CCSM–CAM3 Climate Simulation Sensitivity to Changes in …dust.ess.uci.edu/ppr/ppr_HCD06.pdf ·...

CCSM–CAM3 Climate Simulation Sensitivity to Changes in Horizontal Resolution

JAMES J. HACK, JULIE M. CARON, G. DANABASOGLU, AND KEITH W. OLESON

National Center for Atmospheric Research, Boulder, Colorado

CECILIA BITZ

University of Washington, Seattle, Washington

JOHN E. TRUESDALE

National Center for Atmospheric Research, Boulder, Colorado

(Manuscript received 11 March 2005, in final form 14 November 2005)

ABSTRACT

The latest version of the Community Climate System Model (CCSM) Community Atmosphere Modelversion 3 (CAM3) has been released to allow for numerical integration at a variety of horizontal resolutions.One goal of the CAM3 design was to provide comparable large-scale simulation fidelity over a range ofhorizontal resolutions through modifications to adjustable coefficients in the parameterized treatment ofclouds and precipitation. Coefficients are modified to provide similar cloud radiative forcing characteristicsfor each resolution. Simulations with the CAM3 show robust systematic improvements with higher hori-zontal resolution for a variety of features, most notably associated with the large-scale dynamical circula-tion. This paper will focus on simulation differences between the two principal configurations of the CAM3,which differ by a factor of 2 in their horizontal resolution.

1. Introduction

Solution of the continuous nonlinear differentialequations governing atmospheric motions requires theuse of a discrete approximation, most frequently utiliz-ing finite-difference or spectral methods. The horizon-tal and vertical resolution at which global climate ap-plications are numerically integrated is normally chosenon the basis of computational expense, generallyweighed against gross measures of solution accuracy.To place any confidence in numerical simulations ofclimate, the horizontal and vertical resolution must befine enough to accurately represent the phenomeno-logical motion scales of most importance to the climatesystem. A common spectral truncation used in globalclimate models is a 42-wave triangular truncation(T42), which provides an isotropic representation ofscalar information and very accurately treats featuresand their horizontal derivatives down to approximately

950 km. Motion scales below this truncation limit mustbe treated in some other way, and generally enter thesolution in the form of a forcing term. In a spectralmodel these subgrid-scale terms are evaluated on atransform grid that is also used to evaluate nonlinearterms in the equations, and whose grid intervals aredirectly related to the spectral truncation. In a T42model the transform grid interval is approximately 300km at the equator. These terms are almost alwaysevaluated using parameterization techniques, whichcan be highly nonlinear and are generally functions ofthe explicitly resolved atmospheric state variables. Ide-ally, one would select a horizontal resolution for whichthe solutions are in a convergent regime; that is, one inwhich additional increases in resolution would notgreatly alter the solutions. Under such circumstances itmight also be expected that the behavior of parameter-ized forcing terms would not significantly change withadditional increases in resolution.

Exploration of global atmospheric simulation sensi-tivity to horizontal resolution goes back more than 30yr (Manabe et al. 1970), and has continued sporadicallyin the intervening years (e.g., Manabe et al. 1979; Boer

Corresponding author address: Dr. James J. Hack, NCAR, P.O.Box 3000, Boulder, CO 80307.E-mail: [email protected]

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© 2006 American Meteorological Society

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and Lazare 1988; Boville 1991; Kiehl and Williamson1991; Chen and Tribbia 1993; Pope and Stratton 2002).Most investigations have identified some systematic im-provements related to increases in horizontal resolu-tion. In an earlier version of the CAM, Williamson etal. (1995) showed that many statistics used to charac-terize climate properties began to converge in the rangeof a T63 spectral truncation for midlatitudes. They alsoshowed that scales of motion included at T63 andhigher resolutions were needed to capture the nonlin-ear processes that appear to drive some larger scalecirculations. The more discouraging outcome of thatinvestigation was that they were unable to demonstrateconvergence for many other quantities, even at a T106truncation.

In this paper we will explore simulation sensitivity tohorizontal resolution in the most recent version of theCommunity Climate System Model (CCSM) Commu-nity Atmosphere Model version 3 (CAM3). This modelis the latest in a succession of atmospheric general cir-culation models that have been made widely availableto the scientific community, originating with the Na-tional Center for Atmospheric Research (NCAR)Community Climate Model (CCM). The CAM3 incor-porates a significant number of changes to the dynami-cal formulation, the treatment of cloud and precipita-tion processes, radiation processes, and atmosphericaerosols, and is described in Collins et al. (2006b). Oneof the unique design goals for the CAM3 was to providesimulations with comparable large-scale fidelity over arange of horizontal resolutions. This is accomplishedthrough modifications to adjustable coefficients in theparameterized physics package associated with cloudsand precipitation. The standard configuration of theCAM3 is based on an Eulerian spectral dynamical core,where the vertical discretization makes use of 26 levels(L26) treated using second-order finite differences(Williamson 1988). Three standard configurations ofthe CAM3 are distributed, including horizontal spectraltruncations of T31 (�3.75° transform grid), T42 (�2.8°transform grid), and T85 (�1.4° transform grid). Thediscussion that follows will focus on simulation differ-ences between the T85L26 and T42L26 CAM3 configu-rations, which we anticipate will be the most commonlyused versions. Twenty-two-year uncoupled simulations,using observed sea surface temperatures (SSTs) andobserved sea ice, are used to characterize features ofthe simulated climate at the two resolutions. Thesesimulation characteristics are then contrasted withsimulation properties obtained from the fully coupledCCSM3 (Collins et al. 2006a). Characteristics of theT31L26 simulation are discussed separately in Yeager

et al. (2006) for uncoupled applications, and whencoupled to a nominal 3° resolution ocean model.

2. Tuning of the physical parameterizationpackage

Changes to horizontal resolution in a global atmo-spheric model directly affect the scales of motion avail-able for the explicit solution of the governing equa-tions. For example, doubling the horizontal resolutionfrom T42 to T85 allows motion scales that did not existin the T42 model to be explicitly resolved in the solu-tion of the large-scale equations of motion. A familiaradjustment to compensate for such changes is the needto maintain reasonable energy characteristics for thesmallest resolved scales. In the case of the CAM3 spec-tral model, this requires that the coefficient on the bi-harmonic diffusion operator vary with resolution. Thisvariation is determined experimentally so that in themid- to upper troposphere the two-dimensional kineticenergy spectra have a reasonably well-behaved distri-bution as a function of the high-order wavenumbers(i.e., near the truncation limit), an approach discussedin detail by Boville (1991).

Similarly, the behavior of the parameterized treat-ment of physical processes also changes with resolution,for a variety of reasons. One example is shown in Fig. 1,which illustrates the time series of the resolved three-dimensional advective tendency of temperature overthe tropical West Pacific warm pool (�1°N, 155°E) forthe T42 and T85 CAM3 configurations, along with anobservationally derived version of the same field. Wenote that the T85 time series has been spatially aver-aged to the equivalent T42 area average for a fair com-parison. This quantity is one of the primary destabiliz-ing resolved-scale terms seen by the parameterizedtreatment of moist convection. It is clear from simpleinspection that the fundamental character of this termchanges with resolution in terms of vertical structure,temporal behavior, and the amplitude of the deviationsfrom the long-term mean. The high-resolution time se-ries is much more consistent with observational esti-mates of this quantity (bottom panel). This is admit-tedly a convolved product of both the additional scalesof explicitly resolved motions and their interactionswith the parameterized physics. But as shown in Hackand Caron (2005, unpublished manuscript) the morerealistic behavior of the resolved motion field at higherresolution is very robust and largely determined by theinternal behavior of the dynamical motion field. Asmight be expected, the temporal characteristics of pa-rameterized processes like precipitation or cloud water[e.g., their probability density functions (PDFs)] differ

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significantly, even for cases where the time-mean prop-erties are very similar.

A second example of how changes in resolution canaffect the behavior of parameterized physics is shown inFig. 2. This figure shows the difference in zonally andannually averaged temperature between the T85 andT42 CAM3 simulations, as well as the T42 CAM3 simu-lation and the European Centre for Medium-RangeWeather Forecasts (ECMWF) 40-yr Re-Analysis(ERA-40) reanalysis. The figure shows a systematicwarming of the troposphere, with the largest signals athigh latitudes. The enhanced warming associated withhigher resolution is a desirable signal when comparedwith observational estimates, particularly at high lati-

tudes. It also is a very robust signal that has a weakdependence on the formulation of the parameterizedphysics package and is presumed to be attributable toimproved accuracy in the treatment of the large-scalemotion field. Systematic changes like this warming canhave a significant impact on the treatment of param-eterized processes. For example, the CAM3 exploitsrelative humidity thresholds in the treatment of cloudformation. Systematic changes in the temperature fieldaccompanying changes in horizontal resolution requirethat the selection of these thresholds be revisited inorder to maintain a similar cloud field and similar cloudradiative properties.

Changing free parameters in parameterized physicsformulations, such as relative humidity thresholds orcloud water autoconversion thresholds in pursuit of aspecific simulation goal is most frequently, and oftendisparagingly, referred to as tuning. Tuning generallyinvolves the exploration of simulation sensitivity to alimited number of loosely constrained coefficients inthe parameterized physics. The most common goal is toidentify a parameterization configuration that yieldssimulation results that best agree with observations onsome arbitrary combination of time and space scales.Generally, these time and space scales involve zonallyaveraged seasonal means of quantities like cloud radia-tive forcing (CRF). In the case of CAM3, adjustmentsare made to spatially and temporally invariant coeffi-cients incorporated in the physical parameterizationpackage. For the CAM3, these parameters includelarge-scale relative humidity thresholds on cloud for-mation, rainfall evaporation efficiencies in stratiformand convective precipitation processes, adjustmenttime scales associated with moist convection, and auto-conversion thresholds for transforming cloud and icewater to rainwater and snow, respectively.

The goal of modifying the physics package as a func-tion of horizontal resolution in the CAM3 is to ensurethat the top-of-model (TOM) energy budget is closedand that the top-of-atmosphere (TOA) energy budgetcomponents are as close to observational estimates aspossible, at all resolutions. We note that there is asubtle difference between TOM and TOA since there isa small amount of atmospheric mass above the top ofthe model, where TOM radiative fluxes define the netsource or sink of energy to the simulated atmosphere.The nonlinear tuning exercise is done experimentally,exploiting expert knowledge of the way in which thevarious physical processes are formulated and are likelyto interact. The CAM3 was initially developed at theT42 resolution, and the T31 and T85 configurationswere developed as derivatives. A selected set of globalannual climate metrics for the T42 and T85 CAM3 con-

FIG. 1. Large-scale advective temperature tendency in K day�1

for (top) the T42 CAM3, (middle) the T85 CAM3, and (bottom)Tropical Ocean Global Atmosphere Coupled Ocean–Atmo-sphere Response Experiment (TOGA COARE) dataset (Mon-crieff et al. 1997).

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figurations are shown in Table 1. The TOM annualenergy balance remains well within 0.5 W m�2 for bothresolutions, where the component fluxes in the energybudget are both within 1 W m�2 between the two reso-lutions, and well within observational uncertainty. It isreasonable to ask how these quantities would comparewithout changes to the physics parameterizations. Us-ing the T42 physics parameters, a T85 configurationwould have a TOM and surface energy imbalance of�3.6 W m�2, principally due to differences in the long-wave portion of the energy budget. This would renderthe configuration unsuitable for coupled simulation ap-plications. Most other measures in Table 1 are virtuallyunchanged with the exception of the vertical distribu-tion of cloud, the clear-sky TOM longwave flux, theall-sky net surface longwave flux, and the surface sen-sible heat flux. The change in the cloud distribution isrequired to keep the all-sky top-of-model radiativefluxes relatively unchanged. The changes in the clear-sky longwave flux are attributable to subtle changes inthe vertical distribution of water vapor and changes tothe tropospheric static stability. Generally speaking, theT42 and T85 configurations are very similar to eachother in terms of the large-scale global annual energyand water cycle budget. However, as we will show, thedetails of how this balance is maintained can be quitedifferent depending on the spatial and temporal aver-aging procedures.

An important check on the changes made to thecloud and precipitation processes is to examine the re-sponse of the cloud field to anomalies in SST. Oneapproach exploits the observed linear correlation be-tween longwave cloud forcing (LWCF) and shortwave

cloud forcing (SWCF) as discussed in Kiehl andRamanathan (1990), Ramanathan and Collins (1991),and Kiehl (1994). Figure 3 shows this relationship overthe tropical western Pacific (10°S–20°N, 110°–160°E)for both Earth Radiation Budget Experiment (ERBE)and the T42 and T85 configurations of CAM3. TheERBE data show a strong linear correlation betweenthe SWCF and LWCF, most closely approximated bythe T42 CAM3. The T85 configuration exhibits a non-linear correlation between the CRF anomalies, with amuch greater range in the magnitude of the CRFanomalies. This unusual behavior of the cloud schemein the T85 configuration will require additional re-search at the process level to better understand andimprove the relationship between SWCF and LWCF.More importantly, it does indicate that the radiativebehavior of clouds is likely to be different at differentresolutions due to the changes made to achieve globalenergy balance. This may explain, in part, why theCAM3 exhibits slightly different climate sensitivity as afunction of horizontal resolution (Kiehl et al. 2006).

3. Simulation differences: Uncoupled atmosphere

In many respects, the large-scale simulation proper-ties of the T42 and T85 CAM3 configurations are verysimilar, exhibiting analogous biases with respect to ob-servational data. Fields like surface temperature gener-ally show changes that would be expected from differ-ences in elevation associated with changes in horizontalresolution. One of the larger and more obvious system-atic changes to the simulation is a general warming ofthe troposphere (see Fig. 2), with a relatively wide-

FIG. 2. Zonally averaged annual temperature difference (left) between the T85 CAM3 and the T42 CAM3 and(right) between the T42 CAM3 and ERA-40. Contour intervals are 0.8 K.


spread warming of the tropopause at virtually all lati-tudes. The T85 simulation shows a modest drying of theatmosphere outside of the deep Tropics, most notablyover Northern Hemisphere land areas. There are also acollection of other significant simulation differencesthat are of importance to coupling the atmosphere toother component models. These differences fall intothree categories worthy of discussion: differences in ra-diative forcing, differences in the low-level dynamicalcirculation, and differences in surface water exchange

processes, principally attributable to the precipitationcomponent. Other, more modest differences in quanti-ties like the dynamical circulation of the free atmo-sphere, including aspects of internal variability, are con-tained in companion manuscripts (e.g., Hurrell et al.2006).

As shown in Table 1, the T42 and T85 CAM3 con-figurations exhibit similar global annual average prop-erties. We note that one area where both configurationsdiffer markedly from observations is cloud amount,which to some extent may be associated with uncer-tainty in observational estimates. For example, al-though there is a large difference in low-level cloudamount compared to the International Satellite CloudClimatology Project (ISCCP), the simulated results aremore consistent with the surface-based observations ofWarren et al. (1988). Both configurations exhibit verysimilar energy budget properties on global annual timescales. There is, however, a redistribution of energy inthe system at T85, which is easiest to discuss in terms ofzonal means. The T85 model shows a reduction in out-going longwave radiation (OLR) of approximately 3 Wm�2 in the deep Tropics, and an increase in OLR on theorder of 4 W m�2 poleward of 30°. Similarly, the ab-sorbed solar radiation shows a reduction of �8 W m�2

in the deep Tropics and an increase in the extratropics,maximizing in the storm tracks around 8 W m�2. TheCRF is enhanced at low latitudes, and decreased at highlatitudes, most notably in the storm tracks. The largestdifferences in the tropical radiation budget are gener-ally confined to the western Pacific and Indian Oceans,where both the longwave and shortwave budgets showlarge spatially coherent increases in CRF (see Fig. 4).These regions are convectively active, and show signifi-cant increases in liquid and ice water loading at higherresolution, consistent with the increases in CRF. Gen-erally speaking, CRF and the associated condensed wa-ter loading appear to be biased high when compared toavailable observational estimates. Despite the system-atic biases in the longwave and shortwave radiationbudgets over the Pacific and Indian Oceans, the radia-tive response to the El Niño–Southern Oscillation(ENSO) cycle is generally improved for the higher-resolution configuration. This is especially true for theshortwave response. Figure 5 shows the spatial patternof the anomaly response of monthly averaged differ-ences in absorbed solar and outgoing longwave radia-tion between November 1984 (ENSO warm phase) andOctober 1989 (ENSO cold phase) as seen by ERBEand as simulated by CAM3. Generally, the T85 re-sponse is much stronger than the T42 response, andmore consistent with the observed response. The mostextreme difference is seen in the shortwave response,

TABLE 1. Global annual-mean climatological properties ofCAM3 at T85 and T42.

Property T42 T85 Observation

TOM outgoing longwave radiation (W m�2, � upward)

All sky 233.5 234.4 234.0a

Clear sky 263.2 265.1 264.4a

TOM absorbed solar radiation (W m�2, � downward)

All sky 233.8 234.0 234.0a

Clear sky 288.8 288.7 289.3a

Longwave cloud forcing (W m�2) 29.6 30.7 30.4a

Shortwave cloud forcing (W m�2) �55.0 �54.7 �54.2a

Cloud fraction (%)

Total 62.2 56.1 66.7b

Low 42.2 40.1 28.0b

Medium 21.0 17.3 20.0b

High 36.5 29.3 13.0b

Cloud water path (mm) 0.129 0.122 0.112c

Precipitable water (mm) 24.4 24.3 24.6d

Latent heat flux (W m�2) 82.4 83.8 84.9e

Sensible heat flux (W m�2) 19.7 17.8 15.8f

Precipitation (mm day�1) 2.83 2.87 2.61g

Net surface longwave radiation (W m�2, � upward)

All sky 56.4 58.0 49.4h

Clear sky 86.1 85.8 78.7h

Net surface shortwave radiation (W m�2, � downward)

All sky 158.8 159.1 165.9h

Clear sky 218.6 218.6 218.6h

Annual mean budgets (W m�2, � upward)

TOM 0.27 �0.44Surface 0.24 �0.47

a ERBE TOA (Kiehl and Trenberth 1997).b ISCCP (Rossow and Schiffer 1999).c Moderate Resolution Imaging Spectroradiometer (MODIS;

King et al. 2003).d National Aeronautics and Space Administration (NASA) Water

Vapor Project (NVAP; Randel et al. 1996).e ECMWF (Kållberg et al. 2004).f National Centers for Environmental Prediction (NCEP; Kistleret al. 2001).

g Global Precipitation Climatology Project (GPCP; Adler et al.2003).

h ISCCP flux data (FD; Zhang et al. 2004).

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for which the T42 configuration exhibits an extremelyweak response in the western Pacific. The longwaveresponse is also weaker than observed, and the stron-gest response is positioned well to the east of the ob-served maximum.

Changes in the simulated cloud field and the CRF

introduce relatively large local differences to the netsurface energy budget (see Fig. 6). Some of these dif-ferences can be characterized as local reflections ofchanges to the CRF, a consequence of tuning cloud andprecipitation processes to achieve global energy bal-ance. Others are nonlocal changes, associated with dif-

FIG. 4. (top) The net longwave flux difference and (bottom) net shortwave flux differencebetween the T85 CAM3 and the T42 CAM3 in W m �2.

FIG. 3. Scatterplots showing shortwave vs longwave cloud forcing in the tropical west Pacific warm pool region for (left) the T42CAM3, (middle) the T85 CAM3, and (right) ERBE.


Fig 4 live 4/C

ferences in the low-level dynamical circulation that af-fect properties like surface latent heat fluxes in the sub-tropics. In either case, these changes representsignificant differences in the forcing of the ocean circu-lation, something to keep in mind when we discuss thecoupled model simulation results.

Several of the differences that can be seen in theradiative forcing of the climate system motivated astrong interest in higher resolution. A persistent simu-lation deficiency for most global atmospheric climatemodels is the representation of stratocumulus cloudsalong the eastern coasts of the Atlantic and PacificOcean Basins. Figure 7 shows the change in annuallyaveraged absorbed solar radiation off the coasts ofBaja, Peru, and Namibia. Note the significant reduc-tions in absorbed solar radiation immediately along thecoast, attributable to a much improved representationof stratocumulus cloud cover. Unlike the changes to theradiation budget in the convectively active deep Trop-

ics, these signals are entirely associated with higherhorizontal resolution since they have a very weak de-pendence on the cloud radiative budget tuning. Theeast–west dipole structure in the Southern Hemisphere(SH) plots of absorbed shortwave radiation reflects thedisplacement of stratus clouds toward the coast wherethey should be located. Previous coupled models exhib-ited large warm SST biases in these regions, attributedin part to excessive solar insolation at the surface. Localreductions of more than 40 W m�2 represent a signifi-cant improvement to the simulation. We will discuss theimpact of these radiative changes on the coupled simu-lation in later sections.

Another area where the T85 configuration exhibitssystematic improvements when compared to the T42configuration is with respect to the low-level dynamicalcirculation. The differences in the low-level circulationrepresent noteworthy simulation improvements, al-though some biases continue to exist. We begin by ex-

FIG. 5. (left) The absorbed solar radiation anomaly for the T42 CAM3, the T85 CAM3, and ERBE for ENSOwarm minus cold conditions. (right) The outgoing longwave radiation anomaly for the T42 CAM3, the T85 CAM3,and ERBE for ENSO warm minus cold conditions. Contour intervals are 5 W m �2.

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Fig 5 live 4/C

amining features of the surface wind stress in the east-ern ocean stratocumulus regimes. The upper panels inFig. 8 show the annually averaged surface wind stressoff the west coast of Peru as simulated by the T42 andT85 CAM3 configurations, along with the differencebetween the two. The surface height field, in geopoten-tial meters, is also illustrated using color contours in theT85 and T42 panels. The surface geopotential illus-trates the difficulties in representing structures like the

Andes Mountains at low resolution. One consequenceof the horizontal averaging process is the extension ofelevated topography over the ocean surface, as seen inFig. 8. The bottom panels show surface wind stress asestimated from Earth Remote Sensing (ERS) scatter-ometer retrievals and the differences with the T42 andT85 CAM3 configurations. As can be seen, there isvirtually no equatorward stress on the ocean surface inthe vicinity of the coast in the T42 model. This compo-

FIG. 7. Shortwave cloud forcing difference between the T85 CAM3 and the T42 CAM3 for (left) North America, (middle) SouthAmerica, and (right) Africa. Contour intervals are 5 W m�2.

FIG. 6. Surface energy residual for (top) the T85 CAM3, (middle) the T42 CAM3, and(bottom) their difference in W m �2 for the Tropics. The contour interval for the top andmiddle panels is 25. The contour levels for the bottom panel are (�80.0, �60.0, �40.0, �30.0,�20.0, �10.0, �5.0, �1.0, 0.0, 1.0, 5.0, 10.0, 20.0, 30.0, 40.0, 60.0, 80.0).


Fig 6 live 4/C Fig 7 live 4/C

nent of the wind stress is responsible for the upwellingof cold ocean water in these regions, and its absencewas another simulation deficiency attributed to the gen-eration of warmer than observed sea surface tempera-tures over this region in coupled applications of theCCSM. The lower-right panel shows the magnitude andstructure of the wind stress bias as compared to ERSestimates. A clear improvement in the low-level windfield can be seen in the T85 configuration of the model.The upwelling wind component immediately along thecoast continues to be weak, but the overall structure ofthe T85 low-level wind field represents a major im-provement in circulation. This change in the low-levelcirculation is also a very robust simulation feature that

is entirely attributable to changes in horizontal resolu-tion.

Two other low-latitude areas benefit from circulationchanges associated with higher horizontal resolution.Figure 9 shows surface wind stress over the Pacific Ba-sin for the T85 and T42 configurations along with ERSscatterometer estimates. Two features that have plaguedT42 versions of the CAM include excessively strongtrades in the subtropical Pacific, and a very weak west-erly wind stress on the equator. The T85 configurationpushes both of these biases toward observational esti-mates, with significantly enhanced westerly equatorialwind stress in the central Pacific, and a marked de-crease in the subtropical Pacific trades. This latter dif-

FIG. 8. (top) The surface wind stress vectors for (left) the T85 CAM3, (middle) the T42 CAM3, and (right) their difference. (bottom)The surface wind stress vectors for (left) ERS, (middle) the difference between T85 CAM3 and ERS, and (right) the difference betweenT42 CAM3 and ERS. Color contours show corresponding surface geopotential height fields (PHIS) in units of 10�3 m2 s�2.

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Fig 8 live 4/C

ference is associated with comparably large reductionsin surface latent heat flux. One area where the low-levelcirculation degrades is in the Indian Ocean, which ex-hibits an enhanced and anomalous cross-equatorialflow.

One of the most reliable simulation signals associatedwith higher horizontal resolution in the atmosphere isthe location of the SH storm track. Figure 10 shows thesurface wind stress over the SH for the T42 and T85configurations of the CAM3, along with their differ-ence. This figure clearly shows the poleward migrationof the SH storm track, which is in much better agree-ment with observational analyses. This response is oneof the signals that monotonically improved with higherresolution as shown in Williamson et al. (1995).

The Arctic surface wind simulation changes consid-erably when the CAM3 resolution is increased to T85.Although there continue to be notable differences withanalyses, there are large reductions in circulation bi-ases, where the most prominent difference is a reduc-tion in an anomalous polar summer anticyclone as seenin Fig. 11. This anomalous flow pattern at T42 has beenthought to be a major contributor to bases in the simu-lated Arctic sea ice distribution, which exhibits ice thatis too thick off the Siberian coast and too thin along the

Canadian coast. We will briefly touch on this when wediscuss coupled simulation results.

The final area in which there are conspicuous large-scale simulation differences between the T42 and T85models involves freshwater exchange with the surface.Although changes to the low-level dynamical circula-tion introduce desirable improvements in the evapora-tion of water (e.g., central Pacific subtropics), differ-ences in the net exchange of water are more oftendominated by changes in precipitation. Both configura-tions of the model continue to reproduce the majorfeatures of the hydrological cycle, but the T85 configu-ration exhibits marked redistributions of precipitationat low latitudes, as seen in Fig. 12.

The Pacific intertropical convergence zone (ITCZ)sharpens in the meridional direction at higher resolu-tion, particularly during boreal summer (Fig. 13). Thereare similar improvements to the representation of theAtlantic ITCZ, historically a very difficult feature toreproduce. The improved definition of the PacificITCZ is manifested primarily in the form of reductionsin subtropical precipitation, with very modest increasesin ITCZ precipitation along the equator. Some of themore important differences are seen in the tropicalwestern Pacific, which exhibits a substantial increase in

FIG. 9. (left) The surface wind stress magnitude (color contours) and vectors over the Tropics for (top) the T85 CAM3, (middle) theT42 CAM3, and (bottom) their difference. (right) The surface wind stress for (top) ERS, (middle) the difference between T85 CAM3and ERS, and (bottom) the difference between T42 CAM3 and ERS.


Fig 9 live 4/C

precipitation rate, maximizing northeast of NewGuinea and north of the Solomon Islands. Overall,there is a tendency to move precipitation closer to theequator, and in some cases into the equatorial NorthernHemisphere, as seen in the Indian Ocean. If we use theClimate Prediction Center (CPC) merged analysis ofprecipitation (CMAP) product as our standard ob-served precipitation climatology (Xie and Arkin 1997),these changes can be viewed as improvements. Reduc-tions in precipitation rates over Indonesia and the In-

dian peninsula represent desirable simulation improve-ments, as are the reductions in precipitation rate overthe western Arabian Sea and Gulf of Oman (notshown). The boreal winter simulation shows a signifi-cant and realistic enhancement of the South Pacificconvergence zone (SPCZ), as well as a reduction ofprecipitation in the Northern Indian Ocean (notshown). The improvements to the SPCZ are onlyweakly seen in the annual mean plots shown in Fig. 12,and arise from a weakening of the double-ITCZ-like

FIG. 10. The surface wind stress magnitude (color contours) and vectors over the Southern Hemisphere for (top) the T85 CAM3,(middle) the T42 CAM3, and (bottom) the T85–T42 difference. Contour intervals are 0.02 m s�2 for wind magnitudes and 0.01 m s�2

for differences.

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Fig 10 live 4/C

structure extending across the Pacific just south of theequator, and an enhancement of precipitation in thesouthern extratropical extension of the SPCZ.

Changes to the precipitation distribution represent

significant alterations to the net water exchange be-tween the atmosphere and ocean. The increase in pre-cipitation rate in the tropical western Pacific alters theT42 freshwater budget by approximately 20% and isanother area where we might expect to see large simu-lation differences when these atmospheres are coupledto a fully interactive ocean component model.

4. Simulation differences: Coupled configuration

In this section we provide an overview of the simu-lation differences as a function of resolution in CCSM3coupled configurations with an emphasis on the simu-lation results obtained from the land, ocean, and sea icecomponents. All simulations employ the CAM3 as theatmospheric component, either at T42 or T85 resolu-tion. The land surface model is discretized on the samehorizontal transform grid as the atmosphere: �2.8° atT42, and �1.4° at T85. The ocean and sea ice modelsmake use of a nominal 1° horizontal finite-differencediscretization for all the coupled simulations to be dis-cussed. We will make use of the nomenclature T85x1 torefer to the T85 atmosphere coupled to the 1° oceanmodel, and T42x1 to refer to the T42 atmospherecoupled to the 1° ocean. The T85x1 configuration of thecoupled model has been used to document the CCSM3simulations for international climate-change assess-ment purposes (see Collins et al. 2006a).

a. Atmosphere

In the previous section we discussed a broad class ofatmospheric simulation differences, including a warm-ing and drying of the simulated atmosphere at highresolution, along with three specific classes of simula-tion differences that would affect coupled componentmodels: localized radiation budget differences, large-scale differences in the dynamical circulation, and lo-calized changes in the freshwater budget.

The differences in global annual measures of thecoupled atmospheric simulation for both resolutionsare remarkably similar to the differences documentedin Table 1 for the uncoupled configuration. Otherlarge-scale simulation differences also carry over to thecoupled framework, including the tendency for aslightly warmer and drier simulation at high resolution.The resolution differences in the vertical distribution ofwater, in both condensed and vapor phases, are also inqualitative agreement with the uncoupled solutions(see also Hack et al. 2006). Regional radiation biasesare generally confined to convectively active regions inthe western Pacific and Indian Oceans as in the un-coupled model. Interestingly, the shortwave absorption

FIG. 11. Near-surface wind difference (top) between the T85CAM3 and the T42 CAM3 and (bottom) between the ERA-40and T42 CAM3 in m s�1.


Fig 11 live 4/C

anomalies in the eastern ocean stratocumulus regimespersist in the coupled framework, but are not as appar-ent in the coupled net surface energy budget differ-ences. This is due to adjustments in the other compo-nents of the surface energy budget in response to dif-ferences in SST (e.g., a warmer ocean) and localchanges in the low-level dynamical circulation (affect-ing sensible and latent heat transfers). Generally speak-ing, low latitude differences in the net surface energybudget as a function of resolution are considerablymore complex than shown in Fig. 6. The complexity ofthe resolution response is largely attributable to differ-ences in meridional shifts in deep convection, and theassociated changes to the cloud field and dynamicalcirculation. These differences will become more appar-ent when we discuss the coupled freshwater budget dif-ferences at the two resolutions.

Improvements in the dynamical circulation due to

higher resolution are generally similar to what is seen inthe uncoupled framework. Flow along the easternocean coastlines is improved, excessively strong tradesare reduced, the SH storm track moves poleward inagreement with observations, and seasonal anomaliesin the low-level Arctic circulations are reduced. Themajor exception includes the equatorial Indian Oceanwhere the low-level surface circulation improves alongwith the distribution of diabatic heating. A portion ofthis improvement appears to be associated with thecoupled atmosphere–ocean configuration (e.g., seeHack et al. 2006), and further improves at higher reso-lution.

Although many global annual atmospheric measuresof the energy and water cycles are virtually identical tothe uncoupled simulations, there are some notable localdifferences in the coupled and uncoupled configura-tion. One of the most egregious biases in the coupled

FIG. 12. Annually averaged precipitation over the tropical Indian and Pacific Oceans for (top) the T85 CAM3, (middle) the T42CAM3, and (bottom) the T85–T42 difference. Contour intervals are 1 mm day�1 for total precipitation rate and 0.25 mm day�1 fordifferences.

1 JUNE 2006 H A C K E T A L . 2279

Fig 12 live 4/C

framework is associated with the behavior of the simu-lated hydrological cycle, which exhibits large anomaliesin the exchange and storage of water in the atmospherewhen compared to the uncoupled CAM3 configuration(Hack et al. 2006). One feature is the shift in the surfaceexchange of water from the Northern to SouthernHemisphere Tropics, producing a significant and unre-alistic change to the freshwater budget over the tropicaloceans, most notably during the boreal winter. Thisshift is largely attributable to differences in the precipi-tation distribution. Although precipitation anomaliesappear in both the Atlantic and Pacific Basins, thezonal mean anomaly is dominated by changes over thePacific. This takes the form of an unrealistic enhance-ment of a southern and more vigorous branch of ITCZconvection extending across the Pacific Basin from thewarm pool to the Ecuador coast. There are hints of thistendency in the uncoupled model (see Fig. 12), a ten-dency which is amplified in the coupled configuration.The change to the precipitation distribution is symp-tomatic of the so-called double-ITCZ problem that

plagues many coupled models (e.g., see Davey et al.2002). Figure 14 shows the coupled model precipitationdistribution for the T42x1 and T85x1 configurationsalong with their difference. The figure shows locallysignificant meridional redistributions of precipitation asa function of horizontal resolution. This redistributionof diabatic heating is also associated with changes to thelow-latitude dynamical circulation, which alters the sur-face exchange of latent and sensible energy, and is re-sponsible for the complex differences in the net surfaceenergy budget discussed earlier. This figure also servesto illustrate an important and more general observationabout the contribution of horizontal resolution to manysystematic large-scale biases in the coupled CCSM3. Ascan be seen, the double-ITCZ problem is qualitativelypresent at both resolutions where the localized precipi-tation differences are consistent with the resolution bi-ases seen in the uncoupled model (e.g., poleward ex-tension of the SPCZ at high resolution). This result istypical of many other systematic large-scale coupledsimulation biases, such as measures of internal variabil-

FIG. 13. June–August (JJA) mean precipitation over the tropical oceans for (top) the T85 CAM3, (middle) the T42 CAM3, and(bottom) their difference. Contour intervals are 1 mm day�1 for total precipitation rate and 0.5 mm day�1 for differences.


Fig 13 live 4/C

ity (e.g., the Madden–Julian oscillation; MJO), whichalso show little, if any, sensitivity to changes in hori-zontal resolution (see Hurrell et al. 2006). This resultsuggests that the parameterized treatment of processeslike moist convection may play the most important rolein the introduction of such biases.

b. Ocean

In this section we present a brief summary of theocean model solutions from the T42x1 and T85x1 con-figurations, and refer to other papers in this volume foradditional detail. The ocean component in both con-figurations is identical (for model details see Danaba-soglu et al. 2005), and is initialized with January-meanclimatological potential temperature (�) and salinity (S)(Levitus et al. 1998; Steele et al. 2001) at a state of rest.For consistency, the following analysis of the meanstates is based on the same 30-yr time-mean period

(years 571–600) as in the T85x1 analysis of Large andDanabasoglu (2006, hereafter LD06).

Both the T42x1 and T85x1 ocean simulations showmodest, linear cooling trends after the first 50 years of

FIG. 15. Time- and horizontal-mean global differences betweenmodels and observations for potential (left) temperature and(right) salinity.

FIG. 14. Annually averaged precipitation over the tropical Indian and Pacific Oceans for (top) the T85 CCSM3, (middle) the T42CCSM3, and (bottom) the T85–T42 difference. Contour intervals are 1 mm day�1 for total precipitation rate and 0.25 mm day�1 fordifferences.

1 JUNE 2006 H A C K E T A L . 2281

Fig 14 live 4/C

integration. We compute these average trends as �0.19and �0.24 W m�2 heat losses at the surface betweenyears 400 and 600 in T42x1 and T85x1, respectively. Incontrast, the global-mean S is well preserved in both theT85x1 and T42x1 integrations.

The time- and horizontal-mean global difference pro-files from observations (Levitus et al. 1998; Steele et al.2001) for � and S are plotted in Fig. 15. The T42x1profile is warmer between 250 and 2250 m, and colderelsewhere when compared to the T85x1 profile. Thedepth ranges over which this warming and cooling oc-curs are very similar in all major ocean basins exceptthe deep Pacific where T42x1 is uniformly warmer byabout 0.1°C below 2000 m, in better agreement withobservations. Although the global-mean S is essentiallythe same at both resolutions, the S profiles (Fig. 15)exhibit different vertical distributions. The T85x1 pro-file is fresher than the T42x1 profile in the upper 1500m and saltier below this depth, where all major oceanbasin profiles show the same systematic behavior. TheT42x1 profile generally agrees better with observations.This is particularly true below 2250 m, where the T85x1simulation bias is reduced by one-half in T42x1. Wenote that the different vertical distributions of tempera-ture and salinity in the two configurations are densitycompensating.

Figure 16 shows the time-mean, vertically integratedmass transport (barotropic) streamfunction distributionfrom T42x1 and its difference from the T85x1 solution.A detailed discussion of the T85x1 circulation, includ-ing comparisons with observations, is presented inLD06. In general, all gyre circulation patterns and mag-nitudes are similar in the two configurations suggestingthat the wind stress curls are similar at both atmo-spheric resolutions. Localized differences are at mostorder 5–10 Sv (1 Sv � 106 m3 s�1) in the northern GulfStream, southern Agulhas, and equatorial Pacific gyre.The exception to this is the Southern Ocean where theAntarctic Circumpolar Current (ACC) is driven pri-marily by the zonal wind stress. In T85x1 the latitude ofthe maximum zonal-mean westerlies is in good agree-ment with satellite scatterometer data (Chin et al.1998), and represents an improvement over T42x1. Thesimulated winds, however, are too strong in the latitudeband of the ACC (Yeager et al. 2006, hereafter Y06).The erroneous equatorward displacement of the maxi-mum zonal wind at T42x1, coupled with a modest re-duction in magnitude, produces an equatorward shift inthe ACC with a mean transport at the Drake Passage of177 Sv. This represents a reduction of 15 Sv with respectto mean transport in T85x1, but still exceeds the obser-vational estimate of 134 � 13 Sv [Whitworth (1983) as

corrected by Whitworth and Peterson (1985)]. Gener-ally, the T85x1 configuration exhibits systematicallylarger transport along the ACC, which locally exceeds35 Sv compared to T42x1. Although the magnitude ofthe ACC transport is too strong at high resolution, theinterannual variability of the ACC transport (�15 Sv)is comparable in both configurations of the model.

The SST error patterns and magnitudes are verysimilar in the T42x1 and T85x1 configurations (seeLD06). This is primarily due to the same circulationerrors in each of the coupled configurations, leaving theassociated SST biases virtually unchanged. This is alsotrue in the Southern Ocean where the equatorwardshift of the ACC in T42x1 does not significantly changethe SST errors. Important exceptions are the large posi-

FIG. 16. Time-mean, vertically integrated mass transport (baro-tropic) streamfunction from T42x1 and its difference from theT85x1 solution. (top) Contour intervals are 10 and 20 Sv fortransports smaller and greater than 60 Sv, respectively. Also, thethick and thin (shaded regions) lines denote clockwise andcounterclockwise circulations, respectively. (bottom) The contourinterval is 5 Sv, and the negative differences (thin lines) areshaded.


tive SST biases off the west coasts of South America,South Africa, and Baja California. These biases aremodestly reduced at high resolution, where the spa-tially averaged differences for a 15°-wide strip immedi-ately off these coasts (see Y06) show reductions of ap-proximately 0.8°C off of South America and South Af-rica in the T85x1 simulation, but only 0.1°C off Baja. Asdiscussed earlier, regional changes in the surface windsand absorbed solar radiation between the two atmo-spheric resolutions are the likely contributors to thereductions in the SST biases. However, the principalsource of the remaining biases remains an unresolvedcoupled problem, because subsurface ocean tempera-tures and upwelling patterns also exhibit differences inthe T42x1 and T85x1 ocean solutions.

In general, simulation differences in the sea surfacesalinity (SSS) in the tropical regions primarily reflectthe changes in the precipitation fields. For example, theequatorial and southern tropical Atlantic exhibit exces-sive precipitation rates in T42x1, resulting in reducedSSS when compared to observations. Although precipi-tation rates in T85x1 are improved in these regions, thereduction in precipitation leads to only a small im-provement in the fresh bias by about 0.5 psu when com-pared to observations (see Fig. 1 of LD06).

The average Eulerian-mean meridional overturningcirculation (MOC) is very similar in the two configura-tions, as are the northward transports of heat and fresh-water. The T85x1 MOC shows some modest improve-ments compared to the T42x1 MOC, exhibiting adeeper penetration of the North Atlantic Deep Watercell. It also shows better agreement with some obser-vational estimates of southward flows at high latitude incertain density classes as detailed in Bryan et al. (2006).In contrast with the time-mean MOC, the amplitudes ofdecadal time-scale variability differ substantially. Dec-adal time scale variability for the T85x1 configuration isabout a factor of 2 larger than in the lower resolutionconfiguration. Further details of the MOC compari-sons, particularly for the Atlantic Ocean, are given inBryan et al. (2006).

Finally, in the eastern equatorial Pacific, both solu-tions have a strong semiannual signal in the seasonalcycle of SST anomalies. The characteristics of ENSOvariability are also very similar, as documented by Y06.The T85x1 Equatorial Undercurrent maximum is stron-ger than in T42x1 and closer to the observations ofJohnson et al. (2002). On the other hand, the asymmet-ric structure of the South Equatorial Current (with astronger southern branch) is degraded in the T85x1when compared to the T42x1 simulation (see Figs. 10and 11 of LD06).

Overall, despite large differences in the boundaryforcing of the ocean by the high-resolution atmosphere,the T85x1 simulation is quite similar to the T42x1 simu-lation. Each configuration enjoys specific strengths andweaknesses, although the simulation differences sug-gest that neither configuration is systematically betterwhen compared to observational estimates of the globalocean circulation.

c. Land

From the perspective of the Community Land Model(CLM), there are three aspects of the simulation thatchange with increased resolution. First, because theland model currently runs on the same grid as that ofthe host atmospheric model, a finer-scale representa-tion of the land surface is required. Consequently, theunderlying land surface can change in terms of the dis-tribution of land cover and soil types. Second, the landsurface is forced by and responds to changes in thenear-surface atmosphere (e.g., precipitation, tempera-ture, specific humidity) attributable to changes in atmo-spheric circulation. Third, there may be feedbackmechanisms between the land surface and the atmo-sphere that may amplify or dampen the response of theland surface to the changes in near-surface forcing.There are no scaling or tuning modifications madewithin the land model itself to accommodate higherresolution.

Attributing changes in land surface climatology toany of these mechanisms separately is difficult and out-side the scope of this paper. However, the contributionof the finer-scale land surface representation is likely tobe small compared to other factors. Globally, the landcover types change by at most 0.2%. Regionally, thelargest differences occur where there is extensive coast-line or where land cover is fragmented. Generally, ittakes more substantive changes in land cover to affectCLM regional surface climate (e.g., Oleson et al. 2004).Similarly, the largest change in soil texture (%sand or%clay) is 7%. For the larger regions we examine below,these relative differences are even smaller; a maximumof 3% for land cover change and 2% for soil texture.Therefore, in this section we simply address the issue ofwhether increased horizontal resolution significantly af-fects land surface climatology in response to changes inatmospheric radiative and hydrological forcing and sub-sequent feedbacks. We divide the land surface into con-tinental-sized regions, using subdivisions with knownbiases in the T42x1 simulation, to assess whether higherspatial resolution has improved or degraded the simu-lation (Table 2).

Global land seasonal averages of precipitation,

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evapotranspiration, runoff, air temperature, net radia-tion, and the Bowen ratio are fairly similar at T85x1 andT42x1 resolutions (Table 2). In general, changes in theair temperature correspond to changes in radiativeforcing and/or changes in the partitioning of net radia-tion into sensible and latent heat as reflected by theBowen ratio. There is about a 0.3°C decrease (increase)

in T85x1 global surface temperature in boreal winter(summer). These changes compare favorably to obser-vations, and correspond to decreases (increases) in netradiation.

In boreal winter, higher resolution results in coolertemperatures at low and middle latitudes of NorthAmerica (0.9° and 0.2°C cooling) and Europe (0.8°C;

TABLE 2. CCSM DJF and JJA land surface climatology. P: precipitation (mm day�1), E: evapotranspiration (mm day�1), R: runoff(mm day�1), T: 2-m air temperature (K), Q: net radiation (W m�2), and B: Bowen ratio. Observations are from Willmott and Matsuura(2000) (air temperature and precipitation) and Fekete et al. (2002) (runoff). Italics represents improvements in the T85 simulation ascompared to observations while bold represents deterioration. The asterisk denotes that the T85 simulation is not significantly differentfrom the T42 simulation at the 95% confidence level.

DJF JJA

P E R T Q B P E R T Q B

Global (90°S–90°N, 180°W–180°E)

T85 2.2 1.1 0.8 277.1 51.4 0.6 2.1 1.6 0.7 286.9 88.2 0.8T42 2.3 1.2 0.8 277.4 55.4 0.7 2.1 1.5 0.7 286.6 87.9 0.9Obs 2.0 — 0.7 276.5 — — 2.3 — 1.0 287.1 — —

North America high latitudes (60°–80°N, 170°–60°W)

T85 1.2 0.1 0.2 250.9 �24.0 �13.0 1.9 1.1 2.0 277.5 62.4 0.4T42 1.0 0.0 0.1 249.5 �18.8 �10.1 1.7 1.0 1.5 277.2 57.5 0.5Obs 0.6 — 0.1 246.1 — — 1.3 — 1.0 280.6 — —

North America midlatitudes (45°–60°N, 170°–50°W)

T85 2.1 0.3 1.0* 261.4* �6.1 �1.0 2.1 2.1 0.5 286.1 119.4 0.8T42 2.1 0.3 0.9 261.6 �1.9 �0.6 2.1 2.0 0.6 284.7 107.4 0.7Obs 1.7 — 0.4 259.0 — — 2.4 — 1.2 287.1 — —

North America low latitudes (15°–45°N, 170°–50°W)

T85 2.1 0.9 0.9* 277.8 33.5 0.4 2.5 2.3 0.3 297.8 150.6 1.2*T42 1.9 0.9 0.8 278.7 35.2 0.6 2.5 2.4 0.2 297.2 153.2 1.1Obs 1.7 — 0.6 278.7 — — 3.0 — 0.4 296.4 — —

Amazonia (10°S–0°, 70°–50°W)

T85 6.5* 4.0 2.6 299.8 143.8 0.3 2.5 2.1 0.8 300.8 127.6 1.1T42 6.6 4.2 2.4 299.4 152.2 0.3 1.5 1.4 0.3 301.1 120.0 2.0Obs 9.0 — 5.6 298.7 — — 3.0 — 2.6 298.3 — —

Europe (35°–80°N, 10°W–60°E)

T85 2.2 0.6 1.0 271.9 0.2 �0.7 1.5 1.6 0.1 292.4 116.1 1.3T42 2.1 0.6 1.0 272.7 1.0 �0.7 1.3 1.5 0.2 291.3 109.9 1.5Obs 1.6 — 0.6 269.7 — — 1.7 — 0.5 291.3 — —

North Africa (5°–35°N, 20°W–50°E)

T85 0.2 0.3 0.0 291.6 55.6 6.5 2.2 1.6 0.6 301.8 102.8 1.2T42 0.3 0.3 0.1 291.8 56.5 5.7 1.9 1.4 0.4 302.3 103.9 1.5Obs 0.3 — 0.1 292.3 — — 2.2 — 0.4 302.1 — —

Asia high latitudes (60°–80°N, 60°–180°E)

T85 0.9 0.0 0.0 247.4 �20.1 �16.7 1.9 1.5 1.2 281.4 83.5 0.5T42 0.8 0.0 0.0 245.7 �16.0 �10.6 1.7 1.2 1.4 279.8 74.1 0.6Obs 0.6 — 0.1 243.0 — — 1.5 — 1.4 282.9 — —

Asia midlatitudes (45°–60°N, 60°–160°E)

T85 1.0 0.2 0.1 256.7* �7.6 �0.9 2.3 2.1 0.3 289.1 115.9 0.7T42 1.0 0.2 0.2 256.8 �1.7 0.1 2.2 2.0 0.4 287.6 111.2 0.7Obs 0.5 — 0.1 253.1 — — 2.2 — 0.8 289.0 — —


see Fig. 17). A decrease in downward longwave radia-tion (not shown) and hence net radiation is likely to bepartially responsible for this cooling. At low and middlelatitudes over North America longwave radiation de-creases by 7 and 10 W m�2, and in Europe by 9 W m�2.The 4 W m�2 decrease in global net radiation reflects a6 W m�2 decrease in downward longwave radiation.Global absorbed solar radiation is virtually unchanged.However, there are changes in downward solar radia-tion and albedo in some of these regions that compen-sate somewhat for the decrease in longwave radiationand modulate the cooling. For example, at middle lati-tudes over North America, an increase in downwardsolar radiation along with a decrease in albedo in-creases absorbed solar radiation, which compensatessomewhat for the decrease in longwave radiation. Simi-larly, changes in the partitioning of net radiation intosensible and latent heat also interact with changes inradiative forcing. At low latitudes over North America,a wet bias in precipitation increases with higher reso-lution resulting in a shift in the Bowen ratio towardlarger latent heat fluxes that contribute to cooling. Thecooling in Europe and at middle latitudes over NorthAmerica reduces the T42x1 warm bias in these regions.

However, a cold bias at low latitudes in Asia is en-hanced (not shown) and a cold bias is introduced at lowlatitudes over North America in T85x1.

Warming in the high latitudes of North America andAsia offsets some of the boreal winter cooling de-scribed above. The 3°C warm bias in the T42x1 simu-lation in these regions increases by about 1.5°C athigher resolution. This does not appear to have much todo with the atmospheric radiative forcing since ab-sorbed solar and downward longwave radiation changeby less than 2 W m�2. Changes in atmospheric circula-tion appear to be responsible for the warming in theseregions (Dickinson et al. 2006).

In boreal summer, the increase in global temperatureappears to be primarily driven by the response of theland surface to increases in net radiation caused by in-creases in absorbed solar radiation (not shown). In par-ticular, there are increases in absorbed solar radiationof 22 W m�2 at midlatitudes in North America (1.4°Cwarming), 12 W m�2 in Europe (1.1°C), and 10 and 16W m�2 at mid- and high latitudes in Asia (1.5° and1.6°C). Changes in albedo are minimal at these scales(�0.01), with the exception of high latitudes in Asia,where higher incoming solar radiation is primarily re-

FIG. 17. Seasonally averaged 2-m temperature difference between (left) the T85x1 and observations and (right) the T42x1 andobservations for (top) December–February (DJF) and (bottom) JJA. Observations are from Willmott and Matsuura (2000).

1 JUNE 2006 H A C K E T A L . 2285

Fig 17 live 4/C

sponsible for the increase in absorbed solar radiation.Snow cover in high-latitude Asia is lower in the T85x1model, which results in lower albedo and contributes toincreased absorbed solar radiation. The warming in bo-real summer reduces cold biases in these regions exceptover Europe where a warm bias is introduced at higherresolution.

Over North Africa, land air temperature is somewhatcooler in the T85x1 simulation in both seasons (Fig. 17).In summer, this appears to be due in part to an increasein precipitation and evapotranspiration and a decreasein sensible heat that lowers the Bowen ratio. Radiativeforcing of the surface is also lower due to less incomingsolar radiation. These changes contribute to a year-round cold bias in this region.

The global average hydrologic cycle is not stronglyaffected by changes in horizontal resolution. Precipita-tion and runoff are biased high and low in boreal winterand summer, respectively, in both simulations. How-ever, there are noteworthy changes with resolution atsmaller scales. There is an overactive hydrological cycleat northern high latitudes year-round in the T42x1simulation. This appears to be slightly enhanced athigher resolution. In particular, winter precipitationand snow depth at high latitudes in North America areoverestimated. Consequently, the snowmelt season isdelayed and snow persists into early summer, whichlikely contributes to the cold summer bias. This prob-lem is less severe in high-latitude Asia because ofsmaller biases in precipitation. However, as noted pre-viously, the cold bias in summer in this region appearsto be improved at higher resolution because there arefairly large increases in incoming solar radiation inspring and summer that compensate for the increase inwinter snow depth. Snow appears to melt back at aboutthe same rate as the T42x1 simulation.

Summer runoff at high latitudes in North Americaincreases by 50% at higher resolution and is aboutdouble what the observations suggest. Much of this isdue to a much stronger runoff peak in June caused bymelting of the deeper snowpack. In high-latitude Asia,despite a small increase in winter precipitation andsnow depth, the runoff in summer is actually lower inthe T85x1 simulation. This is because warmer tempera-tures and increased solar radiation combine to meltsnow sooner and create a runoff peak that occurs inMay in the T85x1 simulation as compared to June in theT42x1 simulation. However, the runoff peak in May isstill substantially higher than in the observations.

Other regions that have significant biases in hydrol-ogy in the T42x1 simulation show improvement athigher resolution. Summer precipitation in Europe andNorth Africa has improved slightly. In the Amazonia

region, higher resolution increases dry season precipi-tation by 67%, resulting in favorable increases inevapotranspiration and runoff and cooler tempera-tures. However, improvements in precipitation do notnecessarily translate to improvements in the simulationof runoff in other regions. For example, summer pre-cipitation over North Africa agrees quite well with ob-servations but runoff is biased high at higher resolution,suggesting that improvements to the treatment of run-off processes are likely required.

d. Sea ice

A detailed analysis of the simulation of Arctic sea iceas a function of horizontal resolution is addressed inDeWeaver and Bitz (2006). Therefore, in this sectionwe will only summarize the major findings. Historically,simulated Arctic sea ice in the CCSM model has beentoo thick off the Siberian coast and too thin along theCanadian coast using a T42 atmosphere. Both of thesebiases are significantly reduced by moving to thehigher-resolution T85 atmosphere. The improvementin ice distribution is associated with the improvement inthe surface wind distribution discussed in the context ofthe uncoupled simulation, most notably an erroneousnorth polar summer anticyclone, which is a feature ofthe T42 configuration, but absent at T85. DeWeaverand Bitz (2006) employ an offline sea ice model to ex-plore the reasons for the simulation improvement ingreat detail. Although improvements in the surfacewind forcing of the ice plays a major role in the icedistribution improvement, their results also suggest thatdifferences in thermodynamic forcing of the ice are animportant contributor. We refer the interested readerto DeWeaver and Bitz (2006) for more detail.

5. Summary

Like many other major climate modeling activities,the CCSM global atmospheric model has employed thesame moderately low-resolution truncation for morethan a decade. The development and release of theCAM3 provides the opportunity to comprehensivelyexplore the benefits of exploiting higher horizontalresolutions in a framework that provides comparablelarge-scale simulation fidelity when compared withsimulations using more traditional horizontal trunca-tions.

We have presented selected features of the CAM3simulation as a function of two horizontal spectral trun-cations, T42 and T85, for both coupled and uncoupledconfigurations. The model formulations differ only inthe rate at which energy is dissipated by horizontal dif-


fusion processes, and in the choices of a limited numberof coefficients associated with parameterized convectionand cloud processes. The uncoupled high-resolutionmodel exhibits a number of systematic simulation im-provements, including a desirable systematic warmingof the troposphere, large-scale improvements in thelow-level dynamical circulation, and local improve-ments to the representation of clouds and precipitationregimes, such as the improved structure of the AtlanticITCZ. Despite anomalies in the near cancellation oflongwave and shortwave cloud forcing in the Tropics,the T85 model demonstrates a superior radiative re-sponse to the ENSO cycle, particularly in the shortwavecomponent of the radiation budget. In the context ofatmospheric teleconnections, the T85 CAM3 providesan extremely realistic Pacific–North American (PNA)response, where the amplitude of the T42 response isonly about half as strong (Deser et al. 2006). Usingwarm � cold composites, Deser et al. (2006) also showthat although there is no clear benefit of high resolutionto temperature teleconnections, the T85 model is de-monstrably better than the T42 model with regard toprecipitation teleconnections.

Most of the resolution-dependent simulation im-provements seen in the uncoupled CAM3 carry over tothe coupled model framework. From a sea ice perspec-tive, the higher-resolution atmosphere has a significantpositive impact on the simulation of Arctic ice, asshown by DeWeaver and Bitz (2006). From a land per-spective, the higher-resolution atmosphere produces animproved simulation of air temperature and hydrologyin some regions. Most notably, there is a 0.8°C coolingin boreal winter over Europe, which reduces a warmbias in the T42 simulation. The T85 atmosphere alsoresults in warming during boreal summer at mid- andhigh latitudes over North America and Asia, reducing acold T42 bias. There are, however, some aspects of thesimulation that degrade with the high-resolution atmo-sphere. The boreal winter warm bias at high latitudes inthe T42 simulation is worse at higher resolution. Simi-larly, the overactive hydrological cycle at northern highlatitudes year-round appears to be enhanced, particu-larly over North America.

Despite a number of significant localized changes inthe radiative, dynamical, and freshwater forcing of theocean with the higher-resolution atmosphere, there isvirtually no resolution response seen in the ocean com-ponent simulation. This is perhaps most remarkable inthe eastern ocean stratus regions where the radiativeforcing of the ocean surface is reduced by more than 40W m�2 and the coastal surface wind stress has beensignificantly improved with regard to upwelling. Theselarge surface forcing improvements produce relatively

minor responses to the warm sea surface temperatureanomalies simulated by the ocean component. Thestructure and amplitude of other large-scale simulationanomalies like the Pacific double ITCZ are also largelyindependent of horizontal resolution. The overallocean response to the high-resolution atmosphere leadsto the conclusion that the ocean model solutions fromthe high- and low-resolution configurations are not sig-nificantly different. Similarly, Deser et al. (2006) findthat the structure, period, and amplitude of ENSO arerelatively insensitive to the resolution of the atmo-spheric component model.

Overall, the high-resolution version of the CAM3 ex-hibits a mixed message, especially with regard to thecoupled modeling framework. Although the higher-resolution atmosphere has a limited impact on improv-ing the quality of some component model solutions,there is little question that the overall quality of theatmospheric simulation is improved. Perhaps one of themost important observations about the T85 configura-tion is contained in Fig. 1. It is clear that the propertiesof the resolved-scale motions seen by the parameter-ized physics are much more realistic at T85 than at T42.As such, the T85 truncation would appear to be a moreappropriate resolution at which to test parameteriza-tion techniques developed using observational data. Itremains to be seen why the pointwise-resolved scalemotions appear to be so much more energetic at highresolution, but that modes of internal variability likethe MJO exhibit no improvement with increased hori-zontal resolution (see Hurrell et al. 2006). This is butone of many questions that will be answered as thedifferences in simulation quality associated withchanges in horizontal resolution are explored in greaterdetail.

Acknowledgments. We would like to acknowledgethe substantial contributions to the CCSM project fromthe National Science Foundation (NSF), Departmentof Energy (DOE), the National Oceanic and Atmo-spheric Administration, and the National Aeronauticsand Space Administration. In particular, Hack, Trues-dale, and Caron wish to acknowledge support from theDOE Office of Science, NCAR Water Cycle AcrossScales Initiative, and the Central Research Institute forthe Electric Power Industry (CRIEPI). This study isbased on model integrations performed by NCAR andCRIEPI with support and facilities provided by NSF,DOE, MEXT, and ESC/JAMSTEC.

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