixed layer is not set by the atmospheric surface
ﬂuxes, as currently assumed in all boundary layer theories
and parameterizations, but it is radically modiﬁed by theageostrophic circulations that develop at lateral fronts. FoxKemper et al. (2008, hereafter FFH) and Fox-Kemper and Ferrari(2008, hereafter FF) derive and validate a parameterization
scheme to represent the mixed-layer restratiﬁcation associatedwith frontal instabilities and frontogenesis. The dynamics
associated with coupling between winds and fronts have notyet been cast in a parameterization.
This note introduces the FFH parameterization. It has been
implemented in two global climate models: the Community
Climate System Model/Parallel Ocean Program (CCSM/
POP2, Smith and Gent, 2002) and the Geophysical Fluid

Dynamics Laboratory Coupled Model/Generalized Ocean
Layer Dynamics (CM2.2/GOLD, Delworth et al., 2006, Adcroft
and Hallberg, 2006). So far, the parameterization has beentested in three contexts: 1) in idealized simulations (FFHand FF), 2) in an ocean-only, 3-degree, 100-year simulation ofPOP, and 3) in a 20-year 1-degree coupled ocean-atmosphereCM2.2/GOLD simulation. These tests differ greatly. POP
is a z-coordinate model with the Large et al. (1994) ﬁnescale
mixing parameterization, and GOLD is an isopycnal-coordinate
model with a reﬁned bulk mixed layer model (Hallberg, 2003).
Nonetheless, when the missing physics of frontal instability
restratiﬁcation is approximated by the FFH parameterization,
both POP and GOLD show a reduction in model bias whencompared to control runs without the parameterization. Future
papers will address in more detail the implementation andeffects in these global models.
Dynamics of Mixed Layer Eddies
The weak stratiﬁcation and shallow depth of the mixed layer
lead to submesoscale ageostrophic baroclinic instabilities thatare trapped within the mixed layer (BFF). FFH dub them mixed
layer eddies (MLEs) when they reach ﬁnite amplitude. MLEs
form by extracting energy from fronts. They have slightly
sub-inertial time scales so are fast enough to grow even in thepresence of nightly convective mixing. MLEs are submesoscalefeatures with scales near the mixed layer deformation radius(100m to 5km). Satellite (Munk et al., 2001) and in situ (Rudnick,2001) observations conﬁrm that the ocean is populated witheddies with characteristics consistent with MLE.
Both mesoscale eddies and MLEs drive overturning circulations
that act to slump lateral density gradients, converting steep
isopycnal surfaces to shallower, wavy ones. The slumping
results in a lateral mixing of tracers and in an increase of thevertical density stratiﬁcation. During slumping lighter water
is moved over denser water, and extraction of potential energy
results. BFF and FFH show that the slumping and restratiﬁcation
by mixed layer instabilities quickly outpaces restratiﬁcation
by Rossby adjustment (Tandon and Garrett, 1994) and otherinstabilities (see also Haine and Marshall, 1998).
Since Taylor (1921), eddy diffusivities have been the basic tool
to approximate stirring by eddies. Gent and McWilliams (1990,
hereafter GM) showed that a similar approach can be taken
to represent mesoscale ocean eddies, as long as the lateral
diffusion of buoyancy is accompanied by a vertical buoyancy
ﬂux acting to release potential energy (e.g., Green, 1970). An
eddy-induced velocity streamfunction (see Grifﬁes, 1998, for
implementation) can be used to slump density gradients, hence:releasing potential energy and also transporting buoyancy
down its mean horizontal gradient to achieve lateral mixing.
FFH follow GM in introducing an eddy-induced overturning
streamfunction, but instead of scaling this streamfunction to
produce known horizontal mixing (the GM transfer coefﬁcient),
they derive a scaling for the streamfunction that achieves
the expected release of available potential energy and hence
eliminate any dependence on unknown transfer coefﬁcients.
The scaling was then tested against a suite of high resolution
numerical simulations. The choice to focus on vertical ﬂuxes
was motivated by the fact that MLEs rapidly restratify thesurface mixed layer through vertical exchanges of buoyancy,
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CLIVAR Exchanges

while lateral ﬂuxes associated with MLEs are dominated bylarger scale motions. Also, it was observed during spin-downof mixed layer fronts by MLEs that the vertical ﬂux is nearly
constant while the horizontal flux and diffusivity change
dramatically in time. FFH parameterize the submesoscaleeddies only, so the parameterization is intended to be usedin mesoscale-resolving simulations or in conjunction with amesoscale parameterization (e.g., GM or a recent improvement,e.g., Ferrari et al., 2008).
The FFH parameterization is cast as an expression for theoverturning streamfunction at a front:

Where H is mixed layer depth,
is the buoyancy averaged
over the mixed layer depth, ƒ is the Coriolis parameter and thestructure function is

The streamfunction gives an eddy-induced velocity associatedwith the overturning:which is used to advect buoyancy and other tracers. The
parameterization approximates the eddy ﬂuxes:
The form of the parameterization guarantees a down-gradienthorizontal ﬂux and an upward, restratifying vertical ﬂux. The
scaling found for mixed layer fronts extends to cover the regimeof restratiﬁcation after deep convection, by reproducing thescalings found by Jones and Marshall (1993, 1997) and Haine
and Marshall (1998).
Implementation and Impact in Global Climate Models
In a global climate model, the parameterization must be
modiﬁed. A useful form is,

Introducing the timescale x, for mixing momentum across themixed layer (typically a few days) makes the parameterization
converge to the subinertial mixed layer approximation(Young, 1994) near the equator. Also, differentiability andﬁnite amplitude are preserved as f goes to zero. The ratio of
the grid resolution ∆x to the typical scale of mixed layer fronts
Lf preserves the average vertical buoyancy ﬂux in the face of
weaker buoyancy gradients in coarse-resolution models, which
are assumed to have a white spectrum as in models and data(Hallberg & Gnanadesikan, 2006 and Ferrari and Rudnick,2000). The frontal scale may be either a ﬁxed number, e.g.,
5 km, but observations suggest the mixed layer deformation
radius (Hosegood et al. 2006).
Since the MLEs tend to restratify the mixed layer, it is not
surprising to ﬁnd that the boundary layer thickness is reduced
when the parameterization is introduced (Figure. 1, page19). Furthermore, the action of the parameterization is mostpronounced where mixed layers are deep and horizontal
buoyancy gradients are large. These regions are thoseanticipated by FF by inference from satellite data. The models
show qualitatively similar shoaling of the boundary layer insimilar regions, but quantitatively different responses to theparameterization. It is likely that the different resolutions of
the models contribute signiﬁcantly, and possibly also the oceanonly versus coupled conﬁgurations. In any case, once longer
and more directly comparable resolutions and simulations areavailable a more detailed comparison will follow.
Is a shallower boundary layer or mixed layer more realistic?
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Volume 13 No.1 January 2008

The POP model provides mixed layer depth as well as boundary
layer depth. Figure. 2 shows a comparison to mixed layer
climatologies of the time average of the mixed layer depth
for years 90-100 of the POP model simulation with the MLE
parameterization and the control run without. BMFLI is thede Boyer Montegut et al. (2004) temperature-based mixed
layer climatology and Levitus is the Monterey and Levitus(1997) climatology. Figure. 2 shows a probability distributionof ﬁnding a given difference between the model time-mean
and the climatology at an arbitrary location. It is clear thatthe change induced by the parameterization is larger thanthe difference between climatologies, and that the control
run is biased toward deep mixed layers. Introducing theparameterization reduces this bias: the rms error is reduced
from about 15m to 7m, and the skewness (indicating bias) is
reduced from 2.4 to 0.6.
Conclusions
A new parameterization for restratiﬁcation by mixed layer
eddies is introduced. The parameterization was shown to beeffective in idealized simulations by FFH and FF. It has now
been included in CCSM/POP and CM2.2/GOLD and thisnote demonstrates that it reduces bias over control runs in
preliminary simulations.
References
A. Adcroft and R. Hallberg, 2006: On methods for solving
the oceanic equations of motion in generalized verticalcoordinates. Ocean Modelling. 11, 224-233.
Boccaletti, G., R. Ferrari, & B. Fox-Kemper, 2007: Mixed Layer
Instabilities and Restratiﬁcation. J. Phys. Oceanogr., 37,
2228-2250.
de Boyer Montegut, C., G. Madec, A. S. Fischer, A. Lazar, and D.
Iudicone, 2004: Mixed layer depth over the global ocean: An
examination of proﬁle data and a proﬁle-based climatology.
J. Geophys. Res., 109, C12003, doi:10.1029/2004JC002378.
Capet, X., J. C. McWilliams, M. J. Molemaker, and A. F.
Shchepetkin, 2008: Mesoscale to submesoscale transition in
the California Current system. Part II: Frontal processes, J.
Phys. Oceanogr., in press.
Delworth, T.L., A.J. Broccoli, A. Rosati, R.J. Stouffer, V. Balaji,
J.A. Beesley, W.F. Cooke, K.W. Dixon, J. Dunne, K.A. Dunne,
J.W. Durachta, K.L. Findell, P. Ginoux, A. Gnanadesikan,
C.T. Gordon, S.M. Grifﬁes, R. Gudgel, M.J. Harrison, I.M.
Held, R.S. Hemler, L.W. Horowitz, S.A. Klein, T.R. Knutson,
P.J. Kushner, A.R. Langenhorst, H.C. Lee, S.J. Lin, J. Lu, S.L.
Malyshev, P.C.D. Milly, V. Ramaswamy, J. Russell, M.D.
Schwarzkopf, E. Shevliakova, J.J. Sirutis, M.J. Spelman,
W.F. Stern, M. Winton, A.T. Wittenberg, B. Wyman, F. Zeng,

Fig. 2: Probability distributions of difference between modeled timemean mixed layer depth and observed time-mean mixed layer depth.
POP model with MLE parameterization and the POP control run
are compared to BMFLI and Levitus mixed layer depth climatologies.
(Bin width of 2m).

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and R. Zhang, 2006: GFDL’s CM2 Global Coupled Climate
Models. Part I: Formulation and Simulation Characteristics.
J. Climate, 19, 643–674.
Ferrari, R. and D. Rudnick, 2000: The thermohaline structure of
the upper ocean. J. Geophys. Res., 105, 16,857–16,883.
Ferrari, R., J. C. McWilliams, V. M. Canuto, and M. Dubovikov,
2008: Parameterization of Eddy Fluxes near Ocean
Boundaries. J. Climate, in press.
Fox-Kemper, B., R. Ferrari, and R. W. Hallberg, 2008:
Parameterization of mixed layer eddies. I: Theory anddiagnosis. J. Phys. Oceanogr., in press.
Fox-Kemper, B. and R. Ferrari, 2008: Parameterization of mixed
layer eddies. II: Prognosis and impact. J. Phys. Oceanogr.,
in press.
Gent, P. R. and J. C. McWilliams: 1990, Isopycnal mixing in ocean
circulation models. J. Phys. Oceanogr., 20, 150–155.
Green, J., 1970: Transfer properties of the large-scale eddies andthe general circulation of the atmosphere. Q. J. Roy. Meteor.
Soc., 96, 157–185.
Grifﬁes, S.M., 1998: The Gent–McWilliams Skew Flux. J. Phys.
Oceanogr., 28, 831–841.
Haine, T. W. N. and J. Marshall: 1998, Gravitational, symmetricand baroclinic instability of the ocean mixed layer. J. Phys.
Oceanogr., 28, 634–658.
Hallberg, R., 2003: The suitability of large-scale ocean models
for adapting parameterizations of boundary mixing and a
description of a reﬁned bulk mixed layer model. Proceedingsof the 2003 ’Aha Huliko’a Hawaiian Winter Workshop, P, Muller
and D. Henderson, editors, U. Hawaii, 187-203.
Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies
in determining the structure and response of the winddriven southern hemisphere overturning: Results from themodelling eddies in the southern ocean (MESO) Project. J.
Phys. Oceanogr., 36, 2232–2252.
Hosegood, P., M. C. Gregg, and M. H. Alford, 2006: Submesoscale lateral density structure in the oceanic surface
mixed layer, G. Res. Lett., 33, L22604, doi:10.1029/
2006GL026797.
Jones, H and J.C. Marshall, 1993: Convection with rotation ina neutral ocean; a study of open-ocean deep convection, J.
Phys. Oceanogr., 23, 1009-1039.
Jones, H. and J. Marshall, 1997:Restratification after deep
convection. J. Phys. Oceanogr., 27, 2276-2287.
Lapeyre, G., P. Klein, and B. L. Hua, 2006: Oceanic restratiﬁcation

CLIVAR Exchanges

forced by surface frontogenesis. J. Phys. Oceanogr., 36,
1577–1590.
Large, W., J. McWilliams, and S. Doney: 1994, Oceanic verticalmixing: A review and a model with a vertical k-proﬁleboundary layer parameterization. Rev. Geophys., 363–403.
Mahadevan, A. and A. Tandon, 2006: An analysis of mechanisms
for submesoscale vertical motion at ocean fronts. Ocean
Modelling, 14, 241–256.
Maltrud, M. E. and J. L. McClean, 2005: An eddy resolving
global 1/10 ocean simulation. Ocean Modelling, 8, 31–54.
Monterey, G. I., and S. Levitus, 1997: Climatological cycle ofmixed layer depth in the world ocean. U.S. Gov. Printing
Ofﬁce, NOAA NESDIS, 5pp.
Munk, W., L. Armi, K. Fischer, and Z. Zachariasen: 2000, Spiralson the sea. Proc. Roy. Soc. London, 456A, 1217–1280
Oschlies, A., 2002: Improved representation of upper-ocean
dynamics and mixed layer depths in a model of the North
Atlantic on switching from eddy-permitting to eddyresolving grid resolution. J. Phys. Oceanogr., 32, 2277-2298.
Rudnick, D., 2001: On the skewness of vorticity in the upper
ocean. Geophys. Res. Lett., 28, 2045–2048.
Smith, R. D., and P. R. Gent, 2002: Reference manual for the
Parallel Ocean Program (POP), ocean component of theCommunity Climate System Model (CCSM2.0 and 3.0). Los
Alamos National Laboratory Technical Report LA-UR-02-2484.
[Available online at http://www.ccsm.ucar.edu/models/
ccsm3.0/pop. Also available from LANL, Los Alamos, New
Mexico 87545.]
Tandon, A., and C. Garrett, 1994: Mixed layer restratiﬁcation
due to a horizontal density gradient. J. Phys. Oceanogr., 24,1419-1424.
Taylor, G. I., 1921: Diffusion by continuous movements.
Proceedings of the London Mathematical Society, 20, 196–212.
Thomas, L. N., 2005: Destruction of potential vorticity by winds.
J. Phys. Oceanogr., 35, 2457–2466.
Thomas, L. N., and R. Ferrari, 2008: Friction, frontogenesis,
frontal instabilities and the stratiﬁcation of the surface
mixed layer, J. Phys. Ocean., submitted.
Thomas, L. N., A. Tandon, and A. Mahadevan, 2008:
Submesoscale processes and dynamics, In: M. Hecht, H.
Hasumi (Eds.), Eddy Resolving Ocean Modelling. American
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Young, W. R., 1994: The subinertial mixed layer approximation.
J. Phys. Oceanogr., 24, 1812–1826.

Global Ocean Modeling in the Eddying Regime Using POP
M. Maltrud1, Bryan, F.2 , Hecht, M.1, Hunke, E.1, Ivanova, D.3 , McClean, J.3,4 , Peacock, S.5
1
Los Alamos National Laboratory, 2National Center for Atmospheric Research, 3Lawrence Livermore National Laboratory,
4
Scripps Institution of Oceanography, 5University of Chicago.
Corresponding author: maltrud@lanl.gov

Introduction
The Parallel Ocean Program (POP) was developed at the Los
Alamos National Laboratory (LANL) in the early 1990’s foruse on high performance parallel computers (Dukowicz, etal., 1993). Early emphasis was on performing simulations
requiring computational capability beyond that accessible withother codes. The combination of algorithmic improvements
and access to powerful hardware led to simulation of ocean
circulation in the eddying regime being a major goal of early
and ongoing Department of Energy ocean modelling activities
at LANL.
Expanding on the ground breaking high resolution simulationsof Semtner and Chervin (1988), POP was used in a series of near
global (including all but the Arctic) ocean simulations at 0.28o
horizontal resolution1 (Maltrud et al. 1998). These simulations

showed broad agreement in the geographical distribution ofmesoscale eddy variability with altimeter observations (Fuand Smith, 1996; McClean et al, 1997), but underestimatededdy amplitudes at shorter wavelengths and periods, andmisrepresented smaller scale features of the time mean ﬂow
such as the path of the Gulf Stream and Kuroshio. These global
runs were followed by 0.1o North Atlantic simulations (which
extended from 20S to 78N) that, for the ﬁrst time, used grid cells
smaller than the ﬁrst baroclinic Rossby radius throughout thedomain in a fully thermodynamic, realistic basin scale setting(Smith et al. 2000). These experiments at resolutions of 10km
and ﬁner suggested that a regime shift had been reached, as
both eddy and mean ﬂow quantities (such as the Gulf Stream
separation) much more closely resembled observations. The
Resolution will be denoted by the equatorial longitudinal spacing

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CLIVAR Exchanges

success of the 0.1o POP North Atlantic experiments inspired
the ﬁrst fully global 0.1o simulation (including the Arctic) by
Maltrud and McClean (2005). This article describes somelessons learned in performing these large simulations, andpresents examples of our current high resolution modelling
efforts.
Model Conﬁguration
POP is descended from the Bryan-Cox-Semtner lineage of
geopotential coordinate (“z-level”) models (Grifﬁes, 2004)
formulated on the Arakawa B-grid and using leapfrog time
stepping. Important improvements were introduced in the POPcode, including the surface pressure and implicit free-surface
solution of the barotropic mode (Dukowicz and Smith, (1994))
and formulation of the equations on a general orthogonal grid.
Along with its ability to run efﬁciently on parallel computers,
POP established a signiﬁcant user base, including being selected
as the ocean component of the NCAR Community Climate
System Model (Kiehl and Gent, 2004; Collins et al, 2006).
The general orthogonal grid formulation has been especially
useful for including the Arctic while avoiding the polesingularity and eliminating the necessity of ﬁltering at highlatitudes of the latitude-longitude grid. Until recently, we haveused a ‘displaced-pole’ grid where the North Pole of the gridis smoothly shifted into land (e.g., Canada in Maltrud andMcClean (2005) and Greenland for the CCSM conﬁguration(Yeager et al, (2006)) in a manner that preserves second orderaccuracy of the spatial discretizations. More recently, we havebegun using a tripole grid which in effect splits the polar
singularity in half and moves each half into opposing landmasses (e.g., Canada and Russia). Note that the Southern
Hemisphere (since there is land at the south pole) and theNorthern Hemisphere tropics (in order to have the grid alignedwith the equatorial wave guide and dominant zonal ﬂows)
remain on a latitude-longitude grid.
One drawback of the dipole grids is that grid cell aspect ratios
can be fairly large (e.g., 9% of the ocean cells in Maltrud andMcClean (2005) had a ratio greater than 2). In addition topresenting truncation errors which are much larger in one
direction than the other, very thin cells can severely reducethe model’s time step via CFL limitations, requiring explicit
modiﬁcation of the diffusion coefﬁcients in these areas to allow
a reasonably large time step.
The relationship of vertical to horizontal resolution has notbeen systematically studied in the eddying regime. Verticalresolution should be relatively high near the surface to aid in
the simulation of mixed layer processes. Level thicknesses
should be chosen to increase smoothly in order to preserve
accuracy. These criteria, however, are no different than those
applied to non-eddy-resolving models. Issues that are speciﬁc
to a strongly eddying regime have only begun to be explored(see, for instance, Danioux et al. (2007)).
One major advantage of the free surface formulation in POP
is removing the need to smooth the model bathymetry as
was required by the streamfunction formulation used by its
predecessors. Consequently the 0.28o and 0.1o dipole grids wereprepared without smoothing. However, topographic roughness
at the grid scale can induce very strong vertical velocities, which
in turn can give rise to excess grid-scale dissipation. A partial
bottom cell (PBC) representation of topography has been shown
to improve the representation of topographic interactions, andreduce grid-scale noise (Pacanowski and Gnanadesikan (1998)).
In our most recent 0.1o tripole experiments, we have adopted
the PBC representation of topography, and used a smoothinginterpolant in the preparation of the discrete topography.
6

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Some hand editing of topography is typically still required.
While the depth of sills and the width of passages require lessalteration than at lower resolutions, the greater complexity ofthe topography presents additional features which may require
attention.
Since one goal of eddying simulations is to allow the ﬂow
to evolve with the minimum amount of artiﬁcial diffusion
possible, we have typically used a biharmonic form for the
diffusive terms in both the momentum and tracer equations,since this moves the dominant dissipation scale to higher
wave numbers than for the Laplacian form. In addition, we
also decrease the diffusion coefﬁcients as the grid size getssmaller. For example, if one equates the grid scale advective
and diffusive time scales, the diffusion coefﬁcients scale withthe grid area to the 3/2 power (Maltrud et al (1998)).
With explicit mesoscale eddies in these simulations, it was
hoped that the precise form of the diffusive terms (which act
as a model for eddy processes in coarser resolution climate
models) wouldn’t matter very much, as long they were large
enough to dissipate grid scale noise and resolve the Munk layer
near boundaries. Chassignet and Garraffo (2001) and Bryan etal (2007) show that this is not the case, and the choice of lateraldissipation remains critical (see Hecht et al. (2008) for a reviewof the issue). Smith and Gent (2004) demonstrate improvements
when using an adiabatic closure for tracer mixing in the eddying
regime, but at a substantial computational price. Hunke etal (2007) show that scaling of coefﬁcients with the grid scale
for both biharmonic and aplacian forms is crucial for highlatitudes
Starting a run from an initial condition derived fromobservational data (such as the World Ocean Atlas) can
introduce issues not experienced at coarser resolutions. The
process of geostrophic adjustment can generate currents andwaves that cause the simulation to go numerically unstable.
We have had to start 0.1o simulations with a time step as small
as a few seconds to get through this initial adjustment phase(5-10 model days), compared to an 8-10 minute time step for thesubsequent period. Often, we will also turn off surface forcing
during this initial phase so other possible fast modes are notintroduced into the system. At resolutions coarser than 0.1o(but still eddying), diffusion and slower currents apparently
mitigate this problem to large degree.
Some data sets may be gravitationally unstable in various
locations when using the model’s equation of state, whichcauses a strong response in any hydrostatic model. In POP,either convective adjustment or very strong vertical diffusion(1000 cm2/s) is used to make the water column stable. As aresult, adjacent columns can develop very high horizontal
gradients if one of them has been convected very deeply. Notethat ‘stabilizing’ an initial condition before starting the modelleads to the same problem unless strong horizontal smoothingis also applied. In the end, using a very small time step forseveral days appears to be an adequate solution.
Most of the surface forcing issues are similar to those associatedwith lower resolution models, with a few exceptions. Jayne andTokmakian (1997) showed that discontinuous daily forcing can
excite spurious inertial oscillations which can be removed withtemporal interpolation. In general, one must also be mindful ofthe large difference in spatial scales between the atmosphericreanalysis products typically used for creating surface forcing
and the ocean model grid. This can be very important nearcoastlines, especially for wind stress and fresh water ﬂuxes.
Testing at lower resolution (e.g., 0.4o tests for a 0.1o setup) orwith basin scale conﬁgurations at the same resolution (e.g., 0.1oVolumeVolumeNo.
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Conclusion
From POP’s inception, the goal of our effort has been a better
understanding of ocean circulation and its role in the climate.
We feel eddying ocean models will play a crucial role in futureclimate projection by explicitly simulating eddies, resolving
important small scale topographic and ﬂow features, and moreaccurately simulating transport and ventilation processes.

Figure 2: Time series of the global IBP at 3 locations: Pacific
Subtropical Mode Water at 150oE, 35oN, 300m (solid), Circumpolar
Water at 40oW, 45oS, 500m (dashed) and Atlantic Deep Water at
52oW, 10oN, 2500m (dotted, which has been multiplied by 10 forclarity).
NA for 0.1o global run) can be very useful. However, there arecaveats in addition to the expected drop in eddy energy due todecreased resolution, for example. We have seen a very good
simulation of the Equatorial Undercurrent in a 0.4o global model
deteriorate signiﬁcantly at 0.1o using the exact same forcing,presumably due to the less diffusive nature of the higher
resolution case. Artiﬁcial boundaries in basin models can also
have signiﬁcant effects. We extracted the North Atlantic sector(from 20oS to 78oN) of the Maltrud and McClean (2005) global
grid and applied the same forcing, and the representation of
the Gulf Stream improved dramatically compared to the fully
global domain, with a strong Northwest Corner and realistic
Gulf Stream separation.
Results from the new 0.1o tripole model
As successful as the 0.1o dipole run of Maltrud and McClean(2005) was, there were also some problems. Most conspicuously,
the Gulf Stream reverted back to the less-realistic behaviour
typically seen at lower resolution, unlike the 0.1o North Atlantic
run. In particular, the separation was north of Cape Hatteras
and the North Atlantic Current failed to turn back to thenorthwest around the Grand Banks. In hopes of correcting thesebiases, we conﬁgured a new 0.1o model with the addition of
PBCs on the tripole grid. At the time of this writing, this new
simulation has run 34 years using a repeating annual cycle ofmonthly averaged forcing. Figure 1 (front cover) shows theNorth Atlantic sea surface height variability from the 0.1o tripole
run compared to the 0.1o dipole run and the AVISO gridded
altimeter data. Clearly the new simulation has corrected theNorthwest Corner bias and (less obviously) improved theseparation point. However, it does appear that now there may,in fact, be too much variability.
In order to enhance our understanding of turbulence andtransport in eddying models, we have added a dozen passive
tracers to the current simulation. Following the work of Peacockand Maltrud (2006) using a 3o version of POP, a suite of Impulse
Boundary Propagators (IBPs) are being simulated. A usefulproperty of IBPs is that they give an approximate probability
density function of ventilation times, i.e., the transit time
distribution (TTD) at a given location (Figure 2). Our intention
is to use the IBP simulated by the eddy resolving model as a
metric in evaluating eddy parameterizations used in coarser
resolution models such as the CCSM. As complements to the
IBPs, CFC-11 and a suite of ﬂoat deployments are also being
simulated to assess the ﬁdelity of the solution.

While great progress has been made in optimizing the model
code itself to run efﬁciently on computer systems with 10,000
processors or more, there remain signiﬁcant challenges inrealizing century time-scale coupled climate simulations
with an eddy-resolving ocean component. Analysis tools,
and computational and storage systems available for postprocessing of model output have not kept pace with the systems
used for the simulation itself. In addition, the demands onhuman resources expand many-fold with higher resolution
simulations. The level of detail in the solutions, and theenhanced level of variability, and the resource consequencesof errors require many people looking early and often at theresults, as well as the forcing ﬁelds, initial conditions, and mostother aspects of the model setup.
Acknowledgements
This work was supported by the Department of Energy Ofﬁceof Science Climate Change Prediction Program. The current 0.1o
tripole POP runs are being performed at the National Centerfor Computational Sciences at Oak Ridge National Laboratory.
Thanks to AVISO for the gridded altimeter data.
References
Bryan, F. O., M. W. Hecht, and R. D. Smith, 2007: Resolution
convergence and sensitivity studies with North Atlantic
circulation models. Part I: The western boundary current
system. Ocean Modelling,16, 141-159.
Chassignet, E. P. and Z. D. Garraffo, 2001: Viscosity
Parameterization and Gulf Stream Separation. Proceedingsof the ‘Aha Huliko’a Hawaiian Winter Workshop, P. Muller and
D. Henderson, editors, U. Hawaii, 367-374.
Collins, W.D. et al, 2006: The Community Climate System Modelversion 3. J. Climate, 19, 2122-2143.
Danioux, E., P. Klein and P. Rivière, 2007: Propagation ofwind energy into the deep ocean through a fully turbulent
mesoscale eddy field. Journal of Physical Oceanography,
submitted.
Dukowicz, J. K., R. D. Smith, and R. C. Malone, 1993: A
reformulation and implementation of the Bryan-CoxSemtner ocean model on the Connection Machine. Journalof Atmospheric and Oceanic Technology,10, 195-208.
Dukowicz, J. K. and R. D. Smith, 1994: Implicit free-surface
method for the Bryan-Cox-Semtner ocean model. Journalof Geophysical Research, 99, 7991-8014.
Fu, L.-L. and R.D. Smith, 1996: Global ocean circlation fromsatellite altimetry and high resolution computer simulation.
Bull. Amer. Meteor. Soc., 77, 2625-2636.
Grifﬁes, S., 2004: Fundamentals of Ocean Climate Models, Princeton
University Press.
Hecht M. W., E. Hunke, M. E. Maltrud, M. R. Petersen, and B.
A. Wingate, 2008: Lateral mixing in the eddying regime anda new broad-ranging formulation. Eddy Resolving Ocean
Modelling; M. Hecht and H. Hasumi, editors; Geophysical
Monograph Series, American Geophysical Union.
Hunke, E. C., M. E. Maltrud, and M. W. Hecht, 2007: On thegrid dependence of lateral mixing parameterizations for
global ocean simulations. Ocean Modelling, in press.
Jayne, S. R. and R. Tokmakian, 1997: Forcing and sampling ofocean general circulation models: impact of high-frequency
motions. Journal of Physical Oceanography, 27, 1173-1179.
7

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Kiehl, J.T. and P.R. Gent, 2004: The Community Climate System
Model, version 2. J. Climate, 17, 3666-3682.
Maltrud, M. E., R. D. Smith, A. J. Semtner, and R. C. Malone,
1998: Global eddy-resolving ocean simulations driven
by 1985-1995 atmospheric winds. Journal of GeophysicalResearch,103, 30825-30853.
Maltrud, M. E. and J. L. McClean, 2005: An eddy resolving
global 1/10 degrees ocean simulation. Ocean Modelling, 8,31-54.
McClean, J.L., A.J. Semtner and V.Zlotniki, 1997: Comparisonsof the mesoscale variability in the Semtner-Chervin 1/4°
model, the Los Alamos Parallel Ocean Program 1/6°
model, and TOPEX/POSEIDON data. J. Geophys. Res., 102,
25,203-25,226.
Pacanowski, R. C. and A. Gnanadesikan, 1998: Transient
response in a z-level ocean model that resolves topography

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with partial cells. Monthly Weather Review, 126, 3248-3270.
Peacock, S. and M. Maltrud, 2006: Transit-time distributions
in a global ocean model. Journal of Physical Oceanography,
36, 474-495.
Semtner, A.J. and R.M. Chervin, 1988: A simulation of the global
ocean circulation with resolved eddies. J. Geophys. Res., 93,
15,502-15,522.
Smith, R. D., M. E. Maltrud, F. O. Bryan, and M. W. Hecht, 2000:
Numerical simulation of the North Atlantic Ocean at 1/10
degrees. Journal of Physical Oceanography, 30, 1532-1561.
Smith, R. D. and P. R. Gent, 2004: Anisotropic Gent-McWilliams
parameterization for ocean models. Journal of Physical
Oceanography, 34, 2541-2564.
Yeager, S.G., C.A. Shields, W.G. Large, and J.J. Hack, 2006: The
low-resolution CCSM3. J. Climate, 19, 2545-2566.

Impact of relative atmosphere-ocean resolution on coupled climate models
Roberts, M.J.1, J. Donners,2, J. Harle,3, and D. Stevens.4
1
UK-Japan Climate Collaboration, Met Office Hadley Centre, Exeter, UK; 2UK-Japan Climate Collaboration, NCAS-Climate,
University of Reading, Reading, UK; 3National Oceanography Centre, Southampton, UK; 4School of Mathematics, University of
East Anglia, Norwich, UK
Corresponding author: malcolm.roberts@metoffice.gov.uk

Introduction
There are many examples of ocean processes that are importantfor climate simulation and which require some minimal meshsize for a believable simulation (examples include dense
ﬂow through narrow gaps, boundary currents, deep water
formation, eddy processes involved in Tropical Instability
Waves, Agulhas rings and the Antarctic Circumpolar Current).
However, when considering the ﬁdelity of climate models, wecannot only consider ocean model resolution; if atmosphericmesh size is insufficient to distinguish important oceanic
features, then important coupling and feedback effects may be
excluded. Such a situation was found by Roberts et al. (2004),
in which a 1/3 degree resolution ocean model was coupled toa 280km atmosphere model. The ocean simulation is hugely
improved (compared to an ocean model with 1.25° spacing),
but there are rather few changes to the large-scale atmosphericand climate simulation.
Hence we need to consider the correct balance between
atmospheric and oceanic mesh sizes, so that the most important
processes are captured in both components, in addition to thenecessary coupling and feedbacks. The purpose of this article
is to show several examples where increases in ocean model
resolution also require increases in atmosphere resolution inorder for the coupled effect to be properly realised.
ModelsThe joint DEFRA/NERC-funded UK-Japan Climate
Collaboration (UJCC) project, together with the NCASClimate UK-HiGEM project (Norton et al., 2007), have beendeveloping coupled models based on the Met Ofﬁce Hadley
Centre’s HadGEM1 model (Johns et al., 2006, a conﬁgurationof the Met Ofﬁce Uniﬁed ModelTM(UK)), but with enhanced
resolution. Using atmospheric models with 150km, 90km and
60km mesh sizes at mid-latitudes, and ocean models at 1 degree(with enhancement to 1/3 degree meridionally at the equator)and 1/3 degree, we have formed a matrix of models in which
we can attempt to understand the relative importance of model
resolution in a coupled framework (in a similar way to theJapanese CCSR/NIES/FRCGC groups with their MIROC3.2
coupled model (Hasumi and Emori, 2004) using T42, T106 andT213 atmosphere models and 1.4 degree and 0.25 degree ocean
models). Integrating such models for 50-100 years has been
made possible through use of the Japanese Earth Simulator
8

super computer. Here we describe results from versions of thecoupled HadGEM model (150km atmosphere, 1-1/3 degree
ocean) and HiGEM model (90km atmosphere, 1/3 degree
ocean) as well as the intermediate models.
Results
The impact of model resolution in the coupled framework cantake various forms. Examples will be shown which illustrate:(a) strong local feedbacks between atmosphere and ocean asmesh size is reduced, leading to changes in the coupling of
the components and potential changes to the mean state, and(b) internal ocean processes at higher resolution changing themean ocean climate, which therefore changes the forcing to
the atmosphere.
Tropical Instability Waves
Tropical instability waves (TIWs) in the eastern tropical Paciﬁc
Ocean, caused by mixed barotropic/baroclinic instability, area highly visible sign of ocean variability in observations (e.g.,
Legeckis, 1977). They are often poorly represented in climate
models as, although their zonal wavelength is large (1000-2000km), the cusp of the wave is very narrow. Hence the wave
breaking (the movement of cold water off the equator, andwarm water onto the equator) only begins to be represented
with ocean model mesh sizes of about 1 degree. The SSTchange across a TIW can also be large (2-5C), and hence a highresolution atmosphere is needed to resolve the SST gradients.
The impact of improved TIW representation in a coupledmodel is illustrated in Figure 1. The wind stress divergenceﬁeld is shown for both HadGEM and HiGEM, and overlaid
are the associated SST contours depicting the characteristic
TIWs. It has been shown that perturbations in the wind stressdivergence and curl ﬁelds are linearly related to the underlying
SST gradient in the eastern equatorial Paciﬁc (Chelton et al.,2001). Changes in SSTs are thought to modify the overlying
wind ﬁeld via alterations in the stability of the Atmospheric
Boundary Layer (ABL) and local sea level pressure (Hayes
et al., 1989). Both models resolve the oceanic TIW signature,
though stronger and earlier in HiGEM with its higher zonal
resolution. However, the low resolution HadGEM atmosphereis unable to capture the SST perturbed wind ﬁeld on the lengthscales of the TIWs. The TIW perturbed wind ﬁeld is apparent
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by white shading, associated with the cusp-like features of the
SST contours.
Chelton et al. (2001) describes two measures of oceanatmosphere coupling in relation to TIWs. In the ﬁrst, the degreeof coupling, can be derived from the amplitude of the cosine(sine) dependencies that the wind stress divergence (curl) hason the angle between the SST gradient and wind vectors on
TIW length scales. In the second, the strength of coupling, isdeﬁned as the slope of ﬁt between the downwind (crosswind)
SST gradient and wind stress divergence (curl) on TIW lengthscales. With higher horizontal resolution in both ocean andatmosphere, HiGEM has a signiﬁcantly greater degree andstrength of coupling than HadGEM. However, the degree andstrength of coupling is still less than that derived from satellite
observations by Chelton et al. (2001). This deﬁciency may be
accounted for by increasing the resolution yet further or it may
suggest that the model is not resolving the physics of the systemcompletely. If we degrade the resolution of the SSTs to that of
the atmospheric model in HiGEM, we increase the strength ofthe coupling (i.e. grad(SST) is weakened thus strengthening
the slope d(grad. tau) / d(grad SST)) although the degreeof coupling remains relatively unaffected. By analogy, if weincrease the resolution of the atmosphere in HiGEM to that of
the ocean grid we are likely to resolve sharper features in the
ABL, thus increasing not only the strength of coupling, but also
the degree of coupling on the length scales of the TIWs.
By improving TIW representation in coupled models
through reﬁnement of the horizontal resolution (including
the convergence of ocean and atmosphere mesh size) we
will better resolve the effects of the TIWs on the ABL. Such a
modiﬁcation may manifest itself locally through, for example,
cloud distribution in the eastern equatorial Paciﬁc (Deser etal., 1993) or through an inﬂuence on the mean climate of thetropical Paciﬁc Ocean. An illustration of the latter is described inRoberts et al. (2004), in which the ocean resolution in a coupledmodel is increased to resolve TIWs. It was found that the SST
bias present in the model was signiﬁcantly reduced, and thiswas shown to be due to the explicit representation of TIWs as
an advective process, in which they remove cold water from theequator and replace it with warmer water from off the equator.
The reﬁnement in the SST ﬁeld results in an improvement to theatmospheric winds, which in turn leads to a better simulationof the whole zonal atmospheric Walker circulation (Roberts etal., 2008). It has also been suggested that the modiﬁed ABL may

CLIVAR Exchanges

feedback onto the TIWs themselves (Pezzi et al., 2004). There
are many other regions where such small-scale interactions
may be important to properly simulate large-scale climate (e.g.
Agulhas retroﬂection; O’Neill et al., 2003).
Coastal effects
Coupled climate models often suffer from large biases inregions adjacent to coastlines, most prominently off the easternboundaries of Africa and America, where complex interactions
between atmospheric winds and clouds, and ocean upwellingand SST, are poorly simulated. Although these areas only
occupy 0.5% of the global ocean, they account for 11% of the
global primary production transported to the thermocline and
20% of global ﬁsh catch (Kearns and Carr, 2003), and hence
are an important part of the carbon cycle and our food supply.
Observations show that these regions are very sensitive toclimate change (McGregor et al., 2007).
The impact of ocean and atmosphere mesh size on the seasonal
cycle of SST in a 3x3 degree area along the North African
coast, centred on 30N, 11W, is shown in Figure 2 (page 10).
The thick line is based on Reynolds SST observations from2001-2006 (Reynolds et al., 2002), and model data from thedifferent resolution coupled models averaged over 20 years.
While HadGEM shows signiﬁcant but opposite-signed biases insummer and winter, the HiGEM model follows the observations
more closely. Although a higher resolution in either theatmosphere or the ocean improves the simulation, both areclearly needed to give a good simulation throughout the year.
The increased atmospheric resolution improves the processes
that determine the radiation balance over these stratocumulus
areas in summer, while the ocean resolution probably moderates
the seasonal cycle through a stronger upwelling response which
is important throughout the year.
Hawaiian Lee Countercurrent
Another illustration of the incremental role that ocean andatmosphere mesh size can play on the coupled climate is seenin the simulation of the Hawaiian Lee Countercurrent (HLCC),
which is described in detail by Sasaki and Nonaka (2006).
Simply stated, a wind stress curl caused by the trade winds
interacting with the Hawaiian Islands induces a circulationin the ocean which drives a eastward countercurrent at about
20N extending from west of 160E to the Hawaiian Islands. It isthought that interaction and feedbacks between atmosphericwind stress curl and ocean SSTs and currents cause the HLCC
to stretch such a distance.

Figure 1: Atmospheric boundary layer response to Tropical Instability Waves in the ocean. Daily mean windstress divergence (Nm-2 x 10-7,
shading) and daily mean sea surface temperature (black and white dashed contours, 20C to 26C every 1C) for a) HadGEM and b) HiGEM.
Daily mean ﬁelds taken from the 13th and 5th September of the HadGEM and HiGEM runs respectively.
9

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Volume 13 No.1 January 2008

et al., 2007) will be important tools for future experiments,
particularly as atmosphere model resolution increases.
While it is clearly desirable to continue to develop higher
resolution ocean models, since there are many important
processes that are not properly simulated in the current
generation of models, continued thought must be given to themost appropriate resolution of forcing (be it from observations
or coupled to an atmospheric model) in order that the feedbacks
and interactions are also represented.

Figure 2: Seasonal cycle of SST (degree C) averaged over a 3x3 box
centred on 30N, 11W along the North African coast. Solid line is
Reynolds 2001-2006 observations (Reynolds et al., 2007), short dashes
for the lowest resolution coupled model (HadGEM), dots for a higher
resolution atmosphere, dash-dots for a higher resolution ocean, andlong dashes for both high resolution atmosphere and ocean (HiGEM).
All model data has been averaged over 20 years.
Using the model matrix, the relative roles of atmosphere andocean for inducing this current can be studied. Figure 3 (page19) shows the wind stress curl (colours) and the ocean zonal
current at 35m (contours) for the four coupled models. In the
low resolution HadGEM model (a), there is strong local windstress curl over the Hawaiian Islands, and a weak zonal current.
With higher resolution atmosphere (b), the wind stress curlsignal stretches further west, as does the current. The higher
resolution ocean with low resolution atmosphere (c) showsa current which stretches over to 160E and beyond, but withrelatively modest wind stress curl, while (d) shows the highresolution coupled response, with the local maximum in windstress curl collocating with the stronger ocean current over to160E. Such changes to the circulation cause a warming of thelocal SSTs, which may be related to local changes in clouds
and precipitation.
The Hawaiian Lee Countercurrent is a simple example of howhigher resolution atmosphere and ocean components can leadto changes in simulated circulation. It may well be that changes
to the persistent small-scale wind stress curl features (whencomparing within the model matrix) found in many otherregions (particularly over the Southern Ocean and boundary
currents) might also lead to changes in their simulation andbehaviour, but these will require more detailed analysis.
Discussion
There are many important interactions between the atmosphereand the ocean occurring on small time and space scales, and it isa challenge to represent the most important of these processes inour climate models. Systematic studies of the impact of model
resolution on simulated coupled climate are a useful ﬁrst step,
but other methods of analysis will also be needed to isolate andidentify individual processes.
For example, UJCC has performed experiments with a variety
of atmosphere model resolutions, using AMIP-II SST and seaice forcing (Gates et al., 1999) which is nominally 1° resolution
but is effectively much smoother than this in time and space.
Surprisingly few differences have been found between thesesimulations, and it is reasonable to ask whether using suchsmooth forcing is partly to blame. Experiments in which higher
resolution ocean SSTs have been used to force atmospheremodels (e.g. Chelton et al., 2005) suggest that this can makea signiﬁcant difference to model variability, and hence thedevelopment of higher resolution SST datasets (for example
the Reynolds and OSTIA datasets; Reynolds et al., 2007, Stark
10

Acknowledgements
The model integrations described in this paper were mainly
performed using the Japanese Earth Simulator supercomputerunder the support of JAMSTEC. The work was performed
as part of the NERC UK-HiGEM and the UK-Japan Climate
Collaboration projects, the latter being supported by theForeign and Commonwealth Ofﬁce Global Opportunities Fundand jointly funded by NERC, and the Joint DEFRA and MoD
Integrated Climate Programme - GA01101, CBC/2B/0417Annex C5. Thanks to Len Shaffrey for useful comments.
Further information on UJCC and UK-HiGEM projects can befound at http://www.earthsimulator.org.uk/index.php andhttp://www.higem.nerc.ac.uk/
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Pezzi, L. P., J. Vialard, K.J. Richards, C. Menkes and D.
Anderson, 2004: Inﬂuence of ocean-atmosphere couplingon the properties of tropical instability waves. Geophys. Res.
Lett., 31, L16306, doi:10.1029/2004GL019995.
Reynolds, R. W., C. Liu, T.M. Smith, D.B. Chelton, M.G. Schlax
and K.S. Casey, 2007: Daily high-resolution-blended analyses
for sea surface temperature. J. Climate, 20, 5473-5496.
Roberts, M., H. Banks, N. Gedney, J. Gregory, R. Hill, S.
Mullerworth, A. Pardaens, G. Rickard, R. Thorpe and R.

CLIVAR Exchanges

Wood, 2004: Impact of an eddy-permitting ocean resolution
on control and climate change simulations with a global
coupled GCM. J. Climate, 17, 3-20.
Roberts, M.J., A. Clayton, M.-E. Demory, J. Donners, P.L. Vidale,
W. Norton, L. Shaffrey and I. Stevens, 2008: UJCC: Impactof resolution on the tropical Paciﬁc circulation in a matrix
of coupled models. In preparation.
Sasaki, H. and M. Nonaka, 2006: Far-reaching Hawaiian Lee
Countercurrent driven by wind-stress curl induced by warm
SST band along the current. Geophys. Res. Lett., 33, L13602,
doi: 10.1029/2006GL026540.
Stark, J.D., C. J. Donlon, M. J. Martin, M. E. McCulloch, 2007:
OSTIA: An operational, high resolution, real time, global
sea surface temperature analysis system. Oceans ‘07 IEEE
Aberdeen, conference proceedings. Marine challenges: coastline
to deep sea. Aberdeen, Scotland.IEEE.

Development of a global ocean model with the resolution of 1o×1/2o and 1/8o×1/12o
Nakano, H., M. Hirabara, H., Tsujino, and T. Motoi.
Meteorological Research Institute, Tsukuba, Ibaraki, Japan
Corresponding author: hnakano@mri-jma.go.jp

1. Introduction
Ocean general circulation models (OGCMs) have become more
and more complex and sophisticated during past decades.
The present OGCMs can be used to address various purposessuch as global warming projection, seasonal prediction,and the carbon cycle, but it is hard to design and maintainglobal models for each different purpose. The MeteorologicalResearch Institute (MRI) of the Japan Meteorological Agency isdeveloping a baseline global OGCM to meet these needs.
The resolution of our base-line model is set to 1o×0.5o. Since
mesoscale eddies and swift western boundary currents are
not reproduced in such a low resolution model, it is importantto evaluate the representation error caused by this insufﬁcient
resolution. At the same time, it is necessary to assess thefundamental limitations of the model that are not overcome
by increasing horizontal resolution.
This article presents the assessment of the upper ocean
circulation. The representation issues are assessed by comparing
with a global eddy-resolving model and the fundamental issues
related to changing surface forcings are also addressed.
2. ModelThe Meteorological Research Institute Community Ocean
Model (MRI.COM) (Ishikawa et al., 2005, Tsujino et al., 2006,
Hirabara et al., 2007) is used. Its basic characteristics are listed
in Table 1. Here we describe some unique techniques used in
MRI.COM to run global models. It uses an Arakawa B grid,and coastlines are created by connecting tracer points insteadof velocity points. This is useful for coarse-resolution global
models because a narrow passage can be represented by one
velocity cell. A split-explicit algorithm is used for the barotropic
and baroclinic part of the equations. The vertical coordinate
Table 1: Features of MRI.COM
Grid arrangement Arakawa B(Horizontal)(coastline is on the tracer point)
Grid arrangement Z-level + Parial cell at the lowest level(Verical)
Bottom Boundary Layer (option)
Free-surface
Explicit (Killworth et al., 1991)
M o m e n t u m Quassi-enstrophy conservation scheme
advection(Ishizaki and Motoi, 1999)
Tracer advection
UTOPIA and QUICKEST
Sea Ice model(Thermodynamics) Mellor and Kantha (1989)(Dynamics)
Hunke and Ducowicz (2002)

near the surface follows the surface topography like sigmacoordinate models (Hasumi, 2006), enabling us to adopt a ﬁnevertical resolution near the surface without causing a vanishingof the uppermost layers in the southern ocean, where sea surface
height is signiﬁcantly lower than other regions.
3. Experimental design
In this article we show three experiments differing mainly inhorizontal resolutions and forcings: coarse-CORE run, coarseJRA run, and ﬁne-JRA run,. Basic settings are summarizedin Table 2. The tripolar grid is chosen. The resolution of the
ﬁne-JRA is set to 1/8o×1/12o, which is the necessary resolution
to represent the Kuroshio Extension (Tsujino and Fujii, 2007).
The topography of the coarse-resolution model is modiﬁed torepresent the important current systems. Preliminary results of
the ﬁne-resolution model beneﬁt from this modiﬁcation aroundcomplex archipelagos such as the Philippines Islands.
For the coarse-CORE run, the model is driven by the surface
forcing data of the Co-ordinated Ocean-Ice Reference
Experiments (COREs) (Large and Yeager, 2004). For the coarseJRA and ﬁne-JRA run, the coarse- and ﬁne-resolution models
are driven by the climatological monthly forcings derived fromthe dataset of the Japanese 25-year Reanalysis Project (JRA25)(Onogi et al., 2007). JRA25 provides surface ﬁelds as othermajor reanalyses. It has been reported that JRA25 is in generalcomparable to ERA and NCEP (e.g., Zhao and Li, 2006).
The initial conditions of the coarser resolution model are derived
from the 0.25o version of World Ocean Atlas 2001 (WOA01) and
the Polar Science Center Hydrographic Climatology (PHC),
which is also used for the restoring of the sea surface salinity.
The coarse resolution model is integrated for 50 years to spin-up
the wind-driven circulation. The result of year 40 of the coarseJRA run is interpolated and used as the initial conditions of the
ﬁne-JRA run. The integration time of ﬁne-JRA run is 10 years.
Starting from the Sverdrup balanced states, the Kuroshio
Extension and the Gulf Stream emerge accompanying therecirculation gyres within 3 years in the subtropical gyres of the
ﬁne-JRA run. The dynamical setup of these currents is ﬁnishedwithin about 6 years, which is much faster than starting fromthe observations. The improvement in the tracer distribution
occurs in the upper layer even during this short integration.
This is consistent with the study of western boundary currents
using the “ideal settings” (Nakano et al. 2008). This method
helps to estimate the impact of the resolution.
11

CLIVAR Exchanges

Volume 13 No.1 January 2008

Grid

Tripolar grid (Singularities of bipolar grid is (64oN,80oE) and(64oN,100oW)

Resolution(Coarse resolution)

1 (zonal), 1/2 (meridional), 50 levels + BBL (Nakano and
Suginohara, 2002)(Total 364×368×51)

Resolution(Fine resolution)

1/8o(zonal), 1/12o (meridional), 50 levels. (2884×2152×50)

Thickness of vertical grid

4, 5, 6.5, 7.5, 9, 11.5, 14, 16, 17.5, 18, 18.5, 20, 20.5, 21, 22, 23, 24,
27, 30, 30, 35, 40, 40, 50, 60, 65, 70, 75, 80, 90, 100, 100, 125, 150,
150, 150, 175, 225, 250, 250, 250, 275, 300, 300, 300, 300, 300, 450,
600, 600 (m)ooSurface forcing (1) for Climatological data set derived from Japanese 25 year Reanalysis
Coarse-JRA run and Fine- Project (JRA25) and a bulk formula of Kara et al. (2000).
JRA runSurface forcing (2) for Climatological data set for Co-ordinated Ocean-Ice Reference
Coarse-CORE run
Experiments (CORE).
Salinity restore

8 days for uppermost 4 m.

Normalized hydrology

No

Horizontal diffusion
Parameterization
Horizontal viscosity(Coarse resolution)

Harmonic isopycnal diffusion (coef.= 5x102 m2s-1) +GM-parameterization (coef.=5x102m2s-1).
Harmonic Smagorinsky-like viscosity

Horizontal diffusion
Horizontal viscosity(Fine resolution)

Bi-harmonic diffusion (coef. = 1x10 m s )Biharmonic Smagorinsky-like viscosity

Mixed layer model

Noh and Kim (1999)

8

4 -1

Background viscosity and Tidally driven mixing over rough topography
diffusion(St. Laurent et al., 2002)
Initial state(Coarse resolution)

WOA01(south of 64oN) + PHC
Integration: year 1 – year 50

Initial state(Fine resolution)

End of year 40 of coarse-JRA run
Integration: year 41 – year 50(north of 64oN)

Table 2: Setting of the global ocean simulations
4. Results
Figure 1 (page 19) shows the biases of the annual mean sea
surface temperature of the last two years of the runs. The
patterns are in general very similar. Notable differences are seen
between CORE and JRA runs in the regions of upwelling causedby the local winds. The warm bias off the western coast of thecontinents (California, Ecuador, Chile, Angola) and the cold
bias in the central Paciﬁc are seen in limited areas near the coast
and along equator in the JRA run, but these biases are broader
in CORE run. They are presumably due to the difference in the
resolution of the wind-forcing data; the resolutions of CORE
and JRA are T62 and T106, respectively.
Figure 2 (page 20) shows the difference in the barotropic stream
function between the ﬁne-JRA and coarse-JRA runs. The largest
differences are found in the western boundary and the Southern
Ocean, where the effect of the meso-scale eddies is considered
to be large. The currents in these regions are in general more
realistic, leading to reduced bias in these regions in the ﬁneJRA run (Figure 1). Bias in equatorial region is quite similar
despite the presence of Tropical Instability Waves and many
complicated currents reproduced in the ﬁne-JRA run. The cold
and warm biases in the interior of the subtropical and subpolar
gyres are not reduced by the increase in resolution.
The differences between the experiments are more conspicuousin the subsurface than at the surface. A Taylor diagramshows that biases are reduced in the ﬁne-JRA run in both thetemperature and salinity ﬁelds (Figure 3). This improvement
Figure 3: Taylor diagramf o r t e m p e r a t u re a n dsalinity at 200 m from
40oS to 50oN.

12

is in large part related to the improved representation ofwater mass transport around the recirculation gyres in thesubtropics.
5. Summary
Sensitivity experiments on the MRI.COM global model using
different surface forcing data and resolutions are conducted.
The biases of SST are within 1oC in large part of the oceans but
the equatorial cold bias and the eastern boundary warm bias arefound for all cases. The improvement by increased horizontal
resolution is signiﬁcant near the western boundary currents
and recirculations of the subtropical gyres. The model settings
will be ﬁxed through further analyses including analysis of thethermohaline circulation.
References
Hasumi, H, 2006: CCSR Ocean Component Model (COCO)
Version 4.0. Center for Climate System Research, CCSR
Report No. 25.
Hirabara, M., H. Ishizaki, and I. Ishikawa 2007, Effects of theWesterly Wind Stress over the Southern Ocean on the
Meridional Overturning J. Phys. Oceanogr.,37, 2114-2132.
Hunke, E.C., and J.K. Dukowicz, 2002: The elastic-viscousplastic sea ice dynamics model in general orthogonal
curvilinear coordinates on a sphere-incorporation of metricterms. Mon. Wea. Rev., 130, 1848-1865.
Ishikawa, I., H. Tsujino, M. Hirabara, H. Nakano, T. Yasuda,and H. Ishizaki 2005, Meteorological Research InstituteCommunity Ocean Model (MRI.COM) Manual, Technical
Reports of the Meteorological Research Institute, No. 47, p 189(in Japanese).
Ishizaki, H., and T. Motoi, 1999: Re-evaluation of the TakanoOnishi scheme for momentum advection on bottom relief in
ocean models. J. Atmos. Oceanic. Technol., 16, 1994-2010.
Kara, A.B., P.A. Rochford, and H.E. Hurlburt, 2000: Efﬁcientand accurate bulk parameterizations of air-sea ﬂuxes foruse in general circulation models. J. Atmos. Oceanic. Technol.,17, 1421-1438.
Killworth, P.D., D. Stainforth, D.J. Webb, and S.M. Paterson,
1991: The development of a free-surface Bryan-Cox-Semtner
ocean model. J. Phys. Oceanogr. 21, 1333-1348.
Large, W. B. and S. Yeager, 2004: Diurnal to decadal global
forcing for ocean and sea-ice models: the datasets and ﬂux
climatologies. NCAR Technical Note: NCAR/TN-460+STR.
CGD Division of the National Centre for AtmosphericResearch.
Mellor, L.G., and L. Kantha, 1989: An ice-ocean coupled model.
J. Geophys. Res., 94, 10937-10954.
Nakano, H., and N. Suginohara, 2002: Effects of bottom
boundary layer parameterization on reproducing deep andbottom waters in the world ocean model. J. Phys. Oceanogr.
32, 1209-1227.
Nakano, H, H. Tsujino, and R. Furue, 2008: The Kuroshio
Current System as a jet and twin “relative” recirculation
gyres embedded in the Sverdrup circulation. Dyn. Atmos.
Ocean, in press.
Noh, Y., and H.J. Kim, 1999: Simulation of temperature andturbulence structure of the oceanic boundary layer with
the improved near-surface process. J. Geophys. Res. 104,
15621-15634.
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H.
Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K.
Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T.
Ose, N. Mannoji and R. Taira 2007, The JRA-25 Reanalysis.,J. Meteor. Soc. Japan, 85, 369-432.
St. Laurent, L., H.L. Simmons, and S.R. Jayne, 2002: Estimating
tidally driven mixing in the deep ocean. Geophys. Res. Lett.
29, 2106, doi:10.1029/ 2002GL015633.

VolumeVolumeNo.
13
9 No.3No.1
3 September
September
January2004
2008
2004

Tsujino H., N. Usui, H. Nakano 2006, Dynamics of Kuroshio pathvariations in a high-resolution general circulation model, J.
Geophys. Res., 111, C11001, doi:10.1029/2005JC003118
Tsujino, H., and Y. Fujii, 2007: Improved representation ofcurrents and water massed in the upper layer of the North
Paciﬁc Ocean in eddy-resolving OGCMs. CLIVAR Exchanges,
43, 19-21.

CLIVAR Exchanges

Zhao, Y., and J. Li, 2006: Discrepancy of mass transport between
the northern and southern hemisphere among the ERA-40,
NCEP/NCAR, NCEP-DOE AMIP-2, and JRA25- reanalysis.
Geophys. Res. Lett. 33, 20804, doi: 10.1029/ 2006GL027287.

The MITgcm/ECCO adjoint modelling infrastructure
Heimbach, P., MIT/EAPS, Cambridge (MA), USA
Corresponding author: heimbach@MIT.EDU

Introduction
The availability of an adjoint model as a powerful researchtool complementary to an ocean model was a major design
requirement early on in the development of the MIT generalcirculation model (MITgcm) (Marshall et al., 1997, Marotzke etal., 1999, Adcroft et al., 2002). It was recognized that the adjoint
permitted very efﬁcient computation of gradients of various
scalar-valued model diagnostics, norms or, generally, objectivefunctions with respect to external or independent parameters.
Such gradients arise in at least two major contexts. If theobjective function is the sum of squared model vs. observationdifferences weighted by, e.g., the inverse error covariances,
the gradient of the objective function can be used to optimize
this measure of model vs. data misﬁt in a least-squares sense.
One is then solving a problem of statistical state estimation.
If the objective function is a key oceanographic quantity suchas meridional heat or volume transport, ocean heat content,
or mean surface temperature index, the gradient provides
simultaneously a complete set of sensitivities of this quantitywith respect to all independent variables.
1.1. Adjoint model generation via automatic differentiation
In a formal sense, the tangent linear model (TLM) of a givennon linear model maps perturbations of independent variables
such as the initial state to perturbations in the ﬁnal state andthe resulting objective function. For discretized problems
the adjoint model ADM is the transpose of the tangent
linear model, and the adjoint variables are equivalent to the
Lagrange multipliers of the model. Physicists would referto the adjoint method as the Lagrange multiplier method.
For very complex non linear parent models such as an ocean
general circulation model (GCM), which consists of the orderof 105 lines of code, the task of developing an adjoint model,
which faithfully represents the derivative of that model withrespect to a high-dimensional control vector, is as challenging
as the development of the parent model itself. Furthermore,
the structure of the adjoint model depends on the control
variables chosen, i.e., extending the control space may require
extending the adjoint model (a drastic example is the use ofbottom topography as the control variable in ocean modelling,e.g., Losch and Heimbach, 2007). Finally, if the parent model
undergoes continued improvements, changes in algorithms,
and extensions to incorporate new processes, a simultaneous
update in the adjoint code is almost doomed to fail, unless equal
resources for it would be available.

The route chosen by the MITgcm model developers was tosystematically explore automatic or algorithmic differentiation(AD) (Griewank, 2000). AD is concerned with the accurate and
efﬁcient evaluation of derivatives for functions deﬁned bycomputer programs. Source-to-source AD tools are capableof automatically generating code representing, for example,
the tangent linear model, adjoint model, the Hessian, or otherhigher order derivatives of the non linear parent model code.
The following steps are at the heart of the idea of AD:
1

2

3
4
5

consider a model as mapping from the space of independent
variables controls to the space, often one-dimensional, of
dependent variables (cost, objective/probing function,measure, diagnostic, ....).
this mapping is a composition of a large number of
elementary operations; at an intermediate level one
thinks of one time-step of a time-evolving problem, butultimately each line of code represents such an elementary
operation,at the elementary level, the AD tool knows the derivative
expression to each intrinsic function such as arithmetic andlogical/conditional instructions,the derivative of a composition of mappings can be
rigorously computed via the chain rule; the result of thefull derivative is the product of elementary Jacobians,
the full adjoint operator is the reverse-order product of thetransposed elementary Jacobians.

For time-dependent problems the effect of transposing the TLM
to obtain the ADM is to propagate information backwards intime (one could phrase it in terms of the TLM propagating
perturbations, and the ADM accumulating sensitivities).
The reverse nature of the integration, for transient problems,
requires knowledge of the model state in reverse order. This
is at the heart of the technical difﬁculties to generate efﬁcientadjoint code for large-scale transient applications.
The AD tool chosen for MITgcm was TAMC and its commercial
successor TAF developed by Giering and Kaminski (1998). It
solves the problem of requiring model state variables in reverse
temporal order through a hierarchical ”check pointing” andtargeted ”taping” algorithm which provides an optimal mix
between saving and recomputing required variables. To achievethis, the user has to guide TAF by the insertion of directives
into the parent model to trigger storing of speciﬁc variables.
This approach has rendered exact adjoint computation, i.e.,#

conﬁguration

resolution

time# proc’s

HPC platforms

comments

I

quasi-global

4 deg.

101 - 103 yr

1 to 2PC

coupled ocean/biochem.

II

quasi-global

1 deg.

15 yr

48 - 96

Alktix, IBM DP, NEC SX-6

ECCO production

IIIfully-global LLC

1/4 to 1 deg.

15 yr

72 to 324

Altix, IBM SP 4,5

next-generation ECCOIV

Arctic Ocean

34 km

4 yr

40 to 80

Altix, IBM SP 4,5

coupled ocean/sea-ice

V

Southern Ocean

1/6 deg.

2 yr

600

IBM SP4 and BlueGene/L

eddy-permittingVI

Tropical Paciﬁc

1/6 deg.

1 yr

96

PC Beowulf cluster

eddy-permittingVII

Labrador Sea

20 km

1 yr

8

AMD Opteroncouple ocean/sea-ice

VIII

Irminger Sea

4 to 9 km

15 - 30 days

1 to 32

AMD Opteron, Altix

eddy-resolving

Table 1: Some current adjoint applications
on HPC platforms

13CLIVAR Exchanges

Volume 13 No.1 January 2008

FeatureAdjoint Support

NUMERICAL SCHEMES AND FEATURES
parallel setup
parallel cubed-sphere topology
partial cell topographyhigher-order advection schemes
multi-dimensional advection
staggered time-stepping
vector-invariant momentum
Bryan & Lewis (1979) mixing
bottom dragnonlinear EOS
nonlinear free surface

fullpartlyfull
some (e.g. 3rd order DST)
full
full
full
full
fuyll (linear and quadratic)
some (e.g. JMD95)
partly

PACKAGES
GM/Redibulk formulae
sea-ice
BloECCOopen boundaries
passive tracers

partly (tapering-dependent)
full (Large & Yeager, 2004)
mostlymostlymostly (except Orlanski)
full

Table 2: Major supported model features.
evaluating derivatives in reverse order, exact with respect totheir time-varying model trajectory, practical for large-scale
time-dependent applications.
Another critical point of concern is efﬁcient implementation onvarious high performing computing (HPC) platforms. AD asapplied to the MITgcm ensures that the adjoint model adopts
the same parallel implementation strategy as the forward
model. The adjoint model inherits the forward models’ domain
decomposition. Primitives of the parallel support package ofthe MITgcm have been adjointed by hand to overcome current
limitations of AD tools to handle Message Parsing Interface(MPI) functions. The success of this approach is demonstrated
by the number of adjoint applications that have been run on
various HPC platforms, as listed in Table 1. Technical details
related to hierarchical check pointing, adjoint dump and restartso-called divided adjoint or DIVA, adjoint parallelism, andactive and adjoint variable I/O in the context of MITgcm areprovided in Heimbach et al. (2002, 2005).
1.2. An up-to-date adjoint modelling infrastructure
The MITgcm model repository is transparently managed
on-line via the Concurrent Version System (CVS). Through it
the source code is changed on a daily basis. Currently, a totalof 8 adjoint conﬁgurations, which employ various numerical
schemes, features, and packages are tested every night tocheck whether developments to the forward model retain
differentiability of the code with respect to the AD tool andpreserve the computed gradient. For these conﬁgurations, theadjoint code is automatically generated every night based on
the latest available forward code, and gradients are checked for
13-digit compatibility with respect to reference gradients. This
ensures that up-to-date adjoint code can be maintained for the
MITgcm. Most (but not all) features of the MITgcm can be madepart of the adjoint. A summary of the most important features
is listed in Table 2. Control variables currently supported by
MITgcm are listed in Table 3.
2. Applications
The first sensitivity study using the MITgcm adjoint was
conducted by Marotzke et al. (1999) to investigate effects on
Atlantic meridional heat transport over a one-year period.
The availability of three-dimensional Lagrange multipliers ofthe model state enabled the distinction between dynamical
and kinematical effects of initial temperature anomalies, anddemonstrated the power of the method to separate various
processes affecting the heat transport.
The main driver, however, of the MITgcm adjoint model
development was the Paciﬁc acoustic tomography program(ATOC Consortium, 1998), the World Ocean Circulation
14

Experiment (WOCE), and the satellite altimetric data,
which were for the ﬁrst time becoming available globallyand continuously with the launch of TOPEX/POSEIDON
in 1992 (Stammer et al., 1997). Combining these data withcomplementary observations from other satellite missions(e.g., SST and wind stress) and in-situ data collected during the
WOCE period was thought to best be achieved through oceanstate estimation (OSE) using an ocean model as dynamicalinterpolator e.g., Wunsch (2006).
2.1 Ocean state estimation
The term ”state estimation” is borrowed from control theory,
and as envisioned here, is directed (at the present time) not at
forecasting, but at description and understanding. In control
terminology, it addresses the smoothing problem rather thanthe ﬁltering and prediction ones. As such, and in contrast to”data assimilation” for Numerical Weather Prediction (NWP),a premium is placed on avoiding trajectory jumps which
occur at the analysis times in NWP and having control overthe integrated space/time behaviour of many climatologicallyimportant properties e.g., heat content. Producing such a
dynamically consistent description of the ocean was one ofthe primary goals of the JPL-MIT-SIO consortium called ECCO
Estimating the Circulation and Climate of the Ocean founded
in 1998 with D. Stammer as PI. The successful demonstrationof OSE using the adjoint method came with the release andanalysis of the ﬁrst adjoint method-based quasi-global productby Stammer et al. (2002, 2003).
Since then, various improvements have been made (e.g.,
Heimbach et al., 2006), such as increasing horizontal resolution,
extending the estimation period to 15 years covering 1992 to2006 in the case of ECCO-GODAE (Wunsch et al., 2007) andto 50 years covering 1952 to 2001 in the case of GECCO (Köhl
et al., 2006), update of existing and inclusion of various new
data sources, improvement of various prior error estimates,
notably for representation errors in conjunction with in-situ
data (Forget and Wunsch, 2007), inclusion of an atmosphericboundary layer scheme, and update of numerical algorithms.
See Wunsch and Heimbach (2007) for details.
Within ECCO, complementary estimation approaches are
pursued at JPL, one led by I. Fukumori, using a Kalman ﬁlter
and RTS smoother method (Fukumori 2002), one led by D.
Menemenlis using a Green’s function approach (Menemenlis
et al., 2005b). Both efforts deserve their own description.
The data-constrained solutions are now being used to address
important science questions surrounding decadal climate
variability. Wunsch and Heimbach (2006) and Köhl et al. (2007)
have placed lower bounds on month-to-month meridional heatand volume transport ﬂuctuations in the North Atlantic, andnoted the strong noise which masks weak, but non-signiﬁcantdecadal transport trends, and raises serious sampling andaliasing issues in hydrographic transport estimates. Vinogradov
et al. (2007) analyze the seasonal cycle in global sea-levelvariations, while Wunsch et al. (2007) examine steric andControl variables• initial conditions (T,S,U,V• time-varying air-sea ﬂuxes (buoyancy and momentum)• time-varying atmospheric state (surface air temperature, speciﬁc humidity,
airpressure, downwelling shortwave radiation, wind speed vector)• surface relaxation timescales• time-varying prescribed open boundaries• mixing coefﬁcients• bottom drag• bottom topography (requires special set-up)• passive tracer initial concentrations and ﬂuxes• eddy stresses• sea-ice initial conditions

Table 3: Supported control variables.

VolumeVolumeNo.
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2004

CLIVAR Exchanges

mass contributions to decadal sea level trends, as well as their
vertical partition between the upper and abyssal ocean, which isavailable from the full three-dimensional state estimate. Given
the strong regional signals, they showed that both observationand calculation of global mean sea-level variations remainfragile.
The beneﬁt of using a mean dynamic topography (MDT) toconstrain the ECCO state estimates which is based on the geoidfrom the recent Gravity Recovery And Climate Experiment(GRACE) satellite mission has been demonstrated by Vossepoel(2007) and Stammer et al. (2007). The former provided an
uncertainty assessment of available MDTs ahead of theEuropean Gravity Field and Steady-State Ocean Circulation
Explorer (GOCE) mission. Attempts are now under way (Ponte
et al., 2007) to also use time-varying GRACE data in the formof monthly ocean bottom pressure. More application examplesof the state estimates may be found in Heimbach and Wunsch(2007).
On a regional scale, the feasibility of higher-resolution state
estimation in a nested context has been explored in several
studies. Most have employed boundary conditions from a
global solution as a starting point, and then improved theregional solution through adjusting the open boundaries aspart of the control space. Examples for the Atlantic include
the study by Ayoub (2006), by Gebbie et al. (2006) to study
eddy subduction in the Eastern subtropics, and by Lea et al.
(2006) in an eddy-resolving Irminger Sea context (Table 1, VIII).
An eddy-permitting regional study in the tropical Paciﬁc has
been conducted by Hoteit et al. (2005) (Table 1, VI). In what isarguably the largest adjoint-based estimation effort undertaken
so far, Mazloff (2007) has computed a preliminary solution ofa two-year (2005/06) eddy-permitting (1/6 deg.) Southern
Ocean state estimate (SOSE). This work involves a 600-processor
adjoint model (Table 1,V). It contributes directly to NASA’s
Modelling and Analysis Project (MAP) “ECCO2 – HighResolution Global-Ocean and Sea-Ice Data Synthesis” by J.
Marshall at MIT and L.L. Fu at JPL/Caltech. By way of example,
Figure. 1 page 20 depicts a snapshot of near-surface velocity
taken on May 12, 2006 for iteration 16 of SOSE. Overlaid as awhite line is the sea ice extent. The eddy-permitting characterof SOSE is evident.
2.2 Sensitivity analysis
Application of the MITgcm adjoint for sensitivity analyses
has moved in various directions, and following the AD routehas also enabled us to keep pace with various coupling efforts
within the MITgcm modelling framework.
Köhl (2005) extended the work of Marotzke et al. (1999) toa 10-year study of North Atlantic MOC sensitivities. Theyshow which geographical locations are optimally suited formeasuring MOC changes, and assess dominant mechanisms
inﬂuencing the MOC on various time scales from annual todecadal. Going far beyond these time scales, Bugnion et al.
(2006a,b) considered multi-centennial equilibrium sensitivitiesof the MOC. The sensitivity maps enable quantiﬁcation of therelative role played by buoyancy vs. momentum forcing, andby diapycnal mixing. Furthermore, different surface boundary
conditions resulted in fundamental differences in the sensitivity
patterns, providing a clear conﬁrmation of the importance ofthe type of boundary conditions used for climate-time scale
integrations.
Focusing on the tropical Paciﬁc, Lee et al. (2000) and Fukumori
et al. (2004) investigate the subtropical-tropical water mass
exchange and its impact on the Niño-3 region via adjoint
advection and diffusion of passive tracer-tagged water masses.

Figure 2: Adjoint sensitivity of sea-ice export through Fram Strait
to ice thickness distribution everywhere in the Arctic and sub-Arctic
domain over a 4-year period between 1992 and 1995. The coupled
ocean/sea-ice adjoint model runs on 80 processors and was generatedusing TAF.
In a similar context, Galanti and Tziperman (2003) study midlatitude ENSO–teleconnection mechanisms using the TAMCgenerated adjoint of an early alpha version of MOM4. Theyﬁnd strong sensitivities to planetary Rossby waves, which areampliﬁed in baroclinically unstable regions of the subtropicalgyre, and may account for decadal ENSO variability.
Dutkiewicz et al. (2006) developed an adjoint component of
their biogeochemical model which is coupled to the ofﬂine
adjoint MITgcm dynamical core. The coupled adjoint wasused to investigate controls and limitations to global biologicalproductivity and air-sea carbon ﬂuxes. In particular, regional
patterns of iron vs. light sensitivities highlighted the interplay ofphosphorus, iron and light limitation. Extension of the coupled
ocean/biogeochemical adjoint to enable coupled ocean carbon
cycle studies is anticipated.
In the light of concerns regarding the evolution of the Arctic
sea-ice cover that have received signiﬁcant attention in theongoing International Polar Year (IPY), we have increased oureffort at completing an adjoint component of MITgcm’s fullyﬂedged dynamic/thermodynamic sea-ice model (Heimbachand Menemenlis, 2003, Losch et al., 2008). The coupled
ocean/sea-ice adjoint is now being used in a regional Arctic
conﬁguration, which is a slightly coarsened (roughly 30 km
horizontal resolution) version of the ECCO2 cubed-sphere
setup of Menemenlis et al. (2005a). Figure. 2 depicts transientsensitivities of sea-ice export through the Fram Strait to changes
in ice thickness everywhere in the Arctic and sub-Arctic domain
over a 4-year period between 1992 and 1995. The dominantpattern is a positive signal (an increase in ice thickness by aunit perturbation at position (x, y) will increase sea ice exportby a magnitude shown in the map) expanding away fromthe Fram Strait into the Arctic interior, in agreement with thetime-reversed circulation in the Arctic. Time scales of sea-ice
advection connecting the Fram Strait to various areas of the
Arctic domain become clearly visible.
3. OpenAD: a new AD tool
Given the importance of the AD tool for ECCO and for various
MITgcm applications, we are involved, together with Argonne
National Lab, in the development of a new open-source tool,
called OpenAD (Naumann et al., 2006, Utke et al., 2008) asa backup and complement to the current tools in use. The
project has been supported by NSF’s Ocean Science Divisionand by NASA’s MAP project ECCO2. It represents the largest
AD tool development effort currently under way within thecomputational mathematics and AD community. The tool
has now been successfully applied to the full MITgcm in a
conﬁguration very similar to the one of Bugnion et al. (2006a),i.e. a coarse-resolution setup, including partial cell topography,
convective adjustment, and a GM/Redi parameterization
scheme, all of which are part of the adjoint. To underline thevalue of the availability of complementary AD tools we report15

CLIVAR Exchanges

here two instances in which adjoint gradients between the
two tools were compared for equivalent setups. In one casethe comparison helped to isolate a bug in the TAF tool, in theother case, a bug in OpenAD was found. The MITgcm gradientscomputed by the tools typically agree to within at least 7 digits.
Performance-wise, for the same checkpointing hierarchy (level3 checkpointing), the ratio between a full adjoint and a forward
integration is on the order of 5 using the TAF-generated adjoint,
and 10 using the OpenAD-generated code. The origin of thesedifferences are the different approaches of optimizing storing
vs. recomputation requirements (store-all vs. recompute-all
approaches). Numbers given above are expected to change inthe future.
The OpenAD-generated adjoint has been applied to computesensitivities globally of North Atlantic heat transport over a
100 year period, similar to the work of Bugnion et al. (2006a),
but focusing here on the transient aspect. Figure 3 (page 20)
shows sensitivities of meridional heat transport across 26 N
in the Atlantic to changes in temperature at 1250 m depth, 2(panel a) and 75 (panel b) years prior in the past. Sensitivities
are mediated through a complex interplay of various processes
which act on different time scales, ranging from coastal Kelvinwaves, Rossby waves (here visible snapshots of adjoint waves)and advective processes (again in time-reversed, adjoint
fashion). For instance, while Figure. 3a reﬂects comparatively
local inﬂuences on heat transport changes limited by maximum
wave propagation speed, Figure. 3b shows that on multidecadal time scales, the problem of Atlantic heat transportvariability becomes a global problem, as one might expect.
Many challenges lie ahead. A partial list includes expanding
the work on the interpretation of adjoint sensitivities duals,
extending adjoint calculations to complex coupled models,
further exploring the limit of high-resolution state estimation
with the adjoint method, and using other mathematical tools
which require derivative code such as tangent linear, adjoint
and Hessian models to address problems of climate science. A
variety of tools and adjointable models will undoubtedly be
beneﬁcial to improve conﬁdence in this type of modelling.
Acknowledgement:This work is supported by the National Ocean Partnership
Program NOPP with contributions from NASA, NSF
and NOAA, and additional NASA funding. The ongoingdevelopment is a team effort of the MITgcm and ECCO
groups, involving A. Adcroft, J.M. Campin, S. Dutkiewicz,
C. Evangelinos, I. Fenty, D. Ferreira, G. Forget, R. Giering, C.
Hill, E. Hill, A. Köhl, M. Losch, M. Mazloff, D. Menemenlis,
A. Nguyen, R. Ponte, D. Stammer and J. Utke, and supported
over the years by J. Marshall and C. Wunsch. Thanks are due
to our various ECCO partners who contributed to aspects ofthis work. Computer resources at GFDL, SDSC, NASA/ARC
and NCAR are acknowledged.
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Ayoub, N., 2006, ‘Estimation of boundary values in a North
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Bugnion, V., C. Hill and P. Stone, 2006a: ‘An Adjoint Analysisof the Meridional Overturning Circulation in an Ocean
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Heimbach, P., C. Hill and R. Giering, 2002: Automatic
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Mazloff, D. Menemenlis, S. Vinogradov and C. Wunsch,2006: Combining Altimetric and All Other Data with a
General Circulation Model., in ‘Proceedings of the 15 Years ofProgress in Radar Altimetry Symposium’, Vol. SP-614 of ESA
Special Publication, ESA.
Hoteit, I., B. Cornuelle, A. Köhl and D. Stammer, 2005: ‘Treatingstrong adjoint sensitivities in tropical eddy-permitting
variational data assimilation.’, Q. J. R. Meteorol. Soc. 131,
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Köhl, A., 2005: ‘Anomalies of Meridional Overturning:
Mechanisms in the North Atlantic’, J. Phys. Oceanogr. 358,
1455–1472.
Köhl, A., D. Dommenget, K. Ueyoshi and D. Stammer, 2006: ‘The
Global ECCO 1952 to 2001 Ocean Synthesis’, submitted.
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decadal changes in the ECCO global synthesis’, J. Phys.
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Large, W.G. and S.G. Yeager, 2004: Diurnal to decadal global
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climatologies. Technical Note NCAR/TN-460+STR, NCAR,
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Lea, D.J., T.W.N. Haine and R.F. Gasparovic, 2006: ‘Observabilityof the Irminger Sea circulation using variational data
assimilation’, Q. J. R. Meteorol. Soc. 132, 1545–1576.
Lee, T., J.P. Boulanger, A. Foo, L.L. Fu and R. Giering, 2000: ‘Data
assimilation by an intermediate coupled ocean-atmospheremodel: Application to the 1997-1998 El Nino’, J. Geophys.
Res. 105C11, 26,063–26,088.
Losch, M., D. Menemenlis, J.M. Campin, P. Heimbach and C.
Hill, 2008: ‘A dynamic-thermodynamic sea ice model for
ocean climate modelling on an Arakawa C-grid’, Ocean
Modelling in preparation.
Losch, M. and P. Heimbach, 2007: ‘Adjoint sensitivity of an
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Marotzke, J., R. Giering, K.Q. Zhang, D. Stammer, C. Hill andT. Lee, 1999: ‘Construction of the adjoint MIT ocean generalcirculation model and application to Atlantic heat transportvariability’, J. Geophys. Res. 104, C12, 29,529–29,547.
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Mazloff, M., 2007: Production and analysis of an eddypermitting Southern Ocean state estimate, Ph.D. thesis inpreparation 2007, Massachusetts Institute of Technology,
MIT-WHOI Joint Program, Cambridge MA, USA.
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B. Ciotti, I. Fukumori, A. Köhl, P. Heimbach, C. Henze,
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eddy permitting estimates of the global ocean and sea-ice
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functions to calibrate an ocean general circulation model.
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Marotzke, A. Adcroft, C.N. Hill and J. Marshall, 2002: ‘The
global ocean circulation and transports during 1992 – 1997,
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in sea level patterns’, J. Clim. 2024, 5889–5911.

AOMIP: coordinated activities to improve models and model predictions
Proshutinsky, A1., R. Gerdes2, D. Holland3, G. Holloway4 and M. Steele5
1
Woods Hole Oceanographic Institution, USA. 2Alfred Wegener Institute for Polar and Marine Research, Germany, 3Courant
Institute of Mathematical Sciences, New York University, USA., 4Institute of Ocean Sciences, Canada, 5Polar Science Center,Seattle, USA.
Corresponding author: aproshutinsky@whoi.edu

AOMIP motivation
A note published by Sasowsky (2006) in Eos is one of theimportant motivations for the organization of “Model
Intercomparison Projects” and, in particular, the Arctic Ocean
Model Intercomparison Project (AOMIP): “Of 29 numericalmodeling studies reviewed by Bredehoeft (2005), between sevenand ten included ‘surprises’ where natural phenomena andmodel predictions of those phenomena diverged. Konikow etal. (1997) found that six different groups of modeling experts all
made fundamental errors in implementing the same numericalboundary condition for a model test case. Oreskes and Belitz(2001) identiﬁed three factors—non-uniqueness, temporal andspatial divergence, and subjectivity of model assessment—

inherent in contemporary numerical model predictionsof natural systems that they argue make such predictions
unreliable. These cautions should be taken seriously.”
Interestingly, the initial AOMIP studies have revealed striking
differences among Arctic model results. The question wasand still is: What are the causes of these differences: errors ofmodel implementation, forcing, boundary conditions, model
physics, numerics, or design of numerical experiments?
Some of these questions or probably all of them are relevantto the results of global climate models represented in IPCC
studies. In order to address these questions for the Arctic
Ocean environment, the AOMIP was organized in 2001. It hascreated a broad-based energetic and motivated “community”
17CLIVAR Exchangesof directly involved Arctic modelers from the U.S.A., Canada,
Germany, United Kingdom and Russia (http://efdl.cims.nyu.
edu/project_aomip).
AOMIP foci and approach:AOMIP has been focused on Arctic regional coupled iceocean model intercomparisons and investigations of different
aspects of ocean and sea ice changes for the time period 1948present. Among the major themes were investigations of theorigin and variability of Atlantic Water (AW) circulation (e.g.
Gerdes et al., 2003; Karcher et al., 2002, 2003, 2007; Golubeva
and Platov, 2007; Zhang and Steele, 2007), mechanisms ofaccumulation and release of fresh water (e.g. Steele and Flato,
2000; Proshutinsky et al., 2002; Häkkinen and Proshutinsky,
2004; Steiner et al., 2004; Uotilla et al., 2005; Proshutinsky et al.,2005; Dukhovskoy et al., 2004, 2006a, 2006b), causes of sea level
rise (e.g. Proshutinsky et al., 2002, 2004, 2007b), and the role oftides in the shaping of climate (e.g. Hibler et al., 2006, Holloway
and Proshutinsky, 2007). In the course of these investigations,problems with numerical implementation, and validation
against observations have been emphasized. Some problems
have been found to occur only in a few models, while others
have been found in many or most. Exposing these issues is
an extremely important part of AOMIP, and would have been
difﬁcult or impossible to achieve without coordinated activities.
Preliminary investigations into improving the models havealso been initiated by AOMIP. AOMIP is not a “top-down”
organization that can dictate model changes to all participants.
Rather, a vital function of this project is to expose consistentproblems in numerical models and then to provide a forum forindividual investigators to propose solutions.
The AOMIP approach is to leverage the existing ﬁnancial
support of each participant for a comparative analysis ofdifferent models and scientific results. This strategy has
provided a unique opportunity to coordinate AOMIP studies
via a set of carefully-planned numerical experiments covering
the most important processes and interactions. A clear
advantage was that each AOMIP participant was able to workwith her/his speciﬁc research theme using simulation resultsfrom all AOMIP models and to analyze differences and test
hypotheses using a multi-model suite of outputs. The result
is a synthesis that integrates observational and modeling
efforts toward the overall goal of developing advanced Arctic
models able to accurately reconstruct past, describe current
and predict future Arctic conditions. We view AOMIP as acollaborative frame-work wherein modelers and observers
discuss results, problems, and new ideas, all with the goals ofmodel improvement and better understanding of the Arctic
climate system.
AOMIP models and results
During 2001-2007, the AOMIP group has consisted of a coreof nine principal investigators, and a large number of coinvestigators from different countries (see AOMIP web site).
Model parameters including domains, vertical and horizontal
resolutions, initial and boundary conditions are describedin Holloway et al. (2007) and at the web site http://www.
planetwater.ca/research/AOMIP/modelspecs.html. In
AOMIP, z-coordinate models dominate that are mostly basedon the same original code (Bryan, 1969) but are, nonetheless,sufﬁciently distinct in their detailed treatment of physicalprocesses as to warrant intercomparison. As an example, theInstitute of Ocean Sciences (IOS, Canada) model replaces the
traditional Newtonian formulation of viscous damping withan eddy-topography rectiﬁcation, a parameterization known as
the Neptune effect. A variant of the most-widely used isopycnic
model (Bleck and Boudra, 1981) is represented in AOMIP by
18

Volume 13 No.1 January 2008

the New York University (NYU) model. AOMIP also employs
several versions of a sigma-coordinate model (Blumberg andMellor, 1987) represented, for example by the Goddard Space
Flight Center (GSFC, USA) and International Arctic ResearchCenter (IARC, USA) models. The basic conﬁguration for allparticipating models is to have their ocean model coupled
to a sea-ice model and to be driven by speciﬁed atmosphericforcing ﬁelds.
The sea ice models differ in both dynamics (viscous plastic,
general viscous or elastic-viscous-plastic or cavitating ﬂuid
dynamics) and in thermodynamics (heat and salt fluxes,
number of sea ice categories, layers and snow parameters).
Holloway et al. (2007) provides detailed characteristics for eachof 14 AOMIP models.
Three publications (Proshutinsky et al, 2001a, 2005; Proshutinsky
and Kowalik, 2007a) have outlined the major AOMIP results forthe broad community. The special JGR section “Arctic Ocean
Model Intercomparison Project Studies and Results” waspublished in 2007 (JGR, vol. 12, No C4, 2007), summarizing
results of past AOMIP activities (hereafter AOMIP-2007).
Since 2001, AOMIP has organized 11 workshops, publishedmore than 50 peer-reviewed papers and resulted in morethan 80 presentations. AOMIP workshops took place at
different locations (Naval Postgraduate School, Universityof Washington, Woods Hole Oceanographic Institution,
Geophysical Fluid Dynamics Laboratory, McGill University,and University of Hawaii) in order to involve students fromthese institutions in Arctic studies.
One unresolved question for climate studies is: How to force
regional models at lateral and surface boundaries based on
AOMIP experience? This problem has not received muchattention in AOMIP, since most model southern boundaries lie
far south from the Arctic Ocean and thus their role in the Arctic’s
simulated climate variability may be minimal (e.g. Proshutinsky
et al., 2002 Dukhovskoy et al., 2004, 2006). For example, theGSFC and ICMMG models include the entire North Atlantic
and the other models southern boundary (except the global
CNF, LANL and POL models; see Holloway et al., 2007) islocated at 50N or at least includes the Greenland, Iceland andNorwegian Seas (GIN Sea). The NPS model has all boundaries
closed and no water mass ﬂux is allowed through the lateralboundaries, but a 30-day restoring to monthly temperature andsalinity climatology is used there. Other models assign watertemperature and salinity on inﬂow based on climatology andprescribe “free” T and S outﬂow (i.e. mapped from the interior
grid point,as in the IOS model). At the southern boundary(15° S) of the GSFC model the salinities and temperatures arerelaxed to Levitus et al., (1994a, 1994b) climatology (hereafter
Levitus climatology) values in ﬁve grid rows from the boundary.
Immediate restoring of temperature and salinity is used at
the Mediterranean outﬂow point. The AWI model’s southernmodel boundary approximately runs along 50°N and the open
boundary condition was implemented following Stevens (1991).
This boundary condition allows the outﬂow of tracers and theradiation of waves. At inﬂow points determined by the model,
temperature and salinity are speciﬁed according to Levitus
climatology. The baroclinic part of the horizontal velocity is
calculated from a simpliﬁed momentum balance while thebarotropic velocities normal to the boundary are speciﬁed froma lower resolution version of the model that covers the entire
North Atlantic. Other boundaries are treated as closed walls.
From the several examples described above, one sees that a)
AOMIP has “tested” at least three different approaches (openboundaries, closed “wall” boundaries, and global models), andb) none of the AOMIP models have reported any substantial
continued on page 23

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CLIVAR Exchanges

From Fox Kemper et al, page 2: Parameterising Submesoscale Physics in Global Climate Models.
Fig. 1: Reduction in boundary layer thickness with the introduction of the MLE
parameterization in GOLD (upper) and POP (lower). The average over the last ten yearsof the simulations are shown. The boundary layer is the layer over which ﬁnescale mixing
due to winds and convection is active. Generally, it is less than or equal to mixed layer
depth.

Change of Time-Mean Boundary Layer Depth in GOLD

Change of Time-Mean Boundary Layer Depth in POP

BLD_mle-BLD_control(m)
BLD_mle-BLD_control(m)
BLD_mle-BLD_control(m)
BLD_mle-BLD_control(m)
BLD_mle-BLD_control(m)
BLD_mle-BLD_control
(m)
-75

-75

-75
-75-37.5-75-37.5-37.5 -37.5

0 -37.5

0

0
0 -75

37.5

0

37.5
-37.5
37.5

37.5

75

0

75

37.5 75 75

37.5

75

75

From Roberts et al (page 8): Impact of relative atmosphere-ocean resolution on coupled climate models

Figure 3: Wind stress curl (colour, Nm-2/m) and ocean
zonal current at 35m (contours at 5cms-1 intervals,
eastward currents solid, westward currents dashed).
(a) lowest resolution (HadGEM), (b) higher resolution
atmosphere, (c) higher resolution ocean, and (d) highresolution atmosphere and ocean (HiGEM).

From Nakano et al, page 11: Development of a global ocean model with the resolution of 1˚ x ½˚ and 1/8˚ x 1/12˚

Figure 1: Bias of annual mean sea surface temperature in oC of the last two years of the runs. (a) Coarse-CORE run (b) Coarse-JRA run (c)Fine-JRA run.

19

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Volume 13 No.1 January 2008

From Nakano et al, page 11: Development of a global ocean model with the resolution of 1˚ x ½˚ and 1/8˚ x 1/12
Figure 2: Difference in barotropic stream functions in SV of the last two
years between Fine-JRA and Coarse-JRA run (Fine-JRA minus CoarseJRA). Green lines indicate ±5SV and black lines indicate ±10 SV.

From Heimbach et al., page 13:The MITgcm/ECCO adjoint modelling infrastructure
Figure 1: Snapshot of near-surface speed on May 12, 2006 for iteration 16 of the ECCO Southern
Ocean State Estimate (SOSE). The underlying setup is the MITgcm and its adjoint at 1/6o horizontal
resolution. Overlaid as white line is the sea ice extent as computed with the dynamic/thermodynamic
sea ice model. Courtesy M. Mazloff [2007].

Figure 3: Sensitivities of meridional heattransport across 26N in the Atlantic tochanges in temperature at 1250 m depth 2(a) and 75 (b) years prior in the past. The
calculation was based on the OpenAD
generated adjoint of the MITgcm at 4ohorizontal resolution. Note the different
scales between the panels.

From Proshutinsky et al, page 17: AOMIP: coordinated activities to improve models and model predictions

Fig. 1 Left: Mean April sea surface salinity (triangles) within the Beaufort Gyre, in comparison with mean (open circles) and max/min (horizontalbars) climatological values. As the unrealistic climate restoring term is weakened (longer restoring time constant) the modeled salinity drifts
towards an unrealistically salty asymptotic value of about 33 (Steele et al., 2001). Note that GSFC and IOS models have no restoring corrections
because their restoring time is inﬁnite. Right: Annual anomalies of sea level at 9 tide gauge stations located along the Siberian coastline (red).
The blue line is the 5-year running mean anomalies of the annual mean Arctic Oscillation (AO) index multiplied by 3. The black line is the
SLP anomaly at the North Pole (NCAR/NCEP reanalysis) multiplied by -1. The correlation between sea level and AO before 1996 is higher
than 0.8. After 1996 the AO is low and stable but sea level continues rising.
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CLIVAR Exchanges

From Agarwal et al, page 28: Impact of different Bulk Parameterization schemes of air-sea ﬂuxes on Oceanic heat content in the Tropical Indian
Ocean������ �� �������� ��������� �� ��������� ��������� ���� ������� ������ ����������

From Legler et al, page 33: US CLIVAR Drought Predictability Research Focus

Soil Moisture Percentiles (wrt/ 196020 Figure 1. Spatial extent of the Dust Bowl drought of the 1930s
in August 1934. Percentiles are for soil moisture relative to the19340801
120˚

112˚

104˚

96˚

88˚

80˚

72˚

48˚

48˚

44˚

44˚

40˚

40˚

36˚

36˚

32˚

32˚

28˚

28˚

24˚

24˚
0

1

5

10

20

30

70

percentile

80

90

95

99

85-year period 1915-2003. From University of Washington
Surface Water Monitor, available online at: http://www.hydro.
washington.edu/forecast/monitor

100

Figure 2. Time series of precipitation anomalies averaged over the U.S.
Great Plains region (30°N to 50°N, 95°W to 105°W), from Schubert et al.
(2004). A ﬁlter has been applied to remove time scales shorter than about
6 years. The thin black curves are the results from 14 ensemble members
from C20C global model runs. The green solid curve is the ensemble mean.
The red curve is based on observations. The maps show the simulated (left)
and observed (right) precipitation anomalies averaged over the Dust Bowl
period (1932 to 1938, units mm/day).

21

CLIVAR Exchanges

Volume 13 No.1 January 2008

From Balmaseda et al., page 36: The Ocean Component at ECMWF: Towards a seamless prediction system
Figure 1. Time series of the mixed layer depth in the North
Subtropical Atlantic (5N-28N), from the ocean analysis(black) and from seasonal forecasts initialized in April
and June. The forecasts initialize in April systematicallyoverestimate the depth of the mixed layer.

Mixed Layer Depth in North Subtropical Atlantic
and July
starts0001April
NSTRATL
Mixedlayer depth

-40

-50

-60

-70

-80

-90

-100
2001

Ocean Analysis
Seasonal Forecasts
2002

2003

2004

Time

2005

2006

Figure 2: Impact of the summer 2007 ice anomaly on JuneJuly-August Z500, as measured by the ensemble meandifference between two experiments in which the atmospheremodel is forced by the 2007 analyzed ice coverage and by
climatological ice respectively. The experiments, with 20
ensemble members each, were initialized in May 2007 andrun for the 5 months forced by observed SST. Units are dam.
The 80%, 90% and 95% signiﬁcance level are shown by theblack contours.

2007

2008

2007 JJA: Z500 Ensemble Mean Difference
Analyzed minus Climatological ice cover

10
3

2.5
2

1.5
-0.5

- 0.
5

1

0.5
1

-0.5
-1

-1.5

1

-2
-0.5

-2.5
-3

-10

From Latest News: WOCE Paciﬁc Atlas published, page 39

Left Paciﬁc station positions Right Dissolved oxygen Section P16
22

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continued from page 18
problems with their results associated with open boundary
conditions.
The surface heat and especially fresh water (FW) ﬂux forcing
problem in AOMIP studies has been contentious. Initially,
AOMIP recommended avoiding restoring and ﬂux correction
procedures. Unfortunately, this was not followed by all
AOMIPers, and even some who reluctantly gave up salinity
restoring have since reverted. Among AOMIP models cited
above only three models report a no restoring approach (IOS,
UW, and GSFC). The GSFC model has some drift in ocean
salinity but after ﬁltering the salinity trend it demonstrates
excellent results picking seasonal, interannual and decadal
variability very well. All other modeling teams have voted forrestoring procedures with different restoring time. Steele et al(2001) have shown (Figure 1, page 20) that surface salinity in thecenter of the Beaufort Gyre generally increases with increasing
restoring time. However there are two points marked “IOS”, thesecond of which, also with no restoring, has the lowest surface
salinity which could be explained due to changes in wind
forcing (more converging and respectively more fresh water inthe center of the Beaufort Gyre). Other models may or may nothave this same effect, depending on their restoring and mixing
procedures. Zhang and Steele (2007) and Golubeva and Platov(2007) originally focusing on the problems of the Atlantic water
circulation and trying to explain why their model generates
anticyclonic motion of the Atlantic layer instead of cyclonicrotation practically independently have found that when they
limited oceanic mixing below the surface mixed layer, theyobtained better circulation patterns of the Atlantic layer andalso were able to simulate better vertical water temperatureand salinity proﬁles. They concluded that the upper ocean
saliniﬁcation observed in Figure 1 comes from deeper layers
due to too much mixing. Zhang (personal communication) has
also reported that their model does not need restoring after the
mixing problem was ﬁxed.
Table 1 Project participants.

CLIVAR Exchanges

The role of surface freshwater ﬂux boundary conditions inprognostic Arctic Ocean/sea-ice modeling was also analyzed
by Prange and Gerdes (2006) using a regional numerical model.
Three different applications of freshwater ﬂux formulations
were evaluated. The standard formulation, which serves as abenchmark, takes surface volume ﬂuxes due to precipitation,
evaporation and river runoff into account. The total freshwater
input to the Arctic Ocean by runoff, precipitation and Bering
Strait inﬂow is approximately 6800 km3 yr−1 in the model
setup. The implementation of an Arctic river water tracer inthe standard run enables the calculation of an average mean
residence time of 14–15 years for river water in the Arctic
halocline. The second formulation for surface freshwater ﬂuxes
neglects the volume input, which corresponds to applying‘virtual salinity ﬂuxes’. This simpliﬁcation leads to a rapid
salinity build-up in the upper layers of the Arctic Ocean andcauses a substantial reduction of freshwater export throughFram Strait. The third formulation uses a constant reference
salinity of 35 in the definition of the virtual salinity flux
boundary condition. This approach results in hydrographic
ﬁelds which are very similar to those from the standard run.
Errors in circulation and freshwater transport are small and, formost applications, tolerable. Their results suggest that virtual
salinity ﬂuxes with ﬁxed reference salinity are a reasonableapproximation for Arctic Ocean models with horizontal
resolution of order 100 km.
However, because of large uncertainties in high-latitude
surface fresh water ﬂuxes and inherent model shortcomings, asimulated equilibrium will in general not be realistic in termsof horizontal transports and regional storage of fresh water
without further constraints. A technique that can be used inregional models has recently been applied by Köberle andGerdes (2007). The model was started from initial conditions
and integrated for several periods of the NCEP–NCAR
reanalysis forcing using restoring of surface salinity. The model

Institute, PI(s)Country

Abbreviation

Arctic and Antarctic Research Institute, A. Makshtas

RussiaAARI

Alfred Wegener Institute, R. Gerdes and C. Koeberle

Germany

AWI

Dalhousie University, F. Dupont

CanadaDAL

Florida State University, E. Chassignet and D. Dukhovskoy

USA

FSU

Geophysical Fluid Dynamics Laboratory, S. Grifﬁes, M. Winton

USA

GFDL

Goddard Space Flight Center, S. Hakkinen

USA

GSFC

International Arctic Research Center, B. Hibler, G. Panteleev

USA

IARC

Institute of Marine Sciences, UAF, M. Johnson

USA

IMS

Institute of Ocean Sciences, G. Holloway

CanadaIOS

Jet Propulsion Laboratory, R. Kwok, A. Nguyen

USA

JPL

Los Alamos National Laboratory, E. Hunke

USA

LANL

Massachusetts Institute of Technology, C. Hill

USA

MIT

Naval Postgraduate School, W. Maslowski

USA

NPS

National Center for Atmospheric Research, M. Holland

USA

NCAR

New York University, D. Holland

USA

NYU

Norwegian Polar Institute, Ole Anders Nøst

NorwayNPI

Ocean and Atmosphere Systems, M. Karcher and F. Kauker

Germany

OASYS

Proudman Oceanographic Laboratory, M. Maqueda

UK

POL

Russian Academy of Science, Moscow, N. Yakovlev

RussiaRASM

Russian Academy of Science, Novosibirsk, E. Golubeva

RussiaRASN

Swedish Meteorological and Hydrological Institute, M. Meir

SwedenSMHI

University College London, S. Laxon

UK

UCL

University of Massachusetts, Dartmouth, C. Chen

USA

UMAS

University of Washington, M. Steele, J. Zhang

USA

UW

Woods Hole Oceanogr. Ins, A. Proshutinsky, P. Winsor, A. Condron

USA

WHOI

23

CLIVAR Exchanges

was then reinitialized with the result from the end of the third
cycle of NCEP–NCAR forcing. For the repetition of the fourth
forcing cycle, the restoring of surface salinities was switched off
and replaced by the annual mean climatology of the restoring
term applied as a ﬁxed salt ﬂux to the surface box of the ocean
model. This procedure is not recommended for long-term
integrations with global models as it represents one version of
mixed boundary conditions that lead to unrealistic oscillationsof the large scale ocean circulation.
Goals and planned activities
The ﬁrst 5-year AOMIP research cycle was completed in March
2007 and AOMIP is seeking new funding to pursue its goals,
taking into account new circumstances in Arctic change andscience developments. It is important to continue these studies
because the research and integration activities of AOMIP are
now well established and progressing towards starting model
improvement, testing and validation experiments in order todevelop and verify a comprehensive Arctic regional model andimprove global climate models for the Arctic region. Thus, theoverall AOMIP science goals are:• Validate and improve Arctic Ocean models in a coordinatedfashion.
• Investigate the variability of the Arctic Ocean and sea ice atseasonal to decadal time scales, and identify mechanisms
responsible for the observed changes.
The project’s practical goals are to:• Maintain and enhance the established AOMIP
international collaboration to reduce uncertainties in model
predictions;• Support synthesis across the suite of Arctic models andorganize scientiﬁc meetings and workshops;• Conduct collaboration with other MIPs with a special focus
on model improvements and analysis;• Disseminate findings of AOMIP effort to broader
communities;• Train a new generation of ocean and sea-ice modelers.

Volume 13 No.1 January 2008

Participants and methods
It is expected that 25 institutions (Table 1, page 23) will be
involved in AOMIP studies in the next research cycle. Table
2 shows their interests and foci of research. Realistically
we anticipate each group to perform the experiment(s) thatmost closely follow their already-funded interests but two
coordinated experiments are proposed for all modelinggroups. The ﬁrst experiment will be a ~50-year simulation of
the years 1948-present with (i) improved forcing (see Hunkeand Holland, 2007 for details) and (ii) improved models (seerecommendations in AOMIP-2007). Comparing new improvedand control model runs with observations will allow AOMIP to:(i) estimate results of model improvements; (ii) validate models
against observations and (iii) assess model uncertainties. In
parallel, model results will be used for the investigation of
Arctic climate variability. For example, for AOMIP purposes
GFDL will employ the global atmosphere dataset developedat NCAR by Large and Yeager (2004). This dataset has been
sanctioned by the CLIVAR Working Group for Ocean Model
Development. This experiment will signiﬁcantly increase thenumber of collaborating international scientists interested andable to participate in AOMIP activities.
The second experiment is guided by growing interest in thelonger-term climate variability of the Arctic climate system,
including e.g., the 1930s warm period. The experiment will be
carried out for ~100-year period under forcing reconstructed by
the Alfred Wegener Institute group (Kauker et al., submitted to
JGR; see also AOMIP web site). Results from other participating
models running this experiment will be used to reconstruct ice/
ocean conditions for 1900-present and then to see how AOMIP
models behave during long-term integrations.
AOMIP basic activities include model improvements, process
and climate change studies. Details of some of them aredescribed below and for each institution are shown in Table 2.

Table 2 AOMIP major activities

01 – AARI; 02 – AWI; 03 – DAL; 04-FSU; 05 – GFDL; 06-GSFC;
07 – IARC; 08 – IMS; 09 – IOS; 10 – JPL; 11 – LANL; 12 – MIT;
13 – NCAR; 14 – NPI; 15 – NPS; 16 – NYU; 17 - OASYS; 18– POL; 19 – RASM; 20 – RASN; 21 – SMHI; 22 – UCL; 23– UMAS; 24 – UW; 25 – WHOI.

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Intercomparison and model validation procedures include theimplementation of common grids, coordinated interpolation
techniques, a set of integrated parameters such as heat,
freshwater content, potential vorticity, topostrophy (deﬁned
as fxV·S, where f is Coriolis vector, V is model velocity vector,
and S is gradient of total depth, Holloway et al., 2007), ice areaand extent, etc. These procedures will also include data mining
and reconstruction methods (Panteleev et al., 2007).
Model Improvements
This activity addresses Goal #1: “Validate and improve
Arctic Ocean models in a coordinated fashion,” by focusing
on three broad areas of model improvement. The ﬁrst area
includes the use of observations for model forcing (including
initial and boundary conditions) and validation. Some of therecommended work associated with this was described in
AOMIP-2007 and is related to data collections, gridding andvalidation for AOMIP purposes. The second area is model
numerics and parameterization of different processes (e.g.
mixing, advection). An example is the introduction of theeddy rectiﬁcation effect over sloping topography (Holloway,
2004; Holloway et al., 2007), which is incorporated into several
relatively coarse resolution models (DAL, IOS, POL and RASN;
see Table 2) and has been shown to drive a “cyclonic rim
current” around the Arctic basins. Numerical improvements
include the introduction of second order moments based
advection (e.g. Prather, 1986; Hofmann and Morales Maqueda,
2006; Morales Maqueda and Holloway, 2006; Holloway et al.,2007) and improved mixing (Zhang and Steele, 2007; Golubeva
and Platov, 2007) in several AOMIP models. These activities will
be continued in other models and also expanded to implement
new methods/algorithms/physics. The third area of model
improvement will be the inclusion of processes important
for the Arctic such as tides and ice dynamics. Preliminary
experiments have shown that tidal and inertial sea-ice motion
leads to additional generation of sea-ice mass and can improvemixing and allow heat release from the AW to the bottom of ice(Hibler et al., 2006; Holloway and Proshutinsky, 2007).
Model forcing and validation: Observational data analysis is needed
for model calibration and validation. In particular, small errors
in ice parameters stemming from errors in atmospheric forcing
can translate into serious errors in ocean variables. Hunke andHolland (2007) compared three forcing data sets in global iceocean simulations over 20 years, ﬁnding that minor changes toforcing resulted in substantial discrepancies in the simulations,
not only in the ice parameters, but also in the deep ocean (e.g.
the sense of AW circulation could reverse). Knowing the rangeof model errors due to forcing ﬁelds will allow for more accurate
evaluation of the range of errors associated with the internal
model physics. Among sources for atmospheric and oceanic
data for model forcing and validation there are digital atlases(NCAR/NCEP, ERA-40, NP drifting stations, EWG (1997, 1998,
2000)), current meters compilation (Holloway, 2008) and morethan 500,000 hydrographic stations in AARI archives. As well,
JPL will provide results from Synthetic Aperture Radars and
ICESat to help validate high-resolution modeled sea ice.
Process/climate change studies
These activities address Goal #2: “Investigate variability of the
Arctic Ocean and sea ice at seasonal to decadal time scales, andidentify mechanisms responsible for the observed changes.”
The major strategy for these studies will be the investigation on
how different mechanisms and processes inﬂuence variabilityof the ocean and ice integrated parameters: ice area, ice extent,
ice volume, major ice and ocean circulation patterns or regimes,
vertical T-S structure (parameters of major ocean layers), heatand freshwater content. These studies will help improve models

CLIVAR Exchanges

and allow AOMIP to investigate processes using model results
and observations. This research will be based on the resultsof 50- and 100-year experiments and on a set of experiments
speciﬁcally designed for some of these studies and described
below.
Fresh water and heat: This research will attempt to answer thefundamental questions: How does fresh water/heat enter
the Arctic Ocean system? How does it move about including
undergoing phase changes? How does it ﬁnally exit the system?
First, groups responsible for this activity (Table 2) will evaluate
how well models can reproduce pan-Arctic freshwater and heatbudgets by comparison of model outputs budgets of Serreze
et al. (2006, 2007). We anticipate that most (but perhaps notall) models will achieve freshwater and heat balance in theupper layers including the AW after several decades. How
these balances are actually achieved will provide insight intomodel physics. Second, AOMIP will investigate how well themodels can reproduce the basic water mass structure of the
Arctic Ocean. For example, Fig. 1 (left) shows a comparisonof modeled late winter sea surface salinity within the Beaufort
Gyre from an early AOMIP study (Steele et al., 2001). The result
indicates a salty bias in this variable which had not been clearlyshown until then. Addressing causes of this bias, Zhang andSteele (2007) show how the magnitude of numerical verticalmixing can affect salinity structure within the Beaufort Gyre
(see also the discussion above).
Sea-level variability and change: Proshutinsky et al. (2001b, 2007b)
reported that the majority of AOMIP models for 1954-1989 hadnotable difﬁculties in reproducing Arctic sea-level variability.
The major cause of this problem was the omission of sea-levelvariability associated with changes in atmospheric pressure
and water volume ﬂuxes from river runoff. Model outputs from50- and 100-year models runs will be used to evaluate rates ofArctic Ocean sea level change (see Proshutinsky et al., 2007b
for approach). Among science questions of sea-level rise wewill attempt to understand inconsistencies in the behavior of
Arctic sea-level (rises) and the Arctic Oscillation index (low andstable) in the last decade despite the previous 40-year excellent
correlation (Figure 1, right).
Sea ice: With record minima in 2007 ice concentration, the fate ofice is clearly a critical question for climate research. Our focus
in this component is to evaluate competing explanations forthe recent reductions in ice concentration/extent. If Serreze etal. (2003) are correct ice extent should be signiﬁcantly different
among the AOMIP models that, with the same forcing, havedifferent Atlantic Water (AW) circulations and hence different
patterns of heat loss to the surface. This difference would be
extreme between the Barents/Kara Sea area and the area along
northern Greenland/Canadian Archipelago. If differences in ice
are attributable, at least in part, to different AW circulation, it
may offer one explanation for the greater variance in modeled
ice than in the observations as shown by Johnson et al. (2007).
The major instruments for these studies will be numericalexperiments with and without ice dynamics, with and without
thermodynamics, and with different forcing.
Atlantic water: Circulation of AW in the Arctic has been an
especially important subject of modeling studies because it
is important to (i) conﬁrm or correct the circulation schemes
inferred from observational data; (ii) test and validate the abilityof models to capture the boundary currents and circulation of
the deep layers of the Arctic Ocean; (iii) elucidate the natureand impacts of the cyclonic versus anticyclonic AW flow
regimes in the Arctic Ocean (e.g. Proshutinsky and Johnson,1997); and (iv) determine the underlying reasons why AW
penetrates the Arctic basin in the ﬁrst place. AOMIP simulated25

CLIVAR Exchanges

AW circulation in the Arctic basin differs in intensity and senseof rotation from model to model. In an attempt to understandsuch discrepancies and inconsistencies, AOMIP has overseena series of coordinated modeling experiments, identifying
numerous factors inﬂuencing AW behavior. However, there
are still several competing hypotheses, for example, from
Holloway’s idea (Holloway et al., 2007) that AW circulation isstable and does not change to the Karcher et al. (2007) ﬁndingsthat AW circulation pulsates and can change sense from time
to time. There are difﬁculties associated with each of the above
proposed mechanisms and better understanding of the primarydriving mechanism for the ﬂow of AW into the Arctic, and how
it will vary under changing climate conditions (wind, ice cover,precipitation, heat loss) is greatly needed and will be a primary
focus of AOMIP studies.
Tides: Holloway and Proshutinsky (2007) have introduced
parameterizations for tidal inﬂuence on ocean mixing and on ice
dynamics in a 3-D coupled ice ocean model. They have shownthat tidal forcing leads to an enhanced heat loss from AW and
ice thinning along continental slopes where tides dominate
other motions. On the other hand, this thermodynamic
effect competes with net ice growth during rapid openings
and closings of tidal leads. They recommended testing theirconclusions with a model where tides are included directly
without parameterizations. Several AOMIP groups (Table2) will lead this investigation by running their models withand without tides for at least 50 years to understand thetidal role in the shaping of Arctic climate. We expect that themodel results between these cases will signiﬁcantly differ at
the continental slope and ridges, around islands, and in the
Canadian Archipelago.
Interaction of global ocean with the Arctic: AOMIP global models
will conduct experiments to examine interactions of the Arctic
Ocean with global changes. Major concerns will include change
via signals brought with AW and Paciﬁc water (PW). AW
circulation within the Arctic Ocean was a major topic of recent
AOMIP studies (see above); ongoing AOMIP efforts will focus
on signals penetrating to the Arctic with AW and on how both
outﬂowing AW and PW affect the North Atlantic and global
ocean, including the potential for “freshwater catastrophes”.
Summary
The coordinated community approach to the investigationof Arctic Ocean variability is the only way to assess thedegree of uncertainty in results and conclusions made bydifferent modelers, scientiﬁc groups or institutions. AOMIP
is an important and unique component of present-day Arctic
studies because it improves our understanding of oceanic andsea-ice processes. From this view point, the most signiﬁcant
AOMIP contributions will be: development of regional andimprovement of global climate models; identiﬁcation of model
errors and causes of these errors and model discrepancies
and recommendations for improvements of existing regional
coupled ice-ocean models and GCMs by implementing new
physics and parameterizations for Arctic processes. Withinthese activities, an important contribution will be an assessmentof the state and variability of Arctic sea ice and ocean parametersfor 1900-present.
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Köberle, C. and R. Gerdes, 2007: Simulated Variability of
the Arctic Ocean Freshwater Balance 1948–2001, J. Phys.
Oceanogr., 37, 1628-1644.
Konikow, L. F., W. E. Sanford, and P. J. Campell, 1997: Constantconcentration boundary condition:Lessons from the
HYDROCOIN variable-density groundwater benchmark
problem, Water Resour. Res., 33(10), 2253-2261.
Large, W. B. and S. Yeager, 2004: Diurnal to decadal global
forcing for ocean and sea-ice models: the datasets and ﬂux
climatologies. NCAR Technical Note: NCAR/TN-460+STR.
CGD Division of the National Centre for AtmosphericResearch.
Levitus, S., R. Burgett, and T. Boyer, 1994a: World Ocean Atlas
1994, vol. 3, Salinity, NOAA Atlas NESDIS 3, U.S. Dep. ofComm., Washington D. C.
Levitus, S., R. Burgett, and T. Boyer, 1994b: World Ocean Atlas
1994, vol. 4, Temperature, NOAA Atlas NESDIS 4, U.S. Dep.
of Comm., Washington D. C.
Morales Maqueda, M. A. and G. Holloway, 2006: Second Order
Moment advection scheme applied to Arctic simulation,
Ocean Modeling, 14, 197-221.
Oreskes, N., and K. Belitz, 2001: Philosophical issues in model
assessment, In: Model Validation: Perspectives in Hydrological
Science, M. G. Anderson and P. D. Bates, eds., 23-41, John
Wiley, Hoboken, N. J.
Panteleev, G., A. Proshutinsky, M. Kulakov, D. A. Nechaev,
and W. Maslowski, 2007: Investigation of the summer Kara
Sea circulation employing a variational data assimilation
technique, J. Geophys. Res., 112, C04S15, doi:10.1029/
2006JC003728
Prange M. and R. Gerdes, 2006: The role of surface freshwaterﬂux boundary conditions in Arctic Ocean modeling, Ocean
Modeling,13, 1, ,25-43.
Prather, M. J., 1986: Numerical advection by conservation of
second-order moments, J. Geophys. Res., 91, 6671-6681.
Proshutinsky, A. Y., M. A. Johnson, 1997: Two circulation
regimes of the wind-driven Arctic Ocean, J. Geophys. Res.,
102(C6), 12493-12514, 10.1029/97JC00738.
Proshutinsky, A., M. Steele, J. Zhang, G. Holloway, N. Steiner, S.
Hakkinen, D. Holland, R. Gerdes, C. Koeberle, M. Karcher,
M. Johnson, W. Maslowski, W. Walczowski, W. Hibler, J.
Wang, 2001a: Multinational effort studies differences among
Arctic Ocean models, Eos Trans. AGU, 82(51), 637-637,
10.1029/01EO00365.
Proshutinsky, A., V. Pavlov, R. H. Bourke, 2001b: Sea level risein the Arctic Ocean, Geophys. Res. Lett., 28(11), 2237-2240,
10.1029/2000GL012760.
Proshutinsky, A.Y. , R.H. Bourke, F. McLauglin, 2002: The role of
the Beaufort Gyre in the Arctic climate variability: seasonal
to decadal climate scales, Geophys. Res. Lett., 29(23), 2100,
doi:10.1029/2002GL015847.
Proshutinsky A., I. M. Ashik, E. N. Dvorkin, S. Häkkinen, R. A.
Krishﬁeld, W. R. Peltier, 2004: Secular sea level change in
the Russian sector of the Arctic Ocean, J. Geophys. Res., 109,
C03042, doi:10.1029/2003JC002007.
Proshutinsky, A., J. Yang, R. Krishﬁeld, R. Gerdes, M. Karcher,
F. Kauker, C. Koeberle, S. Hakkinen, W. Hibler, D. Holland,
M. Maqueda, G. Holloway, E. Hunke, W. Maslowski, M.
Steele, J. Zhang, 2005: Arctic Ocean Study: Synthesis ofModel Results and Observations, Eos Trans. AGU, 86(40),

CLIVAR Exchanges

368, 10.1029/2005EO400003.
Proshutinsky A., Z. Kowalik, 2007a: Preface to special
section on Arctic Ocean Model Intercomparison Project(AOMIP) Studies and Results, J. Geophys. Res., 112, C04S01,
doi:10.1029/2006JC004017.
Proshutinsky A., I. Ashik, S. Häkkinen, E. Hunke, R. Krishﬁeld,
M. Maltrud, W. Maslowski, J. Zhang, 2007b: Sea levelvariability in the Arctic Ocean from AOMIP models, J.
Geophys. Res., 112, C04S08, doi:10.1029/2006JC003916.
Sasowsky, I. 2006: Model Veriﬁcation and Documentation Are
Needed, Eos, 87(25).
Serreze, M. C., J. A. Maslanik, T. A. Scambos, F. Fetterer, J.
Stroeve, K. Knowles, C. Fowler, S. Drobot, R. G. Barry, andT. M. Haran, 2003: A new record minimum Arctic sea ice
and extent in 2002. Geophys. Res. Letts, 30: 1110, doi:10.1029/
2002GL016406.
Serreze, M. C., A. P. Barrett, A. G. Slater, R. A. Woodgate, K.
Aagaard, R. B. Lammers, M. Steele, R. Moritz, M. Meredith,and Craig M. Lee, 2006: The large-scale freshwater cycleof the Arctic. J. Geophy. Res., 111(C11), doi:10.1029/
2005JC003424.
Serreze, M. C., A. P. Barrett, A. J. Slater, M. Steele, J. Zhang and K.
Trenberth, 2007: The large-scale energy budget of the arctic,
J. Geophys. Res., 112, D11122, doi:10.1029/2006JD008230.
Steele, M., and G. Flato, 2000: Sea ice growth, melt andmodeling: A survey, In: The Freshwater Budget of the Arctic
Ocean, edited by E.L. Lewis, 549-587, NATO Advanced
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Steele, M., W. Ermold, S. Häkkinen, D. Holland, G. Holloway,
M. Karcher, F. Kauker, W. Maslowski, N. Steiner, J. Zhang,
2001: Adrift in the Beaufort Gyre: A model intercomparison,
Geophys. Res. Lett., 28(15), 2935-2938, 10.1029/2001GL012845,
2001.
Steiner, N., Holloway, G., Gerdes, R., Hakkinen, S., Holland,D., Karcher, M. J., Kauker, F., Maslowski, W., Proshutinsky,
A., Steele, M., Zhang, J., 2004: Comparing modeled
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Ocean. Ocean Modelling, 6, 265-284.
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Holloway, M. Karcher, F. Kauker, M. Steele, N. Yakovlev, J.
Zhang, and A. Proshutinsky, 2005: An energy-diagnostics
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Modelling, DOI: 10.1016/j.ocemod.2004.11.003.
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the Atlantic Water layer circulation in the Arctic Ocean, J.
Geophys. Res., 112, C04S04, doi:10.1029/2006JC003732

27

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Volume 13 No.1 January 2008

Impact of different Bulk Parameterization schemes of air-sea ﬂuxes on oceanic heat content in the TropicalIndian Ocean
Agarwal, N., R. Sharma and V. K. Agarwal
Meteorology & Oceanography Group, Space Applications Center (ISRO) India
Corresponding author: rashmisharma_01@yahoo.co.in

1. Introduction
Understanding the variability of oceanic heat content andits anomalies is quite important in studying the temporal
evolution of the coupled ocean-atmosphere system. It is oneof the important parameters that leads to cyclogenesis. In thetropical Indian Ocean, especially the Bay of Bengal, which isfrequently struck by tropical cyclones, heat content plays a
key role in determining the conditions favourable for such
events. Due to the lack of in-situ observations over the oceans,estimation of heat content (or any other oceanic parameter,say mixed layer depth, sub-surface isotherm depth etc.) has
been a problem. More recently with the deployment of Argo
profiling floats (Argo Science Team 2001) the observation
network in the world oceans has improved, but still, complete
3-dimensional oceanic ﬁelds from these ﬂoats are not available
on regular basis. Ocean general circulation models therefore,play a key role in estimating these ﬁelds. However theirperformance in turn depends upon the atmospheric forcingsand the parameterization schemes used in the model. In thepresent study we have made use of two different air-sea ﬂux
bulk parameterization schemes in an ocean general circulation
model and examined their impacts on the simulated oceanic
heat content of the Indian Ocean.

upper 40 meters. The bottom topography is based on 1/12o by
1/12o resolution data from the U. S. National Geophysical Data
Centre. Monthly climatological river discharge data for 3000
rivers were downloaded from the UNESCO site and used.

2. Experimental setup
The model used in the present study is the Modular Ocean
Model (Pacanowski and Grifﬁes, 2000) version 3.1 (MOM-3)
which has been set up for the global domain excluding polar
regions (80°S - 80°N) with variable horizontal resolution of from
0.5 degrees in the Indian Ocean to 2 degrees in the rest of theoceans. There are 38 levels in the vertical with 8 levels in the

3. Results
The model was able to reproduce the climatological patterns ofheat content (x107 J/m2) for the tropical Indian Ocean in boththe experiments with E2 simulations closer to climatology.
However both the runs showed an underestimation in heat
content especially in the Arabian Sea. More than the meanpattern, regions of strong heat content variability are important

Using climatological temperature and salinity (Levitus 1982)
the model was spun up from rest for 20 years forced byclimatological winds (Hellerman and Rosenstein 1983) and withrestoring boundary conditions for SST and sea surface salinity.
Later the model was run for the two-year period (2003-2004)
forced with scatterometer winds from QuikSCAT. Two setsof simulations were carried out. In one experiment (E1) weused the parameterization scheme of Large and Pond (1982)
to compute the exchange transfer coefﬁcients of momentum,
sensible and latent heat ﬂuxes, while in the other experiment(E2) the parameterization scheme proposed by Kara et. al.
(2000) was used. Latent and sensible heat ﬂux components arecomputed using the model’s SST. Daily heat content up to the
20° isotherm depth was computed from both the experiments.
This was then compared with the climatological values obtained
from Levitus temperature proﬁles. Heat content derived frommodel simulations was also checked against those obtained
from Argo profiling float measurements collected during
2003-2004.

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2004

CLIVAR Exchanges������ �� ���� �� ������ � ��� ��� ���

for air-sea exchange processes. Figure 1 (page 21) shows
the standard deviation of the heat content computed fromexperiment E2 over the two-year (2003-2004) period. Strongest
variability centers are in the eastern equatorial Indian Ocean,the southeast Arabian Sea and along the eastern boundary of the
Bay of Bengal. These regions acquire signiﬁcance as they play
important role in Indian Ocean dipole events (eastern equatorial
region), summer monsoon dynamics (southeast Arabian Sea)
and cyclone formation (Bay of Bengal). A detailed study
connecting the heat content variations and air-sea exchange
processes will be taken up separately.
A detailed comparison using co-located (~9500 proﬁles) Argoobservations for the same period was made. Four regions viz.,Arabian Sea (5°-25° N and 40°-80° E), Bay of Bengal (5°-25° N
and 80°-100° E), Somali (5° S-5° N and 40°-80° E) and the easternequatorial Indian Ocean (5° S-5° N and 80°-100° E) were selected
for comparison. Performance of the two bulk formulations, E1
and E2 in simulating heat content against heat content derived
from Argo ﬂoats are shown in Figures 2 and 3 respectively. The
y-axis represents observation and x-axis model heat content.
The scatter is similar in three regions except for the Arabian Sea
where the Kara et. al. (2000) formulation (E2) shows a better
performance (8 % improvement over E1). Although E2 resultsin improvement, yet some amount of bias in simulated heat
content still persists in the Arabian Sea. Correlations between
E2 heat content and Argo heat content are better than 0.7 except
for the eastern equatorial Indian Ocean, where correlation is
0.44.
4. Conclusions
The present study has examined the impact of two different airsea bulk parametrization schemes on 20°C isotherm heat content
obtained from ocean general circulation model simulations.
The main ﬁndings can be summarized as follows:• The model performs reasonably well in simulatingheat content in the tropical Indian Ocean with twoparameterization schemes.

• The two schemes did not lead to very different results as
regards to heat content simulations, albeit Kara et al. (2000)
shows slight improvement in the simulation in the ArabianSea. The bias in this region was also reduced with the Karaet al (2000) formulation.
• This is a preliminary analysis and we intend to do detailed
and rigorous analysis on the impact of different bulk
formulations on ocean model simulations.
Acknowledgements
We thank Director, Space Applications Centre, Ahmedabad for
his encouragement. Fruitful discussions with Dr. Abhijit Sarkar
are gratefully acknowledged. MOM3 code and related data sets
were obtained from Geophysical Fluid Dynamics Laboratory.
Argo data were downloaded from ftp://ifremer.fr.
References:
Argo Science Team, 2001: Argo: The global array of proﬁling ﬂoats,
in Observing the Oceans in the 21st Century, edited by C. J.
Koblinsky and N. R. Smith, pp. 248-258, GODAE Proj. Off.,
Melbourne, Australia.
Hellerman, S., Rosenstein, M. , 1983: Normal monthly windstress over the world ocean with error estimates. J. Phys.
Oceanogr., 13, 1093–1104.
Kara, A. B., Rochford, P. A. and Hurlburt, H. E., 2000: Efﬁcientand accurate bulk parametrizations of air-sea ﬂuxes for use
in general circulation models. J. Atmos. and Oceano., Tech.,17, 1421-1438.
Large, W.G., Pond, S., 1982: Sensible and latent heat ﬂux
measurements over the ocean. J Phys. Oceanogr., 12, 464482.
Levitus, S., 1982: Climatological atlas of the world ocean, NOAA
Prof. Pap. 13, 173 pp., Natl. Oceanic and Atmos. Admin.,Silver Spring, Md.
Pacanowski, R. C., Grifﬁes, S. M., 2000: MOM 3.0 Manual,
Geophysical Fluid Dynamics Laboratory, NOAA.

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Volume 13 No.1 January 2008

Report from the CLIVAR Working Group on Ocean Model Development (WGOMD)
Pirani, A1., S. Griffies2, and H, Banks31
ICPO, 2GFDL, Princetown, USA, 3UK Met Office, Hadley Centre, UK
Corresponding author: anna.pirani@noc.soton.ac.uk

The 7th Session of the CLIVAR Working Group on Ocean
Model Development (WGOMD) was held on the 25-26 August
2007, generously hosted by H. Drange of the Nansen Centre(NERSC) and the Bjerknes Centre (BCCR) in Bergen Norway.
This meeting focused on the major issues that the panel needs
to make progress on, namely the CORE experiments and ocean
model evaluation metrics. Reports on regional and institutional
activities from individual WGOMD members are available on
the meeting web page (http://www.clivar.org/organization/
wgomd/wgomd7/wgomd_bergen.php).
The WGOMD meeting was preceded by the Layered Ocean
Model Workshop (see the following web page for details:
http://oceanmodelling.rsmas.miami.edu/lom/index.html)
and the CLIVAR Workshop on Numerical Methods in Ocean
Models (http://www.clivar.org/organization/wgomd/nmw/
nmw_main.php), that is summarised below. The WGOMD
meeting coincided with the Inaugural Meeting: Southern
Ocean Physical Oceanography and Cryosphere Linkages(SOPHOCLES) (http://clic.npolar.no/theme/sophocles.php).
WGOMD joined part of a session of the SOPHOCLES meetingand contributed some presentations outlining the CORE
experiments. The SOPHOCLES community is interested in
the CORE-II framework, particularly run at high resolution,
though processes of interest such as water mass formation, aresensitive to details of the experimental protocol, in particularsalinity restoring and coastal run-off. The question was raisedof how restoring is applied under sea ice.
Review of WGOMD activities
The terms of reference of WGOMD have been updated throughthe removal of the qualiﬁer on the ﬁrst term of reference that
previously limited WGOMD activities in stimulating thedevelopment of ocean models for research in climate and relatedﬁelds to decadal and longer timescales at global scales. Thisfocus on longer, global timescales originated from WGOMD’soriginal role as support for WGCM. WGOMD will extend its
activities to shorter and smaller spatial scales including regionaland coastal problems, as well as involvement in ENSO-related
issues.
A central mission of WGOMD is to facilitate the maturation ofocean models, and the use of ocean models in well deﬁned andAttendees of the WGOMD Panel Meeting, Bergen, Norway.
30

reproducible ocean modelling simulations. WGOMD aims torealize this mission by (a) providing pedagogical peer-review
survey papers that document models and the experimental
design of simulations, see for example Grifﬁes et al. (2000),and (b) by organizing topical workshops that bring elements of
the oceanography community together to discuss research anddevelopment areas relevant to increasing the scientiﬁc integrityof models and their simulations.
Realizing this mission (or some aspect of it) allows WGOMD
to provide scientiﬁcally based advice to other CLIVAR panels
and to WGCM.
Summary of Workshop on Numerical Methods in Ocean
ModelsPrior to the WGOMD panel meeting, the WGOMD, withassistance from the Layered Ocean Model (LOM) group,
organized the workshop “Numerical Methods in Ocean
Models” on August 23-24, 2007 in Bergen, Norway.
The evolution of ocean models is prompted by a growing rangeof high proﬁle scientiﬁc and engineering applications. These
applications range from reﬁned resolution coastal and regionalmodelling forecast systems, to centennial-millenial global earthsystem models projecting future climate. Groups worldwide
are working to improve the integrity of ocean models for use
as tools for science research and engineering applications. This
work involves a signiﬁcant number of fundamental questions,such as what equations to solve, which coordinate system tosolve the equations in, what horizontal and vertical mesh is
appropriate, what physical parameterizations are required, andwhat numerical algorithms allow for computational efﬁciency
without sacriﬁcing scientiﬁc integrity. Furthermore, given theincreasing size of many applications, as well as difﬁculties ofdoing everything in just one group, there is a growing level ofcollaboration between diverse groups. This collaboration spans
the spectrum of algorithm sharing to the merger of previously
disparate code bases.
The numerical methods workshop aimed to foster the maturationof ocean models by supporting enhanced collaboration between
model developers. It did so by bringing together nearly 100of the world’s top ocean model developers and theoreticians.
Presentations were given throughout each day, with plentyof opportunity for interactions, debate, and networking. The
workshop emphasized fundamentals of ocean model numericalmethods and physical parametrizations. The relevance of aparticular approach gauged by its ability to satisfy the needsof various applications. This workshop provided a venue forparticipants to educate one another on the latest advances inocean model development.
Coordinated Ocean-ice Reference Experiments (COREs)
Overview
COREs provide benchmark experiments for global oceanice models, and a step towards developing an Ocean Model
Intercomparison Project (OMIP). The CORE-I proof of concept
project includes seven international modelling groups andconsists of three ocean model coordinate classes (geopotential,
isopycnal and hybrid). A peer-review paper is currently in
preparation illustrating CORE-I (see below) results with theseven ocean-ice models each run for 500 years (Grifﬁes et al.,2008).

VolumeVolumeNo.
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CLIVAR Exchanges

Attendees at the Layered Ocean Model Group Workshop “Numerical Methods in Ocean Models” August 23-24th, Bergen, Norway
The current COREs do not constitute Ocean Model
Intercomparison Projects (OMIPs) as WGOMD is not prepared
to formally sanction the present forcing dataset protocols until
the community has had time to provide feedback. This meansthat the set of COREs outlined below are research projects thatare voluntarily conducted by interested scientists and there is
no formal oversight committee or data repository arrangementin place. This does not prevent the project from eventually
evolving into an OMIP.
Ocean-ice model experiments are useful since they are lesscostly than fully coupled experiments, they can be used in
hindcast mode to reproduce the history of ocean and ice
variables and hence help in the interpretation of observations,they allow for the understanding of processes in the absenceof biases introduced by the atmospheric model and hence
potentially give superior representations (compared to theocean component of a coupled model) of key physical, chemicaland biological processes and so help in model development.
The question of the usefulness of CORE relative to coupledexperiments received considerable attention. On the one
hand, running CORE-type experiments facilitates testing
ocean parameterisations in a framework that is isolated fromthe sensitivities generated by running in coupled mode. On
the other hand, there is the view that testing multiple ocean
models is less beneﬁcial than assessing changes in the context
of coupling to an atmosphere model.
Three CORE experiments have been endorsed by WGOMD,
and these are outlined below. The CORE framework is not
limited to these three experiments.
CORE-IThis experiment aims to investigate the climatological oceanand sea-ice states realised through multi-centennial simulations
forced by idealised repeating normal year forcing that has
been derived from 43 years of interannually varying forcing,retaining synoptic variability with a seamless transition from31 December to 1 January (Large and Yeager, 2004).
After being initially proposed in 2004, CORE-I has reached acritical mass with a community-wide proof of concept approvaland seven ocean-ice models (including geopotential, isopycnal
and hybrid coordinate models) that have been run for 500
years with the repeat ‘normal year forcing’ of Large and Yeager(2004). The experiment has yielded a wide variety of results,
with as many questions raised as answered. Broad comparison

projects such as this achieve much in this way by raisingquestions, which then motivate further research. Without sucha comparison, questions would remain unasked, and thus
unanswered. A peer-review paper with 24 authors (Grifﬁes etal., 2008) is under review at Ocean Modelling.
Performing 500 year runs highlighted that the stability ofAtlantic MOC was an issue for some models in CORE-I,
causing the project to falter for sometime. Some groups were
not able to maintain a quasi-stable MOC for the CORE-I multicentennial simulations without applying a non-trivial salinity
restoring, also necessary to damp drifts in deep water massproperties. The participating groups were given the freedom
to choose their own salinity restoring depending on eachmodel’s sensitivity. Initial results on the sensitivity of the MOC
solution to model resolution indicate that models with a ﬁnerhorizontal resolution (in the North Atlantic Ocean) appear tobe less sensitive to details of the SSS restoring.
CORE-II
This experiment aims to investigate the forced response of
the ocean in hindcast mode. The experiment will be forced by
the interannually varying dataset from 1958-2004 (soon to be
updated to 2006) of Large and Yeager (2004).
The initalisation of the CORE-II hindcast simulation is the
key issue that needs to be addressed by the CORE-II protocol,
particularly if more than the evolution of the upper ocean
is to be analysed. Models can be initiated sub-optimallyfrom the existing reanalysis period starting in the 1950s andcycling through the simulation multiple times, though still
having to ignore the analysis of the ﬁrst few years of the ﬁnal
realisation because of adjustment. An additional limitation ofthis approach is that the existing reanalysis period is biased tothe steady increase in the NAO index between the mid-1960sand mid-1990s.
Model drift, particularly below 400m, can be removed bysubtracting the trend from a climatologically forced control
simulation, assuming that the system is linear.
CORE-III
This experiment aims to investigate the response of an ocean
forced with normal year forcing (as in CORE-I) to a freshwater
perturbation resulting from increased melt water run-off
distributed around the Greenland coast. This experimental
design with a hosing perturbation of 0.1Sv, proposed by
Gerdes et al. (2005, 2006), is motivated by possible increases
31CLIVAR Exchanges

in Greenland melt water that could occur as a result ofanthropogenic global warming. This runoff prescription
contrasts to the practice of applying a freshwater perturbation
uniformly over a North Atlantic box, as done with the Coupled
Model Intercomparison Project (CMIP) for use in paleo-studies.
The alternative runoff prescription from CORE-III provides amore realistic distribution of a water ﬂux anomaly relative tothe pathways of the North Atlantic meridional overturning
circulation, and so can be useful to understand transient
responses of the ocean. The proposed perturbation is slightly
stronger than the average increase in meltwater ﬂux fromGreenland estimated over the next 500 years and it is kept
constant during the 100 year experiment, which is unlikely tobe the case in reality.
The experiment would be spun up with CORE-I normal year
forcing, with the last 100 years repeated with the freshwater
perturbation. Continuing the simulation for a recovery period
would be optional. The choice of surface boundary condition
remains open and could be coupled or partially-coupled, forexample by an anomaly-EBM. WGOMD members are interested
in exploring the CORE-III design.
Ocean Model Evaluation
The fourth term of reference of WGOMD states that one of
the responsibilities of this working group is “to stimulate thevalidation of ocean models when used in stand alone mode
and as part of a coupled ocean-atmosphere model, using
oceanographic data and other methods.” There is a need for acommunity-wide comprehensive best practice for the overall
comparison and evaluation of models that is not solely basedon ‘favourable’ diagnostics.
WGOMD Repository for the Evaluation of Ocean Simulations(REOS): WGOMD is planning to develop a website hostinga peer-reviewed clearing house on how ocean models can besystematically assessed with respect to observed datasets tomonitor simulation skill, characterize the structure of model
biases, assess the impact of numerical/physical choices andguide further investigations. The website will share methods,
views on best practices and observational dataset quality
with the wider modelling and data assimilation community.
Different modelling groups already have extensive model
evaluation practices and experience with comparing model
simulations to observed data. The CLIVAR GSOP data synthesis
community is also in the process of organizing the evaluation ofsynthesis products and PCMDI has plans to develop a website
on climate model evaluation.
Metrics can only be deﬁned as being useful if they are relevant
for application, for example, as benchmarks to compare
model development or to help understand ocean variability
and mechanisms in models. There are different requirements
depending on the focus of the assessment. Metrics will beclassiﬁed according to priority and complexity. Metrics need tobe adaptable to cope with differences that arise from analyzing
different model resolutions, such as calculating transports
through straits in models with different degrees of resolution.
Quantitative methods (space-time collocation, filtering,statistical analyses, etc.) will be identiﬁed.
WGOMD Future Direction
WGOMD has previously focused mainly on the science of ocean
models. It will now broaden this emphasis towards applying
ocean modelling to scientiﬁc questions including topics suchas decadal prediction and high resolution models. WGOMD
continues to support WGCM in understanding climate change,
while also supporting the CLIVAR regional basin panels and
GSOP. WGOMD also plans to develop links with the WCRP
32

Volume 13 No.1 January 2008

Working Group on Numerical Experimentation (WGNE),
having expertise to contribute, particularly in terms of setting
model standards for decadal prediction, ocean data assimilation,
regional modelling and modelling biogeochemical cycles.
References
Gerdes, R., S. M. Grifﬁes, and W. Hurlin, 2005: Reaction of
the oceanic circulation to increased melt water ﬂux fromGreenland – a test case for ocean general circulation models.
CLIVAR Exchanges, 10, 28-31.
Gerdes R., W. Hurlin, and S. M. Grifﬁes, 2006: Sensitivity of a
global ocean model to increased run-off from Greenland.
Ocean Modelling, 12, 416-435.
Grifﬁes, S.M, C. Böning, F. O. Bryan, E. P. Chassignet, R. Gerdes,
H. Hasumi, A. Hirst, A. M. Treguier, and D. Webb, 2000:
Developments in ocean climate modelling. Ocean Modelling,
2, 123-192.
Griffies, S. M., A. Biastoch, C. Böning, F. O. Bryan, G.
Danabasoglu, E. P. Chassignet, M. H. England, R. Gerdes,
H. Haak, R. W. Halberg, W. Hazeleger, J. Jungclaus, W. G.
Large, G. Madec, A. Pirani, B. L. Samuels, M. Sheinert, A.
Sen Gupta, C. A. Severijns, H. L. Simmons, A. M. Treguier,
M. Winton, S. Yeager, and J. Yin, 2008: A Proposal for
Coordinated Ocean-ice Reference Experiments (COREs).
Ocean Modelling, in preparation.
Large, W. B. and S. Yeager, 2004: Diurnal to decadal global
forcing for ocean and sea-ice models: the datasets and ﬂux
climatologies. NCAR Technical Note: NCAR/TN-460+STR.
CGD Division of the National Centre for AtmosphericResearch.

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CLIVAR Exchanges

US CLIVAR Drought Predictability Research Focus
Legler, D1., D. Gutzler2, S. Schubert3
1
US CLIVAR Office, 2University of New Mexico, 3NASA Goddard Space Flight Center, USA
Corresponding author: legler@usclivar.org

Long-term (multi-year) droughts have tremendous societal
and economic impacts on the United States and many othercountries throughout the world. Estimates of the costs ofdrought to the United States alone range from $6-$8 billion
annually with major droughts costing substantially more (e.g.,$62B in 1988 according to NOAA). The Dust Bowl drought of
the 1930s, which at its maximum extent covered much of thecontinental U.S. (Figure 1 page 21) is ranked as one of the top
domestic weather-related disasters of the 20th century (http://
www.weathermatrix.net/education/century/nws.txt). Recent
population increases in water-limited regions have increased
vulnerability to drought at the same time that climate change
projections suggest that drought conditions may become more
extreme in the 21st Century, particularly in the southwestern
United States (NRC, 2007).
The US is implementing a National Integrated Drought
Information System (NIDIS, 2004 – see www.drought.gov), toserve as a dynamic and accessible drought risk information
system that provides users with the ability to determine thepotential impacts of drought, and the decision support tools
needed to better prepare for and mitigate the effects of drought.
While NIDIS has a strong focus on the U.S., drought is a global
phenomenon, both in terms of the forcing elements and thepotential commonality of local processes that operate to makesome regions more susceptible to drought than others. As such,conﬁdence in our understanding of drought processes (remoteand local forcing, feedbacks, etc.) will be signiﬁcantly advanced
by efforts to properly quantify, analyze and simulate regional
drought wherever it occurs. Recent studies (e.g., Hoerling andKumar, 2003; Schubert et al., 2004; Seager et al., 2005) suggestthat such simulations are feasible using current atmosphericmodels forced by observed large-scale SST anomalies (Figure
2 page 21). There are, however, still major uncertainties about
the relative roles of the different ocean basins, the strength ofthe land-atmosphere feedbacks, the role of deep soil moisture,
the nature of long-term SST variability, the impact of globalchange, as well as fundamental issues about predictability ofdrought on multi-year time scales.
This general approach to the long-term drought problem
requires advanced global modelling and data assimilation
capabilities, as well as global observational data sets formonitoring and diagnosing drought, and validating andinitializing models. CLIVAR, together with its sister WCRP
program, GEWEX, can play an important role in advancing
these capabilities and integrating research on long-term drought
into operational prediction and NIDIS-type systems.
US CLIVAR has begun two initial projects addressing theseissues. The first, DRought In COupled Models Project(DRICOMP) aims to increase community-wide diagnostic
research into the physical mechanisms of drought and toevaluate drought simulation in current models. The second is
the establishment of a short-term Working Group to facilitate
progress on the understanding and prediction of long-term(multi-year) drought over North America and other droughtprone regions of the world, including an assessment of the
impact of global change on drought processes. The Drought
Working Group is one of several US CLIVAR has initiated (theothers being Ocean salinity, MJO simulation and prediction,
and Climate interactions in the western boundary current

regions). Major aims of these limited lifetime Working Groups
include expediting planning and implementation of scientiﬁc
activities as well as leveraging wider participation in addressing
scientiﬁc issues of critical importance. They can also facilitate
joint activities between U.S. CLIVAR Panels and between U.S.
CLIVAR and other national and/or international programs,
even to those somewhat beyond the traditional scope of
CLIVAR. All Working Groups have international participation
through membership and organized workshops.
The DRICOMP and Drought Working Group activities are
brieﬂy described in this article. Both are clearly motivated by
North American drought, but we recognize the obvious global
relevance of drought, and we welcome opportunities to use
US research to help promote a true international coordination
effort targeting drought.
DRought In COupled Models Project (DRICOMP)
Following the highly successful US led CMEP (Coupled
Model Evaluation Project - http://www.usclivar.org/science.
html#model) that supported more than 20 projects, the resultsof which were presented at an international workshop in March
2005, the Drought in Coupled Models Project (DRICOMP),
focuses on evaluation of a variety of existing model products toaddress issues such as the roles of the oceans and the seasonal
cycle in drought, the impacts of drought on water availability,and distinctions between drought and drying trends. Small,multi-agency supported supplemental grants were awarded
for analysis and evaluation of drought in several previously
generated simulation ensembles. These included unforced
control runs archived as part of the WCRP CMIP3 multimodel dataset at the PCMDI; multi-model simulations of
20th-century climate and the long stabilized simulations with
forcing held ﬁxed at future climate conditions; paleo-climate
simulations; the downscaled high resolution model datasetsthat are part of the North American Regional Climate Change
Assessment Program (NARCCAP); coupled model seasonal
hindcasts carried out under the Seasonal Prediction Model
Intercomparison Project (SMIP2); and others.
Seventeen projects were selected for DRICOMP funding. Most
projects address regional drought in North America, but somewill explore drought in the Sahel, Asia, S. America, and Africa.
The list of projects can be found at the US CLIVAR web site(http://www.usclivar.org/science_status/DRICOMP_awards.
html). Similar efforts are also under way as diagnostic sub
projects of the WCRP CMIP3 program. The results of the
DRICOMP projects will be reviewed at a Drought Workshop
to be organized by the US CLIVAR Drought Working Group(see below) in late 2008.
U.S. CLIVAR Drought Working GroupThe Drought Working Group incorporates a broad range ofexpertise including modelling, observations, applications(NIDIS), and diagnostics. A few international representatives
contribute as well. The speciﬁc tasks of the Drought Working
Group are to: 1) coordinate and encourage the analysis ofobservational data sets to reveal antecedent linkages of multiyear droughts; 2) propose a working deﬁnition of drought
and related model predictands of drought, 3) coordinate
evaluations of existing relevant model simulations, 4) suggest
and coordinate new experiments (coupled and uncoupled)designed to address some of the uncertainties mentioned in
33

CLIVAR Exchanges

the introduction (i.e., roles of the oceans, land atmosphere
feedbacks, deep soil moisture, etc.), and to contribute to NIDISrelated drought risk assessment; 5) examine the prospects
for using land data assimilation products for operational
monitoring, assessment and hydrological applications; and
6) organize a community workshop in 2008 to present anddiscuss the results. The working group will also help coordinate
key aspects of the (US) long-term drought research agenda
outlined in the recent drought workshop recommendations(e.g. Schubert et al., 2005) and interact with the developing
NIDIS program to communicate current drought prediction
and attribution capabilities.
As initial products, the Working Group compiled a list of recent
papers on drought research, an inventory of accessible data
sets for observational studies, and a list of AMIP and coupledmodel runs that are relevant to drought. These products
can be obtained through the Working Group’s web page at
http://www.usclivar.org/Organization/drought-wg.html.
The Working Group has also initiated an effort to develop
and compare the definitions and robustness of drought
indices (including onset and demise) so that “drought” isquantiﬁable and veriﬁable for the purposes of model prediction
experiments, drought monitoring and early warning, andfacilitating communication between the applications andmodeling/research communities.
Lastly, the Working Group has initiated a set of idealized
experiments using models at GFDL, NCAR, NASA/GMAO,
COLA, NCEP, and LDEO and prescribed forcings (SST
variability, warming trends, and soil moisture) hypothesized toinﬂuence drought over North America. These forcings are thesame for each model and are imposed as a monthly climatology
plus a ﬁxed prescribed anomaly. The model integrations arewell under way and expected to be complete by Spring 2008.
The Working Group will analyze the results from the multiple
model runs to identify and compare dynamical mechanisms
leading to drought, characterize the drought-like responses
to ENSO vs global warming, assess the models’ sensitivity toinclusion of deep soil moisture, and other aspects of drought
and its predictability. Where possible, the runs (which are all
global) will be made accessible to the community.
Results from these simulations will complement the DRiCOMP
effort and the many long term coupled model runs that havebeen analyzed as part of the IPCC climate change assessment

Volume 13 No.1 January 2008

process. A workshop will be organized in late 2008 where theresults from the Drought Working Group and DRICOMP efforts
will be reviewed and discussed.
The long-term drought problem can be an important umbrellaissue to bring together the relevant research expertise ofinternational programs such as CLIVAR (with its focus on large
scale and ocean-atmosphere coupling), GEWEX (focusing on
regional scales and land-atmosphere coupling), and perhaps
others in the Earth System Science Partnership (ESSP – www.
essp.org) to improve our capabilities to respond to the climate
information and prediction needs of the many droughtimpacted regions of the world. We look forward to working
with the international climate research community to enhancecoordination of our activities.
[For further information on US CLIVAR Working Groups,
DRICOMP, and other CLIVAR activities in the US, see the US
CLIVAR web pages: http://www.usclivar.org]
References
Hoerling, M.P. and A. Kumar, 2003: The perfect ocean fordrought. Science, 299, 691-699.
NIDIS, 2004: Creating a Drought Early Warning System for the
21st Century: The National Integrated Drought Information
System (NIDIS). A report of the National Oceanic andAtmospheric Administration and the Western Governor’s
Association. Available online at: http://www.westgov.
org.
NRC, 2007: Colorado River Basin Water Management:
Evaluating and Adjusting to Hydroclimatic Variability.
Available online at: http://books.nap.edu/catalog.
php?record_id=11857
Schubert S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J.
T. Bacmeister. 2004: On the cause of the 1930s Dust Bowl.
Science, 303, 1855–1859, doi: 10.1126/science.1095048.
Schubert, S., R. Koster, M. Hoerling, R. Seager, D. Lettenmaier, A.
Kumar, and D. Gutzler, 2005: Observational and Modelling
Requirements for Predicting Drought on Seasonal to
Decadal Time Scales. Available online at: http://gmao.
gsfc.nasa.gov/pubs/
Seager, R., Y. Kushnir, C. Herweijer, N. Naik, and J. Velez, 2005:
Modelling of tropical forcing of persistent droughts andpluvials over western North America: 1856-2000. J. Climate,18, 4068–4091.

The International CLIVAR Climate of the 20th Century Project: Report of the Fourth Workshop
Kinter, J.,1 and C. Folland2
1
COLA, IGES, Calverton, Maryland, 2 Met Office Hadley Centre, Exeter, Devon, EX1 3PB, UK
Corresponding authors: kinter@cola.iges.org, chris.folland@metoffice.gov.uk

The International CLIVAR Climate of the 20th Century Project(C20C; Folland et al., 2002) held its Fourth Workshop on 13-15
March 2007 at the Hadley Centre for Climate Change of the
Met Ofﬁce, Exeter, UK. The workshop reviewed progress on
coordinated climate simulations and analyses, including new
results on 20th century climate variability, and developed plansfor new integrated model experiments with various degrees
of ocean-atmosphere-land coupling. In keeping with the longstanding theme of the C20C project, forcing data sets, including
a new version of the HadISST sea-surface temperature andsea-ice data set were discussed. A new version of HadISST isdue for release in 2009, with signiﬁcantly higher resolution andincorporating (Advanced) Along Track Scanning Radiometer
satellite sea surface temperature data. There was also
considerable discussion of how to coordinate C20C experiments
34

with related international research programs evaluating thestratosphere’s role in climate, and the effects of land use andland cover change on the variability and predictability ofclimate. The workshop was well attended with 37 participantsfrom 19 different institutions. The workshop web site (http://
www.iges.org/c20c/workshops/200703/home.html) includes
downloadable copies of the presentations and summaries of
the three break-out group discussions. In the following, wesummarize the key ﬁndings and plans agreed to by project
participants.
A total of 32 presentations on various aspects of C20C results
were made in the course of the workshop. Besides overview
talks on the C20C project, there were presentations on the resultsof several C20C numerical experiments intended to address
the issues of both predictability and attribution of observed

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climate anomalies over the past century or more, understandingof which is critical to interpret and evaluate the projectionsof future climate change. Features of the Earth’s climate,particularly extremes, such as droughts and ﬂoods, the Asianmonsoon, monsoons in the Americas and west Africa, andvariability of the North Atlantic were all examined. C20C is also
concerned with simulating climate trends and so provides aninterface with more formal climate change detection projects.
Some of the reported experiments were done in the “traditional”
C20C methodology, with atmospheric general circulation
models (AGCMs) forced with specified, observed timevarying sea surface temperature (SST) and sea ice as well as
greenhouse gases (GHG) and aerosols. The C20C methodology
was expanded, as a result of a special meeting in Prague,
Czech Republic on 1-3 July 2005, to include the “pacemaker”
experimental design. In “pacemaker” runs, a global AGCM
is forced by speciﬁed SST and sea ice only in a limited region
where the interaction between ocean dynamics and atmosphericcirculation is likely to produce systematic low-frequency
variations. Elsewhere, the feedback between the atmosphereand the ocean is simulated, either by inclusion of a mixed
layer model of the upper ocean or with a full coupled generalcirculation model (CGCM). One region where the coupled
ocean-atmosphere system sets the pace for climate anomalies
is the eastern tropical Paciﬁc where El Niño and the Southern
Oscillation (ENSO) produces quasi-regular excursions of SST
and surface winds with a broad spectrum of periods between
4 and 7 years (e.g. Lau and Nath, 2003). One of the workshop
break-out groups reviewed plans for further pacemaker
experimentation.
Fully coupled experiments by C20C members when comparedto observed data are also regarded as part of C20C where
their main aim is to investigate coupled mechanisms. A recent
example is an investigation of the mechanisms of the Atlantic
Multidecadal Oscillation and its climatic inﬂuences (Knight etal, 2006 and previous work).
Another extension of the C20C experimental protocol was
suggested by the land-surface modelling community interested
in assessing the effects of land use and land cover change on
climate. By specifying the time-varying state of the global
land surface vegetation, these experiments can determine theimpact of changes in land cover. A new major international
project called, Land Use and Climate, IDentiﬁcation of robust
impacts (LUCID; introduced in Newsletter 4, http://www.
atm.helsinki.ﬁ/ILEAPS/), which is being organized under
the auspices of International Geosphere-Biosphere Program(IGBP) Integration Land Ecosystem – Atmosphere Processes
Study (iLEAPS; http://www.atm.helsinki.ﬁ/ILEAPS/) and theWorld Climate Research Program (WCRP) Global Water andEnergy Experiment (GEWEX), is closely linking its numericalexperimental plan with C20C. Another of the workshop breakout groups discussed plans for land use and cover change
experiments as well as ways of coordinating with LUCID.
A third break-out group considered the further improvementof forcing data sets, particularly the HadISST SST and sea ice
set. Brief summaries of the three break-out sessions are given
below.
There was also a presentation and discussion of possible
coordination with a related international project for the SPARC
initiative (Stratospheric Processes And their Role in Climate;
http://www.atmosp.physics.utoronto.ca/SPARC/index.html).
In particular, Paul Kushner (U. Toronto) described a new setof experiments being undertaken by SPARC on Dynamics andVariability. There has been increasing interest in the stratosphere

CLIVAR Exchanges

community about the possibility of downward propagation of
signals from the stratosphere to the troposphere and even to
the surface. In the context of C20C, Scaife et al (2005) showed
this idea was important for understanding the climatic effectsof the North Atlantic Oscillation on decadal time scales. The
new experiments, to be conducted in AGCM and CGCM
simulations, are expected to evaluate the consequences of poor
resolution of stratospheric dynamics in such models and to
determine how the stratosphere might inﬂuence the coupled
ocean-atmosphere system, both in terms of current climate
variability and the potential role of the stratosphere in futureclimate change. A set of experiments was proposed that include
identical model runs with and without a resolved stratosphere(“high-top” and “low-top” runs), and stratospheric modellinggroups are encouraged to conduct interactive stratosphericchemistry runs using the climate forcings datasets available
from the C20C project (http://grads.iges.org/c20c/c20c_
forcing/home.html).
Pacemaker Experiments
Several groups have already undertaken pacemaker
experiments, primarily with the eastern tropical Paciﬁc region.
Several studies have examined the relationship between ENSO
and the Asian monsoon, but it is not clear if a consensus on
the relationship has emerged from the ensemble of model
simulations. A few experiments have also been done withspeciﬁed north Atlantic SST, but, again, no clear conclusion has
been drawn. The break-out group that reviewed pacemakerprogress to date, found that there are a number of outstanding
questions that need to be addressed with the methodology,
including investigation of other speciﬁed-SST regions, inclusionof ocean dynamics, comparison with traditional C20C runs
as well as runs with speciﬁed climatological SST, inclusion of
uncertainty in the speciﬁed SST, and possible extensions of themethod to decadal predictability and climate change issues.
The break-out group recognized the inherent limitations of theﬁrst generation of pacemaker experiments. In particular, theprocedures for prescribing SST anomalies in these experiments
do not take full account of the two-way interactions of theatmosphere-ocean system in various regions of interest.
Moreover, these experiments do not address important issues
related to the origin of the SST anomalies themselves. Novel
coupling techniques and experimental designs are needed todelineate the roles of various feedback processes in climate
variability on various time scales.
Land-Surface Simulation Experiments
The problem under investigation is to determine the changes
in the land surface that can and do drive climate change, witha focus in the near term on the west African monsoon andpersistent droughts such as that in the Sahel in the 1970s and1980s and in North America during the Dust Bowl years. A setof experiments was deﬁned that address these foci. One set ofruns will be done with and without the observed changes inland use in conjunction with the full set of forcing functions(SST or fully coupled, GHG, etc.). Another set duplicates theﬁrst set, but with land surface changes only in selected regions
to gauge the local and remote effects. A third set of simulations
is planned with idealized land surface scenarios only in west
Africa to consider possible origins of the persistent droughtin the Sahel.
One aspect that is especially exciting about these experiments
is that the LUCID experimental plan is explicitly linked to the
C20C protocol. In fact, a comprehensive list of experiments has
been developed for the LUCID project in which several of theplanned model simulations are C20C model integrations. The
prospects for coordination between C20C and LUCID provide
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CLIVAR Exchanges

Volume 13 No.1 January 2008a basis for leveraging the work done in each project. Currently,
eight of the C20C modelling groups have agreed to participate
in the joint experiments.

SPARC committee of the Dynamics and Variability Coupled
Stratosphere-Troposphere System project (SPARC Newsletter
29, July 2007).

Forcing Data Sets
Plans are in place for the development of the next generation
HadISST2.0 SST and sea ice observational data set, expected tobe released in 2009. The group at the Hadley Centre is placingemphasis on reducing biases, increasing resolution in more
recent decades and estimating uncertainties, primarily in theSST ﬁeld. In the satellite era, development of HadISST2.0 will
beneﬁt from a close working relationship with the Global
Ocean Data Assimilation Experiment High-resolution Sea
Surface Temperature Pilot Project (Donlon et al, 2007). Other
external forcing data were also discussed, and it was strongly
recommended that, for those groups whose models have thecapability, detailed aerosol treatments be used. The group also
recommended that the standard set of forcing data be reviewed
for all future C20C simulations, including SST and sea ice, GHG,
stratospheric ozone, and stratospheric volcanic aerosol.

A special session on C20C is planned for the 82nd Annual
Meeting of the American Meteorological Society in New
Orleans on 22-23 January 2008. A ﬁfth C20C workshop willtake place in late 2009, tentatively in Australia..

Future Plans for C20C
Intercomparison of the ability a number of the C20C models to
reproduce key features of twentieth century climate variability
and change when run in classical C20C mode are under way andare planned to be submitted for publication in early 2008.
As described above, the future C20C simulations areintended to address questions raised in previous rounds tohelp better understand mechanistic questions relating toseasonal and decadal predictability and forecasting, and toprepare to contribute to the Fifth Assessment Report (AR5)of the Intergovernmental Panel on Climate Change (IPCC).
Experimental designs are in place for more detailed pacemakersimulations and land use change experiments. There are
already strong ties in place to the CLIVAR Working Groupon Seasonal-to-Interannual Prediction (WGSIP) and now to
the LUCID project. A C20C representative is now also on the

Acknowledgements
Chris Folland was supported by the Joint Defra and MoD
Programme, (Defra) GA01101 (MoD) CBC/2B/0417_Annex
C5.
Jim Kinter was supported by the US National Science
Foundation (ATM-0332910), National Oceanic and Atmospheric
Administration (NA05OAR4310034) and the National
Aeronautics and Space Administration (NNG04GG46G).
References
Donlon, C., et al, 2007: The Global Ocean Data Assimilation
Experiment High-resolution Sea Surface Temperature Pilot
Project, 2007: Bull. Amer. Meteor. Soc., 88, 1197-1213.
Folland, C.K., J. Shukla, J. L. Kinter III, and M. J. Rodwell,
2002: C20C: The Climate of the Twentieth Century Project.
CLIVAR Exchanges, 7, No. 2 (June 2002), 37-39, (http://
eprints.soton.ac.uk/19305/01/ex24.pdf).
Knight, J.R., Folland, C.K. and A.A. Scaife, 2006: Climatic
Impacts of the Atlantic Multidecadal Oscillation. Geophys.
Res. Lett., 33, L17706. doi: 10.1029/2006GL026242.
Lau, N.-C. and M.-J. Nath, 2003: Atmosphere–Ocean Variationsin the Indo-Paciﬁc Sector during ENSO Episodes. J. Climate,
16, 3-20.
Scaife, A., J. Knight, G. Vallis and C.K. Folland, 2005: A
stratospheric inﬂuence on the winter NAO and North
Atlantic surface climate. Geophys. Res. Lett. 32, L18715, doi:
10.1029/2005GL023226.

The Ocean Component at ECMWF: Towards a seamless prediction system
Balmaseda, M.A., L. Ferranti and F Molteni
ECMWF, Reading, UK.
Corresponding author: Balmaseda@ecmwf.int

Although originally implemented as part of the operational
seasonal forecasting system in 1997, the scope of the ocean
component at ECMWF has been steadily widening with time. It
is now also used for the operational monthly forecasting system(since October 2004), and in the near future it will be part of themedium range forecasting system. On the longer time scales,
the ocean component is also required for the decadal forecasts
conducted within the ENSEMBLES project. The ocean modelis an integral part of both the forward model (coupled oceanatmosphere-land-wave system), and the ocean data assimilation
system needed to provide the ocean initial conditions forthe coupled forecasts. The coupled forecasts usually requirecalibration, achieved by performing a series of hindcasts for a
sufﬁciently long historical record. The ocean initial conditions
for the calibrating hindcasts are obtained by conducting an
historical ocean reanalysis, which has the additional value ofproviding information about the ocean variability at different
time scales (from days to decades).
The Ocean Analysis System
The ocean data assimilation system is based on the HOPE-OI
scheme: The ﬁrst guess is given by forcing the HOPE (Hamburg
Ocean Primitive Equations) ocean model with daily ﬂuxes ofmomentum, heat, and fresh water, while the observations are36

assimilated using an Optimal Interpolation (OI) scheme. The
HOPE ocean model (Wolff et al., 1997) uses an Arakawa E gridhorizontal discretization and variable bottom topography.
Several modiﬁcations to the original code were introduced in
the operational conﬁguration in 2001: The horizontal resolution
is 1o x 1o with equatorial refinement (i.e., the meridional
resolution increases gradually towards the equator, where it
is 0.3o in the meridional direction). There are 29 levels in thevertical, with a typical vertical thickness of 10 meters in theupper ocean. The vertical mixing is based on Peters et al., (1988).
The barotropic solver, originally implicit, was made explicit as
described in Anderson and Balmaseda (2005).
A new ocean analysis system (ORA-S3) was introduced
operationally in August 2006. The ORA-S3 system has several
innovative features, including an on-line bias correction
algorithm, the assimilation of salinity data on temperaturesurfaces, and assimilation of altimeter-derived sea levelanomalies and global trends. A detailed description of theanalysis system is provided in Balmaseda et al (2007a). Alsoa selection of historical and real-time ocean analysis products
can be seen at www.ecmwf.int/products/forecasts/d/charts/
ocean/.
The ﬁrst-guess is obtained from integrating the HOPE ocean

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model from one analysis time to the next, forced by daily
ERA40/OPS ﬂuxes (ERA40 ﬂuxes from the period January
1959 to June 2002 and NWP operational analysis thereafter).
The representation of the upper ocean interannual variability
is improved when using the ERA40 wind stress (Uppala et al.,2005), although the stresses are biased weak in the equatorial
Pacific. The fresh water flux from ERA-40 (PrecipitationEvaporation, denoted P-E) is known to be inaccurate. ORA-S3
uses a better but by no means perfect estimate, obtained by’correcting’ the ERA-40 precipitation values (Troccoli andKållberg, 2004). The surface salinity is relaxed to climatologicalvalues with a time scale of 1 year.
When designing a data assimilation system for seasonal
forecasts several considerations need to be taken into account.
It is important to represent the interannual/decadal variability
in the ocean initial conditions, and therefore strong relaxation toclimatology is not recommended. On the other hand, in order
to avoid spurious trends and signals due to the non-stationary
nature of the observing system, the ocean analysis mean stateshould not differ too much from the observed climate. It is also
important to avoid large initialization shocks in the coupledmodel, which may damage the forecast skill. In ORA-S3 wehave tried to strike a balance between the above requirements:
the weight to observations has been reduced and the relaxation
to climatology is considerably weak (10-years time scale). Thishas been possible because an additive bias correction has been
included (Balmaseda et al., 2007b).
The ORA-S3 consists of an ensemble of ﬁve simultaneous ocean
analyses that contribute to the creation of the ensemble ofprobabilistic forecasts. In addition to providing initial conditions
for forecasts, the ocean reanalysis is an important resource forclimate variability studies (Balmaseda et al., 2007c).
The ocean model in the coupled system: current practiceand limitations.
In coupled mode, the ocean model provides information
about the SST and the ocean current to the atmosphere andwave model, which return ﬂuxes of heat, momentum andfresh water ﬂux. The coupling is done daily in the seasonal
forecasting system and hourly in the monthly forecasting
system. There is not a dynamical ice model. Instead, the ice
concentration is persisted during the ﬁrst 15 days, after whichdamped persistence is used for an additional 15 days, whenclimatological values are used. To produce the coupled forecasts,
the coupled model is initialized with atmospheric and ocean
analyses, and integrated forward in time for 7 months in thecase of the seasonal forecasts (once a month) and 32 days for themonthly forecasts (currently once a week). No relaxation or ﬂux
correction is applied during the forecasts, which are correcteda-posteriori using a ﬁx set of calibrating hindcasts.
Currently, the monthly forecasting system uses the same
ocean model conﬁguration as the seasonal forecasting system.
Vitart et al. (2007) and Woolnough et al. (2006) have shown the
beneﬁts of having an active ocean in forecasts of the Madden
Julian Oscillation (MJO) at monthly time scales. Their work also
demonstrates that a better representation of the mixed layer
process is needed to improve the predictions of the MJO. In
particular, they highlight the importance of the diurnal cycle
and its rectiﬁcation in the representation of the intra-seasonalvariability.
Improvements in the representation of mixed layer processes
are likely to be important in the skill of seasonal forecasts.
Figure 1 (page 22) shows the seasonal prediction of mixed layer
depth (MLD) in the north subtropical Atlantic from April andJune initial conditions (red curves). The forecasts starting from

CLIVAR Exchanges

April systematically overestimate the MLD (compared with theanalysis in black). The forecasts initialized in June, when themixed layer is already shallow, are not biased. Results fromocean-only runs (not shown) suggest that the seasonal bias in
the MLD forecasts is partly due to errors of the ocean model.
The lack of a prognostic ice model may be a shortcoming of
the current forecasting system. For instance, the signiﬁcantreductions in Arctic ice cover during the 2007 Northern
Hemisphere (NH) summer are not correctly represented in
the ECMWF seasonal forecasting system. Experimental results
indicate that this anomalous ice cover has an impact on the NH
atmospheric circulation. Figure 2 (page 22) shows the Z500
ensemble mean differences between a set of experiments forcedby daily varying analyzed ice cover and another set forcedby the daily climatological ice cover (as used in the seasonal
forecasts). Both sets have been forced by the same prescribedvalues of SST. The experiments, consisting of 20-ensemble
members each, were initialized in May and integrated forward
for 5 months. Although the impact in Z500 shows moderate
amplitude, it is statistically signiﬁcant, and in phase with theobserved anomaly in the North Atlantic, indicating a potential
beneﬁt from proper sea-ice treatment in the seasonal forecasting
system. However, the predictability of sea ice anomalies incoupled models is still poorly understood, and it is likely that
accurate initialization of sea-ice properties is needed to predict
such anomalies a few months in advance.
The a-posteriori forecast correction relies on the validity of thelinear approximation, which may not always be appropriate.
For instance, there is evidence that warm biases in NINO3 affectthe behaviour of the coupled system by reducing the amplitudeof the interannual variability in this region. Another potential
non linearity is the deep mixed layer bias described above,
which will affect the thermal inertia of the ocean and persist
the spring anomalies for too long. The excessive persistenceof spring temperature anomalies in the North Atlantic is acommon error in the DEMETER seasonal integrations (vanOldenborgh, 2007) and needs to be investigated further.
Future plans
ECMWF has adopted the NEMO ocean model (http://www.
lodyc.jussieu.fr/NEMO/) as the future ocean component for
all the different operational and research activities. Work isneeded for the evaluation of the suitable horizontal and verticalresolution and the treatment of the sea-ice. The initializationof the ocean will be done by NEMOVAR, a variational data
assimilation system currently under development. The
NEMOVAR system, derived from the existing OPAVAR system(Weaver et al., 2005), is a collaborative project between several
European institutions (http://www.cerfacs.fr/globc/research/
assimilation/assimilation_sheet.html).
References
Anderson, D. L. T., and M. Balmaseda, 2005: Overview of ocean
models at ECMWF. ECMWF Seminar Proceedings. Seminar on
Recent developments in numerical methods for atmosphericand ocean modelling, 6-10 September 2004, 103-111.
Balmaseda, M., A. Vidard & D. Anderson, 2007a: The ECMWF
System-3 ocean analysis system. ECMWF Tech. Memo.
No. 508.
Balmaseda, M., D. Dee, A. Vidard & D.L.T. Anderson, 2007b:
A multivariate treatment of bias for sequential data
assimilation: Application to the tropical oceans. Q. J. R.
Meteorol. Soc., 133, 167–179.
Balmaseda, M.A., D.L.T. Anderson, and F. Molteni, 2007c:
Climate Variability from the new System 3 ocean reanalysis.
ECMWF Newsletter 113., Autumn 2007.

37

CLIVAR Exchanges

Oldenborgh, G.J. van, 2007: Probabilistic seasonal
forecast verification with the Climate Explorer Poster:Workshop on seasonal prediction, 4/6/2007-7/6/2007,
B a rc e l o n a , S p a i n , W C R P. h t t p : / / w w w. k n m i . n l /publications/fulltexts/wcrp.pdf
Peters, H, Gregg, M C and Toole, J M, 1988: On theparameterization of equatorial turbulence. J. Geophys.
Res., 93, 1199-1218.
Troccoli, A. & P. Kållberg, 2004: Precipitation correction in the
ERA-40 reanalysis. ERA-40 Project Report Series, No. 13.
Uppala, S., and coauthors, 2005. The ERA-40 Reanalysis. Q. J.
R. Meteorol. Soc.131,Part B, 2961-3012.
Vitart, F., S. J. Woolnough, M. A. Balmaseda and A. M. Tompkins,

Volume 13 No.1 January 2008

2007: Monthly Forecast of the Madden-Julian Oscillation
using a coupled GCM. Mon. Wea. Rev., 135, 2700-2715.
Weaver A.T., C. Deltel, E. Machu, S. Ricci, N. Daget, 2005. A
multivariate balance operator for variational ocean data
assimilation. Q.R.J. Meteorol. Soc. 131, 3605-3625.
Wolff, J., E. Maier-Reimer and S. Legutke, 1997. The
Hamburg Ocean Primitive Equation Model. Deutsches
Klimarechenzentrum, Hamburg, Technical Report No. 13.
Woolnough, S. J., F. Vitart and M. A, Balmaseda, 2006: The roleof the ocean in the Madden-Julian Oscillation: Sensitivityof an MJO forecast to ocean coupling. Q. J. R. Meteorol. Soc.,
in press.

Global Ocean Ship-based Hydrographic Investigations Panel (GO_SHIP) - First Meeting
Hood, M.
Intergovernmental Oceanographic Commission (IOC), Paris, France
Corresponding author: m.hood@unesco.org

One of the action items developed at the November 2005
International Repeat Hydrography and Carbon workshop in
Shonan Village, Japan, was to establish a small interdisciplinary
advisory group to bring together interests from physicalhydrography, carbon, and biogeochemistry to develop
guidelines and advice for the development of a globally
coordinated network of sustained ship-based hydrographic
sections that will become an integral component of the ocean
observing system after the end of CLIVAR (post-2013).
Taking these suggestions forward, the IOCCP, CLIVAR,
and the SOLAS-IMBER Carbon Group each approved thedevelopment of this advisory group in 2006, and earlier thisyear, the Observations Coordination Group of the IOC-WMO
Joint Technical Commission on Oceanography and Marine
Meteorology (JCOMM) and the GCOS-GOOS-WCRP Ocean
Observations Panel for Climate (OOPC) strongly endorsed its
development.
The ﬁrst meeting of the Hydrography Panel was held in Victoria,
BC, Canada on 1-2 November. Over the next 18 months, thisgroup will develop a draft strategy that will be circulated forreview and comments in mid 2008, with a ﬁnal strategy to be
published in late 2009. Speciﬁcally, the Terms of Reference
for this advisory group (the Global Ship-based Hydrographic
Investigations Panel, GO-SHIP) are as follows:i. To develop the scientiﬁc justiﬁcation and general strategy fora ship-based repeat hydrography network, building on existing
programs and future plans, that will constitute the core global
network, post-CLIVAR; considerations should include:· a set of basic requirements to deﬁne a coordinated repeat
hydrography network (e.g., sample spacing, repeat
frequency, recommended core measurements, etc.);· an inventory of existing and planned sections that meetthose criteria;· an assessment of other observing programs that caneither contribute to or use hydrography data (e.g., Argo,
OceanSITES, GeoTraces, etc.);· an assessment of data release needs to meet research andoperational objectives;· an inventory of on-going or planned scientiﬁc synthesis
activities (basin and global) that might beneﬁt from closer
collaboration; guidelines for the transition from the CLIVAR
hydrographic program to the new system, including
sections, data and information management, and synthesis
activities.
ii. To develop guidelines for a single global information anddata center for ship-based repeat hydrography; and,38

iii. To review and provide guidance on the need to update the
WOCE hydrographic manual, including a review and updateof data quality control issues.
GO-SHIP members include: Chris Sabine (NOAA - PMEL,USA), Nicolas Gruber (ETH, Switzerland), Arne Koertzinger
and Toste Tanhua (IfM-GEOMAR, Germany), Bernadette
Sloyan (CSIRO, Australia), Greg Johnson (NOAA-PMEL, USA),and Masao Fukasawa (JAMSTEC, Japan).
This ﬁrst meeting reviewed discussions and decision fromthe Shonan Village meeting and began drafting sections of
the strategy on science goals, temporal and spatial sampling
requirements, core and recommended variables, survey lines
to be included as part of the sustained system, data release
policies, and data and information center needs. The groupalso reviewed the WOCE hydrographic manual chapters andsuggested lead reviewers and authors to update each section.
It is envisaged that the advisory group will develop a report
within a <2 year period that will be circulated widely for
consultation and consensus on the way forward. The ﬁnal
strategy will be presented at OceanObs 09.
The IOCCP (Maria Hood) and CLIVAR (Nico Caltabiano) areproviding project ofﬁce support for the advisory group activities.
For more information visit the GO_SHIP site at http://ioc.
unesco.org/IOCCP/Hydrography/GOSHIP.html and theInternational Repeat Hydrography and Carbon Workshop site
at http://ioc/unesco.org/ioccp/RepeatHydrog2005.htm.

VolumeVolumeNo.
13
9 No.3No.1
3 September
September
January2004
2008
2004

CLIVAR Exchanges

CLIVAR SSG-15, WMO Headquarters, Geneva, Switzerland. 11-14 September 2007
Howard Cattle, Director ICPO
Corresponding author: hyc@noc.soton.ac.uk

There was a full agenda for the 15th CLIVAR Scientiﬁc SteeringGroup (SSG) meeting, the papers and power points for which
can be accessed from the SSG-15 webpage at www.clivar.org/
organization/ssg/ssg15/ssg15.php. Following a welcome by
the Deputy Secretary General of WMO, the meeting initially
heard presentations from the three other WCRP core projects
and a number of other programmes and activities, including
the WMO World Climate Programme (WCP), IGBP IMBER,
THORPEX and WGNE and, later, national programmepresentations from the US and Japan. Presentations were madeby the co-chairs of each of the CLIVAR panels and working
groups, including, on the ﬁnal day, ESF MedCLIVAR. These
presentations outlined key progress, future plans and issues
arising for the SSG to address. What was clear is that there
is substantial momentum and progress on many importantscientiﬁc topics. Breakout groups with plenary reporting
followed these presentations, aimed at providing a review ofthe directions of CLIVAR science areas against the science areasummary papers and CLIVAR “Forward Look” from SSG-14(see www.clivar.org/organization/ssg/ssg14/ssg14.php).
The SSG also addressed the ways in which CLIVAR iscontributing to the WCRP cross-cutting topics of atmosphericchemistry and climate (AC&C), anthropogenic climate change(ACC), climate extremes, monsoons and seasonal and decadal
prediction. It was clear that CLIVAR can make, and is making,important contributions in all of these areas, including AC&C.
With CLIVAR accepting the responsibility at the meeting of
the Joint Scientiﬁc Committee of WCRP in Tanzania last Aprilof taking the lead (or co-lead, with GEWEX) in shaping anddeveloping the last four of these activities, it was clear thatthe cross cuts will need to be an increasing focus for CLIVAR’sefforts in the future.
Another important item was a plenary discussion on how
CLIVAR should evolve as it moves to it’s sunset date of 2013

and the need to deﬁne how CLIVAR science will be covered
under WCRP post 2013. Current WCRP ﬁnancial stringencies,
outlined by the Director of WCRP, led to an extensive discussion
on whether the SSG should make some early changes in theoverall panel and working group structure of CLIVAR. The
SSG were presented with various organizational options but
for the present agreed not to signiﬁcantly disrupt CLIVAR and
its activities, preferring to leave the present structure in placeout to the 2010 timeframe at which time a reorganization would
be needed to accommodate a ﬁnal analysis and assessment
phase.
With a view to deﬁning the legacy of CLIVAR, the SSG agreed
to hold a 2nd CLIVAR Science Conference in 2011 time framewith a ﬁnal closure meeting in 2013. Part of the aim of the2011 Conference will be to assess achievements and identify
major outstanding science questions. The SSG also recognized
the importance of the 2009 World Climate Conference-3 andagreed to seek ways for CLIVAR to input to it. It also endorsed
developing plans by the Ocean Observations Panel for Climate
and CLIVAR’s Global Synthesis and Observations Panel for an
OceanObs’09 Symposium.
The SSG heard two science lectures on the 3rd day of it’s meeting:“The changing Southern Ocean carbon sink” by Nikki Gruber,
ETH, Zurich and “Climate Extremes in a warmer climate – a
focus on Europe and the Alps” by Martin Beniston, Universityof Geneva which were warmly appreciated. Amongst otheritems the SSG also considered how to improve CLIVAR’s
outreach. It identiﬁed in particular the need to engage on thiswith WCP, especially in respect of applications of CLIVAR
science, and the need to increase CLIVAR’s linkages to otherscience areas such as those represented by IMBER.
A full list of the SSG’s Recommendations and Actions can befound on the SSG-15 webpage.

Latest News: WOCE Paciﬁc Atlas PublishedThe second volume of the series of atlases based on thehydrographic sections worked between 1990 and 1997 during
the World Ocean Circulation has now been published. The
atlas has large format colour plates of vertical sections,horizontal property maps on both depth and density surfaces
and property-property plots (see illustration on page 22). The
plotted properties are temperature, salinity, potential density,
neutral density, dissolved oxygen, silica, nitrate, nitrite,
phosphate, CFC-11, CFC-12, delta3 helium, tritium, delta14
carbon, alkalinity and total carbon. Each atlas contains a DVD ofthe ﬁgures which are also available online at http://www-pord.
ucsd.edu/whp_atlas/paciﬁc_index.html (note that the DVD
version contains additional ﬁgures). The atlas was produced
by Prof. Lynne Talley at Scripps Institution of Oceanography
with funding from the US National Science Foundation. The
ﬁrst atlas covering the Southern Ocean and produced by Alex
Orsi and Thomas Whitworth III (Texas A and M University)
was published in 2003. The Indian and Atlantic Ocean volumes
will be available later in the year. The printing costs of the atlas
series has been funded by a grant from BP. Recipients are asked
to pay the mailing costs from regional distribution points inAustralia, Canada, Japan, Korea, UK and USA. If you wish

to receive a copy please contact Mrs Jean Haynes (jchy@noc.
soton.ac.uk).
Full citation.
Talley, L. D., Hydrographic Atlas of the World Ocean Circulation
Experiment (WOCE). Volume 2: Paciﬁc Ocean (eds. M. Sparrow,
P. Chapman and J. Gould), International WOCE Project Ofﬁce,
Southampton, UK, ISBN 0-904175-54-5. 2007

39

Contents

CLIVAR Exchanges

Volume 13 No. 1, January 2008

Editorials

2

Parameterizing Submesoscale Physics in Global Climate Models

3

Global Ocean Modeling in the Eddying Regime Using POP

5

Impact of relative atmosphere-ocean resolution on coupled climate models

5Development of a global ocean model with the resolution of 1o×1/2o and 1/8o×1/12o

11

The MITgcm/ECCO adjoint modelling infrastructure

13

AOMIP: coordinated activities to improve models and model predictions

17

Impact of different Bulk Parameterization schemes of air-sea ﬂuxes on oceanic heat
content in the Tropical Indian Ocean

28

Report from the CLIVAR Working Group on Ocean Model Development (WGOMD)

30

US CLIVAR Drought Predictability Research Focus

33

The International CLIVAR Climate of the 20th Century Project: Report of the FourthWorkshop34

The Ocean Component at ECMWF: Towards a seamless prediction system

36

Global Ocean Ship-based Hydrographic Investigations Panel (GO_SHIP) - First Meeting

38

CLIVAR SSG-15

39

Latest News: WOCE Paciﬁc Atlas Published39

The CLIVAR Newsletter Exchanges is published by the International CLIVAR Project Ofﬁce
ISSN No: 1026 - 0471
Editors:
Layout:Printing:

Stephen Grifﬁes (GFDL), Helene Banks (UK Met Ofﬁce Hadley Centre), Anna Pirani(ICPO) and Howard Cattle
Sandy Grapes
Technart Limited, Southampton, United Kingdom

CLIVAR Exchanges is distributed free of charge upon request (email: icpo@noc.soton.ac.uk)
Note on Copyright
Permission to use any scientiﬁc material (text as well as ﬁgures) published in CLIVAR Exchanges should beobtained from the authors. The reference should appear as follows: Authors, Year, Title. CLIVAR Exchanges,
No. pp. (Unpublished manuscript).
The ICPO is supported by the UK Natural Environment Research Council and NASA, NOAA and NSF throughUS CLIVAR.
If undelivered please return to:International CLIVAR Project Ofﬁce
National Oceanography Centre Southampton
University of Southampton Waterfront Campus
Southampton, SO14 3ZH, United Kingdom
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