1 A Prototype Two-Decade Fully-Coupled Fine-Resolution CCSM ...

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A Prototype Two-Decade Fully-Coupled Fine-Resolution CCSM Simulation
Julie L. McClean 1,2 , David C. Bader 3,2 , Frank O. Bryan 4 , Mathew E. Maltrud 5 , John M. Dennis 4 ,
Arthur A. Mirin 2 , Philip W. Jones 5 , Yoo Yin Kim 1 , Detelina P. Ivanova 2 , Mariana Vertenstein 4 ,
James S. Boyle 2 , Robert L. Jacob 6 , Nancy Norton 4 , Anthony Craig 4 , and Patrick H. Worley 3
1 Scripps Institution of Oceanography, La Jolla, CA 92093
2 Lawrence Livermore National Laboratory, Livermore CA 94551
3 Oak Ridge National Laboratory, Oak Ridge, TN 37831
4 National Center for Atmospheric Research, Boulder, CO 80303
5 Los Alamos National Laboratory, Los Alamos, NM 87545
6 Argonne National Laboratory, Argonne, IL 60439
PUBLISHED
Ocean Modelling, 39, 10-30, 2011
Corresponding author address:
Dr Julie McClean
Climate, Atmospheric Science and Physical Oceanography Division
Scripps Institution of Oceanography
University of California, San Diego
9500 Gilman Drive, 0230
La Jolla, CA 92093-0230, USA
Ph: 858 534 3030/Fax: 858 534 9820
Email: jmcclean@ucsd.edu
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Abstract
A fully coupled global simulation using the Community Climate System Model (CCSM) was
configured using grid resolutions of 0.1° for the ocean and sea-ice, and 0.25° for the atmosphere
and land, and was run under present-day greenhouse gas conditions for 20 years. It represents
one of the first efforts to simulate the planetary system at such high horizontal resolution. The
climatology of the circulation of the atmosphere and the upper ocean were compared with
observational data and reanalysis products to identify persistent mean climate biases. Intensified
and contracted polar vortices, and too cold sea surface temperatures (SSTs) in the subpolar and
mid-latitude Northern Hemisphere were the dominant biases produced by the model. Intense
category 4 cyclones formed spontaneously in the tropical North Pacific. A case study of the
ocean response to one such event shows the realistic formation of a cold SST wake, mixed layer
deepening, and warming below the mixed layer. Too many tropical cyclones formed in the North
Pacific however, due to too high SSTs in the tropical eastern Pacific. In the North Atlantic
anomalously low SSTs lead to a dearth of hurricanes. Agulhas eddy pathways are more realistic
than in equivalent stand-alone ocean simulations forced with atmospheric reanalysis.
Keywords:
Numerical modelling, atmospheric circulation, ocean circulation, ocean eddies, tropical cyclones.
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1. Introduction
Simulating the Earth System realistically under present day conditions, a necessary precursor to
climate projection, represents a major scientific challenge. The veracity of such simulations is
undermined by missing and inadequately represented processes in component models together
with feedback errors within and among components (Large and Danabasoglu, 2006).
Historically, a successful approach to reducing model biases related to poor fluid dynamical
simulation is to increase the spatial resolution of the component models such that a greater
fraction of the dynamical processes are explicitly resolved, and fewer are parameterized. Errors
in the representation of physical processes may dominate systematic model biases and these
process-related biases may not necessarily decrease in magnitude with increasing resolution.
However, understanding how the explicit simulation of small-scale physical processes impacts
the depiction of coupled processes as well as large-scale climate features in a fully coupled
global system is an essential step towards reproducing a realistic present day climate system.
Here we present results from a two-decade, present-day simulation of the Community Climate
System Model version 4 (CCSM4) where a weather-scale resolution atmospheric model is
coupled to an eddying ocean model using atmospheric and ocean/sea-ice components having
approximately five and ten times the horizontal resolution of standard climate component
models. Others have coupled combinations of high and low-resolution components in the CCSM
framework such as Gent et al. (2009) who use 0.5° atmospheric/land and nominal 1° oceanic/ice
components. This is the first study, to our knowledge, where all components have fine enough
horizontal resolution for both the ocean and the atmosphere to explicitly simulate turbulent
instabilities of the large-scale circulation.
Generally the use of finer resolution in atmospheric global climate models has led to systematic
improvements in the fidelity of atmospheric features, particularly those associated with the largescale
dynamical circulation (Hack et al. 2006). Williamson et al. (1995) showed that the scales of
motion included at T63 (~1.9° transform grid) and higher resolutions were needed to capture the
non-linear processes that appear to drive some larger scale flows. The coupled atmosphere-ocean
dynamics of tropical cyclones are correctly simulated only by the use of very-high horizontal
resolution in both the atmosphere and ocean. In the ocean, the amount of heat mixed downwards
by typhoons may be an important component of global meridional heat transport (Sriver and
Huber, 2007). Accordingly, their explicit simulation in global climate models may be key toward
making assessments of the impact of anthropogenic forcing on mean climate, variability and
extreme events (Emanuel, 2008; Korty et al. 2008; Bengtsson et al. 2007).
Decreasing the horizontal mesh spacing to around 10 km in ocean-only simulations has
improved the depiction of the mean state and variability of the ocean circulation relative to that
in simulations using grid spacing of about 20-50 km (Smith et al., 2000; Hurlburt and Hogan,
2000; McClean et al., 2002; Oschlies, 2002, Maltrud and McClean, 2005; Bryan et al., 2007).
Bryan et al. (2007) discuss particulars of these studies and comment that the improvements
appear to represent a regime shift in the flow dynamics as the grid spacing approaches roughly
10 km. This is generally attributed to the first baroclinic mode Rossby radius being reasonably
well resolved up to approximately 50 degrees latitude (Smith et al., 2000, Figure 1) by such a
grid.
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Specifically improvements generally result when fast narrow (about 100-200 km) flows such as
the Gulf Stream and most of the mesoscale variability (defined as having spatial scales of 50 to
500 km on timescales between 20 and 150 days) are explicitly resolved, together with their eddymean
flow interactions that hitherto were not simulated. Mesoscale variability accounts for about
50% of the variability in the global ocean (Chelton et al., 2007). The Los Alamos Parallel Ocean
Program (POP) ocean general circulation model has been shown to resolve much of the oceanic
mesoscale, both in regional (Smith et al., 2000) and global domains (Maltrud and McClean,
2005, McClean et al., 2006; 2008) when configured with a nominally 0.1º grid.
Understanding the role and importance of mesoscale eddies in the meridional overturning
circulation (MOC) is needed to improve the depiction of present-day conditions in climate
simulations. Böning et al. (2008) argue that in the Southern Ocean eddy fluxes compensate
increased Ekman transport during decadal-scale positive Southern Annular Mode (SAM) events.
Screen et al. (2009) report eddy-driven warming to the south of the Polar Front in the southern
ocean one to three years after a positive SAM anomaly from a 1/12° global ocean primitive
equation model. They consider the enhanced poleward heat flux to be likely associated with the
intensified mesoscale eddy activity. Another example of a mesoscale process of importance to
climate is the Agulhas leakage at the southern tip of Africa, which is characterized by variability
on intraseasonal to interannual timescales. Agulhas eddies, which are resolved in finer resolution
simulations, but are absent in the oceans of standard climate models are the main source of the
warm and salty waters carried towards the North Atlantic as the upper limb of the Atlantic MOC
(Biastoch et al., 2008).
The primary goal of this paper is to examine the general fidelity of air-ocean interaction
processes in the fully coupled fine-resolution CCSM4 simulation. We start by describing the fine
resolution coupled model set-up and then compare the state of the simulated atmosphere and the
upper ocean with observational data and reanalysis products to identify persistent mean climate
biases. Next we examine the explicit simulation of the ocean’s response to a spontaneously
generated typhoon event in the North Pacific. Finally we compare the pathways of Agulhas
eddies as depicted by CCSM4 and altimetry data to appreciate the role of an active atmosphere
when modeling these climatically important mesoscale eddies.
2. Coupled Model Description
The global climate model used in this study is a prototype version of CCSM4; its components are
the Community Atmospheric Model version 3.5 (CAM3.5), the Community Land Model version
3 (CLM3; Dickinson et al., 2006), POP version 2.0 (POP2) and the Community Ice Code version
4 (CICE4.0). CAM3.5 uses the Lin-Rood finite-volume dynamical core (Lin, 2004) and an
updated CAM3 convection parameterization (Neale et al., 2008; Richter and Rasch; 2008). It
was run on a 0.23° x 0.31° spherical grid with 26 levels. POP (Dukowicz and Smith, 1994) was
configured on a tripole grid (Murray, 1996) that has poles in both Canada and Russia, resulting
in a nearly isotropic distribution of resolution in the high northern latitudes. The horizontal grid
spacing at the equator is 0.1º, with the latitudinal spacing decreasing with cosine (latitude).
There are 42 vertical levels, varying smoothly in thickness from 10m at the surface to 250m at
the maximum depth of 6000m. CICE 4.0 (LANL, 2008; Hunke and Dukowicz, 1997) is based on
an Elastic-Viscous-Plastic (EVP) rheology, uses multiple ice thickness categories (Bitz and
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Lipscomb, 1999), and runs on the same horizontal grid as POP; CICE improvements are
highlighted in Gent et al. (2009). Although this resolution may cause the EVP model to operate
at its limits of validity, CICE has been extensively used at 9km resolution (Maslowski et al.,
2004). In addition, some issues that are manifest only at high resolution have previously been
identified and corrected (Lipscomb et al. 2007).
The POP configuration is essentially identical to that used by Maltrud et al. (2010), though we
will highlight some key elements here. The ocean topography interpolated onto this grid was
formed from a combination of ETOPO2 (http://www.ngdc.noaa.gov/mgg/fliers/01mgg04.html)
and BEDMAP (http://www.antarctica.ac.uk//bas_research/data/access/bedmap/) bathymetric
data sets, with the two smoothly blended between 68°S and 71°S. The interpolation used a
weighting scheme that decreased quadratically with distance from the model grid point over a
length scale equal to twice the local grid spacing. In order to improve topographic interactions
with the flow, partial bottom cells (PBCs; Pacanowski and Gnanadesikan, 1998) were
implemented, with partial cell thicknesses limited to be no thinner than the thickness of the
uppermost cell (10 meters), or 10% of the full cell thickness, whichever was greater. Only a few
key passages (the Strait of Gibraltar, Canadian Archipelago) were manually modified to
accommodate sufficient transport, or to terminate large estuaries. The CCSM4 overflow
parameterization (Danabasoglu et al., 2010) was not implemented in the version of POP used in
this experiment.
POP used a second order accurate centered difference advection scheme for both horizontal
momentum and tracers. Although this method can result in numerical over- and under-shoots,
tests using the flux-limiting advection scheme in CCSM4’s POP were judged to be too diffusive
at high resolution. Subgrid scale horizontal mixing was parameterized using biharmonic
operators for momentum and tracers. The viscosity and diffusivity values vary spatially with the
cube of the average grid length for a given cell (see Maltrud et al., 1998) and have equatorial
values, denoted by the subscript 0, of ν 0 = -2.7 x 10 10 m 4 /s and κ 0 = -0.3 x 10 10 m 4 /s, respectively.
Vertical mixing coefficients for momentum and tracers were obtained from the K-Profile
Parameterization (Large et al., 1994). Background values for vertical tracer diffusion range from
10 −5 m 2 /s near the surface to 10 −4 m 2 /s at depth, with viscosity values an order of magnitude
higher. Density is calculated from the equation of state for seawater derived by McDougall et al.
(2003). Freshwater fluxes to the ocean are applied as a virtual salt flux. Unlike the simulation of
Maltrud et al. (2010), the time step was 4.65 minutes to allow for higher amplitude and higher
frequency forcing.
POP was initialized with a two-step process. First, the model was started at rest using potential
temperature and salinity from the World Hydrographic Program Special Analysis Center (WHP
SAC) climatology (Gouretski and Koltermann, 2004). High amplitude transients required that a
time step of approximately one minute be used for several simulated days to avoid numerical
instability. This ocean state was then used to initialize a 2-year CCSM simulation configured
with a 0.5° atmosphere, the final state of which became the initial condition for the simulation
described here. The standard initialization option in CICE was used whereby the specified nonlinear
sea-ice thickness distribution was the same at each grid point over the entire domain
poleward of 70°N and 60°S. This initialization option was chosen rather than use the ice state
from the existing 2-year simulation as the ice in that simulation was showing indications of poor
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performance. We experimented with starting from an initial state with no ice however that failed
probably because significant shocks due to large fluxes in the Polar regions accompany such a
start up. CAM was at rest initially, with prescribed profiles of temperature, vapor and trace gas
concentrations and was allowed to come into equilibrium. Aerosol concentrations were
prescribed in the same manner as in earlier CCSM3 simulations (Collins et al., 2006). Presentday
conditions were represented by 1990’s green house gas (GHG) concentrations, particularly
355 ppmv for CO 2 . The land surface in CLM was initialized with climatologically observed
surface types and vegetation.
The components of the coupled model exchanged information at different time intervals, with the
atmosphere, sea ice, and land models coupling every time step (15 minutes), and the ocean every
6 hours. The ocean coupling interval was chosen as a compromise between an improvement in
stability and accuracy over daily coupling that is still used in lower resolution versions of CCSM,
and efficiency considerations associated with the additional communication and component
synchronization required by more frequent coupling. It should be noted that the fluxes supplied
to the ocean are computed using much more frequent updates of the atmospheric variables, then
averaged over the ocean’s coupling period. The coupled code executed at 0.43 simulated years
per day on 512 nodes on the Lawrence Livermore National Laboratory’s Atlas supercomputer.
This machine has 1152 total nodes, each containing eight, 2.4 GHz processors connected by a
high speed network.
3. Climatology of the Coupled System
In this section we compare the climatology of the simulated circulation of the atmosphere and
the upper ocean with observational data and reanalysis products to identify persistent mean
climate biases. We use averages from model years 13-19 whenever possible. This period is
chosen as a trade-off between using years that are no longer clearly dominated by the adjustment
of the component models to their initial conditions and the need for a sufficiently long period for
the statistics to be meaningful. In addition, comparisons will be made with two other simulations
that use this POP model configuration to provide contrasting realizations of the ocean and
ocean/ice without the complexity of an active atmosphere. One is a global coupled CICE 4.0 and
POP2.0 simulation forced with the repeat annual cycle (normal-year) Coordinated Ocean-ice
Reference Experiment (CORE) dataset (Large and Yeager, 2004; Griffies et al., 2009), with the
6-hourly forcing averaged to monthly values. The other is the stand-alone 0.1° POP2.0
simulation of Maltrud et al. (2010). We then offer explanations for the large-scale ocean and seaice
biases in the subpolar oceans largely in terms of atmospheric circulation biases. Details of
the CLM results will not be discussed here.
The coupled model used the standard set of atmospheric parameter (tuning) constants employed
in the Gent et al. (2009) simulations, thus the model’s climatology is very similar to those of
earlier CCSM simulations (Collins et al. 2006; Bala et al., 2008). Significantly, the net energy
imbalance at the top of the atmospheric model was a small net energy input of less than 1 Wm -2 .
Similarly, the zonal mean distribution of net energy flux at the model top was very close to
values from Clouds and Earth’s Radiant Energy System (CERES) observations, although the
longwave and shortwave components had compensating mean zonal errors between 5 and 15
Wm -2 (in a mean of ~250-300 Wm -2 ) in the mid-latitudes, which is in excess of the 1-4 Wm -2
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observational uncertainty in the CERES data (Loeb et al., 2008). The primary driver of the global
atmospheric circulation is the net differential heating as a function of latitude (Sellers, 1969; and
others). Since the net flux errors are small, we would not expect them to have a significant
influence on the short seven-year climatology presented here. On the other hand, smaller but
persistent longer term trends in the model biases may result from the component errors,
especially when cloud feedbacks are considered in climate change scenarios. While the model
climatology overall is very good considering that no tuning was conducted, below we highlight
biases that would have implications for the mesoscale coupled atmosphere-ocean features.
A comparison of precipitation climatologies from CCSM4 and Xie-Arkin (1997) observations
reveal errors that exceed 5 mm day -1 across a long and narrow band in the tropical north Pacific
(Fig. 1). It also shows that higher resolution does not improve the common problem of a double
or split inter-tropical convergence zone (ITCZ) (Lin, 2007), manifested as the ~5 mm day -1
precipitation deficit over the tropical western Pacific ocean and the Bay of Bengal. Neale and
Slingo (2003) noted that increasing resolution alone does not solve the problem. More recent
studies, including those of Wapler et al. (2010) and Boyle and Klein (2010), attempt to
understand the complex interactions between microscale cloud physics and mesoscale
atmospheric dynamics that are missing in GCM simulations. This remains an active topic of
research, as the solution remains elusive. Less serious errors, such as the too extreme
precipitation over the mountain ranges on the western coasts of the Americas, expose the
requirement to adjust precipitation and cumulus parameterizations to properly reproduce the
physics on the correct scales.
The difference between climatological annual mean potential temperature from the upper-most
ocean level (5m) for model years 13-19 and 0.25° daily Reynolds et al. (2007) satellite-derived
SST data averaged over 1982-2008 (Fig 2a) is seen in Fig. 2b. Cold biases dominate in the
Northern Hemisphere (NH) with extreme values of 10° and 6°C, respectively (Fig. 2b) occurring
in the subpolar North Atlantic (NA) and Pacific (NP); in the subtropics they are up to 1°C. The
highest warm biases are due to poleward displacements of the Gulf Stream (GS) and Kuroshio
Current Extension (KE); more modest biases are found across much of the tropical NP (< 1°C)
and in the Southern Ocean (SO) (up to 2°C). In upwelling zones along the west coasts of North
and South America SST is generally up to 2°C too cold. Off the west coast of Africa surface
waters between 0°and 20°S show a bias of the same magnitude but opposite sign; these
contrasting biases are likely due to the errors in the representation of the dominant processes
controlling the regional upwelling. Warm biases of greater than 7°C were reported in these
upwelling zones in the standard CCSM3 (Collins et al. 2006). Gent et al. (2009) attribute their
reduced SST biases in these regions to better-resolved orography that situates stronger winds
closer to the coast, inducing increased upwelling.
Equivalent seasonal climatological SST bias figures for years 13-19 (not shown) primarily show
that the NH cold biases persist all year. In the NH subtropical gyres the extent and magnitude (up
to 1.5°C) of the cold biases are greatest in boreal summer and fall, defined here as June-July-
August (JJA) and September-October-November (SON), respectively. The positive biases
associated with the KE and the GS are smaller in extent and magnitude relative to those in the
December-January-February (DJF) and March-April-May (MAM) seasons. In the SO cold
biases of generally up to 0.5°C are seen across the entire region in JJA and SON; the biases are
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almost all positive in DJF (up to 2.5°C) with reduced warm biases occurring during MAM. In the
eastern tropical Pacific the largest cold bias both in extent and magnitude (>2°C) is found in
MAM. Maximum warm biases of >2.5° and >1°C, occur in JJA in the western tropical Indian
Ocean and central tropical Pacific, respectively.
Temperature and salinity biases also appear below the surface as SST and sea surface salinity
(SSS) errors are transmitted into the interior by downwelling processes. Fig. 3 shows the
magnitude of the subsurface drifts averaged over various latitude bands (78°S-50°S, 50°S-20°S,
20°S-20N, 20°N-50°N, and 50°N-90°N). In the Polar regions, the temperature biases are
strongest near the surface and show the same pattern as SST. However in the subtropics the
trend is small at the surface and increases with depth as anomalously warm mode waters are
ventilated into the gyres. In the tropics, the pattern reflects changes in the layered equatorial
current system. The depth dependence of the salinity trend (Fig. 3b) is similar to that of
temperature in the tropics and the Polar regions, while that of the subtropical NH is quite
different to that of temperature. While the surface average shows a moderate freshening, the
Atlantic and Pacific display opposite trends, with an overall increase in SSS in the Atlantic and
decrease in the Pacific (not shown).
Insight into some of the causes of the large-scale SST biases in the high and mid-latitudes are
obtained by examining the atmospheric circulation. In Figs. 4a and c we show annual
climatologies of 500 mb geopotential height and sea level pressure from European Center for
Medium-Range Forecasts-European Reanalysis 40 (ECMWF-ERA40) along with their
differences from CCSM4 (Figs 4b, d). In Fig. 5a surface wind stress vectors and their
magnitudes from QuikSCAT (September 1999-August 2007) are shown together with the vector
differences and their magnitudes between CCSM4 and QuikSCAT (Fig. 5b). The CCSM4 fields
are averaged over model years 13-19. A major feature of this simulation is the intensification and
contraction of both polar vortices through the depth of the troposphere. In Fig. 4b, the
geopotential height anomalies are less than -100 m over Eurasia, while positive anomalies
greater than 40 m can be seen over large areas between 40° and 60°S. The sea level pressure
difference field (Fig. 4d) depicts the surface manifestation of this problem, with excessively deep
low-pressure systems over the poles and intensification of the subtropical highs. The circulation
around the intensified Azores High likely contributes to the anomalously cold SSTs in the
tropical Atlantic. Similarly the wind stress vector differences between CCSM4 and QuikSCAT
show that CCSM4 winds are too strong over the subpolar NP and NA oceans and over much of
the SO (Fig. 5b). Particularly, CCSM4 wind stress magnitudes (plot not shown) exceed the
QuikSCAT values (Fig. 5a) by up to 0.09 Nm -2 in the eastern subpolar NA where the winds are
southwesterly. In this area root-mean-square (RMS) wind stress magnitude differences between
QuikSCAT and the National Oceanography Center flux climatology are about 0.04 Nm -2 (Risien
and Chelton, 2008); clearly here the model-data wind stress magnitude differences exceed the
uncertainty in the QuikSCAT wind stresses. The contraction and intensification of the polar
vortex, and resulting biases in the planetary wave structure, are described by Boville (1984). The
problems most likely result from errors in the gravity-wave and mountain wave
parameterizations, which lead to an anomalously strong polar-night jet trapping energy in the
troposphere.
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The NH winter sea-ice pack extends eastward across the entire Labrador Sea and reaches across
the East Greenland Sea into the Norwegian Sea (not shown). This is most likely caused by the
overly intense polar atmospheric circulation rather than the zonally-symmetric initial ice
configuration poleward of 70°N. In the Arctic the seasonal amplitude of total ice area (Fig. 6a) is
seen to increase during the first 8 years of the simulation and then maintains the same too large
magnitude relative to NASA Team Special Sensor Microwave/Imager (SSM/I) observations
(Cavalieri et al., 1996; 2008) for the remaining years of the simulation. The SSM/I data is for the
period 1980-1999. Similarly time series of area-averaged Arctic ice thickness and concentration
(not shown) do not show any significant trends after the initial ice spin-up phase during the first
decade of the simulation. In the Southern Hemisphere (SH) sea-ice is initially placed everywhere
poleward of 60°S; however its area then shrinks rapidly over the first five years of the simulation
and a seasonal cycle results whose amplitude is quite realistic (Fig. 6b). Here as well, time series
of area-averaged ice concentration and ice thickness (not shown) in the second decade of the
simulation show no significant trends and their respective seasonal amplitudes are generally
repeating. Hence, memory of the sea-ice initial condition diminishes rapidly. In general the
biases in the polar/subpolar winds, typically excessive northwesterlies (Fig. 5b), would produce
excessive equatorward ice advection in the Nordic Seas whereas over the SO they would advect
ice towards the South Pole.
The too extensive sea-ice pack in CCSM4 and the displaced North Atlantic Current (NAC) cause
anomalously cold surface waters to be located too far to the east in the NA subpolar gyre.
Evaporative cooling of the surface water accompanies the overly strong subpolar winds that
produce too strong Ekman currents that advect these cold surface waters toward the southeast,
some of which enter the equatorward eastern boundary current (Fig.7). Flatau et al. (2003; Fig.
12a) show the location of the 7° and 8° isotherms, a rough indicator of the NAC axis, during
positive and negative phases of the North Atlantic Oscillation from SST observations during the
1990s. In CCSM4 these isotherms (Fig. 7) are located even further eastwards than their observed
counterparts during the positive NAO phase when anomalously strong wind stresses advect the
NAC front towards the southeast. Subsequently in CCSM4 the anomalously cold water circulates
around the subtropical gyre contributing to the cold bias seen in Fig. 2b. This same process is
active in the subpolar NP.
To appreciate the fidelity of the large-scale upper-ocean CCSM4 ocean circulation we compare
mean sea surface height (SSH) for years 13-19 with the 0.5° mean dynamic ocean surface
topography (MDOT) product of Maximenko et al. (2009) (Figs 8a and b). They combined
satellite radar altimetry with the geoid model of the GRACE Recovery and Climate Experiment
(GRACE) and synthesized upper-ocean velocities from 15-m drifting ocean buoys, hydrographic
profiles, and ocean wind; inclusion of the latter data improved the depiction of the fine-scale
dynamic topography. The product is global in coverage at latitudes equatorward of 65°. For
contrast with CCSM4, we also consider the mean SSH field from stand-alone global 0.1° POP
(Maltrud et al., 2010) for years 95-99 (Fig 8c). The patterns and magnitudes of mean SSH from
CCSM4 agree best with MDOT (Fig. 8a). The difference plot between POP and MDOT (Fig.
9b) highlight overly high POP mean SSH values in western boundary current frontal and
recirculation regions: The Tasman Front in the southwestern Pacific near New Zealand, the
Agulhas Current System, the KE and the GS-NAC. In the SO to the south of Australia and New
Zealand and in the southeastern Atlantic sector POP values are more negative relative to MDOT.
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CCSM4-MDOT (Fig. 9a) shows much more modest differences in these regions, except for
narrow bands associated with the western boundary current and Antarctic Circumpolar Current
(ACC) fronts. In the central subtropical regions of CCSM4 mean SSH values are order 10 cm
lower than MDOT; these regions generally coincide with the cold SST biases identified in Fig
2b. CCSM4 is slightly more negative (10-20 cm) relative to MDOT where the NAC fails to
properly form the Northwest Corner and in the Labrador and Bering Seas.
To further understand the realism of the ocean circulation in CCSM4 we integrated mean mass
transports (years 13-19) through choke points and compared the results with available
observational estimates (Table 1). We use directions here only to differentiate inwards and
outwards flow across a section, channel or opening. The mean mass transport through Drake
Passage is 169 Sv (1 Sv = 10 6 m 3 s -1 ); the observed value is 136.7 ± 7.8 Sv (Cunningham et al.,
2003). The Indonesian Throughflow is -13.8 Sv; recent new measurements by Sprintall et al.
(2009) estimate it to be -15 Sv (into the Indian Ocean). Transport through the Mozambique
Channel is 18.5 Sv southward compared to 16.7 Sv southward observed by Ridderinkhof et al.
(2010). The flow through the Florida- Bahamas Strait is 20.5 Sv while Larsen (1992)’s value
from cable data is 32.3±3.2 Sv. The Windward Passage transport in CCSM4 is low (0.17 Sv into
the Caribbean Sea) however observational estimates have shown significant variability; a more
recent estimate is an inflow of 3.6 Sv (Smith et al., 2007). Across the Greenland-Scotland ridge
(GSR) the net CCSM4 transports are all northward: 6.5 Sv (Denmark Strait), 1.0 Sv (Iceland-
Faroe Ridge) and 5.5 Sv (Faroe-Scotland Ridge). Observational estimates vary with net estimates
of 0.8 Sv northward (Østerhus et al., 2005) and 3 Sv southward (Olsen et al., 2008) through the
Denmark Strait, 3.8 Sv northwards (Østerhus et al., 2005) and 2.1 Sv southward (Olsen et al.,
2008) across the Iceland-Faroe Channel, and 3.8 northward (Østerhus et al., 2005) and 2.1
southward (Olsen et al., 2008) through the Faroe-Scotland Channel. These biases, especially
those across the GSR, are likely due in part to the overly strong polar atmospheric circulation.
To provide a context for these mass transport results we examine the mean mass circulation in
CCSM4 by considering the zonally-integrated flow: the MOC, and the closely related meridional
heat transport (MHT). We use model years 15-19 as the covariances between velocity and
temperature were not archived before year 15. Meridional overturning stream functions for the
global and Atlantic oceans are shown in Fig. 10. The shallow low latitude cells with upwelling
branches at the equator are the most intense features in Fig. 10a with maximum strengths of
around 39 and 38 Sv centered at 60 m in the NH and Southern Hemisphere (SH), respectively,
with the majority of the mass flux sinking within 30° of the equator. In the subpolar zones, the
shallow surface cell is weak (5 Sv equatorwards) in the NH while the Deacon cell in the SH has
a maximum of 38 Sv flowing towards the equator. The strong clockwise cell in the upper 2000-
3000 m of the water column whose southward branch represents the transport of North Atlantic
Deep Water (NADW) connects to the Deacon Cell at intermediate depths leading to strong
upwelling at 60°S. Below it the counter-clockwise deep cell whose lower branch represents the
transport of Antarctic Bottom Water (AABW) has a maximum value of 18 Sv in the SH centered
at around 3500 m. The Atlantic overturning cell (Fig. 10b), driven by downwelling north of
50°N, has a maximum strength of 18 Sv just south of 40°N centered at 1000m, 14Sv crosses the
equator into the SH, and 15 Sv exits the South Atlantic. The strength of the bottom cell is 5 Sv.
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These values can be compared with those from the equivalent stand-alone POP simulation
(Maltrud et al., 2010). In the Atlantic the maximum overturning strength is 25 Sv, 18 Sv crosses
into the SH and then exits the South Atlantic, and the strength of the bottom cell is 3 Sv. The
strength of the global bottom cell is 12 Sv and the Deacon Cell is 43 Sv. Relative to the forced
stand-alone simulation the strength of the Atlantic mid-depth overturning cell is too weak and the
bottom cell is too strong in CCSM4.
The excessive NH ice cover will inhibit convective activity in the subpolar NA of CCSM4
resulting in lower NADW transport. However in the SO convective activity will be enhanced.
The too-strong SO zonal winds will cause increased evaporative cooling producing too cold
surface waters at convection sites in the Ross and Weddell Seas. Fig. 2b and the seasonal SST
bias figures for JJA and SON support this assertion since cold SST biases are found across the
entire SO south of 60°S during these seasons. As well the formation of too-thick (although not
too extensive) ice in the first decade of the simulation would also have contributed to producing
overly dense surface waters by increasing the salinity of local surface waters. The resulting
enhanced convective activity will produce excessive formation of Antarctic Bottom Water
(AABW) hence the overly robust bottom cell circulation seen in the MOC plots.
Further support for these assertions can be obtained by looking at the seasonal climatological
mixed layer depths (MLDs) from CCSM4 (model years 13-19), stand-alone POP (model years
95-99), and observations (Figs 11 and 12) using the 2° resolution global climatology by de Boyer
Montégut et al. (2004). The criterion used to determine the MLD is a threshold value of
temperature from a near-surface value at 10 m depth (ΔT=0.2°C). The temperature criterion
rather than the density criterion is used, as the spatial coverage of the observed density based
MLD estimates is very incomplete in some regions. Figs. 11 a-c show the winter (DJF) MLDs
from observations, CCSM4, and POP; their difference fields are seen in Figs 11 d-e. Clearly the
MLDs in the subpolar NA and Norwegian Sea are too shallow (Fig. 11d), while in POP (Fig 11e)
they are too deep relative to observations. The CCSM4 MLDs are largely all less than POP in
these regions (Fig. 11e). The shallow ML biases in CCSM4 are due to the too extensive ice cover
inhibiting deep convection, and hence deep water formation, in the Labrador and Nordic Seas
during this season. Conversely, too much convective activity may be taking place in POP. In the
Arctic the CCSM4 MLDs are deeper than either the observed or POP values. In CCSM4 the
Arctic temperature stratification is very low in the upper water column hence the overly deep
MLDs are an artifact of the temperature-based criterion. It should be noted that in stand-alone
POP the upper-level potential temperature and salinity was restored to climatology on a time
scale of 30 days in regions of observed diagnosed/climatological ice.
In the austral winter (JJA) MLDs in both POP and CCSM4 are too deep in the ACC relative to
observations (Figs. 12a, b, c, d, and e). In the Pacific sector of the SO POP MLDs are generally
deeper than those from CCSM4, while to the south of Australia and southeastwards of New
Zealand CCSM4 has deeper MLDs (Fig. 12f). The reasons for these differences are complex and
require further study. The wind stresses used to force POP did not include surface currents in the
drag law calculation. In a recent study Hutchinson et al. (2010) using an eddy-resolving
quasigeostrophic model of the SO showed that in simulations forced in this manner too much
power is inputted to the SO eddy field such that it becomes too energetic. On the other hand,
surface currents are included in the CCSM4 calculation however the polar atmospheric biases
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over-ride any benefit. Finally, the actual MLD definition may be misleading in an area of low
stratification. In the Weddell and Ross Seas CCSM4 shows deeper MLDs than POP (Fig.12f),
probably an outcome of the too cold, too saline surface waters conjectured above. AABW
formation is too weak in POP while it is likely too strong in CCSM4.
Together these results indicate a consistent understanding of the depiction of the global MOC
(Fig 10a) from CCSM4. The wind biases in the two hemispheres result in too little convection in
the NA and too much in the SO. In turn this leads to a weak mid-depth overturning cell with a
too robust cell at depth. A comparison with the POP global MOC shows the CCSM4 deep cell to
be intruding into waters as shallow as 2000 m at 38°S whereas in POP the mid-depth cell extends
to depths of 2700 m at the same location.
It is clear that the CCSM4 MOC biases primarily arise from the contracted and intensified polar
atmospheric vortices. Another possible although more minor cause contributing to these biases
may be the representation of the deep overflows in the NA. As is typical with z-level models,
overflows of dense water tend to be diluted as they flow along the ocean floor. For example, the
Denmark Strait Overflow Water (DSOW) is too warm by 1-2ºC in the Irminger Basin due to
excess mixing with relatively warm overlying water. As noted in the model description section
this version of POP does not use any overflow parameterization and tests using the diffusionbased
Beckmann and Döscher (1997) scheme showed minimal improvement in overflow
characteristics in the 0.1º global POP configuration of Maltrud and McClean (2005).
So how realistic is the associated global total mean heat transport from CCSM4? We compare it
with observational estimates based on atmospheric reanalyses by Trenberth and Carron (2000)
and from stand-alone POP (Fig. 13a). The CCSM4 MHT is larger (smaller) than POP to the
south of 30°S (from 30°S to the equator) and lies almost entirely outside of the observed
uncertainty in the SH. It increases to the north of the equator but underestimates the observed
and POP MHT maxima at 15°N. Further north it decreases to a level between those of
observations and POP, again mostly not within the observed uncertainty. Further we decompose
the MHT from the two simulations into their respective total, mean and eddy heat transport
components (Fig 13b). Equatorward of 45°S both the mean and eddy contributions are generally
lower in CCSM4 than in POP especially in the ACC and the NH mid-latitude western boundary
currents, consistent with the weaker MOC in CCSM4. Between 45° and 60° S the CCSM4
southward eddy fluxes are consistently larger than those in POP. In the NH subtropics (20° and
40° N) POP eddy heat fluxes range from 0.1 to 0.2 PW southward whereas in CCSM4 they are
up to 0.15 PW northwards. These two latitude ranges coincide with those previously identified as
being dominated by cold SST biases, and hence are the likely explanation for the behavior of the
CCSM4 eddy fluxes in those regions.
4. Tropical Cyclone Event
In this section we move away from the mean zonally-averaged construct used earlier to
understand ocean heat flux. Here we consider a specific tropical cyclone event and calculate the
ocean heat convergence associated with its passage. Our context however continues to be about
understanding the important mechanisms controlling the movement of mass and heat in this
simulation.
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A very positive feature of the simulation is its ability to generate and propagate intense tropical
cyclones when environmental conditions favor their development. To identify events in model
years 13 through 19, we created animations of the 850 mb relative vorticity field (ζ 850 ), which
was calculated from the 850 mb winds interpolated to a spectral resolution T63 grid (Bengtsson,
et al. 2006). Possible cyclone events were associated with persistent vorticity centers exceeding 5
x 10 -5 s -1 . More detailed analyses of individual storm events was performed using the full high
resolution output.
Compared to climatology, few storms were generated in the tropical NA, due to the anomalously
low SSTs (Fig. 2b) and accentuated Azores High (Fig. 4b). The animation of ζ 850 shows easterly
waves forming off Africa, however no hurricanes developed in the tropical Atlantic. A few
waves that survived upon reaching the Gulf of Mexico did develop into storms as a result of the
more favorable local conditions. Conversely, the too-high SSTs in the central tropical North
Pacific (Fig. 2b; 160°E - 120°W) permitted too many tropical storms to form. Nevertheless, the
high resolution of the simulation permits the spontaneous generation and recurvature of Saffir-
Simpson Category 4 typhoons, as seen in an event with sustained 850 mb wind speeds of 66.5
ms -1 from July-August of year 19 over the western tropical Pacific.
The SST response under and to the right of the track is seen in Fig. 14a. Observational studies of
the upper ocean response to the passage of extreme tropical storms such as those by Jacob et al.
(2000) and Jaimes and Shay (2009) guide our interpretations. The cold SST "wake” is produced
by the entrainment of cold subsurface water either through Ekman pumping or by instability of
the vertical shear of forced near-inertial oscillations (Jaimes and Shay, 2009). A surface
temperature drop of more than 3.6°C is seen at station 9 (133°E, 26°N) on 08/04/19, a day after
the passage of the storm over this location, relative to the pre-storm conditions on 07/29/19 (Fig.
14b). The ocean mixed layer deepened from about 40 m to 80 m, while the temperature below
the bottom of the mixed layer and above 150 m increased by up to 2°C. Surface salinities
increased from about 34.6 to 34.73 (not shown). By 08/18/19 the near-inertial oscillations had
largely abated (not shown), and the mixed layer shallowed to almost pre-storm conditions and
warmed by 0.8°C.
The amount of net surface heating, Q, needed to restore the upper water column to pre-storm
temperatures can be approximated by:
Q = ∫∫∫ ρ 0
C P
ΔTdhdWdL = 5.8 ×10 20 J
where ΔT is the magnitude of the negative part of the temperature anomaly, h is the depth to
which the negative part of the temperature anomaly extends, and dW and dL are the cross-track
and along-track extents, respectively (Emanuel, 2001). The cross-track extent was identified by
differencing SST before the storm and 24 hours afterwards; h was calculated using a 0.2° C
threshold value of temperature from the 10 m value 24 hours after the storm’s passage. Emanuel
(2001) obtained a similar value of Q using approximate values for one NA hurricane event. The
amount of heat mixed downwards into the oceans by typhoons may be an important component
of the global meridional heat transport (Sriver and Huber, 2007). A calculation of Q for all
typhoon events and a subsequent integration in time (such as a year) to estimate the average
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cyclone-induced ocean heat convergence awaits a simulation where the number of hurricane
occurrences more correctly reflect current climatic conditions.
5. Ocean Mesoscale and Agulhas Eddies
The majority of ocean variability is generally accounted for by mesoscale fluctuations; Chelton
et al. (2007) report that more than 50% of the variability over much of the World Ocean is
accounted for by eddies with amplitudes of 5-25 cm and diameters of 100-200 km. Fig. 15 shows
the mesoscale RMS sea surface height anomaly (SSHA) from CCSM4 for years 15-19 and the
AVISO-blended altimetry product from TOPEX/POSEIDON and ERS 1 and 2 (TPERS) for
1997-2001. The ocean model fields were subsampled every 7 days and binned to match the
spatial and temporal sampling of the data. The mesoscale energy is obtained by band-pass
filtering the SSHA between 20-150 days; we are aware that it also contains energy at scales
longer than a few hundred kilometers. The overall comparison generally shows good agreement
in terms of the magnitude and spatial distribution of the variability, however in the ACC and the
KE the mesoscale variability is unrealistically large in places; this is likely due in part to a
barotropic intraseasonal response to the overly strong winds in that region. As well, at latitudes
higher than 50° sampling limitations and the objective analysis procedure produce an
underestimated TPRES signal (Brachet et al., 2004).
A significant improvement in CCSM4 is the depiction of the variability associated with Agulhas
eddies in the South Atlantic. The unrealistic band of strong SSH variability extending
northwestward across the basin that was seen in two earlier global stand-alone 0.1° POP
simulations, without (Maltrud and McClean, 2005; Fig. 4b) and with PBCs (Maltrud et al., 2010)
on dipole and tripole grids, respectively, and forced with different synoptic fluxes, is not seen in
CCSM4. This bias was attributed to Agulhas eddies predominately following the same toonorthwestward
path across the South Atlantic and reaching waters offshore of South America at
about 12-15°S.
We hypothesize that the two-way coupling of 0.1° POP to the high-resolution atmosphere in
CCSM4 has produced more realistic Agulhas eddy pathways and surface dissipation locations.
Examination of the CCSM4 time-evolving latent and sensible heat fluxes, wind stress curl and
Ekman pumping velocities reveals the signature of Agulhas eddies in the Cape Basin region
(west of South Africa) in all these fields; to the west their signatures are barely discernable.
These exchanges and feedbacks likely reduce the range of the eddy sea surface height
displacements causing them to start to spin-down. This assertion is supported by the significant
reduction in mesoscale variability westward of the Cape Basin region (Fig. 15a).
The POP/CICE and the two stand-alone POP simulations were forced with atmospheric fluxes
that were in part derived from National Center for Environmental Prediction/National Center for
Atmospheric Research (NCEP/NCAR) reanalysis products (Kalnay et al., 1996). Uppermostlevel
model potential temperature was included in the calculation of the net surface heat flux
along with the NCEP turbulent fluxes. The 40-year NCEP reanalysis products have a spectral
resolution of triangular wavenumber 62 (T62) or about 210-km grid spacing. Rouault et al.
(2003) found that the NCEP model tends to underestimate latent and sensible transfers from the
Agulhas Current and the Retroflection. They attribute this to the model’s inability to represent
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the fluxes over the warmest waters in the core of the current. Since the core is only 80-100 km
wide they suggest that the one-degree SST product used to force the atmospheric model is
inadequate to resolve the Agulhas Current and its mesoscale variability. Underestimated
turbulent fluxes and the lack of feedbacks would result in less eddy spin-down in the forced
simulations, consistent with the strong band of mesoscale variability found across much of the
South Atlantic.
To further quantify the causes of the improvement in CCSM4 we compared Agulhas eddy tracks
from satellite altimeter-derived sea surface height anomaly from AVISO for 2004-2008 and from
CCSM4 for model years 15-19 (Figs. 16a and b). We identified and tracked eddies using the
Okubo-Weiss parameter, W, which measures the relative importance of deformation and rotation
and is given by the sum of squares of the normal and shear components of strain minus the
square of the relative vorticity (Isern-Fontanet et al., 2003; 2006). Eddies were identified by
closed contours of W = -0.8 x 10 -12 s -2 . AVISO shows three repeating (41, 42%, and 17%)
northwesterly-directed pathways located progressively equatorward of each other by 2.5°of
latitude; they terminate progressively eastward of each other at 32°W and 24°S, 25°W and 26°S,
and 18°W and 26°S. CCSM4 shows two repeating tracks (78% and 13%) directed toward the
northwest about 4° apart; they terminate at 23°W, 25°S and 25°W, 21°S. Some latitudinal spread
is seen in the cluster of pathways constituting the primary pathway. Their signature disappeared
slightly to the east of the observed dissipation locations. For contrast we show tracks from the
coupled POP/CICE simulation forced with normal year CORE forcing. It has two eddy tracks
(90% and 10%) that extend all the way across the South Atlantic (Fig. 16c). The dominant
northwestward pathway has too much of a northward component; the cluster of tracks
comprising it have almost no latitudinal spread and they all terminate at about 14°S.
To support our assertion that the POP eddies are both generated with higher amplitudes and
maintain higher amplitudes during their passage across the Cape Basin than their observed or
CCSM4 counterparts we have calculated the evolution of the along-track amplitude of SSHA for
long-lived anticyclonic eddies from AVISO, CCSM4, and POP/CICE together with their
respective means (Fig. 17). The mean SSHA amplitudes at the formation locations from AVISO
and CCSM4 are about 40-50 cm while it is 100 cm from POP/CICE. These values decrease to
about 30 cm after 300 days both for AVISO and CCSM4 but only drop to 50 cm in the case of
POP/CICE. Clearly POP/CICE eddies are shed from the Agulhas Retroflection with higher
SSHA amplitudes than those derived from observations or in CCSM4. After 300 days their mean
amplitudes decrease to the values of the other cases in their formation regions.
Finally we examined the subsurface signature of a particular CCSM4 Agulhas eddy that
followed the black track in Fig. 16b by identifying it in time-evolving fields of 10°C isotherm
depth (not shown). The eddy was shed from the Agulhas retroflection at the beginning of year 15
and was last identified at the surface using the tracking procedure above in March of year 17 at
about 23°W, 25°S. The subsurface signature of the eddy can be identified as a coherent feature
for at least 8-9 months after it was no longer identifiable at the surface and is last seen at depth at
about 35°W, 23°S.
6. Discussion and Conclusions
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We examined the general fidelity of air-ocean interaction processes in a prototype two-decade
fully coupled fine resolution CCSM4 simulation. The simulation’s climate is very reasonable
considering that no tuning was done to correct for finer model resolution. As noted by Pope and
Stratton (2002), any changes in resolution require significant testing to properly adjust the
multiple constants in resolution dependent parameterizations. In other versions of CCSM, the
scale adjustments for parameterization constants (model tuning) required many simulations, each
lasting several decades, which was beyond the scope and resources of this investigational study.
This simulation was extremely computationally expensive and only one simulation could be
conducted, therefore we needed to minimize risk by choosing component setups with minimal
changes from those that had already been successfully conducted. Our choices for the coupled
model setup therefore were guided by our experience with a forced stand-alone 0.1° POP
simulation on this same grid (Maltrud et al. 2010), low resolution fully-coupled CCSM
simulations (Collins et al., 2006; Bala et al. 2008), and the Gent et al. (2009) CCSM simulation
that was configured with higher than standard-resolution atmosphere/land components (0.5°).
Given the resource constraints tests of increases in vertical resolution and other subgrid scale
parameterizations in both the atmosphere and ocean await investigation in future simulations.
The major high latitude biases in the simulation were dominated by problems in the atmospheric
model’s circulation, which were likely a major cause in the drift and worsening SST errors. The
intensified and contracted polar vortices point to problems with the scale sensitivity of the
parameterization constants associated with gravity and mountain wave sub-grid scale schemes,
exacerbated by the non-linearities associated with the polar filter required to keep the solutions
stable on the fine spacing on the spherical grid. Early test simulations of atmosphere-only runs
forced by climatological surface conditions showed some signature of the high-latitude
problems, but they were not as severe and the belief was that a fully coupled simulation would
bring the atmosphere into a more realistic state. In reality, the biases worsened as a result of the
coupling and introduced problems into the ocean.
Higher resolution does little to correct the large-scale biases in the radiation and precipitation
fields that most likely result from subgrid-scale parameterization weaknesses (Pope and Stratton,
2002). The anomalous high latitude atmospheric circulation was reflected in surface wind
stresses that were too strong in the subpolar NA, NP, and SO. As a result, cold SST biases and
excessive ice were created in the subpolar NH. These anomalously low SSTs caused a dearth of
hurricanes in the North Atlantic, while too high SSTs in the tropical eastern Pacific resulted in an
excessive number of tropical systems forming in the NP.
Encouragingly however the coupled simulation provided an important example of an improved
depiction of mesoscale processes important to climate relative to the forced ocean simulations.
The amplitude of SSHA in eddies being shed from the Agulhas Retroflection are unrealistically
high in the forced simulations; their amplitudes remain unrealistically high as they move across
the South Atlantic basin following a too northwesterly path. These higher amplitudes are
attributed to the coarseness of the NCEP atmospheric fluxes used to force the ocean model and
the SST product used as a bottom boundary condition for the NCEP atmospheric prediction
model. Exchanges and feedbacks between the high-resolution atmosphere and ocean are
implicated as the cause for the more realistic CCSM4 Agulhas eddy SSH displacements at the
time of their formation and their spin-down in the Cape Basin. Therefore it is likely that the
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resulting respective energetics of the Agulhas eddies in the coupled versus the stand-alone
models determine their differing pathways and surface dissipation locations. Unrealistically high
mean SSH was seen in the western boundary currents and recirculation regions, including the
Agulhas System, of a forced stand-alone high-resolution ocean simulation. On the other hand,
CCSM4 in these regions was in much better agreement with the observed dynamic topography
estimate from the combined altimetry, GRACE geoid, and surface drifter data. Again the low
resolution atmospheric fluxes forcing the stand-alone model are likely to be a contributor to these
biases. Hence demonstrable improvements in the depiction of both the mean and mesoscale
variability of the upper ocean circulation were found in the fully coupled fine resolution
simulation relative to the stand-alone ocean simulations.
In all, this prototype simulation demonstrates the computational feasibility and model readiness
to execute coupled high resolution simulations and highlights the future potential of very high
resolution, long coupled climate runs to examine such features as decadal variability or the
impact of ocean mixing by tropical storms on ocean heat transport. More importantly, the
simulation shows the potential for the spontaneous simulation of important coupled mesoscale
features that were previously impractical in a global coupled model, particularly the generation
of intense tropical cyclones under the correct environmental conditions.
Toward the goal of improving the performance of the ocean model in the coupled system, we are
conducting stand-alone ocean or coupled ocean/ice simulations configured as consistently as
possible with the coupled model set-up. In this way, we can shed light on ocean biases without
the complexity of an active atmosphere. These results can then help us understand the ocean’s
behavior in the fully coupled system. Hence we are running stand-alone 0.1° POP with the model
ocean surface velocities incorporated into the drag law calculation as is done in CCSM4. We
wish to know if the eddy field will be weakened as found by Hutchinson et al. (2010). The
current wind stress forcing formulation will also likely provide too much momentum to the
western boundary currents hence we hypothesize that the biases seen in the mean SSH of POP
(Figs. 8 and 9) may be reduced once ocean surface currents are included in the stress calculation.
Global coupled 0.1° POP/CICE is also being run and evaluated to improve the fidelity of the
coupled ice/ocean system, especially the ice cover in the Southern Hemisphere in austral
summer. Finally, the effects of using the baroclinic restratification parameterization developed
by Fox-Kemper et al. (2008) and Fox-Kemper and Ferrari (2008) will be tested in future
simulations. It is based on the concept that ageostrophic baroclinic instabilities form in the ocean
surface mixed layer at horizontal fronts and act to re-stratify the ocean. This submesoscale
parameterization has already been evaluated in stand-alone 0.1º POP (Fox-Kemper et al., 2011),
and in the coupled context should help to re-stratify the upper ocean following the passage of
typhoons.
The promising results of the present investigation have lead to a multi-institutional effort to
develop a CMIP (Coupled Model Intercomparison Project)-capable high resolution version of
CCSM, involving multiple dynamical cores and systematic evaluation of the scale dependence of
sub-grid scale schemes. We hope to perform accurate hindcasts of twentieth century climate, as
well as projection of twenty-first century climate, which will explicitly simulate climate change
at small scales, including climatologies of extreme events. These simulations will provide the
means to study mesoscale air-sea coupling over long time scales and understand its importance
17

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to climate change. A recent study by Bryan et al. (2010) is a forerunner of such analyses. They
found by comparing this CCSM simulation with others having lower resolution components that
it is only when the ocean model largely resolves eddies that the characteristics of observed
frontal scale ocean –atmosphere interaction are captured.
Acknowledgements
This work was supported by the Office of Science (BER), U.S. Department of Energy at the
Lawrence Livermore, Oak Ridge, Argonne, and Los Alamos National Laboratories under
contracts DE-AC52-07NA27344 (AAM, DCB, DPI, JSB, JLM), DE-AC05-00OR22725 (PHW,
DCB), DE-AC02-06CH11357 (RLJ), DE-AC52-06NA25396 (PWJ, MEM, and as an SIO
subcontract: JLM and YYK), respectively and by grants DE-FG02-05ER64119 (JLM, YYK),
and DE-PS02-07ER0706 (JMD). NASA contract 1273575 (via U. Maine) also supported JLM
and YYK. Participation of FOB, JMD, MV, NN, and AN were supported by the National
Science Foundation through its sponsorship of NCAR. Computer time was provided by the
Department of Energy as part of the Multiprogramatic and Institutional Initiative at Lawrence
Livermore National Laboratory. Altimeter products are from Ssalto/Duacs, Aviso and Cnes,
ERA-40 reanalysis came from ECMWF and NCAR, and QuikSCAT winds are from the
Scatterometer Climatology of Ocean Wind (SCOW) data set found at
http://cioss.coas.oregonstate.edu/scow/. We thank both reviewers and the guest editor of this
special edition for their helpful comments and suggestions.
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Figure Captions
Fig. 1. Annual average precipitation rate (mm/day) from Xie-Arkin observations (1997) (top)
and CCSM4 (bottom) for model years 13-19.
Fig. 2. (a) Climatology of daily 0.25° Reynolds et al. (2007) satellite-derived SST (°C) data
averaged for 1982-2008. (b) Difference field (°C) between the mean potential temperature for
model years 13-19 from the upper-most model level (5m) and the observed SST climatology.
Contour line is 0°C.
Fig. 3. CCSM4 ocean potential temperature (left) and salinity (right) change from model year 19
– model year 1. Red line depicts high latitude regions (50º-90º), green lines are mid-latitudes
(20º-50º), blue line is the tropics (20°S-20°N), and black lines are global. Thick and thin lines
represent the Northern Hemisphere and Southern Hemispheres, respectively.
Fig. 4. (a) Annual average of geopotential height at 500 mb from ERA40 and the (b) difference
(m) of the model annual average geopotential height at 500 mb (m) and the ERA40 field. (c)
23

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1038
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Annual average of sea level pressure (mb) from ERA40 and the (d) difference (mb) of the model
annual average sea level pressure and the ERA40 field.
Fig. 5. (a) Annual average wind stress vectors (Nm -2 ) from QuikSCAT superimposed on their
vector magnitudes (color contours). (b) Wind stress difference vectors (Nm -2 ) between annuallyaveraged
CCSM4 and QuikSCAT superimposed on the magnitudes of their vector differences
(color contours). Model wind stresses are averaged for model years 13-19.
Fig. 6. Time series of (a) Arctic and (b) Antarctic total ice area (km 2 ) from CCSM4 for years 0-
19 (red) and from SSM/I (blue) for 1980-1999. The SSM/I data are for the period 1980-1999.
Fig. 7. North Atlantic mean (a) velocity (ms -1 ) and temperature (°C) from the uppermost level
(5m) of the ocean component of CCSM4 and (b) wind stress (Nm -2 ) and 5m temperature (°C) for
years 13-19. Thin black contour line is 7°C.
Fig. 8. (a) Mean dynamic ocean surface topography (cm) from Maximenko et al. (2009). Mean
sea surface height (cm) from (b) CCSM4 for model years 13-19 and (c) a stand-alone tripole 0.1°
POP simulation for model years 95-99. The same grid is used in the two simulations.
Fig. 9. Difference fields (cm) between (a) CCSM4 mean sea surface height (model years 13-19)
and mean dynamic ocean surface topography (MDOT), and between (b) stand-alone tripole 0.1°
POP sea surface height (model years 95-99) and MDOT.
Fig. 10: Mean meridional overturning streamfunction (Sv) for model years 15-19 for the (a)
global ocean and (b) the Atlantic Ocean. Negative contours are dashed and denote a counterclockwise
sense of flow.
Fig. 11. Mixed layer depth (m) climatologies for December-January-February from de Boyer
Montégut et al. (2004) observations, (b) CCSM4 (model years 13-19), and (c) POP (model years
95-99). Differences (m): (a) CCSM4 – data, (b) POP – data, and (c) CCSM4 – POP. MLD
criterion is based on a ΔT=0.2°C from the near-surface temperature value at 10 m.
Fig. 12. Mixed layer depth (m) climatologies for June-July-August from de Boyer Montégut et
al. (2004) observations, (b) CCSM4 (model years 13-19), and (c) POP (model years 95-99).
Differences (m): (a) CCSM4 – data, (b) POP – data, and (c) CCSM4 – POP. MLD criterion is
based on a ΔT=0.2°C from the near-surface temperature value at 10 m.
Fig. 13: (a) Zonally-integrated global ocean total meridional heat transport (PW) from CCSM4
(model years 15-19), POP (model years 95-99) and Trenberth and Caron (2000) atmospheric
reanalysis estimates. (b) Zonally-integrated global ocean total, mean, and eddy meridional heat
transports from CCSM4 and POP.
Fig. 14. (a) Track of a Category 4 tropical cyclone event in July-August of model year 19
superimposed on the SST (°C) cold water "wake” under and to the right of the track of the center
of the storm in the tropical northwest Pacific. (b) Vertical profiles of temperature (°C) at station
9 (133°E, 26°N) that correspond to pre-storm conditions (07/29/19), a day after the passage of
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the storm’s center (08/04/19), and the water column once the near-inertial oscillations have
largely abated (08/18/19).
Fig. 15. Mesoscale RMS SSHA (cm) from (a) the ocean component of CCSM4 for years 15-19
and (b) the AVISO-blended (TOPEX/POSEIDON and ERS 1 and 2) altimetry for 1997-2001.
Fig. 16. Agulhas eddy pathways from (a) AVISO altimetry, (b) CCSM4, and the (c) coupled
POP/CICE simulation. Numbers are percentage of times that eddies follow the same track.
Fig. 17. Evolution of along-track amplitude of SSHA (cm) as a function of time since eddy
formation for AVISO (upper), CCSM4 (middle), and POP/CICE (lower).
25

1084
Channel/Strait/Opening
CCSM4 Observed
(Sv)
(Sv)
1 Sv = 10 6 m 3 s -1
References for
observational
estimates
Barents Opening 3.1 northward 1.5±0.1
northward
Ingvaldsen et al.
(2004)
Bering Strait 1.4 northward 0.8±0.16
northward
Woodgate and
Aagaard (2005)
Canadian Archipelago 1.2 southward 0.7-2.0
southward
Sadler (1976),
Fissel et al. (1998)
Melling (2000)
Denmark Strait
6.5 net
southward
0.8 net
northward
3 net southward
Østerhus et al.
(2005)
Olsen el al. (2008)
Drake Passage 168.9 eastward 136.7±7.8
eastward
Cunningham et al.
(2003)
English Channel 0.17 northward 0.1-0.2 Otto et al. (1990)
northward
Pacific Equatorial
30.7 eastward 26 eastwards Kessler et al. (2003)
Undercurrent (140°W)
Faroe-Scotland Channel 5.5 net
northward
3.8 net
northwards
2.1 net
southward
Østerhus et al.
(2005)
Olsen el al. (2008)
Florida-Bahamas Strait 20.5 northward 32.3±3.2 Larsen (1992)
Fram Strait
3.1 net
southward
4±2 & 2±2 net
southward
Schauer et al.
(2004)
Iceland-Faroe Channel 1.0 net
northward
3.8 net
northward
1 net southward
Østerhus et al.
(2005)
Olsen el al. (2008)
Indonesian Throughflow 13.8 westward 15.0 westward Sprintall et al.
(2009)
Mozambique Channel 18.5 southward 16.7 southward Ridderinkhof et al.
(2010)
Taiwan-
Luzon Straits:
2.2 northwards
3.4 westwards
1.6 southward
6±3 westward
3.0 westward
Wang et al. (2004)
Tian et al. (2006)
Qu (2000)
Windward Passage 0.17 southward 3.6 southward Smith et al. (2007)
1085
1086
Table 1: Depth-integrated mass transports (Sv) through selected channels, straits, and openings
from CCSM4 and observations.
26

Fig. 9: Difference fields (cm) between (a) CCSM4 mean sea surface height (model years 13-
19) and mean dynamic ocean surface topography (MDOT), and between (b) stand-alone
tripole 0.1° POP sea surface height (model years 95-99) and MDOT.
9