Future climate prediction systems will include ocean models at eddy-admitting to eddy-resolving resolution, i.e.  ° on the horizontal or finer. The development and calibration of such models requires the us

Bernard Barnier

Mélanie Juza

Thierry Penduff

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ASSESSING THE REALISM OF OCEAN SIMULATIONS AGAINST HYDROGRAPHY AND ALTIMETRY  
 
 
Thierry Penduff
1, Mélanie Juza
1, and Bernard Barnier
1 
LEGI-CNRS, BP53, 38041 Grenoble Cedex 9, France. 
Corresponding author: Thierry.Penduff@hmg.inpg.fr 
 
Introduction 
Future climate prediction systems will include ocean 
models  at  eddy-admitting  to  eddy-resolving  resolu-
tion, i.e. ¼° on the horizontal or finer. The develop-
ment and calibration of such models requires the use 
of  more  accurate  numerical  schemes,  and  the  im-
provement  of  physical  subgrid-scale  parameteriza-
tions for the ocean interior and its boundaries, includ-
ing air-sea interactions that drive the global ocean cir-
culation  and  its  feedbacks  to  the  atmosphere.  The 
DRAKKAR  consortium is  developing a  hierarchy  of 
basin-scale to global ocean models (Barnier et al, this 
issue)  to  simulate  and  study  the  ocean  variability 
driven  by  realistic  atmospheric  conditions  over  the 
last 50 years without data assimilation. These oceanic 
hindcasts should help understand the nonlinear inter-
actions  between  fine  scale  processes  and  large-scale 
ocean dynamics, better interpret and take advantage 
of satellite and in situ observations (see Penduff et al, 
2006, for an overview). However, numerical simula-
tions  require  quantitative  model-observation  mis-
match  evaluations  to  guide  dynamical  studies  and 
further model improvements, and careful dynamical 
assessments 
 
This paper presents an assessment method of model 
solutions  against  two  complementary  datasets:  the 
ENACT-ENSEMBLES hydrographic profile database
1 
which covers the period 1956-present and includes 7.4 
million  temperature/salinity  (T/S)  reports  from  hy-
drographic sections, moored arrays, floats, ARGO and 
XBT observations; and the Ssalto/Duacs multimission 
Sea Level Anomaly (SLA) weekly maps from altime-
ter  measurements
2  available  since  1993.  DRAKKAR 
models simulate the evolution since the late 1950’s of 
T, S, velocity, sea-surface height (SSH), sea-ice charac-
teristics,  and  oceanic  concentrations  of  two  tracers 
(CFC11, 
14C) released in the atmosphere over that pe-
riod.  These  variables  are  stored  as  successive  5-day 
averages during the integrations. Dynamical outputs 
are  then  collocated  with  real  observations  for  com-
parison purposes. This paper focuses on the 1958-2004 
global ¼° ORCA025-G70 simulation (Barnier et al, this 
issue), and its 2°-resolution counterpart driven by the 
same surface forcing. 
 
Data preprocessing  
The collocation procedure linearly interpolates model 
T/S fields at the geographical locations, depths, and 
instants when real T/S profiles where collected. Only 
                                                 
1  http://www.mersea.eu.org/Insitu-Obs/1-Insitu-Data-
ENACT.html 
2http://www.aviso.oceanobs.com/html/donnees/welcome
_uk.html 
quality-checked (unflagged) observations are consid-
ered. Model profiles are stored in the same format as 
observed ones to facilitate their dissemination. Collo-
cated profiles are then processed jointly to character-
ize  the  structure  of  T/S  model  biases  in  space  and 
time. Model SSH fields are interpolated as observed 
SLA  maps,  i.e.  weekly  and  on  a  1/3x1/3°  Mercator 
grid, from 1993 to 2004. Collocated SLA databases are 
obtained  by  masking  them  where  and  when  (either 
real or simulated) sea-ice is present, by removing at 
each grid point their respective 1993-1999 mean, and 
by removing their global spatial average every week. 
Lanczos filters may then be applied to split collocated 
SLA  fields  (and  thus  evaluate  the  model  skill)  into 
distinct  wavenumber-frequency  ranges.  We  focus 
here  on  the  interannual  SLA  variability,  i.e.  with 
timescales longer than 18 months. 
 
Upper ocean heat and salt contents  
The upper ocean, which varies and interacts with the 
atmosphere on a wide range of time and space scales, 
requires a dedicated assessment in terms of heat and 
salt content (HC and SC). Each color dot in Figure A 
shows,  for  the  50-450m  layer  and  the  period  1998-
2004,  a  collocated  bias  (¼°  global  simulation  minus 
ARGO) of HC and SC. A cold fresh bias, whose me-
dian reaches -3°C at 250m and -0.5 psu at the surface, 
can  be  seen  north  of  the  simulated  North  Atlantic 
Current.  Indeed,  this  current  progressively  tends  to 
block over the Mid-Atlantic Ridge, like in many mod-
els at this resolution, and lets cold and fresh subpolar 
waters  invade the region  off  the  Grand Banks. Two 
other shifts are revealed in the Antarctic Circumpolar 
Current  (ACC),  which  locally  departs  from  its  ob-
served route near 45° and near 90°E. Another signifi-
cant T/S bias (reaching +2°C and +0.2 psu at 200m), 
not fully understood yet and subject to present inves-
tigations,  is  revealed  in  the  Kuroshio  region.  The 
warm and  salty bias  visible in  the  northwestern In-
dian Ocean is due to a spurious mixing of the Red Sea 
overflow; our present work on bottom boundary layer 
parameterizations  will  hopefully  reduce  it.  The 
DRAKKAR group is also working on improving the 
surface forcing function to limit the warm and salty 
equatorial bias seen in the tropical Indian and Atlantic 
basins.  Over  the rest of the global  ocean,  collocated 
model and ARGO profiles show smaller biases after 
several decades of integration.  
 
The black and green lines in Figure B illustrate in the 
Sargasso Sea how ARGO is being used to assess the 
mixed layer annual cycle simulated at ¼° resolution, 
in  terms  of  monthly  heat  content  (MLHC),  depth 
(MLD), and temperature (MLT). The median MLHC 
simulated there over 1998-2004 appears overestimated between November and April, by up to a factor of 2 in 
winter.  The  lower  panels  show  that  this  substantial 
bias is due to a winter MLD that is twice as deep as 
observed, and not to a warm bias (ARGO and simu-
lated MLTs are almost identical). This approach also 
helps evaluate hydrographic sampling errors: our re-
sults  shows  that,  in  this  region,  ARGO  accurately 
samples  the  distributions  (median,  percentiles)  of 
monthly  MLHC,  MLD  and  MLT:  blue  lines  (full 
model)  and  black  lines  (subsampled  model)  are  re-
markably  similar.  Hydrographic  sampling  error 
evaluation  and  model-observation  intercomparisons 
are presently being extended at global scale. 
 
Sea level interannual variability 
The 2° and ¼° DRAKKAR simulations are assessed in 
terms of interannual SLA variability (ISV) after collo-
cation onto altimetric maps. Because the 2° grid is re-
fined meridionally to 1/3° at low latitudes, both mod-
els simulate the ISV observed there with realistic and 
comparable  amplitudes  (Figure  C).  At  higher  lati-
tudes,  the  ISV  magnitude  gets  more  realistic  at  ¼° 
resolution, especially in the eddy-active Gulf Stream 
(GS), North Atlantic Current and ACC (both shifted 
as  mentioned),  Kuroshio,  and  Agulhas  region.  A 
space-time  analysis  of  the  ISV  is  shown  for  the  GS 
region in Figure D. SLA fields from both simulations 
are projected on the 1
st EOF of the observed ISV in this 
area. Its spatial structure Eo and associated principal 
component Po show that the GS latitude follows the 
NAO index in the real ocean with a 9-month lag (right 
panel). The ¼° model represents 19% of this mode’s 
variance, which is modest but much greater than in 
the 2° model (2%). Moreover, the ¼° model captures 
much better the delay between the NAO forcing and 
the GS’s response (7.8 months instead of 3.2 months at 
2°  resolution),  yielding  a  better  correlation  with  the 
observed ISV (0.59 instead of 0.49). Investigations of 
this kind are being extended to other key areas and 
basins of the World Ocean. 
 
Conclusion 
This  quick  overview  of  our  “collocated”  assessment 
approach has highlighted certain strengths and weak-
nesses of 50-year DRAKKAR simulations with respect 
to  complementary  (and  recent)  observations.  More 
complete assessments are underway in other regions, 
depths  and  time  ranges,  and  should  contribute  to 
guide  model improvements.  Our next  multi-decadal 
climate-oriented simulations will be compared against 
the  same  observational  databases,  to  precisely 
quantify the model sensitivities (e.g. time-mean state, 
various  modes  of  variability,  drifts)  to  thermal  and 
mechanical  surface  fluxes,  to  resolved  physical 
processes  (e.g.  mesoscale  turbulence,  nonlinearities, 
scale interactions, etc.), and to numerical choices (e.g. 
resolution,  schemes,  parameterization  of  non-
hydrostatic,  mixing  or  diffusive  processes).  These 
tools can also help evaluate existing or future ocean 
observing  systems  in  terms  of  sampling  errors,  and 
strengthen  the  link  between  the  observational  and 
numerical oceanographic communities.  
 
Ocean  observations,  especially  prior  to  the  recent 
ARGO-plus-altimeter  “golden  age”,  are  both  rare 
with respect  to  typical scales of  motion (the  Rossby 
radius) and dispersed in time and space. On the statis-
tical side, model-observation mismatches can always 
be computed, but estimating the robustness or signifi-
cance of these skills may be difficult. Besides the ex-
tension of our evaluations to various regions, times-
cales,  periods  and  depth  ranges,  advantage  should 
thus  progressively  be  also  taken  of  complementary 
observational datasets (e.g. satellite SSTs, current me-
ters, tide gauges, lagrangian trajectories, etc). 
 
Acknowledgments 
DRAKKAR-France  acknowledges  the  computational 
support from IDRIS, and the support from CNES for 
the  development  of  model-observation  comparison 
methods and tools. 
 
References 
Montegut, C.D., G. Madec, A. 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, 
DOI: 10.1029/2004JC002378. 
Penduff, T., B. Barnier, A.M. Treguier, P.Y. Le Traon, 
2006 : Synergy between ocean observations and 
numerical  simulations:  CLIPPER  heritage  and 
DRAKKAR  perspectives.  Proceedings  of  the 
Symposium 15 Years of Progress in Radar Altime-
try, Venice. 
 
 
Figure A. Mixed layer heat and salt content biases from the 
¼° simulation collocated with ARGO profiles over 1998-
2004. Each color dot quantifies a synoptic model bias.  
 
Figure  B.  Top  left:  Local  mixed  layer  depth  (MLD)  esti-
mates  from  ARGO  in  February  (1998-2004).  The  MLD 
corresponds to a 0.2°C temperature change (Montegut et 
al,  2004).  Other  panels:  Monthly  mixed  layer  statistics 
(heat content in 10
9 J/m
2, depth in m, temperature in °C) 
over 1998-2004 in the Sargasso Sea from the full ¼° simu-
lation (blue), ARGO (green), and the model collocated with 
ARGO (black).  Medians (thick lines) and 17%/83% per-
centiles (dashed) characterize monthly distributions. 
  
Figure C. 1993-2004 standard deviations (cm) of observed 
and simulated low-frequency (LF) SLAs.  
 
Figure D. Left: normalized 1
st EOF Eo(x,y) of the observed 
LF SLA in the Gulf Stream region. Center: Principal com-
ponent Po(t) in cm associated with Eo (green), low-passed 
filtered  NAO  index  N(t)  (blue),  projection  of  simulated 
SLAs on Eo (P¼(t) in black for the ¼° model, P2(t) in ma-
genta for the 2° model). Right panel: variances of Po, P¼, 
and P2 scaled by the variance of Po (line 1). Correlation of 
Po, P¼, and P2 with Po (line 2). Lag between N and Po, P¼, 
P2 in months (line 3). 1/4° Simulation Observations 2° Simulation
Salt Content
103 kg.m-2
1/4° model
bias
Heat Content
109 J.m-2
1/4° model
bias
Mixed
Layer
Heat content
(109 J.m-2)
Mixed
Layer
Temperature
(°C)
Mixed
Layer
Depth
(m)
A
B
C
D
Observed SLA: 1st EOF
at interannual frequency
1/4° run: full output
1/4° run: coloc. on ARGO
ARGO observations
Obs.
1
1
9.0
1/4°
0.19
0.59
7.8
2°
0.02
0.49
3.2
Var
Corr
Lag
NAO
index
Obs.
2°
1/4°

ASSESSING THE REALISM OF OCEAN SIMULATIONS AGAINST HYDROGRAPHY AND ALTIMETRY

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ASSESSING THE REALISM OF OCEAN SIMULATIONS AGAINST HYDROGRAPHY AND ALTIMETRY

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