The Low Resolution CCSM3

Stephen G. Yeager ∗, Christine A. Shields,

William G. Large, James J. Hack

National Center for Atmospheric Research

Boulder, Colorado

For the Journal of Climate CCSM Special Issue

August 16, 2005

∗Corresponding author: Stephen G. Yeager, National Center for Atmospheric Research, P.O.

Box 3000, Boulder, CO 80307. (e-mail: yeager@ucar.edu)

Abstract

The low resolution fully coupled configuration of the Community Climate

System Model version 3.0 (CCSM3) is described and evaluated. In this most

economical configuration, an ocean at nominal 3◦ resolution is coupled to

an atmosphere model at T31 resolution. There are climate biases associated

with the relatively coarse grids, yet the coupled solution remains comparable

to higher resolution CCSM3 results. There are marked improvements in the

new solution compared to the low resolution configuration of CCSM2. In

particular, the CCSM3 simulation maintains a robust meridional overturning

circulation in the ocean, and it generates more realistic Nino variability. The

improved ocean solution was achieved with no increase in computational cost

by redistributing deep ocean and midlatitude resolution into the upperocean

and the key water formation regions of the North Atlantic, respectively. Given

its significantly lower resource demands compared to higher resolutions, this

configuration shows promise for studies of paleoclimate and other applications

requiring long, equilibrated solutions.

2

1 Introduction

Climate modelling inevitably requires a compromise between greater model sophis-

tication and realism, on the one hand, and faster, more efficient throughput, on

the other. For applications where the trade-off must necessarily emphasize the lat-

ter, it is essential to develop and evaluate low resolution model versions. The low

resolution version based on CSM1 (Boville and Gent 1998) was developed as an

extension primarily for paleo-climate applications and so was referred to as Pale-

oCSM. It included improved ocean physics and its features included a more realistic

El Nino Southern Oscillation (ENSO) variability (Otto-Bliesner and Brady 2001)

and a robust meridional overturning circulation (Otto-Bliesner et al. 2002).

More recently, version 2 of the Community Climate System Model (CCSM2) was

released (Kiehl and Gent 2004). The atmospheric component was the Community

Atmosphere Model version 2.0 (CAM2.0), and the Parallel Ocean Program (POP)

replaced the NCAR CSM Ocean Model (NCOM). The standard ocean model reso-

lution was nominally 1 degree in the horizontal with 40 vertical levels. The attempt

to replace PaleoCSM with a low resolution version of CCSM2 was not successful.

This deficient model is referred to as 2T31x3 to relect its CCSM2 base and its hor-

izontal resolution; T31 spectral truncation (3.75◦ by 3.75◦ transform grid) for the

atmosphere and land, and nominally 3 degrees in the ocean and sea ice components

with 25 vertical levels in the ocean. It was found to be unsatisfactory in several

respects. In particular, the meridional overturning circulation of the North Atlantic

Ocean spins down in present day scenarios (T. Stocker, personal communication

2003), rendering the model unsuitable for studies of thermohaline collapse in past

and future scenarios.

An overview of the latest coupled model (CCSM3) is provided by Collins et al.

(2005a). The ocean component (Danabasoglu et al. 2005) has two possible resolu-

3

tions; nominal 1◦ horizontal with 40 vertical levels and nominal 3◦ horizontal with 25

levels, which has roughly 17 times fewer grid points. Uncoupled, these POP configu-

rations will be referred to as x1ocn and x3ocn, respectively. The sea-ice component,

which has the same horizontal resolution as the ocean, is version 1 of the Community

Sea-Ice Model (CSIM), as described by Briegleb et al. (2004). The CAM3 imple-

mentations and performance are described in Collins et al. (2005b). The standard

atmospheric configuration has been T42 (2.8◦ by 2.8◦ transform grid) for more than

a decade (Hack et al. (1994); Kiehl et al. (1998)), and the current uncoupled ver-

sion will be referred to as T42cam, while T85cam and T31cam refer to higher and

lower uncoupled resolutions, respectively. The above models are combined in three

standard CCSM3 coupled versions: T85x1 for the highest, T31x3 for the lowest and

T42x1 for an intermediate. Comparisons of T85x1 and T42x1 physics and solutions

can be found in Hack et al. (2005a) and Large and Danabasoglu (2005).

The purpose of this paper is to describe the novel aspects of the T31x3 con-

figuration, contrast its coupled behavior in present day conditions with the highly-

constrained forced solutions of T31cam and x3ocn, evaluate its performance relative

mainly to T42x1 but also to T85x1, and present the relative computational costs,

so that informed decisions can be made regarding the utility of this low resolution

version of CCSM3. In other words, do the benefits of drastically reduced wall clock

time and CPU cost outweigh the disadvantages associated with some degradation

in the climate simulation? At a minimum, the T31x3 configuration must satisfy

the demand for an inexpensive, yet not unrealistic, climate system model suitable

for routine multi-century or even longer integrations required for some paleoclimate

and biogeochemistry work. All data from these simulations are freely available, and

more extensive evaluation beyond what is presented here is encouraged.

It is essential that both high and low resolution model evolution follow the same

development path so that major new model improvements can span all resolutions.

4

However, the CCSM2 experience demonstrated that in order to qualify as a viable

low resolution climate model, a very different ocean model configuration would be

needed. This development is detailed in Section 2, together with a description of the

resolution-specific modifications made to the atmosphere and sea-ice components.

The coupled spinup of T31x3 over nearly 900 years is presented in Section 3 and

compared to higher resolution spinups. Section 4 describes how the uncoupled low

resolution atmospheric simulation differs from T42cam and to what extent these

differences transfer to the coupled solutions. The quality of the ocean and ice simu-

lations is addressed in sections 5 and 6, respectively. The interannual variability of

the T31x3 coupled control, in particular the ENSO simulation, is examined in sec-

tion 7. The final section compares the computational requirements and efficiencies

of the various CCSM3 configurations.

5

2 Development of low resolution CCSM3

The strategy adopted for developing a low resolution version of CCSM3 is based

on the primary uses of CSM1 and the experience with CCSM2. It involves recon-

figuring the ocean grid, modifying the ocean physics and retuning atmosphere and

sea-ice parameters. The first priority is to maintain a robust meridional overturning

circulation (MOC) in the North Atlantic. Estimates from observations place the

strength of this overturning at about 18 Sv with an error range of 3 − 5 Sv (Talley

et al. 2003). The corresponding values from T85x1 and T42x1 are about 22 Sv and

19 Sv, respectively (Bryan et al. 2005). The target minimum for T31x3 is 14 Sv, so

that it would not be weaker than observed by more than T85x1 is too strong.

The second priority is to have an equatorial circulation and ENSO variability

that is comparable to that produced at higher resolution. The mean zonal velocity

in the Equatorial Undercurrent (EUC) should approach the observed, > 100 m/s

(Johnson et al. 2002), with 80 − 120 cm/s the target range. Equatorial Pacific

variability is affected by biases in the ocean mean state, including the simulation of

the EUC core and equatorial thermocline. ENSO is not particularly well simulated

in T85x1 (Deser et al. 2005), so the hope for T31x3 in this regard is only that its

associated interannual SST variance not be significantly worse than at either of the

two higher resolutions.

The overall experience with T31 truncation grids in the atmosphere has been

positive since the simulation quality is comparable in many ways to T42 but at less

than half the computational cost. Nevertheless, there are a number of systematic

biases that are intrinsically associated with the lower resolution grid. The major

challenge in configuring a T31 atmosphere for CCSM3 is to maintain the quality of

the top-of-atmosphere (TOA) radiation budget, which is strongly modulated by the

simulated cloud field.

6

Finally, the distribution and thickness of sea-ice in both hemispheres is consid-

ered. Climate sensitivity depends on the thickness, which observations in the central

Arctic place in the 2 to 3m range. Sea-ice coverage depends on many factors includ-

ing surface winds and ocean currents and heat transport. The tendency for ice area

to be too extensive, especially at low resolution, needs to be minimized.

2.1 The T31 Atmosphere

A relatively strong sensitivity of the simulated cloud field to changes in horizon-

tal resolution has long been a feature atmospheric models such as CAM (e.g., see

Williamson et al. (1995); Hack et al. (2005a)), despite a significant evolution in the

parameterization of cloud processes. Maintaining good agreement with satellite es-

timates of Earth’s radiation budget is especially important to coupled applications

of the model, as shown by Gleckler et al. (1995) and Hack (1998). Changes to the

cloud field associated with the T42 to T31 grid change produce a >2 W/m2 bias in

the top of the atmosphere (TOA) global annual mean energy balance. There is a

1.5 W/m2 reduction in outgoing longwave radiation, and 0.7 W/m2 increase in ab-

sorbed solar radiation. Biases in the meridional distribution of cloud forcing show

increases in extratropical longwave cloud forcing and slightly reduced equatorial

cloud forcing. The shortwave cloud forcing is slightly enhanced in the extratropics,

and significantly reduced in the deep tropics. These changes are also reflected in

systematic biases to the surface energy budget.

To counter some of the biases associated with the T31 grid, the formulation of

the cloud process parameterization scheme was adjusted to include a collection of

small changes to autoconversion and relative humidity thresholds, along with small

changes to rainwater evaporation efficiencies to bring the TOA energy budget back

into balance. The changes to the cloud scheme also bring the meridional distribution

of the net TOA energy budget into better agreement with observations and the

7

higher resolution configuration of the model. Significant systematic biases in the

components of the energy budget remain, as will be discussed in Section 4, along

with other well known sensitivities related to resolution.

2.2 The x3ocn Ocean and Sea-Ice

Unlike 2T31x3, the North Atlantic Meridional Overturning Circulation (NAMOC)

did not collapse in a non-standard CCSM2 coupling of a T31 CAM2.0 atmosphere

and one degree ocean (2T31x1). This result suggested that a stronger overturning

could be achieved by making the x3ocn grid more like the x1ocn in the deep water

formation regions of the North Atlantic including the Denmark Strait overflow be-

tween Iceland and Greenland. The deep water mass formation is confined to small

areas of the Labrador and GIN (Greenland, Iceland, Norwegian) Seas. In going

from low resolution CCSM2 to low resolution CCSM3, the horizontal resolution is

enhanced in these regions in two ways. First, we take advantage of the converging

meridians at the northern pole of the model grid. For numerical reasons, this pole

is not at the geographic pole, but displaced in CCSM2 to 80N, 40W in Greenland.

Further displacement to 75N, 40W in CCSM3 places more meridians in all the ocean

areas surrounding Iceland and the southern half of Greenland (Fig. 1), including the

Labrador Sea and Denmark Strait. Second, the zonal grid lines are redistributed to

become more dense in these ocean areas, less dense at more southern latitudes, and

removed from the Greenland land mass (Fig. 1). The grid cell density increases by

a factor of about 2 in the Labrador Sea and by a factor of 4 in the Denmark Strait.

A number of different grids were explored, and in the end, it was not necessary to

increase the total number of horizontal grid lines from 100 displaced-pole meridians

and 116 zonal grid lines.

Another benefit of the new x3ocn grid is that the grid cell aspect ratio of merid-

ional length to zonal length is closer to its ideal value of 1 over more of the ocean

8

than in CCSM2, particularly in the midlatitude Pacific and throughout the South-

ern Ocean. A downside is that grid density at midlatitudes, near where boundary

currents seperate, is sacrificed in order to augment grid density at higher latitudes.

However, there is little impact on the western boundary current (WBC) simulation,

which is already poor at low resolution. WBC differences are much larger between

resolutions, because the lateral viscosity coefficients are more than an order of mag-

nitude higher near some western boundaries in the x3ocn compared to the x1ocn,

and tracer mixing coefficients are one-third larger. Finally, an additional benefit of

the increased resolution over the Canadian Archipelago is that it is possible to open

a relatively realistic Northwest Passage between Baffin Bay and the Beaufort Sea

(Fig. 1).

Of course this x3ocn grid may not be suitable for either past or future epochs

when the distribution of continents and/or convection sites is different. Simulation

of worlds in the distant past, such as the Permian (Kiehl and Shields 2005), is

now possible in CCSM3 since the ocean grid can be reconfigured for any topography

compatible with a dipole mesh grid, with both poles over land. For such experiments,

it is possible to configure a preliminary grid to diagnose convection sites, then assess

whether there is a more optimal grid that would increase the resolution at these

locations. Experience with the present-day low resolution simulations suggests that

this would be advantageous.

The choice of vertical grid in an ocean model also requires a compromise between

computational cost and physical realism. While the high resolution ocean models

have 45 vertical levels in CSM1 and 40 levels in CCSM2 and CCSM3, all the low

resolution models are limited to 25. The vertical grid spacing, ∆Z, as a function

of depth is strongly constrained by the number of levels as shown in Fig. 2. The

CCSM2 x3ocn vertical grid spacing was identical to that of Paleo-CSM and greater

than the x1ocn at almost all depths. Since the overturning circulation in 2T31x1

9

was satisfactory and given the importance of upper ocean processes in driving the

MOC and equatorial ocean, the vertical grid for the CCSM3 x3ocn was constructed

to more closely resemble the x1ocn near the surface (Fig. 2, inset). Adding more

vertical levels was found to improve the North Atlantic MOC, but this could also be

achieved without adding to the computational cost by simply redistributing the 25

grid levels in x3ocn so that enhanced upper ocean resolution was balanced by much

larger vertical grid spacing in the deep ocean. The upper layer is now only 8m thick,

as opposed to 10m in x1ocn and 12m in previous versions of x3ocn. No significant

detriments have yet to be ascribed to the increased vertical spacing below 300m.

In all CCSM configurations, the atmosphere is coupled to the land and sea ice

every hour to resolve large diurnal changes in solar radiation and surface tempera-

ture. Since there are much smaller variations in SST, the ocean and atmosphere are

coupled only once per day. The first low resolution coupled integrations of CCSM3

had identical physics to T42x1, including an idealized diurnal cycle of solar heating

of the ocean (Danabasoglu et al. 2005), but produced a continually worsening upper

ocean solution in the western equatorial Pacific and rather anemic ENSO variabil-

ity in the east, despite generally good SST fields. The positive feedback cycle and

specific ocean model response in the west are discussed in Large and Danabasoglu

(2005). These problems were greatly ameliorated by removing the diurnal cycle from

T31x3. Apparently, cold-biased SSTs are required to compensate for a propensity

of the coupled T31x3 model to rain too much in this region, as discussed in Section

4. The ocean sensitivity to the diurnal solar cycle is examined in T85x1 by Danaba-

soglu et al. (2005), who show that the idealized diunal solar cycle improves several

aspects of the ocean model solution. Although these benefits are lost in T31x3,

the equatorial simulation becomes stable. The option of including the diurnal cy-

cle should be exercised in applications where equatorial coupled feedbacks are not

catastrophic, as would be the case if atmospheric rainfall, evaporation and ocean

10

freshwater transport were found to balance.

A fortuitous consequence of removing the diurnal solar cycle from T31x3 is that

doing so tends to increase the SST variance associated with ENSO-like variability

in the central and eastern Pacific. The effect is much less than in T85x1 (Danaba-

soglu et al. 2005), but further improvement was achieved by implementing a simple

center-differenced advection scheme instead of the upwinding used in the x1ocn

(Danabasoglu et al. 2005). The downside of this physics change was the genera-

tion of much larger numerically induced extrema in both temperature and salinity.

These overshoots were acceptably small everywhere except in the North Sea, where

the development of negative salinities was linked to the routing of excess net fresh-

water flux from the Baltic Sea so as to prevent the growth of salinity anomalies in a

marginal sea not connected to the active ocean. The associated numerical problems

in T31x3 are avoided by redistributing this freshwater flux farther north over the

Norwegian Sea where there is more open communication with the global ocean.

The final issue to be resolved to make T31x3 a viable tool for climate research

was the tendency for sea-ice to become too thick in the central Arctic and too

extensive, especially in the Northern Hemisphere. One likely cause of the problem

is that the ocean heat transported from the North Atlantic to the Arctic is either

too small, or too deep to melt sufficient ice. Since attempts to address such model

deficiencies have not been successful, a simple fix was to lower the snow and ice

albedos below observed values. These albedos are characterized by the cold and

warm ice albedos and the cold and warm snow albedos. The respective values are

0.49, 0.42, 0.77 and 0.65 in T31x3, down from 0.53, 0.46, 0.82 and 0.70 in T42x1 and

T85x1. Other than differences in horizontal grid and the albedo changes, the T31x3

sea-ice implementation is identical to that used in the standard control integration

outlined by Holland et al. (2005).

11

3 The T31x3 Spin-Up

We now examine the first 880 years of the T31x3 integration under the present

day (1990) atmospheric conditions given by Otto-Bliesner et al. (2005). The ocean

component was initialized with World Ocean Atlas 1998 climatology (Levitus et al.

1998), merged with PHC Arctic data (Steele et al. 2001), hereafter WOA/P. The

model physics remained constant for the final 850 years of the run, following a change

from upwinded to centered-differenced ocean advection at year 30. At year 133, the

freshwater imbalance from the Baltic Sea was redistributed (Section 2.2). This was

accompanied by a one-time, nonphysical correction of North Sea salinities back to

the WOA/P values.

Figure 3 shows the globally averaged time series of surface temperature for both

the T31x3 and T42x1 simulations. By this measure, T31x3 produces a remarkably

stable climate. It becomes colder than the observed NCEP climatology by about

0.5◦C and does not exhibit the cooling trend seen in T42x1, which by year 800 is

about 0.2◦C warmer than observed. The surface temperature trend in T42x1 is

dominated by the southern hemisphere extratropics and is associated with a linear

trend of increasing SH sea ice. In contrast, sea ice in T31x3 is stable in both the

northern and southern hemisphere, although the ice volume and area significantly

exceed observational estimates (Section 6).

In the ocean, neither T31x3 nor T42x1 reaches equilibrium by year 880, because

of the long deep ocean time scales. However, the drift in global mean ocean temper-

ature of T31x3 is small and nearly linear at approximately 0.01◦C/century (Fig. 4b).

Corresponding rates for the T42x1 and T85x1 controls have the opposite sign and

are -0.04◦C/century and -0.05◦C/century, respectively. The T31x3 trend reflects a

positive bias in total surface heat flux into the ocean of about 0.05 W/m2 (Fig. 4a),

compared to -0.2 W/m2 for both higher resolution models. At the same time about

12

0.05 W/m2 passes in the bottom and out the top of the atmosphere, which is roughly

a quarter as much as in the higher resolutions. By year 800, the global mean ocean

temperature is only about 0.04◦C warmer than the WOA/P climatology. Time se-

ries comparisons to WOA/P of zonally averaged ocean temperature as a function of

depth (not shown) indicate that much of the trend in Fig. 4b is due to a warming

in the Pacific between 400 and 2500m. In contrast, the drift in the T42x1 ocean is

primarily due to Pacific cooling everywhere below about 1000m.

The freshening trend in global average ocean salinity (Fig. 4c) is small (−4×10−4

psu/century), but not as small as either T42x1 (−2 × 10−4 psu/century) or T85x1

(−.5 × 10−4 psu/century). The North Sea salinity adjustment is evident at year

133. Around year 800, the global mean salinity is only 0.003 psu fresher than the

WOA/P climatology but continues to exhibit a linear trend. This trend is mostly

due to freshening above 500m in the Pacific, Indian, and Southern Ocean regions.

Relatively short-lived transients associated with the spin-up have largely disap-

peared by year 200. Time series plots show that model startup from a state of

rest triggers large amplitude fluctuations in almost all global ocean measures. For

example, Drake Passage transport (Fig. 4d) drops by more than 40Sv during the

first 100 years. After year 500 it becomes relatively steady at 110 - 120Sv. Despite

a slow recovery, this transport is still below the observed range estimated by Whit-

worth (1983) and corrected by Whitworth and Peterson (1985). Figure 4e shows

that the the highest priority requirement for T31x3 is achieved. The strength of the

North Atlantic MOC, as given by the maximum Atlantic overturning below 500m

and north of 28◦N (Fig. 4e, thick line), maintains a steady value of around 16 Sv

after initial fluctuations. The range estimated by Talley et al. (2003) is shown for

comparison. These large amplitude early transients underscore the importance of

multi-century climate simulations which permit analysis of a climate system which

has reached a quasi-equilibrated state.

13

4 The Atmospheric Simulation

In most respects, the T31 uncoupled atmosphere (T31cam) bears a close resemblance

to the T42cam simulation, and in general there is a similar correspondence between

the atmospheric solutions in coupled T31x3 and T42x1. However, prior experience

developing low resolution configurations of the atmospheric model has revealed a few

strong resolution-dependent model biases that can have important ramifications in

the fully coupled system. The specific T42x1 to T31x3 differences discussed below

are concerned with the precipitation, especially in the tropics, the low level dynamics

(winds), radiation and surface air temperature. In most cases, uncoupled resolution

sensitivity is similar, and so differences between T31cam and T42cam are useful in

understanding differences in the coupled solutions.

However, an important example of different coupled and uncoupled resolution

sensitivity is seen in the annual average precipitation along the equatorial west

Pacific. The region between 150◦E and the dateline is characterized by a strong west

to east decrease, which the Large and Yeager (2004) balanced climatology gives as 7.7

→ 3.9 mm/day. This rainfall is similar in T42cam (7.8 → 3.5 mm/day), but lower in

T31cam (6.2 → 3.0 mm/day). This reduced T31cam equatorial Pacific precipitation

gradient is seen in Fig. 5a. The increase due to coupling in T42x1 is only about

10% at 150◦ E and even less farther east. In contrast, precipitation in T31cam

coupled to a x3ocn with a diurnal cycle nearly doubles throughout the region (11.5

→ 6.0 mm/day). The serious consequences of this excessive precipitation are noted

in Section 2.2, and prompted the removal of the diurnal cycle from the T31x3 ocean

model. A clean comparison finds that this change alone reduces the precipitation

by about 1.7 mm/day in the west and by 3 mm/day at the dateline, so that the

gradient in T31x3 (Fig. 5b) becomes a more feasible 10.0 → 4.2 mm/day. Even

so, this precipitation remains larger at low resolution when coupled, as opposed to

14

smaller when uncoupled.

The global distributions of annual average precipitation for T31x3 and T42x1

shown in Fig. 5, and for T85cam, T42cam, T85x1 and T42x1 in Hack et al. (2005b)

share many of the same large-scale characteristics. Resolution sensitivity is relatively

small compared to the large systematic precipitation differences from observations

in both coupled (Large and Danabasoglu 2005) and uncoupled (Hack et al. 2005a)

simulations, so comparisons with observations are not repeated in Fig. 5. The most

significant bias is the zonal band of excess rainfall in the tropical South Pacific.

This pattern is symptomatic of the ”double” ITCZ problem, which is enhanced by

coupling. Only the coupled simulations produce the overly extensive rainfall over

the tropical Atlantic, because the source of the problem is the warm SST biases that

develop off south-west Africa in all coupled configurations (Large and Danabasoglu

2005). Both coupled and uncoupled models produce excessive precipitation over the

African continent, and too little in the South Atlantic off the coast of Brazil, but

Fig. 5 shows that there is little change in the coupled biases with lower resolution.

The excessive meridional shift in tropical precipitation between DJF and JJA

which occurs in the high resolution CCSM3 (Hack et al. 2005b) is seen also in T42x1

and T31x3. Zonal mean precipitation curves for both lower-resolution CCSM3 con-

trols are nearly identical for boreal winter and summer (not shown), with slightly

lower peak precipitation rates than in T85x1. Anemic interannual precipitation

variability between 10S and 10N in the equatorial Pacific in the T31x3 is also quite

similar to that seen in T85x1 (Hack et al. 2005b), a result which is related to defi-

cient ENSO variability in each of the CCSM3 integrations. To first order, the T31x3

exhibits the same mean, seasonal, and interannual precipitation biases as the higher

resolution versions, and is not noticeably worse in terms of simulated hydrological

cycle than the more expensive CCSM3 resolutions.

The low-level dynamical circulation in T31cam exhibits large-scale anomalies

15

that have a mostly zonal character, so zonal averages of ocean wind stress compo-

nents are used to display their coupled manifestation in Fig. 6. In general, model

winds are too strong at all resolutions, as shown by the comparison of zonally-

averaged wind stress magnitude (Fig. 6c). This is especially true at storm track

latitudes in both hemispheres and in the Northern Hemisphere trade wind zone, but

there is slightly anemic wind stress over the Arctic in CCSM3 due to weaker than

observed meridional stress. The observed mean stress is computed from coupling

2000-2004 6-hourly blended QuickSCAT scatterometer winds (Milliff et al. 2004) to

monthly observed SST. In both uncoupled and coupled atmospheric models there is

an unrealistic migration of the Southern Hemisphere storm track toward the equa-

tor as resolution is lowered. However, the weaker T31x3 zonal stress and greater

displacement conspire to give better agreement with observations at some latitudes,

particularly ∼55S where T31x3 wind stress magnitude coincides with the peak in

observed Southern Ocean westerlies. Similarly, T42x1 is an improvement over T85x1

at some latitudes. An unrealistic weakening of westward wind stress in the equato-

rial Pacific of T31cam relative to T42cam is not a strong bias when the atmospheres

are coupled, and the storm tracks are the only latitudes where significant change

with resolution is evident in the coupled solutions. The excessive convergence of

meridional wind associated with the ”double ITCZ” in the Pacific is present for all

resolutions in Figure 6b. The effects of these dynamically related resolution sensi-

tivities on the ocean and sea-ice of the coupled system are discussed in sections 5

and 6, respectively.

The T31cam simulation also exhibits important large-scale differences from T42cam

in the radiation budget that are associated with the behavior of parameterized cloud

processes. These biases are seen both at the TOA and at the surface and are strongly

correlated with similar anomalous structures in the precipitation (eg. Fig. 5) and

precipitable water fields. They are especially apparent in the Indian Ocean extend-

16

ing into the tropical western Pacific, and along the South Pacific Convergence Zone.

Spatially coherent signals exceeding 10 W/m2 are seen at the TOA in both the

longwave and shortwave radiation budgets. The corresponding surface signals are

evident in the net surface heat flux difference of Figure 7, which is dominated by

changes in the radiative components. The contributions from the longwave com-

ponent appear to be associated with biases in clear-sky radiative transfer which

are largely explained by a systematic drying of the atmosphere in regions of deep

convection. The net absorbed solar radiation in these regions is also significantly in-

creased, with large regions exhibiting increases of 20 W/m2 or greater. Wittenberg

et al. (2005) show that the range of available estimates of tropical surface heat flux

across the Pacific, averaged from 5◦S to 5◦N, is between 40 and 100 W/m2. Ranges

at least as large are expected at other longitudes, so such estimates are not able

to discriminate between T31cam and T42cam fluxes, even though the differences in

Fig. 7 are significant. In the coupled models these radiation differences are similar,

but noticeably weaker, particularly over the Indian Ocean and Tropical West Pacific.

More significant energy budget differences are associated with relatively minor shifts

in circulation features, and in the distribution of sea ice at high latitudes (section

6).

The change from mid- to low-resolution coupled CCSM3 results in significantly

lower surface temperatures throughout the Eurasian Arctic, especially in the Barents

Sea region where temperatures drop more than 12◦C below the T42x1 mean. This is

by far the largest surface temperature difference between the two coupled solutions

anywhere. The warm bias relative to observations which exists in T42x1 in this

region becomes a cold bias in T31x3, of nearly equal magnitude. The coupled

feedbacks related to ice growth in the Barents Sea region complicate the attribution

of this bias, which arises as a result of the resolution-related sensitivities of both

CAM3 and CSIM and their complex coupled interactions. Although the ice coverage

17

in this region becomes too extensive, the colder T31x3 Arctic has the advantageous

effect of reducing the higher than observed DJF land surface temperatures which

exist over the Eurasian continent in both T42x1 and T85x1 (Collins et al. 2005a)

by up to 4-6 ◦C.

5 The Ocean Simulation

Figure 4e shows that after year 400, the strength of the NAMOC as given by the

maximum Atlantic overturning below 500m and north of 28N, becomes relatively

steady between about 14 and 18 Sv. Multidecadal averages are roughly 16 Sv,

which is well within the target for low resolution CCSM3 (Section 2) as well as the

error ascribed by Talley et al. (2003) to their observational estimates (18 ± 3 − 5

Sv). For comparison, a typical value for the strength of the Atlantic MOC in low

resolution CCSM2 after 200 years is only 6Sv, while for PaleoCSM the max Atlantic

overturning was too strong at around 30 Sv. The global overturning in T31x3

generally tracks that of the North Atlantic with a positive offset of about 6Sv.

The latitude-depth distribution of the mean MOC in T31x3 is shown in Figure

8, for both the globe and the North Atlantic. The 6 Sv offset is not uniform, but

confined to the vicinity of the maximum around 40◦N and 700m depth, in accord

with observationally based estimates of ∼8 Sv for the amplitude of the North Pacific

deep water cell (Talley et al. 2003). The max NAMOC is lower, but not worse,

than in both the T42x1 (∼20 Sv; see Bryan et al. (2005)) and the forced x3ocn

(>20 Sv), and the maximum is found at a similar latitude and depth in all three

ocean solutions. The T42x1 and x3ocn have very comparable Atlantic overturning

streamfunctions with more concentrated flow near 60◦N (∼10 Sv reaching ∼1500

m) associated with the deep western boundary current downstream of the Denmark

Strait and Faroe Bank overflows. Weaker deep water formation at high latitudes in

the Atlantic appears to be the primary cause of the reduced overturning circulation

18

when the low resolution ocean is coupled to the T31 atmosphere.

The less vigorous overturning in T31x3 is consistent with a much reduced north-

ward heat transport in the Atlantic relative to all other model configurations (Fig. 9,

lower panel). The peak value of about 0.8 PW at approximately 25◦N is smaller

than either inferred from ocean observations, ∼1.27 PW (Ganachaud and Wunsch

2003), or implied by surface heat flux climatologies, ∼1.1 PW (Large and Yeager

2004). There can be little doubt that the T31x3 underperforms in this regard.

But it appears that the ocean model is not wholly to blame, since x3ocn forced with

observed atmospheric boundary conditions generates a much more reasonable trans-

port. Boning et al. (1996) find a direct linear relationship between North Atlantic

heat transport and NAMOC strength, with variations between similar physical mod-

els caused by different wind and thermohaline forcing in the north. It follows that

the forcing differences between T31x3 and x3ocn are the likely cause of the reduced

North Atlantic MOC and heat transport in the former. This weakness of coupled

Atlantic heat transport relative to uncoupled is also seen in the high resolution ocean

configurations. However, the coupled configurations generate more global total heat

transport, due to increased Pacific transport when coupled to an atmospheric model.

Apart from uniformly high transports near 50◦N, all curves in the global panel of

Figure 9 appear to fall within the error bars of global meridional heat transport

obtained from inverse methods applied to WOCE hydrographic data (Ganachaud

and Wunsch 2003).

Figure 10 shows how the mean current structure of the Equatorial Pacific in

T31x3 compares both to observations (Johnson et al. 2002) and the standalone

ocean solution (x3ocn). The maximum zonal speed of the EUC in T31x3 is less than

90 cm/s (bottom panel), but still within the target range (Section 2). Westward

surface currents extend too deep in the eastern half of the Pacific in both coupled and

uncoupled ocean solutions compared to observations, but this is a bias seen in the

19

high resolution ocean solutions as well (Fig. 10 of Large and Danabasoglu (2005)).

In the west, there is too much vertical shear near the surface of T31x3, because

low wind variability fails to generate the westerly wind bursts seen in observations

(and present in the observed forcing of the x3ocn), but again, the low resolution

model would appear to be no worse in this regard than T42x1 or T85x1 (Large and

Danabasoglu 2005). The most significant degradation of T31x3 relative to x3ocn

(and T42x1) is that the the core of the EUC west of about 230◦E is constant at

about 100m depth. In contrast, the observations show that the EUC core deepens

westward of 230◦E, reaching ∼200m at 160◦E (top panel). As a result of this bias,

the EUC source waters are too warm in T31x3.

A series of sensitivity experiments have shown that this flattening of the EUC

core is related to excess precipitation in the western Pacific warm pool. In T31x3,

this problem would be catastrophic if the model were configured with an ocean

diurnal cycle, because the resulting warmer equatorial SST would increase the pre-

cipitation and stabilize the ocean, thereby increasing the SST even more. Therefore,

the cold SST bias relative to the Reynolds and Smith SST climatology (Reynolds

and Smith 1994) in the central equatorial Pacific (Fig. 11) is essential in order to

avoid such a runaway situation. Thus, removing the ocean diurnal cycle in the low

resolution CCSM3 improves the subsurface equatorial solution, but at the cost of

physical realism. Another consequence of excess coupled model rainfall, in partic-

ular south of the equator, is that this more symmetric forcing produces zonal flow

that is also much too symmetric about the equator. For example, both T31x3 and

T42x1 generate both northern and southern branches of the westward-flowing South

Equatorial Current (SEC), but in T31x3 (as well as T85x1), the SEC is nearly as

strong south of the equator as to the north, instead of being much weaker as in

observations (see Fig. 11 of Large and Danabasoglu (2005)).

The most serious deficiencies of the SST simulation in T31x3 are the same as

20

those seen in the higher resolution CCSM3 controls: large errors in the vicinity of

poorly represented western boundary currents as well as in the eastern boundary

upwelling regions of the major basins (Large and Danabasoglu 2005). While the

mean equatorial Pacific SST has a more negative bias in T31x3, the seasonal cycle

along the Equator is not obviously worse than in the highest resolution simulation.

It has the same erroneous double peak east of 200◦E seen in the T85x1 (Large and

Danabasoglu 2005). In T31x3, the amplitude of the seasonal variation is too large

as opposed to too small in T85x1, but the same phase biases are present in both

configurations.

The existence of warm mean SST biases in the stratocumulus regions off the

subtropical continental west coasts of South America (Peru/Ecuador/Chile), North

America (Baja/Southern California), and southwest Africa is a problem in all CCSM3

configurations, and demonstrated for T85x1 by Large and Danabasoglu (2005). In

these eastern subtropical ocean regions, the two most significant differences between

T42cam and T31cam are the representation of stratus clouds and the overall wind

stress forcing of the ocean. Potentially problematic is the tendency for both to am-

plify the warm SST bias. T31cam exhibits a reduced stratocumulus cloud cover in

the oceanic regions one to two grid points off the coast, resulting in significantly

increased absorbed solar radiation which can easily exceed 50 W/m2 seasonally.

Also, the upwelling favorable longshore surface wind stress is too weak in T42cam

compared to observations and even weaker in T31x3 (not shown). Such weakening

of the subtropical dynamical circulation would be expected to produce less coastal

upwelling, and contribute to even warmer surface temperatures.

The severity of eastern boundary SST anomalies at all coupled resolutions is

quantified in Table 1 which lists the climatological difference of model SST from

observed, averaged over strips within 15◦ longitude of the west coasts. Unexpectedly,

T31x3 has biases lower than T42x1 along all subtropical eastern boundaries, and

21

lower than T85x1 everywhere but along the coast of South America. This result

likely follows from x3ocn exhibiting generally less of a bias than x1ocn. At all

resolutions, coupling exacerbates these ocean biases. Lower SST anomalies in the low

resolution ocean are related to colder subsurface temperatures, not enhanced coastal

upwelling. This suggests that there are differences in large scale ocean circulation

between the models which account for the differences in severity of the problem

and which more than compensate for the inherent warming tendencies of T31cam.

However, the T31x3 bias off Africa appears to be still too large to improve the

tropical Atlantic precipitation (Fig. 5), as was achieved with prescribed coastal

temperatures and salinities in Large and Danabasoglu (2005).

Table 2 compares various aspects of ocean circulation in T31x3 to other model

configurations and to a set of observed ocean benchmarks: North Atlantic MOC

strength (NAMOC) (Talley et al. 2003), peak northward Atlantic heat transport

(NAHT) (Bryden and Imawaki 2001), volume transport between Florida and Cuba

(FCT) (Hamilton et al. 2005), Drake Passage transport (ACC) (Whitworth (1983);

Whitworth and Peterson (1985)), the Indonesian throughflow (ITF) (Gordon 2001),

and the Bering Strait throughflow (BST) (Roach et al. 1995). Both the ACC trans-

port through Drake Passage and the Gulf Stream transport between Florida and

Cuba are too small, but probably for different reasons. The ACC compares quite

well to observations in both x1ocn and x3ocn, so it is likely the coupled forcing that

is to blame; T31 storm track migration towards the equator (Section 4) implicates

the zonal winds. The southern hemisphere westerlies that drive the ACC are too

strong in all coupled configurations, but the latitude of the peak in zonal mean

winds shifts systematically northward with decreasing atmospheric resolution. In

the case of T31x3, this shift is nearly 10◦ at Drake Passage, which results in a zonal

stress that is weaker than observed at ACC latitudes by as much as 0.07 N/m2. As

a consequence, the T31x3 ACC is low, but the T42x1 and T85x1 transports are

22

higher than observed because these configurations generate generally stronger than

observed stress over latitudes between 50◦S-60◦S (Fig. 6).

The Florida-Cuba transport (FCT) is too small in both uncoupled and coupled

low resolution configurations, but too high in both x1ocn and T42x1. This suggests

that the larger lateral viscosity required by the lower resolution numerics, and the

poorer representation of ocean topography and coastlines retard the transport in

both the forced x3ocn and coupled T31x3. Other factors such as sea-ice extent may

be contributing to the smaller than observed transport from the Pacific to Arctic

through the Bering Strait, because the x3ocn value is more reasonable. Finally,

the Indonesian Throughflow (ITF) in T31x3 falls in the estimated range, while this

transport appears too strong in T42x1.

6 The Sea-Ice Distribution

The equilibrium ice model solution in T31x3 is characterized by excessive Northern

Hemisphere (NH) ice. Figure 12 shows mean aggregate ice area and ice thickness

from the final 5 years of the integration. The thick line in the ice area plots (top

panels) shows the observed climatological location of 10% ice coverage derived from

1979-1999 Special Sensor Microwave/Imager (SSM/I) satellite data (Comiso 1999).

Apart from a small region in the Greenland Sea, the NH ice edge is too extensive

throughout the Arctic. In contrast, both higher resolution configurations of CCSM3

show deficient ice coverage in the Barents Sea (Holland et al. 2005). The T31x3

ice model bias is related to an atmospheric surface temperature cold bias in the

Barents Sea of more than 12◦C relative to T42x1 (section 4). Both configurations

generate surface temperature biases in this region relative to observations, but of

opposite sign. The NH ice thickness distribution is qualitatively quite similar to

that of T42x1, but thicker; the mean ice thickness in the central Arctic is 3.5-4m,

compared to the observed value of 2-3m and the T42x1 value of 2.5-3m. As in T42x1,

23

there is an unrealistic accumulation along the coast of Eastern Siberia and deficient

ice buildup along the Canadian coastline, the latter of which DeWeaver and Bitz

(2005) have linked to poor Arctic summer surface wind forcing at low atmospheric

resolution.

In the Southern Hemisphere (SH), ice concentration in the T31x3 is reduced

relative to the T42x1 in the Eastern Atlantic and Indian Ocean sectors, resulting

in somewhat better agreement with the line of observed 10% ice coverage (compare

to Fig. 5 of Holland et al. (2005)). There is too much ice coverage in the quadrant

centered at Cape Horn. This is probably related to biased wind forcing in this region

(Fig. 6), among other factors. There is also excessively thick ice on the eastern side

of the Antarctic Peninsula, although it would not appear to be significantly worse

than in T42x1 (Holland et al. 2005).

In general, there is increasingly excessive NH ice coverage as CCSM resolution

is lowered. Figure 13 shows that the T42x1 ice area bias in the NH is roughly

doubled in the T31x3 throughout the year, with the largest deviation from observed

in the wintertime. However, T31x3 aggregate ice coverage in the SH is less extensive

than T42x1 from summer through winter, and hence more in line with observations

(Fig. 13, lower panel). As in the NH, the largest deviations from observed sea ice

area occur in wintertime.

7 Interannual Variability

ENSO-like variability in the T31x3 is qualitatively quite comparable to the observed

record over one particular 50-year period near the end of the simulation. The min

and max Nino3.4 region anomalies over these years (830-880) are -1.7 and 2.9 ◦C

compared to -1.9 and 2.7 ◦C for observations between 1950 and 2000. The frequency

of large amplitude anomaly events is also very comparable to the observed record.

The number of large, positive Nino3.4 anomaly events (> 1◦C) over the time period

24

above is 8 for T31x3 and 7 for observed; the number of large, negative events (<

−1◦C) is 5 for T31x3 and 8 for observed.

By employing a moving 50-year window over several hundred years of model

integration, the mean and range of the standard deviation for each Nino region was

computed for the three different CCSM3 configurations. The results (Fig. 14) show

that there are significant variations in modelled ENSO variability over the course

of the control simulations. Whereas comparison of individual observation-length

segments usually highlights differences in Nino variability between the CCSM3 res-

olutions, the overlap of the standard deviation ranges suggests a basic similarity. As

is common in coupled climate models (eg, see Wittenberg et al. (2005)), equatorial

SST variability is relatively high in the western Pacific (Nino4, Nino3.4), and low

compared to observed in the eastern equatorial Pacific (Nino3, Nino1+2). The nat-

ural rise in SST variability from the west to the east in the Pacific does not occur

in CCSM, at any resolution. Although T31x3 has the lowest mean variance in the

three easternmost Nino regions, it is highest in the Nino4 region. As hoped, its

range includes the mean values of the higher resolutions in all 4 measures.

Despite the qualitative realism of the T31x3 Nino3.4 time series mentioned above,

comparing the power spectra of 50-year segments of Nino3.4 from the model with

that of the data record over 1950-2000 reveals a general shift in the peak of power

towards higher frequencies than is observed, a result seen in both higher resolution

configurations (Deser et al. 2005). For example, over model years 830-880, both

T31x3 and T42x1 show broad peaks in power centered near a period of 2 years

instead of near 4 years as in nature, with less overall variance in T31x3 than in

T42x1.

However, wavelet analysis reveals that over the course of the T31x3 simulation,

time periods can be found during which there is a much more realistic peak of

Nino3.4 spectral power than in T42x1. Figure 15 panels a and b show the wavelet

25

power spectra of the Nino3.4 index for T31x3 and T42x1, respectively, over 400

years of integration near the end of the runs. To the right, time-averaged wavelet

power of model Nino3.4 anomalies are compared to observations (1950-2000, in

red). There is a clear focus of wavelet power near a period of 2 years in T42x1

throughout the 400 years, but the period of peak power is much less well-defined in

T31x3 and occasionally shifts to longer periods. During the time interval 650-700,

in particular, Nino3.4 wavelet power in T31x3 peaks between a period of 4-6 years,

generating a time-average spectrum whose shape closely resembles observed, but

with lower maximum power (Fig. 15a, green curve). The long-term mean (480-880)

for T31x3 does peak near a period of 2 years, but of course no observed record of

equivalent length is available for comparison.

In contrast, no 50-year interval can be found when the T42x1 wavelet power

shows a similar shift to longer periods. Although the time period 810-860, for

example, does show a relative increase in power at longer periods, the peak remains

at 2 years (Fig. 15b, green curve). For T42x1, the long-term mean power curve (480-

880) faithfully represents the frequency distribution of power for observation-length

segments of the control integration.

The time history of scale-averaged wavelet power in the period band of 3 to 8

years (equivalent to average variance in this band, see Torrence and Compo (1998)) is

shown for T31x3, T42x1, and observations in Figure 15 panel c. This frequency band

is where observations of the last half-century show maximum power for Nino3.4 SST.

The T31x3 integration goes through several multi-decadal segments when variance in

this band increases dramatically, to levels comparable to observations. The intervals

660-690 and 700-730 are particularly notable. Nino3.4 variance in the T42x1 and

T85x1 (not shown) control integrations does not reach the same levels in the 3-8

year band.

The T31x3 simulation of other major modes of climate variability shows the

26

same basic level of skill as in T42x1, with significantly greater discrepancy between

CCSM3 and observation than between different versions of the CCSM3 model. Fig-

ure 16 shows the first empirical othogonal function of mean DJFM sea level pressure

north of 20◦N (top panels) and monthly non-seasonal sea level pressure south of 20◦S

(bottom panels) for T31x3 (years 700-879), T42x1 (years 700-879), and NCEP ob-

servations (1948-2002) . The observed patterns of pressure variation are known as

the Arctic Oscillation (AO) and Antarctic Oscillation (AAO), respectively. We have

used the full NCEP-NCAR Reanalysis back to 1948 for both hemispheres, despite

indications that data quality over Antarctica is lower prior to 1979 (Marshall 2002).

Both model resolutions generate an AO which is much more tripolar than observed,

with a strong North Pacific signal which is barely seen in nature. This mode ex-

plains more variance in both models than in observations, and it appears to be

more strongly exhibited in T31x3, with larger amplitudes and even greater variance

explained.

In the southern hemisphere, both T31x3 and T42x1 generate an AAO which is

too weak over the continent of Antarctica and too strong in the band between ∼30-

50◦S. Extensions of the polar maximum into the Atlantic, eastern Indian, and eastern

Pacific sectors are not as pronounced as observed, at either resolution. The T42x1

does seem to do a somewhat better job than T31x3 of reproducing the enhanced

variability which is observed in the Southern Ocean near 120◦W. Still, the resolution-

related differences between T31x3 and T42x1 are slight compared to the inherent

biases seen in the CCSM3 family of model solutions.

A 1% per year increasing CO2 experiment branched off of the T31x3 control

at year 400 indicates that the transient climate response of the T31x3 (change in

global average surface air temperature at the point of doubling of CO2) is 1.4◦C.

This value is a 20-year average centered about the point of doubling. The equivalent

numbers for the T85x1 and T42x1 resolutions are 1.5◦C and 1.4◦C, respectively. The

27

transient response to greenhouse gas forcing in fully coupled CCSM3 does not show

an unambiguous increase with increasing resolution, as is found to be the case for

CAM3 equilibrium sensitivity (Kiehl et al. 2005). At the point of quadrupling of

CO2 in the 1% increase experiments, the response is 3.5◦C, 3.3◦C, and 3.4◦C for

T85x1, T42x1, and T31x3, respectively. Thus, the climate sensitivity of the fully

coupled low-resolution CCSM3 is not significantly different from that of the higher

resolution configurations.

8 Comparative Computational Efficiency

The significant economies associated with the low resolution CCSM3 are quantified

in Figure 17. Performance data compiled from load balancing tests run on a variety

of platforms have been plotted for each model configuration. The number of years

of coupled model integration achievable per wall clock day is related to the total

number of CPUs applied. The points plotted generally represent the best of a series

of performance tests, and all ordinate values should be understood as approximate.

Some of the platforms included are experimental at this stage. Also, the load bal-

ancing work has not been completed, and further refinement is likely to result in

increased performance on the machines at Oak Ridge as well as on the Linux clusters

at NCAR (Intel Xeon).

Direct comparisons between resolutions are only possible for select configura-

tions. On the NCAR IBM Power 4, with 128 CPUs, going from T85x1 to T42x1

results in an increase in simulated years per day (syd) by more than a factor of

2.5. On the Cray X1 at Oak Ridge, T31x3 is more than 3 times faster than T42x1

when both models are run on 76 (multi-stream) processors. This results in a model

throughput of 35 syd, the highest yet achieved for any coupled CCSM3 configu-

ration. Running T31x3 on 16 processors of a Linux server (NCAR, Intel Xeon)

generates as many simulated climate years per day as running the T85x1 on 192

28

processors of an IBM power 4 supercomputer.

The slope of the line through the data point and the origin in Figure 17 gives the

simulated years per day per CPU, a measure of efficiency. Higher slopes are more

desirable, indicating that more climate simulation can be completed with fewer

resources. The three rays drawn show the maximum efficiency achieved at each

resolution of CCSM3. All of the T31x3 test cases have higher efficiency than the most

efficient T42x1 case. As expected, T85x1 is the least efficient configuration, and all of

these cases fall in the lower right hand sector of the plot where large increases in CPU

power are needed to achieve even modest gains in model throughput. Comparing

performance numbers on either of the two IBM supercomputers at NCAR shows that

there are much higher efficiency gains going from T42x1 to T31x3 than from T85x1

to T42x1. This is related to the simultaneous reduction of both atmosphere and

ocean resolutions in the low resolution CCSM3. Changing from T42x1 to T31x3

reduces the number of atmosphere grid points by almost a factor of two, but it

reduces the number of ocean grid points by a factor of almost 17. This drastic

reduction in resolution puts T31x3 in a performance class by itself.

9 Discussion and Conclusion

The results of the previous sections show that several features of the coupled climate

at T31x3 are notably worse than in T42x1: the ice in the Northern Hemisphere is

even more excessive; the Atlantic heat transport is relatively anemic; and SH storm

tracks are shifted further towards the equator. Many of these biases can be traced to

inherent deficiencies of the individual component models at low resolution. Although

the T31cam solution is similar in most respects to T42cam, there are low-level dy-

namical circulation differences as well as systematic biases related to parameterized

cloud processes. Weaker deep water formation in the x3ocn contributes to a less

vigorous thermohaline circulation and an anomalously low heat transport in T31x3.

29

In some instances however, the uncoupled biases do not leave strong signatures in

the coupled solution. For example, large scale radiation budget biases in T31cam are

not as large in T31x3, and reduced stratocumulus and coastal wind forcing off sub-

tropical west coast regions do not exacerbate the positive SST biases in the coupled

context.

In fact, many aspects of the low resolution coupled solution compare quite favor-

ably with the higher resolution configurations. The T31x3 generates a more stable

climate than T42x1, with less ocean temperature drift, which increases the utility of

T31x3 as a tool for climate studies. There does not appear to be a systematic degra-

dation in modelled ENSO-like variability as CCSM3 resolution is lowered. On the

contrary, T31x3 at least once switched into a regime where ENSO variability has a

quite realistic spectral power distribution, unlike higher resolution configurations in

which ENSO power consistently peaks at a period of near 2 years. A related result is

that the T31x3 maintains a passable Pacific equatorial undercurrent despite having

less than 1/3 of the longitudinal resolution of the T42x1. The eastern boundary

ocean SST bias which is present in all configurations of CCSM3 is least severe in the

T31x3. The ACC transport in T31x3, while too weak, is closer to the observed value

than in either high resolution coupled configuration. The Indonesian Throughflow

is within the observed range, although probably not for the correct reasons. Ice

coverage and thickness in the southern hemisphere appear to be at least as good as

in T42x1, and the seasonal cycle of total SH ice area is slightly closer to observed in

the T31x3 configuration. The simulation of modes of atmospheric variability such

as the AO and AAO and the transient climate response to anthropogenic forcings

are not significantly degraded in the T31x3 compared to the more standard CCSM3

configurations. Finally, the magnitude of the meridional overturning in T31x3 is

within the error bars of observation and maintains its strength over many hundreds

of years of integration. This represents a significant improvement in CCSM low

30

resolution modelling.

Whether or not the shortcomings of the T31x3 climate are acceptable in light of

the very large gains in efficiency described above (Fig. 17) is clearly a question which

must be answered by the individual researcher. This evaluation will necessarily

depend upon the nature of the phenomena under investigation. But efficiency is

not the only benefit of T31x3; its unexpected skill in several measures relevant to

climate studies will also recommend its use.

31

Acknowledgments

This study is based on model integrations that were performed by NCAR and

CRIEPI with support and facilities provided by NSF, DOE, MEXT, and ESC/JAMSTEC.

This work would not have been possible without the concerted effort of the entire

staff of the Climate and Global Dynamics Division at NCAR who are responsible

for creating and running CCSM3. We thank George Carr for the data on CCSM3

computational performance.

32

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35

Figure Captions

1. Low resolution ocean horizontal grid from CCSM2 (top) and from CCSM3

(bottom).

2. Ocean vertical grid cell height as a function of depth for CCSM2 x3 (25 levels),

CCSM3 x3 (25 levels), and CCSM3 x1 (40 levels).

3. Time series of globally annually averaged surface temperature (K) for control

simulations T31x3 and T42x1. The asterisk indicates the climatological ob-

served (NCEP) value.

4. Annual mean time series of a) global mean total surface heat flux into the

ocean, b) global mean ocean temperature, c) global mean ocean salinity, d)

ocean mass transport through Drake Passage, and e) maximum meridional

overturning streamfunction (below 500m and north of 28◦N) for the global

(thin) and Atlantic (thick) oceans. Asterisks in panels b and c represent

WOA/P global mean values after interpolation to the gx3v5 grid. The bars in

panels d and e represent the observed ranges for Drake Passage transport and

Atlantic overturning strength, respectively.

5. a) Climatological annual mean tropical precipitation difference (T31cam -

T42cam). Global annual mean precipitation rate averaged over years 861-880

for b) T31x3 and c) T42x1. Units are mm/day.

6. Climatological zonal-mean of the a) zonal component, b) meridional component,

and c) magnitude of the surface wind stress over the ocean (N/m2). Thirty-

year averages of T85x1 and T42x1, and a twenty-year average of T31x3 are

plotted alongside a 5-year mean stress computed from coupling 2000-2004

QuickSCAT winds to observed SST.

36

7. Climatological annual mean tropical difference (T31cam - T42cam) in net surface

energy budget (W/m2).

8. 20-year mean (years 861-880) global (top) and Atlantic (bottom) Eulerian merid-

ional overturning streamfunction from T31x3. Contour intervals are +/- 2, 4,

6, 10, 14, 16, 20, 40, 60 Sv. Shaded where positive.

9. Mean global (top) and Atlantic (bottom) northward ocean heat transport. The

solid curves correspond to 20-year means from fully coupled 1990 control so-

lutions while the dashed curves are for years 1996-2000 of standalone ocean

solutions forced with observed atmospheric state fields at high (x1ocn) and

low (x3ocn) resolutions. The global heat transport is total and thus includes

eddy transports, but the Atlantic heat transport includes only the Eulerian

mean component. Observed estimates with error bars are shown.

10. Mean Pacific zonal velocity at the Equator from observed measurements (top),

x3ocn (middle), and T31x3 (years 861-880) (bottom).

11. Mean (years 861-880) Pacific equatorial SST compared to observed SST clima-

tology.

12. Five year mean (876-880) T31x3 aggregate ice area (top) and ice thickness

(bottom) for both hemispheres.

13. Climatological (years 700-799) mean seasonal cycle of sea ice area for T31x3

and T42x1 compared to SSM/I observations.

14. Nino region temperature standard deviations for each CCSM3 coupled config-

uration compared to observed values. A 50-year running window is applied

to several hundred years of model integration (years 400-880 for T31x3 and

T42x1; years 200-600 for T85x1) to derive a mean and a range of standard

37

deviation values. For observations, there is a single 50-year window cover-

ing 1950-1999. The monthly time series have had the mean seasonal cycle

removed, are detrended, and have had a Welch window of bin size 3 applied

before the standard deviation is computed.

15. The wavelet power spectra of the Nino3.4 SST index over years 480-880

of a) T31x3 and b) T42x1, using the Morlet wavelet. Cross-hatching indi-

cates the cone of influence where edge effects become important, and the 90%

confidence level is overlayed. The global wavelet spectrum (time-averaged

over 480-880, black) is shown to the right, compared to a particular 50-year

time average as well as to the observed spectrum (1950-2000, red). Panel

c shows the time series of wavelet power scale-averaged over the band be-

tween 3 to 8 year periods for T31x3 (black), T42x1 (green), and observations

(red). Horizontal lines in panel c indicate 90% confidence levels. (Wavelet soft-

ware was provided by C. Torrence and G. Compo, and is available at URL:

http://paos.colorado.edu/research/wavelets/)

16. The first EOF of mean December through March mean sea level pressure north

of 20◦N (top panels), and the first EOF of mean monthly sea level pressure

south of 20◦S (bottom panels), for T31x3 (years 700-879), T42x1 (years 700-

879), and NCEP observations (1948-2002). The seasonal cycle was removed

from the monthly time series to produce the EOFs in the bottoms panels.

17. Computer performance results for each CCSM3 configuration on a variety

of common platforms. The number of simulated years per wall clock day is

plotted against the number of CPUs used. The slope between the origin and

each data point indicates years/day/CPU, a measure of efficiency. A ray is

drawn to the highest efficiency case for each resolution with slopes of 1.04,

0.14, and 0.09 years/day/CPU for T31x3, T42x1, and T85x1, respectively.

38

Table Captions

1. Area-averaged climatological SST bias (◦C) within 15◦ longitude of the west

coasts of three continents; South America (between 40◦S and Equator), North

America (between 18◦S and 38◦N), and Africa (between 30◦S and Equator).

2. Measures of ocean general circulation in uncoupled and coupled CCSM3 in-

tegrations compared to observed estimates of North Atlantic MOC strength

(NAMOC), peak northward Atlantic heat transport (NAHT), volume trans-

port between Florida and Cuba (FCT), Drake Passage transport (ACC), the

Indonesian throughflow (ITF), and the Bering Strait throughflow (BST).

39

Figure 1: Low resolution ocean horizontal grid from CCSM2 (top) and from CCSM3

(bottom).

40

Figure 2: Ocean vertical grid cell height as a function of depth for CCSM2 x3 (25

levels), CCSM3 x3 (25 levels), and CCSM3 x1 (40 levels).

41

Figure 3: Time series of globally annually averaged surface temperature (K) for

control simulations T31x3 and T42x1. The asterisk indicates the climatological

observed (NCEP) value.

42

Figure 4: Annual mean time series of a) global mean total surface heat flux into the

ocean, b) global mean ocean temperature, c) global mean ocean salinity, d) ocean

mass transport through Drake Passage, and e) maximum meridional overturning

streamfunction (below 500m and north of 28◦N) for the global (thin) and Atlantic

(thick) oceans. Asterisks in panels b and c represent WOA/P global mean values

after interpolation to the gx3v5 grid. The bars in panels d and e represent the

observed ranges for Drake Passage transport and Atlantic overturning strength,

respectively.

43

Figure 5: a) Climatological annual mean tropical precipitation difference (T31cam

- T42cam). Global annual mean precipitation rate averaged over years 861-880 for

b) T31x3 and c) T42x1. Units are mm/day.

44

Figure 6: Climatological zonal-mean of the a) zonal component, b) meridional com-

ponent, and c) magnitude of the surface wind stress over the ocean (N/m2). Thirty-

year averages of T85x1 and T42x1, and a twenty-year average of T31x3 are plotted

alongside a 5-year mean stress computed from coupling 2000-2004 QuickSCAT winds

to observed SST.

45

Figure 7: Climatological annual mean tropical difference (T31cam - T42cam) in net

surface energy budget (W/m2).

46

Figure 8: 20-year mean (years 861-880) global (top) and Atlantic (bottom) Eulerian

meridional overturning streamfunction from T31x3. Contour intervals are +/- 2, 4,

6, 10, 14, 16, 20, 40, 60 Sv. Shaded where positive.

47

Figure 9: Mean global (top) and Atlantic (bottom) northward ocean heat transport.

The solid curves correspond to 20-year means from fully coupled 1990 control solu-

tions while the dashed curves are for years 1996-2000 of standalone ocean solutions

forced with observed atmospheric state fields at high (x1ocn) and low (x3ocn) res-

olutions. The global heat transport is total and thus includes eddy transports, but

the Atlantic heat transport includes only the Eulerian mean component. Observed

estimates with error bars are shown.

48

Figure 10: Mean Pacific zonal velocity at the Equator from observed measurements

(top), x3ocn (middle), and T31x3 (years 861-880) (bottom).

49

Figure 11: Mean (years 861-880) Pacific equatorial SST compared to observed SST

climatology.

50

Figure 12: Five year mean (876-880) T31x3 aggregate ice area (top) and ice thickness

(bottom) for both hemispheres.

51

Figure 13: Climatological (years 700-799) mean seasonal cycle of sea ice area for

T31x3 and T42x1 compared to SSM/I observations.

52

Figure 14: Nino region temperature standard deviations for each CCSM3 coupled

configuration compared to observed values. A 50-year running window is applied

to several hundred years of model integration (years 400-880 for T31x3 and T42x1;

years 200-600 for T85x1) to derive a mean and a range of standard deviation values.

For observations, there is a single 50-year window covering 1950-1999. The monthly

time series have had the mean seasonal cycle removed, are detrended, and have had

a Welch window of bin size 3 applied before the standard deviation is computed.

53

500 600 700 800

0.5

1.0

2.0

4.0

8.0

16.0

32.0

PERI

OD (Y

EARS

)

90%

a.

0.5 1 2 3 4 8 12 oC2

Time-average

0.0 7.5 15.0POWER (oC2)

480-880650-700OBS

500 600 700 800

0.5

1.0

2.0

4.0

8.0

16.0

32.0

PERI

OD (Y

EARS

)

90%

b. Time-average

0.0 7.5 15.0POWER (oC2)

480-880810-860OBS

500 600 700 800MODEL YEAR

0.2

0.4

0.6

0.8

AVG

VARI

ANCE

( o C2 )

c.

T31x3T42x1OBS

1960 1980 2000YEAR

Figure 15: The wavelet power spectra of the Nino3.4 SST index over years 480-880

of a) T31x3 and b) T42x1, using the Morlet wavelet. Cross-hatching indicates the

cone of influence where edge effects become important, and the 90% confidence level

is overlayed. The global wavelet spectrum (time-averaged over 480-880, black) is

shown to the right, compared to a particular 50-year time average as well as to

the observed spectrum (1950-2000, red). Panel c shows the time series of wavelet

power scale-averaged over the band between 3 to 8 year periods for T31x3 (black),

T42x1 (green), and observations (red). Horizontal lines in panel c indicate 90%

confidence levels. (Wavelet software was provided by C. Torrence and G. Compo,

and is available at URL: http://paos.colorado.edu/research/wavelets/)

54

Figure 16: The first EOF of mean December through March mean sea level pressure

north of 20◦N (top panels), and the first EOF of mean monthly sea level pressure

south of 20◦S (bottom panels), for T31x3 (years 700-879), T42x1 (years 700-879),

and NCEP observations (1948-2002). The seasonal cycle was removed from the

monthly time series to produce the EOFs in the bottoms panels.

55

Figure 17: Computer performance results for each CCSM3 configuration on a variety

of common platforms. The number of simulated years per wall clock day is plotted

against the number of CPUs used. The slope between the origin and each data point

indicates years/day/CPU, a measure of efficiency. A ray is drawn to the highest

efficiency case for each resolution with slopes of 1.04, 0.14, and 0.09 years/day/CPU

for T31x3, T42x1, and T85x1, respectively.

56

x3ocn x1ocn T31x3 T42x1 T85x1

South America 0.87 1.16 2.24 2.54 1.7

North America 0.73 0.7 0.76 1.68 1.61

Africa 0.93 1.32 2.95 4.0 3.21

Table 1: Area-averaged climatological SST biases (◦C) within 15◦ longitude of the

west coasts of three continents; South America (between 40◦S and Equator), North

America (between 18◦S and 38◦N), and Africa (between 30◦S and Equator).

57

x3ocn x1ocn T31x3 T42x1 T85x1 Observed

NAMOC (Sv) 20 22 16 19 22 18±3 − 5

NAHT (PW) 1.1 1.2 0.8 1.0 1.1 1.07 - 1.27

FCT (Sv) 17 29 17 29 28 25±1

ACC (Sv) 145 140 115 177 193 134±13

ITF (Sv) 9 14 10.5 16.5 14.5 10 - 15

BST (Sv) 1.0 1.0 0.4 0.9 1.0 0.83±0.5

Table 2: Measures of ocean general circulation in uncoupled and coupled CCSM3

integrations compared to observed estimates of North Atlantic MOC strength

(NAMOC), peak northward Atlantic heat transport (NAHT), volume transport be-

tween Florida and Cuba (FCT), Drake Passage transport (ACC), the Indonesian

throughflow (ITF), and the Bering Strait throughflow (BST).

58