JUNE 1998 1179H A C K E T A L .

q 1998 American Meteorological Society

The Hydrologic and Thermodynamic Characteristics of the NCAR CCM3*

JAMES J. HACK, JEFFREY T. KIEHL, AND JAMES W. HURRELL

National Center for Atmospheric Research,+ Boulder, Colorado

(Manuscript received 15 May 1997, in final form 29 September 1997)

ABSTRACT

Climatological properties for selected aspects of the thermodynamic structure and hydrologic cycle are pre-sented from a 15-yr numerical simulation conducted with the National Center for Atmospheric Research Com-munity Climate Model, version 3 (CCM3), using an observed sea surface temperature climatology. In mostregards, the simulated thermal structure and hydrologic cycle represent a marked improvement when comparedwith earlier versions of the CCM. Three major modifications to parameterized physics are primarily responsiblefor the more notable improvements in the simulation: modifications to the diagnosis of cloud optical properties,modifications to the diagnosis of boundary layer processes, and the incorporation of a penetrative formulationfor deep cumulus convection. The various roles of these physical parameterization changes will be discussedin the context of the simulation strengths and weaknesses.

1. Introduction

One of the more important long-standing problemsin global modeling of the climate system and its sen-sitivity to increased greenhouse gases is how to accu-rately include the effects of the various components ofthe hydrologic cycle into the governing meteorologicalequations. In the past, earth’s hydrologic cycle has beencharacterized as an aspect of the climate system thatwas simply controlled by the general circulation (e.g.,see Riehl 1965). It is now clear, however, that the detailsof evaporation, precipitation, runoff, and water transportare very much an integral part of the general circulation,representing a major component of the overall energybudget, particularly for the thermally driven circulationsin the Tropics and subtropics (Chahine 1992).

Water in any phase is a strongly radiatively activeatmospheric constituent, and changes in water phase area major source of diabatic heating in the atmosphere.Consequently, the large-scale moisture field plays a fun-damental role in the maintenance of the general circu-lation and climate, where the sources, sinks, and a largecomponent of the transport responsible for its time evo-lution are inadequately understood. It is generally rec-ognized that our ability to numerically model climate

* An electronic supplement to this article may be found on the CD-ROM accompanying this issue or at http://www.ametsoc.org/AMS.

1 The National Center for Atmospheric Research is sponsored bythe National Science Foundation.

Corresponding author address: Dr. James J. Hack, NCAR/CGD,P.O. Box 3000, Boulder, CO 80307-3000.E-mail: jhack@ucar.edu

and climate change is fundamentally limited by a lackof understanding of the interaction of moist processesand the large-scale radiation field, particularly with re-spect to clouds (e.g., Stephens and Webster 1981; Cesset al. 1990). Clouds are a central component in the hy-drologic cycle since they directly couple dynamical andhydrological processes in the atmosphere through therelease of the latent heat of condensation and evapo-ration, through precipitation, and through the verticalredistribution of sensible heat, moisture, and momen-tum. They play an equally critical role in the large-scalethermodynamic budget through the reflection, absorp-tion, and emission of radiation, and they are directlyinvolved in the chemistry of the earth’s atmosphere.Efforts to realistically incorporate these processes on aplanetary scale are hampered by the wide range of im-portant space and time scales contained in the atmo-sphere’s general circulation. Cloud-scale processes in-volving phase change influence the behavior of the at-mosphere on all time and space scales but operate onscales of motion distinctly separate from those of thelarger-scale circulation. Because of this scale separation,their collective effects on the general circulation areparameterized as a function of the large-scale fields. Theform of the parameterized treatment of the principalcomponents of the hydrologic cycle strongly influencesthe fidelity of global climate simulations.

The formulation of the National Center for Atmo-spheric Research (NCAR) Community Climate Modelversion 2 (CCM2) was a significant departure from ear-lier versions of the CCM, which suffered from highlysimplified physical parameterizations, particularly forthe determination of surface temperature, surface energyexchanges, boundary layer transfers, moist convection,

1180 VOLUME 11J O U R N A L O F C L I M A T E

and the diagnosis of cloud amount and its interactionwith the radiation field. Most of the CCM1 physicalparameterization components were replaced with con-siderably more sophisticated methods for treating thesekey climate processes (Hack et al. 1993). Althoughmany aspects of the CCM2 simulation have been shownto be significantly more realistic when compared withpredecessor models (e.g., Hack et al. 1994; Kiehl et al.1994; Hurrell et al. 1993), a number of important sys-tematic deficiencies continued to plague the simulation,many of which would introduce serious climate driftsif the CCM2 were to be coupled to interactive land, sea-ice, and ocean component models. One of the moreglaring weaknesses included several aspects of the sim-ulated hydrologic cycle, which was extremely activewhen compared to observational estimates (e.g., Lau etal. 1995).

A principal objective for the development of theCCM3 was to address the more serious systematic errorspresent in the CCM2 simulation, so as to make the at-mospheric model more suitable for coupling to otherclimate component models, such as in the NCAR Cli-mate System Model described by Boville and Gent(1998). The vast majority of this development activitytherefore focused on further improving the formulationof several key physical parameterizations (Kiehl et al.1996). These improvements are reflected in a consid-erably more credible climate simulation for the CCM3(e.g., Kiehl et al. 1998a; Kiehl et al. 1998b; Hurrell etal. 1998). In this paper we will discuss selected aspectsof the thermodynamic structure and hydrologic cycle assimulated by CCM3. We will examine characteristics ofthe mean state, annual cycle, and aspects of variabilityin response to El Nino–Southern Oscillation (ENSO).We will also discuss the physical reasons for these char-acteristics in the context of the changes in physical pa-rameterization. Comparisons will be made with avail-able observational data. Since there is considerable un-certainty in many measures of the hydrological cycle,and some aspects are not observed on a global basis,we will also occasionally include CCM2 simulation re-sults to provide a stable reference point for illustratinghow selected simulation metrics have changed.

2. Parameterization changes and the globalhydrologic cycle

The CCM3 is the fourth generation in the series ofNCAR’s Community Climate Model. Most aspects ofthe model’s dynamical formulation and implementationare identical to the CCM2. The most important changesto the model formulation have been made to the col-lection of parameterized physics. When compared to theCCM2, changes to the physics most relevant to the glob-al hydrological cycle fall into three major categories:modifications to the representation of radiative transferthrough both clear and cloudy atmospheric columns;modifications to the atmospheric boundary layer, moist

convection, and surface energy exchange formulations;and the incorporation of a sophisticated and compre-hensive land surface model (LSM) developed by Bonan(1996). The LSM is a one-dimensional model of energy,momentum, water, and CO2 exchange between the at-mosphere and land. It accounts for ecological differ-ences among vegetation types, hydraulic and thermaldifferences among soil types, and allows multiple sur-face types, including lakes and wetlands, within a singlegrid cell. LSM replaces the prescribed surface wetness,prescribed snow cover, and prescribed surface albedosemployed in the CCM2, as well as the CCM2 mathe-matical formulations for evaluating land surface fluxes.The incorporation of the land surface model greatly im-proves regional aspects of the simulated surface climate,particularly with regard to diurnal and seasonal cycles,where the specific simulation characteristics are shownin Bonan (1998) and will not be examined here.

Let us begin by discussing the remaining two cate-gories of modified physics components in the contextof globally and annually averaged properties of theCCM3 simulated climate, the CCM2 simulated climate,and the corresponding observational estimates (shownin Table 1). These particular global averages are highlystable measures of model performance, where even rel-atively small differences generally indicate significantsystematic changes in the simulation properties. We be-gin with the suite of changes incorporated in the pa-rameterization of clouds and radiation. Changes to theclear-sky radiation formalism include the incorporationof trace gases (CH4, N2O, CFC11, CFC12) and someminor CO2 bands in the longwave parameterization, andthe incorporation of a background aerosol in the short-wave parameterization (Kiehl et al. 1998a). All-skychanges include improvements to the way in whichcloud optical properties are diagnosed (Kiehl 1994a;Hack 1998a), the incorporation of the radiative prop-erties of ice clouds, and several other minor modifica-tions to the parameterization of convective and layeredcloud amount (Kiehl et al. 1998a). The global impactof these modifications can be seen in Table 1, wheresystematic biases in the clear-sky and all-sky outgoinglongwave radiation and absorbed solar radiation are sub-stantially reduced to well within observational uncer-tainty, while maintaining very good agreement withglobal observational estimates of cloud forcing. Theprincipal effect of the clouds and radiation modificationsis to improve the top-of-atmosphere (TOA) and surfaceenergy budgets, both regionally and globally (e.g., seeKiehl et al. 1998b). In particular, these parameterizationchanges greatly reduce net surface solar radiation re-sulting in a decrease in the warm July surface temper-ature bias over the Northern Hemisphere, reductions inthe systematic overprediction of precipitation overwarm land areas, and improvements in the simulatedstationary wave structure (e.g., see Hack 1998a). Thereduction in the global annual latent heat flux (whichwe will use as one measure of the overall strength of

JUNE 1998 1181H A C K E T A L .

TABLE 1. Global annual average properties.

Observations CCM2 CCM3

Outgoing longwave radiation (W m22)all skyclear sky

234.8a

264.0a

241.10271.87

236.97266.22

Absorbed solar radiation (W m22)all skyclear sky

238.1a

286.3a

245.35295.49

236.88286.42

Longwave cloud forcing (W m22)Shortwave cloud forcing (W m22)

29.2a

248.2a

30.76250.14

29.25249.54

Cloud fraction (percent)totallowmiddlehigh

52.2b–62.5c

26.0c–43.8d

18.0c

14.0c

52.8630.5222.2028.89

58.8334.7520.8434.62

Cloud water path (mm)Precipitable water (mm)Latent heat flux (W m22)Sensible heat flux (W m22)Precipitation (mm day21)Net surface solar radiation (W m22)Net surface longwave radiation (W m22)

0.0700–0.0813e

24.7f

78.0h

24.0h

2.69g

168h

66h

—25.52

104.049.323.58

180.8962.58

0.046523.3989.9720.47

3.09171.05

60.68

Annual mean budgets (W m22)TOA energy budgetsurface energy budgettotal water (P 2 E)

4.254.950.00

20.0920.07

0.00

a ERBE.b Nimbus-7 (Hurrell and Campbell 1992).c ISCCP (Rossow and Zhang 1995).d Warren et al. (1988).e Greenwald et al. (1995), all-sky average.f NVAP (Randel et al. 1996).g Xie and Arkin (1996).h Kiehl and Trenberth (1997).

the hydrologic cycle) associated with cloud optical prop-erty changes represents approximately 3 W m22 of thedecrease shown between CCM2 and CCM3.

The remaining category of physics changes have thegreatest impact on the simulated hydrologic cycle. Thefirst of these includes revisions to the formulation of theatmospheric boundary layer (ABL) parameterization,resulting in greatly improved estimates of boundary lay-er height, and a substantial reduction in the overall mag-nitude of the hydrologic cycle, approximately 8 W m22

in the global annual mean when compared to the CCM2.Since the ABL modifications minimally affect the netsurface energy balance, the reduction in latent heat fluxis offset by a comparable increase in sensible heat flux(as seen in Table 1). Thus, the revised boundary layerformulation results in a more realistic partitioning of thesurface turbulent heat flux. Parameterized convectionhas also been modified in CCM3 where moist convec-tion is now represented using the deep cumulus for-malism of Zhang and McFarlane (1995) in conjunctionwith the scheme developed by Hack (1994) for theCCM2. A Sundqvist (1988) style evaporation of strat-iform precipitation is also incorporated in the CCM3,playing an important role in maintaining the simulated

thermodynamic structure of the lower troposphere.These changes result in an additional reduction to themagnitude of the global hydrologic cycle (approxi-mately 3 W m22) along with a number of other desirableimprovements, such as a smoother distribution of trop-ical precipitation and a warmer tropical troposphere.The additional reduction in latent heat flux is generallyconfined to regions of deep convection, most notablyin the ITCZ, and is the singlemost important simulationfeature contributing to the improvement in the impliedmeridional ocean heat transport (Hack 1998b). Othernoteworthy changes in global measures associated withthe modified convection parameterization are a largeincrease in high cloud amount and a decrease in pre-cipitable water (see Table 1). Finally, surface roughnessover ocean surfaces is diagnosed as a function of surfacewind speed and stability in the CCM3, resulting in morerealistic surface flux estimates for low wind speed con-ditions. The combination of these changes to the majormoist physics parameterizations results in more than a10% reduction in the annually averaged global latentheat flux and the associated precipitation rate.

Several of the more relevant global measures of thehydrologic cycle exhibit interesting, albeit relatively

1182 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 1. Annual cycle of globally averaged precipitable water, pre-cipitation, and E 2 P, for the CCM3 and corresponding observationalestimates.

weak, seasonal behaviors. Figure 1 shows the seasonalcycle of precipitable water (representing the storage ofwater vapor in the atmosphere), precipitation, and thedifference between evaporation and precipitation, for

the CCM2, CCM3, and the corresponding observationalestimates. The estimates of precipitation rate are takenfrom Xie and Arkin (1996), who have constructed globaldistributions of monthly precipitation by combining datafrom gauge observations, several different satellite re-trieval estimates, and predictions from the EuropeanCentre for Medium-Range Weather Forecasts(ECMWF) operational forecast model. Observationalestimates of precipitable water are taken from the Na-tional Aeronautics and Space Administration (NASA)Water Vapor Project (NVAP), which combines watervapor retrievals from the TIROS-N Operational VerticleSounder (TOVS) and the Special Sensor Microwave/Imager (SSM/I) platforms with radiosonde observations(Randel et al. 1996).

The CCM simulations and NVAP analysis all showa clear annual cycle in precipitable water, with minimain December and maxima during July and August. TheCCM2 exhibits the largest global water vapor contentand the largest amplitude in the global annual cycle, 4.9mm compared with just under 3.5 mm shown in theNVAP data. The CCM3 is systematically drier than theNVAP estimates, approximately 1.3 mm in the globalannual mean, with a slightly weaker annual amplitudeof just under 2.0 mm. Kiehl et al. (1998a) attribute theremaining bias in the TOA clear-sky OLR to the pre-cipitable water dry bias present in the CCM3 simulation.In all cases the global annual cycles shown in the firstpanel of Fig. 1 are dominated by a stronger seasonalcycle in precipitable water over the Northern Hemi-sphere. The seasonal cycle in precipitable water is fueledby an imbalance in the globally averaged evaporationand precipitation, where the precipitation term exhibitsa similar seasonal peak during the Northern Hemispheresummer months. The imbalance responsible for the an-nual cycle in precipitable water turns out to be verysmall, measured in fractions of a watt per square meter,as shown in the third panel (where the curve labeledOBS is derived from the NVAP and Xie and Arkindatasets). As suggested by the first two panels, theCCM3 does a good job of simulating the phase of thisimbalance with slightly weaker amplitude when com-pared to the observational estimates. The CCM2 showsnoticeable differences in both amplitude and phase.

Another important role of water in the atmosphere isin the form of clouds. Although cloud condensate is notcarried as a prognostic variable in the CCM3, it is adiagnosed quantity that strongly affects the global ra-diation budget. In the global annual mean the verticallyintegrated condensed water (or cloud water path) is con-siderably smaller than the atmospheric water vapor con-tent, but contributes to an important radiative forcingon the climate system as measured by longwave andshortwave cloud forcing (see Table 1). The globally av-eraged cloud fraction exhibits a clear seasonal cycle inboth the CCM2 and CCM3, with maximum cloudamount occurring during the Northern Hemisphere win-ter months (Fig. 2). A comparable seasonal cycle is not

JUNE 1998 1183H A C K E T A L .

FIG. 2. Annual cycle of globally averaged total cloud, cloud water,and shortwave cloud forcing (SWCF), for the CCM3 and correspond-ing observational estimates.

seen in either of the corresponding observational da-tasets, which are derived from International SatelliteCloud Climatology Project (ISCCP) (Rossow and Zhang1995) and from Nimbus-7 (Hurrell and Campbell 1992).The ISCCP and Nimbus-7 observations bracket the

CCM2 and CCM3 results, where the CCM3 cloudamount is systematically larger than in CCM2 through-out the entire year. The greater total cloud amount inCCM3 is largely the result of an increase in high cloudthat accompanied the introduction of the Zhang–Mc-Farlane scheme for treating deep cumulus convection.

The CCM3 annual cycle in cloud amount is reflectedin a somewhat similar seasonal variation in condensedwater, as shown in the middle panel of Fig. 2, wherethe principal maximum in cloud water path occurs inJanuary, with a weak secondary maximum occurringduring June and July. The satellite-derived cloud liquidwater path data obtained from Greenwald et al. (1995)shows a distinct seasonal cycle, but with a much stron-ger amplitude and virtually completely out of phase withthe CCM3. The Greenwald et al. (1995) data are for the4-yr period 1988–91, and represent an ocean-only all-sky field of view for nonprecipitating clouds, where nothresholding has been applied to the data. Satellite-de-rived data for which precipitating clouds have not beenfiltered exhibit a similar seasonal behavior with system-atically larger values (as suggested by the global annualmean shown in Table 1). An ocean-only cloud wateraverage for CCM3 is also shown in the middle panelfor more direct comparison with the satellite-deriveddata. It exhibits a slightly stronger seasonal cycle incloud condensate, but is basically the same as the morecomplete global average. The all-sky representation ofthe satellite-derived data is more consistent with theaveraging process used for the CCM3 simulated con-densed water path since clear-sky conditions are alsoincluded in those averages. One important difference,however, is that cloud water is not differentiated fromcloud ice in the CCM3 (i.e., the CCM3 data includestotal condensate) and the Greenwald data includes onlyliquid condensate. The large discrepancy between theobservational estimate and the CCM3 simulation ofglobally averaged cloud water is mostly attributable todifferences in the Tropics and subtropics (as we willshow later) for reasons that are not well understood. Thephysical reasons for the difference in the phase of theseasonal cycle are also difficult to understand, sinceocean-only cloud water averages from the CCM3 ex-hibit the same kind of seasonal behavior as the morecomplete global average. Another way to try to indi-rectly explore the actual seasonal cycle in cloud wateris to use the Earth Radiation Budget Experiment (ERBE)shortwave cloud forcing (SWCF) (bottom panel of Fig.2). The increases in CCM3 SWCF during the Northernand Southern Hemisphere summer months are consis-tent with the increases in cloud water path shown in themiddle panel. The fact that the ERBE SWCF data alsoindicate a similar seasonal behavior suggests that theannual cycle in cloud water path is likely to be moresimilar in phase to the CCM3 than to current satelliteestimates of this quantity. The role of the partition be-tween ice and liquid condensate in contributing to the

1184 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 3. Annual cycle of evaporation, precipitation, and E 2 Paveraged over land and ocean surfaces for the CCM3. Annual meansare indicated in parentheses.

discrepancy in seasonal cycle requires further investi-gation.

Finally, we look at the annual cycle of evaporation,precipitation, and their difference (E 2 P), as averagedover land and ocean surfaces for both the CCM2 andCCM3. As seen in Fig. 3, the CCM3 exhibits system-atically weaker evaporation and precipitation amountsover land and ocean surfaces when compared to theCCM2. Weak, but similar, seasonal variations in thesequantities exist over ocean surfaces, whereas the twomodels show considerably different exchange charac-teristics over land surfaces. Both models show a sea-sonal cycle of evaporation over land maximizing duringthe Northern Hemisphere summer months. The ampli-tude of the CCM3 annual cycle is less than one-half aslarge as in the CCM2, and 40% smaller in terms of theannual amplitude. Land precipitation differences areequally large. The CCM3 shows little evidence for astrong annual cycle in precipitation, whereas the CCM2shows a well-defined July maximum in precipitation,with a secondary maximum during April. As in the caseof evaporation, precipitation over land is substantiallyreduced in the CCM3 when compared with CCM2, withmaximum monthly average differences exceeding 1.5mm day21. The simulated differences in evaporation andprecipitation over land are largely attributable to thecloud optical property parameterization modifications(Kiehl 1994a; Hack 1998a) and to the incorporation ofa considerably more realistic land surface treatment(Bonan 1996). The net result, as measured by E 2 P,is a noticeably weaker transfer of water from the oceansto the land in the CCM3 throughout the year. Both sim-ulations show similar annual cycles in the land- andocean-averaged E 2 P, where the ocean surplus andland deficit are systematically smaller in the CCM3.

3. Mean-state seasonal simulation properties

a. Temperature and specific humidity

Two of the most basic of all climate properties arethe structure of the temperature and water vapor field.To interpret the simulation quality of these fields, wecompare them to the National Centers for Environmen-tal Prediction (NCEP) global reanalyses (Kalnay et al.1996). The archive we use consists of twice-daily anal-yses at 17 pressure levels in the vertical from which 15-yr (1979–93) December–February (DJF) and June–Au-gust (JJA) climatologies were constructed. The resultsare truncated from T63 to T42 resolution for comparisonto the pressure-interpolated fields from CCM3. Figures4 and 5 show the CCM3 seasonal DJF and JJA zonalaverages of temperature and specific humidity differ-ences from the respective NCEP climatology.

Overall, the CCM3 simulation does a very crediblejob of reproducing the analyzed thermal structure, wherethe simulated temperatures are within 1–2 K of the an-alyzed field for the domain bounded by 508N and 508S

and 200 mb. The CCM3 simulation exhibits a weakwarm bias in the low to middle portion of the tropicaltroposphere, as compared to the very weak cold biasseen in the CCM2. This warming of the Tropics accom-panied the inclusion of the deep convection scheme. The

JUNE 1998 1185H A C K E T A L .

FIG. 4. Zonally and seasonally averaged differences in temperature (CCM3 2 NCEP) for DJF and JJA.

CCM3 continues to poorly simulate polar tropopausetemperatures, which can be from 10–14 K colder thananalyzed. Cold polar tropopause simulations have beendocumented to be a pervasive problem in atmosphericgeneral circulation modeling (Boer et al. 1992) and rep-resents an ongoing simulation deficiency in the variousversions of the NCAR CCM, despite the many other

major simulation improvements. Williamson and Olson(1998) have shown a marked improvement in the polartemperature bias, where the bias is reduced by morethan a factor of 2, in response to the replacement of theCCM3 Eulerian dynamical core with a semi-Lagrangiandynamical core. Their results suggest that a large com-ponent of the CCM3 polar thermal bias may be attrib-

1186 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 5. Zonally and seasonally averaged differences in specific humidity (CCM3 2 NCEP) for DJF and JJA.

utable to numerical approximation errors associatedwith large-scale advection. Tropical upper-tropospheretemperatures are also more poorly simulated in theCCM3. Both seasons exhibit a zonal-mean cold bias ofbetween 3 K and 4 K at the tropical tropopause. Thistype of cold bias was also seen in the CCM2 simulation,although it is more pronounced in the CCM3. The en-hancement of this bias also accompanied the introduc-tion of the deep convection scheme, and the associatedincrease in upper-level tropical cloud amount. Despitethe problems with properly representing tropopausetemperatures, the CCM3 does a very good job of cap-

turing variations in tropopause height. This attribute ofthe simulation is readily seen in zonally averaged tem-perature plots (not shown) and in regional thermody-namic profiles (which will be shown later).

The tropospheric warm bias present in the JJA CCM2simulation poleward of 408N has become a weak coldbias in the CCM3. The CCM2 Northern Hemispherewarm bias was primarily a consequence of inadequaciesin the diagnosis of cloud optical properties, which con-tributed to an excessive input of solar energy at thesurface. Improvements to the parameterization of cloudsand radiation, along with the introduction of a more

JUNE 1998 1187H A C K E T A L .

FIG. 6. Zonally and seasonally averaged precipitable water distri-bution for the CCM3 and CCM2 simulations, and for the NCEP andNVAP water vapor analyses.

realistic land surface model, addressed this very serioussimulation deficiency. As in the CCM2, simulated tem-peratures at high latitudes in the lower troposphere con-tinue to be colder than analyzed.

The zonally averaged cross sections of specific hu-midity differences shown in Fig. 5 are not as useful ameasure of the simulation quality as are the temperaturedifferences. There are large uncertainties in the analyzedmoisture field (e.g., Trenberth and Guillemot 1995),where the water vapor distribution strongly dependsupon the collection of parameterized physics associatedwith a particular analysis cycle. Consequently, thesemoisture biases are most useful for illustrating similar-ities in the behavior of the CCM3 against a standardanalysis product, but should not be viewed as a defin-itive quantification of model error. For this reason wewill also compare the simulated water vapor field withthe NVAP climatology and with regional climatologiesconstructed from radiosonde data. When comparedagainst the NCEP analysis, the CCM3 simulated mois-ture distribution appears to be in reasonably good agree-ment. There is evidence of a meridionally broad low-level dry bias on the order of 1 g kg21 in both DJF andJJA, with a weak moist bias in the ITCZ. The largestdifference appears poleward of 308N during JJA wherea pronounced dry bias extends up through the middletroposphere, with maximum values of 3.5 g kg21 at 358Nnear the surface. This dry bias is in sharp contrast withthe CCM2 simulation, which exhibited a modest moistbias in this region, principally due to its cloud opticalproperty deficiencies and relatively crude representationof land surfaces.

Because of the uncertainties in any single analysis ofthe water vapor field, we also compare the CCM3 withthe NVAP precipitable water climatology, the verticalintegral of the specific humidity. Figure 6 shows thezonal- and seasonally averaged precipitable water forCCM3, CCM2, NVAP, and NCEP. This figure shows asimilar meridional bias in the JJA meridional distribu-tion of water vapor, where a pronounced NorthernHemisphere dry bias is clearly indicated by the NVAPdata, exceeding 5 kg m22 (or 5 mm) over most of theregion between the equator and 408N (where NCEP isalso dry when compared with NVAP). Note that theCCM2 simulation tended to be slightly more moist thanthe NVAP analyses in both seasons, with a moderatemoist bias over the mid- to high latitudes of the NorthernHemisphere during JJA.

The horizontal distribution of precipitable water, andits difference from the NVAP dataset, are shown in Figs.7 and 8 for DJF and JJA. The CCM3 produces relativelyminor seasonal changes in the precipitable water dis-tribution except over the Amazon basin, central Africa,and in the vicinity of the Indian subcontinent. Theseseasonal excursions of the meridional maximum in pre-cipitable water are primarily responsible for the merid-ional shifts seen in the zonal mean. Despite relativelygood agreement in the zonal-mean values, there are

some significant regional differences between the sim-ulated water vapor field and NVAP. The DJF simulationshows a large spatially coherent dry region stretchingfrom the southern Indian Ocean eastward through theSouth Pacific convergence zone (SPCZ), and a secondfeature extending across the equatorial Atlantic, North-ern Africa, and into Northern India. These differencestypically exceed 6 mm, representing approximately a15%–20% error. Similar, but more severe, regional dif-ferences are seen in the JJA simulation. The atmosphereover warm continental surfaces appears to be system-atically dry, exceeding 10 mm over large areas, wherethese differences represent local errors of 30% or more.Even oceanic regions exhibit a significant dry anomaly,as seen in JJA over much of the western Pacific.

The structure and magnitude of the precipitable watererrors shown in Figs. 6–8 depend very strongly on theparticular analysis product used to represent the realatmosphere. For example, the differences between theNCEP analyses, ECMWF analyses, and NVAP clima-tology can produce similar patterns to what is seen inFigs. 7 and 8. Another way to help verify the charac-teristics of systematic errors in temperature and moisture

1188 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 7. Global distribution of precipitable water as simulated by the CCM3 for DJF (top panel) and the difference with respect to theNVAP analysis.

in the simulation is to compare vertical thermodynamicprofiles produced by the model with in situ radiosondemeasurements at locations having long, high quality,observational records. This is typically done for ap-proximately 50 sites as a part of model developmentactivity, where we present results from two of them.Since the more severe moisture biases tend to be con-

fined to the Tropics and midlatitudes, we have selectedlocations representing midlatitude ocean, and tropicalwestern Pacific climates. The two sites, which were pre-viously used to evaluate the quality of the CCM2 ther-modynamic simulation (Hack et al. 1994), are centeredover the Azores (38.78N, 27.18W) and the Yap Atoll(9.48N, 138.18E). The Azores radiosonde data makes

JUNE 1998 1189H A C K E T A L .

FIG. 8. Global distribution of precipitable water as simulated by the CCM3 for JJA (top panel) and the difference with respect to theNVAP analysis.

use of a single reporting station, while the Yap sound-ings incorporate two radiosonde stations. Profiles oftemperature and specific humidity are shown in Figs. 9and 10 for the months of January and July, respectively.The climatological observational record is given at dis-crete locations in the vertical where the dots representthe long-term average and the error bars indicate one

standard deviation with respect to observed interannualvariability. The CCM3 temperature and specific humid-ity data are shown on the same diagrams by the solidline.

The overall quality of the CCM3 simulated thermo-dynamic structure at these two sites is mixed. The Jan-uary lower to midtropospheric temperature structure is

1190 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 9. Vertical climatological profiles (January and July averages) of temperature and specifichumidity for the CCM3 and radiosonde observations over the Azores.

improved at both sites, eliminating the weak cold biasexhibited by the CCM2 (see Fig. 11; Hack et al. 1994).However, simulated tropopause temperatures are colderthan observed, despite the fact that tropopause heightsare well represented. The January water vapor distri-bution over Yap is quite dry in the low to midtropo-sphere and too moist in the upper troposphere. DuringJuly, the temperature structure over the Azores repre-sents an overall improvement when compared to CCM2,reducing a mid- to upper-tropospheric warm bias and amore severe cold bias at the tropical tropopause. TheYap temperature profile shows warmer than observedvalues in the middle troposphere and a cold bias at thetropical tropopause. The more serious thermodynamicdeficiencies occur in the July water vapor profiles, whereboth sites are severely dry between the top of the at-mospheric boundary layer and middle troposphere. Oth-er radiosonde sites suggest that the form of this moisturebias is a fairly widespread characteristic of the CCM3water vapor distribution. The vertical distribution of theanomaly, dry lower troposphere, and moist upper tro-posphere is a signature of the deep convection scheme,and is perhaps the most serious degradation of the

CCM3 simulation when compared to the CCM2, forwhich the July water vapor structure was very well re-produced in the Tropics.

A concise characterization of the simulated thermo-dynamic structure is the vertical profile of equivalentpotential temperature, a measure of the atmosphericmoist static stability. Figure 11 illustrates the Januaryand July ue distributions at the Azores and Yap sites forthe CCM3, CCM2, and radiosonde observations. Theoverall improvement in the January Azores profile isquite remarkable, as is the improved simulation of themid- to upper troposphere during July. As suggested byFigs. 9 and 10, however, the lower-tropospheric profileof ue is markedly degraded for the Yap profiles in bothseasons, and for the July Azores profile. The Yap pro-files also show ue anomalies associated with a warm andmoist bias between 200 and 400 mb.

b. Cloud water

It is widely accepted that clouds (i.e., water in con-densed form) exert an important forcing on the climatesystem as a regulator of the radiative heating field. The

JUNE 1998 1191H A C K E T A L .

FIG. 10. Vertical climatological profiles (January and July averages) of temperature and specifichumidity for the CCM3 and radiosonde observations over the Yap Atoll.

global magnitude of this cloud forcing is shown in Table1 for both the shortwave and longwave portions of theTOA radiation budget. Ironically, cloud processes are avery poorly understood aspect of the climate system,representing one of the principal sources of uncertaintyin the modeling of global climate (e.g., Cess et al. 1990).Cloud parameterization schemes are either diagnostic,for which cloud properties are parametrically derivedfrom large-scale state information, or prognostic, whichintroduce additional large-scale state variables to rep-resent the cloud field. The CCM3 cloud parameteriza-tion scheme is a diagnostic approach, where both cloudamount and the corresponding cloud optical propertiesare simply evaluated from several other large-scaleproperties of the simulated flow field (Kiehl 1994a;Hack 1998a; Kiehl et al. 1998a). Even though cloudwater is not carried as a prognostic variable in theCCM3, it strongly influences the simulated global andregional energy budgets (Kiehl et al. 1998b). The cli-matological distribution of cloud water is therefore wor-thy of some discussion.

The first thing to note about condensed water in theatmosphere is how remarkably small it is when com-

pared to water stored in vapor form (see Table 1). TheCCM3 simulated global annual mean of 46.5 g m22 isaround 500 times smaller than the analogous value forprecipitable water, yet is of comparable climate impor-tance in terms of modulating the global radiation bal-ance, the so-called clouds and climate problem (e.g.,Wielicki et al. 1995; Kiehl 1994b). Zonally and sea-sonally averaged distributions of condensed water pathare shown in Fig. 12 for the CCM3, and for the satellite-derived values (ocean only) of Greenwald et al. (1995)and Weng and Grody (1996). The Greenwald data in-clude the 4-yr period 1988–91 and represent an all-skyfield of view, where the contributions of precipitatingclouds to the liquid water path have been filtered (sinceliquid water path retrievals are unreliable when precip-itation is present). The Weng and Grody data includethe 9-yr period 1988–96. As mentioned earlier, theCCM3 results include both water and ice condensate,while the Greenwald data, and Weng and Grody data,are for liquid clouds only.

The CCM3 results exhibit well-defined condensedwater maxima in the extratropical storm tracks, alongwith a very weak secondary maximum (only during

1192 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 11. Vertical climatological profiles (January and July averages) of equivalent potentialtemperature for the CCM3, CCM2, and radiosonde observations over the Azores and YapAtoll.

JJA) associated with the ITCZ. The satellite-derivedcloud water path shows a very different meridional dis-tribution, where both datasets show an obvious maxi-mum in the ITCZ. The Greenwald data shows little me-ridional definition of extratropical features, while theWeng and Grody data exhibits better meridional sepa-ration, mostly due to pronounced minima in subtropicalcloud water amount. The Southern Hemisphere extra-tropical maximum seen in the simulated data is essen-tially nonexistent in the satellite data. The lack of ex-tratropical maxima in the satellite retrievals may be re-lated to the absence of column-integrated ice conden-sate. The amplitude of the satellite-derived cloud liquidwater path is also systematically larger than what issimulated by the CCM3 for the region bounded by 408Nand 408S (where the zonally averaged satellite retrievalsincluding precipitating clouds show an additional 20%–25% increase in magnitude). This discrepancy is par-ticularly large in the ITCZ where both satellite datasetsshow cloud water amounts that are two to three times

the total condenstate diagnosed by the CCM3. The sea-sonal behavior of the simulated zonal average of con-densed water shows a strong seasonal oscillation in mag-nitude at the high latitudes, where the summer hemi-spheres contain 40% or more cloud condensate than intheir respective winter season. The Greenwald satelliteestimate also suggests a similar seasonal oscillation, butmostly for the Northern Hemisphere.

The DJF and JJA meridional distributions of totalcloud amount are shown in Fig. 13 for CCM3, ISCCP,and Nimbus-7. These curves show a clear meridionalshift in the seasonal location of the ITCZ and subtropicalcloud features, but little evidence for large extratropicalchanges in total cloud fraction. In regions where totalcloud fraction does exhibit large seasonal changes (e.g.,the subtropics), the simulated cloud water path onlyweakly reflects the change in cloud amount. Thus, thezonally averaged cloud water path does not appear tobe strongly correlated with more subjective measures ofcondensed water, such as total cloud amount. Never-

JUNE 1998 1193H A C K E T A L .

FIG. 12. Zonally and seasonally averaged cloud water path (DJFand JJA) for the CCM3, Greenwald et al. (1995), and Weng andGrody (1996) cloud water climatologies.

FIG. 13. Zonally and seasonally averaged total cloud amount (DJFand JJA) for the CCM3, ISCCP, and Nimbus-7.

theless, the CCM3 cloud amount does show reasonablygood agreement with the observational estimates, par-ticularly the Nimbus-7 climatology. We note that thelocal agreement between all three cloud fraction datasetsis strongest for the JJA ITCZ, the same region wherethe CCM3 cloud condensate is most different from thecorresponding satellite-derived cloud water estimates.

Figure 14 shows the DJF and JJA global distributionof condensed water path for the CCM3 simulation. Theextratropical storm tracks are clearly defined in thesefigures, as are the dry subsidence regions in the sub-tropics (identified by well-defined minima in the cloudwater distribution). Condensed water path generally ex-ceeds 100 g m22 in the storm tracks, where maximumvalues exceeding 150 g m22 occur during the respectivesummer season (e.g., Gulf of Alaska during JJA). Drydesert regions, such as over the southwestern UnitedStates, northern Africa, and the high deserts over theinterior of Eurasia are also easy to identify in both pan-els. Surprisingly, the ITCZ and other climate featuresassociated with deep cumulus convection are only weak-ly represented. The strongest ocean features are seen inthe eastern ocean basins, in particular, the eastern trop-

ical Pacific. This is in sharp contrast to the observationalestimates of cloud water path, such as Greenwald et al.(1995) and Weng and Grody (1996), which show well-defined cloud water structures associated with the ITCZ.This is indeed an unexpected result, given the strongagreement in the TOA radiation budget between theCCM3 and ERBE observations (Kiehl et al. 1998b).Since the satellite-derived data does not include cloudice, the actual differences are likely to be even largerthan suggested by Fig. 12. The reasons for such largedifferences between lower-latitude simulated and sat-ellite-derived cloud water path are not well understoodand are a topic for additional research. For example,despite the more realistic representation of the cloudfield optical properties in CCM3, the low-latitude dis-crepancy between observed and CCM3-simulated con-densed water remains similar to what was seen in CCM2(Hack 1998a). This type of discrepancy presents an in-teresting scientific opportunity to better understand therelationship between large-scale observational estimatesof cloud water (e.g., subgrid-scale variability issues re-lated to retrieval) and the treatment of parameterizedradiative transfer (e.g., modeling assumptions related tothe cloud field; anomalies in cloud absorption) in at-mospheric general circulation models (AGCMs).

1194 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 14. Global seasonal distribution of cloud water path as simulated by the CCM3 for DJF (top panel) and JJA (bottom panel).

JUNE 1998 1195H A C K E T A L .

FIG. 15. Zonally and annually averaged evaporation rate for theCCM3 and CCM2.

FIG. 16. Zonally and annually averaged precipitation rate forthe CCM3, CCM2, and Xie and Arkin (1996) precipitation cli-matology.

FIG. 17. Zonally and annually averaged E 2 P for the CCM3 andCCM2.

c. Evaporation and precipitation

The evaporation and precipitation fields illustrate theproperties of water exchange between the atmosphereand the underlying surface. The zonal- and annuallyaveraged evaporation rate is shown in Fig. 15 for theCCM2 and CCM3 (where a comparable global obser-vational dataset does not exist). The evaporation curvesclearly show that the most vigorous transfer of water tothe simulated atmosphere occurs in the subtropics. Thisfeature is particularly obvious for the CCM3 simulation,which exhibits well-pronounced evaporation maximanear 208N and 208S. Overall, the CCM3 evaporationrates are significantly and systematically weaker thanfor the CCM2. The suppression of surface evaporationis especially obvious in the deep Tropics, where it isreduced by more than 1.4 mm day21 (;40 W m22). Thisenhanced suppression of evaporation in the vicinity ofITCZ convection is a more realistic feature of the CCM3simulation, showing good agreement with correspond-ing oceanic estimates (e.g., see Oberhuber 1988; Doneyet al. 1998; Kiehl 1998).

The zonal- and annually averaged precipitation rateis shown in Fig. 16 for the CCM2, CCM3, and the Xieand Arkin (1996) analysis. The precipitation distributionalso shows a systematic reduction when compared withCCM2 and is more consistent with the Xie and Arkinestimates. Most notable is that the magnitude of extra-tropical reductions in precipitation are on par with pre-cipitation changes in the ITCZ. The zonally and an-nually averaged net surface exchange of water, E 2 P,is shown for CCM2 and CCM3 in Fig. 17, and expressedin energy units (where 1 mm day21 ; 29.055 W m22).The regions 108–408N and 108–408S are well-definedsource regions of total water, where the deep Tropicsand extratropics represent regional sinks of total water.The ITCZ water sink is much broader and more sym-metric about the equator in the CCM3 simulation, wherevery large differences along and just south of the equator

are a consequence of both reduced evaporation and in-creased rainfall. The implied meridional export of waterfrom source regions to sink regions is quite differentfor the two models, where the Southern Hemispheresubtropics are the principal source of water poweringthe atmospheric hydrologic cycle in the CCM3.

As seen in the annual mean, the CCM3 exhibits asystematic decrease in evaporation for both seasonswhen compared to the CCM2 (Fig. 18), where the moresignificant extratropical differences occur in the North-ern Hemisphere during JJA (in response to changes incloud optical properties, land surface representation, andboundary layer formulation). The most noticeable re-sponse when comparing CCM3 to CCM2 is the pro-nounced reduction of evaporation in the ITCZ region,a signal clearly associated with the introduction of theZhang–McFarlane deep convection scheme. The sup-pression of evaporation rates elsewhere in the subtropicsand extratropics is a simulation response primarily as-sociated with changes to the atmospheric boundary layer

1196 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 18. Zonally and seasonally averaged evaporation rate (DJFand JJA) for the CCM3 and CCM2.

FIG. 19. Zonally and seasonally averaged precipitation rate (DJFand JJA) for the CCM3, CCM2, and Xie and Arkin (1996) precipi-tation climatology.

formulation. The seasonal zonal averages of precipita-tion (Fig. 19) show considerably greater differences be-tween the CCM2 and CCM3 than suggested by the an-nual mean. Seasonal maxima in ITCZ precipitation aresubstantially reduced and are more consistent with theXie and Arkin climatology. Although reduced in am-plitude, simulated precipitation rates in the midlatitudestorm track regions continue to be slightly higher thanin the observational estimates. Two features worth not-ing are the improvement in the simulation of theSouthern Hemisphere subtropical minimum during JJA,and the anomalous shift in the DJF ITCZ maximummore than 108 north of both the Xie and Arkin andCCM2 locations. These panels show a clear tendencyfor the simulated tropical precipitation maximum to re-main in the Northern Hemisphere year round, in sharpcontrast with most observational estimates, which sug-gest a seasonal migration of ITCZ precipitation acrossthe equator. The large changes to both evaporation andprecipitation produce large differences in the seasonaldistribution of the net water exchange at the surfacebetween the CCM3 and its predecessor model (see Fig.20). The CCM3 simulates a considerably weaker sea-

sonal meridional excursion of the net water sink in thedeep Tropics, largely due to the weak seasonal migrationof ITCZ precipitation. The subtropical water source re-gion in the winter hemispheres is much weaker as well,mostly due to a reduction in meridional extent. Alsonote the relatively large Northern Hemisphere extra-tropical differences, which are mostly attributable tochanges in the total water exchange over land surfaces.

Figures 21 and 22 show the DJF and JJA global dis-tribution of precipitation for the Xie and Arkin clima-tology and the CCM3 simulation. Overall, the CCM3does a very credible job of simulating the principal fea-tures of the observed precipitation distribution. TheNorthern Hemisphere DJF extratropical storm tracks areespecially well represented, as is the general pattern oftropical precipitation. Note that the seasonal behaviorof extratropical precipitation in both hemispheres, ex-hibiting enhanced storm track precipitation in the winterhemisphere, is well simulated by the CCM3. Low pre-cipitation rates in the subtropics with well-defined min-ima in the eastern oceans are realistically represented.The southeast Asian monsoon is particularly well re-produced, representing one of the more notable simu-lation improvements when compared to the CCM2. The

JUNE 1998 1197H A C K E T A L .

FIG. 20. Zonally and seasonally averaged E 2 P (DJF and JJA)for the CCM3 and CCM2.

areal extent of the monsoon precipitation into northernIndia and south into the southern Indian Ocean, with awell-defined maximum in the Bay of Bengal, is wellcaptured in the simulation. The overall pattern of pre-cipitation exhibits a more coherent and spatially smoothstructure when compared to the CCM2, and better re-sembles the Xie and Arkin precipitation distribution andamplitude characteristics. Nevertheless, a number of sig-nificant biases are apparent in the simulated seasonalstructure. The DJF pattern shows an anomalous precip-itation maximum in the western tropical Pacific, posi-tioned well north of the observed precipitation maxi-mum (which is also seen as a TOA radiative anomaly).Vigorous ITCZ convection extends too far east in thePacific Ocean, and convection in the Indian Ocean ispositioned too far to the north with evidence of a doubleITCZ not reflected in the observational data. WesternPacific convection extends too far south into north-western Australia, and there is a tendency to lock pre-cipitation over the Andes Mountains, a problem thatplagues many other AGCMs, including the CCM2. TheJJA distribution shows excessive convective activityover Central America erroneously extending eastward

into the Caribbean, with weaker than observed precip-itation in the eastern Atlantic. There is also an anom-alous precipitation maxima over the Arabian Peninsula,an unrealistic meridional separation of the ITCZ in thewest-central Pacific, and an anomalous precipitationmaxima in the north subtropical central Pacific. TheCCM3 exhibits more of a tendency to form double ITCZstructures, as suggested by both the DJF and JJA sea-sonal means. This characteristic appears to be an at-tribute of the deep convection scheme, which tends notto maintain deep convective structures along the equator.Despite these deficiencies, the overall simulation of pre-cipitation represents a notable improvement when com-pared with the CCM2.

The simulated evaporation field is shown in Fig. 23for DJF and JJA. Both seasons show a clear evaporationminimum in the ITCZ, with extensive regions of highevaporation in the respective winter hemispheres. TheDJF distribution shows evaporation maxima in theNorthern Hemisphere western oceans (along the Ku-roshio and Gulf Stream currents) as well as in the west-ern Arabian Sea, all well exceeding 8 mm day21 (;240W m22). Another extensive region of high evaporationis located in the subtropical western Pacific, providingmuch of the moisture supply for the convective featuresto the south and west. A relatively small, but vigorousregion of high evaporation is seen in the eastern Pacific,to the north and east of the anomalous convective ac-tivity discussed in Fig. 22. Significant evaporation ratesare also seen over much of South America and SouthernAfrica. During JJA, an extensive and coherent regionof high evaporation stretches across the southern oceans,with maximum rates occurring in the Southern IndianOcean. Other significant features are present in the east-central subtropical Pacific, the western subtropical At-lantic, and in the Arabian Sea just south of the anom-alous convective activity over the Arabian Peninsula.Large evaporation rates are also seen over the north-central and southeastern parts of the United States, In-dia, and large portions of southeast Asia.

The evaporation field shows a distinct minimumalong the equator in the vicinity of deep ITCZ convec-tion in both seasons. This characteristic represents asignificant improvement when compared with CCM2,which only weakly captured the observed equatorialminimum (e.g., see Fig. 15). The reduction in evapo-ration is of particular importance to the simulation of arealistic surface energy budget over the tropical westernPacific warm pool (e.g., see Kiehl 1998), where obser-vational estimates suggest tropical evaporation reachesa relative minimum. This east–west gradient in evap-oration over the western tropical Pacific is reasonablywell represented by the CCM3, and is particularly ob-vious in the JJA distribution.

The net water exchange with the surface, E 2 P, isshown in Fig. 24. The major tropical precipitation fea-tures are clearly visible, with local water deficits ex-ceeding 300 W m22 (;10 mm day21) in the long-term

1198 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 21. Global DJF distribution of precipitation for the Xie and Arkin (1996) precipitation climatology, and as simulated by the CCM3.

JUNE 1998 1199H A C K E T A L .

FIG. 22. Global JJA distribution of precipitation for the Xie and Arkin (1996) precipitation climatology, and as simulated by the CCM3.

1200 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 23. Global seasonal distribution of evaporation as simulated by the CCM3 for DJF (top panel) and JJA (bottom panel).

JUNE 1998 1201H A C K E T A L .

FIG. 24. Global seasonal distribution of E 2 P as simulated by the CCM3 for DJF (top panel) and JJA (bottom panel).

mean. The subtropics are the clear source of water forthe simulated general circulation, particularly in theSouthern Hemisphere as suggested by the zonal meanshown in Fig. 17. A large seasonal cycle in E 2 P existsover much of South America, central and southern Af-

rica, India, and southeast Asia, mostly a consequenceof the seasonal migration of deep convection in responseto solar insolation. Similar seasonal variability is seenover most of Europe extending into central Asia, andover North America. Most of Europe and a large portion

1202 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 25. Equatorial (108N–108S) precipitation anomalies for the 15-yr period 1979–93 as derivedfrom the Xie and Arkin (1996) precipitation climatology.

of North America can be characterized as water sourceregions during JJA, and water sink regions during DJF.

4. Simulated ENSO response

Equatorial SST anomalies associated with ENSO pro-vide a very useful framework for evaluating observedlocal and extratropical responses in numerical simula-tions of climate. Here, we examine the sensitivity of theCCM3 simulation for the 1979–93 period, which in-cludes a number of ENSO cycles, including the verystrong 1982 ENSO event. ERBE provides a unique ob-servational dataset on cloud radiative forcing for a por-tion of this period, which can be used to evaluate thesimulated TOA response to ENSO forcing (e.g., Kiehlet al. 1998b). The Xie and Arkin (1996) dataset alsoprovides a unique observational check on the simulatedprecipitation response to ENSO. Figures 25 and 26 areHovmoller diagrams showing precipitation anomalies asestimated by Xie and Arkin over the equatorial region(averaged between 108N and 108S) for the period Jan-uary 1979–December 1993, and for the CCM3 simu-lation for the period January 1979–July 1993. The Xie

and Arkin dataset shows strong positive precipitationanomalies in response to the major warming events ex-tending eastward across the tropical Pacific. Similarly,a number of strong negative anomalies, correspondingto the cold phase of the observed ENSO cycle, are seenin the central Pacific. During the 1982 warm event, alarge precipitation anomaly, exceeding 3 mm day 21,clearly extends across the entire Pacific basin. TheCCM3 does an exceptionally good job of capturing boththe structure and magnitude of the anomaly pattern. Thewest-central Pacific anomalies are also generally wellrepresented in the CCM3 simulation, where the eastwardextension of the precipitation changes are very accu-rately reproduced, even for the weaker SST events.

The simulated ENSO response can also be illustratedby examining the longer-term average precipitation dif-ferences between a warm and cold event, an analysistechinique that helps to maximize the observed re-sponse. Figure 27 shows the seasonal differences be-tween DJF 1987 and DJF 1989 (i.e., warm minus coldevent) for the Xie and Arkin precipitation data and theCCM3. The Xie and Arkin dataset shows a very largeincrease in precipitation over the central Pacific, along

JUNE 1998 1203H A C K E T A L .

FIG. 26. Equatorial (108N–108S) precipitation anomalies for the 15-yr period 1979–93 assimulated by the CCM3.

with reductions to the west and south (e.g., in the SPCZ)of this region for the DJF seasonal average. The CCM3does a very good job in reproducing this precipitationanomaly pattern. Although the positive anomaly doesnot exhibit the identical meridional extent, the generalstructure and magnitude of the response is very wellcaptured. The nonlocal extratropical ENSO responsepattern is considerably weaker, but is reproduced to alarge extent over large portions of the Northern Hemi-sphere. In particular, note the positive precipitationanomaly over the southeastern United States and alongthe west coast of North America. Similar response pat-terns are seen over portions of the Eastern Hemisphere.During JJA (not shown) the Xie and Arkin data exhibitsa positive anomaly centered in the western Pacific ex-tending eastward across the entire Pacific basin, withweak negative anomalies flanking the maximum in theprecipitation response. In this case, the CCM3 simu-lation of the ENSO response is not as good, where themaximum response is mislocated in the central Pacificwith an anomalously large negative anomaly to thesouthwest. Despite the poor positioning, the equatorialresponse is reasonably well captured in the simulation

with a magnitude similar to what is indicated by theobservational data. These results, as with most of thesimulation properties that have been presented, repre-sent a significant improvement in the CCM’s precipi-tation sensitivity to variability in surface forcing.

5. Concluding remarks

We have illustrated selected aspects of the simulatedthermodynamic structure and hydrologic cycle for theNCAR CCM3 in the context of parameterizationchanges to key thermodynamic and hydrologic com-ponents. The revised parameterizations have had a majorimpact on the simulated climate of the CCM, wheremany of the traditional measures of simulation qualityare now much closer to observational estimates.Changes to the diagnosis of cloud optical properties andthe incorporation of a sophisticated land surface modelhave greatly improved the surface energy budget andthe corresponding simulation of the surface climate overland. This results in a considerably weaker annual cyclein hydrologic processes over land surfaces, whichstrongly influences the global behavior of the hydrologic

1204 VOLUME 11J O U R N A L O F C L I M A T E

FIG. 27. DJF87–DJF89 (warm–cold) precipitation anomalies for the Xie and Arkin (1996) precipitation climatology (top panel) andCCM3 (bottom panel).

cycle. Modifications to the parameterization of the at-mospheric boundary layer and moist convection alsosignificantly alter, and generally improve, the simulatedevaporation and precipitation characteristics, both glob-ally and regionally.

The simulated temperature field in the Tropics iswarmer in the CCM3 when compared to the CCM2,

exhibiting a weak warm bias in the middle tropospherewhen compared to global analyses. This warming is inlarge part a response to the introduction of the Zhangand McFarlane (1995) scheme for deep convection, asis the more pronounced cold bias near the tropical tro-popause. In most other respects the temperature field isnot significantly changed when compared to the CCM2.

JUNE 1998 1205H A C K E T A L .

The zonally averaged large-scale moisture field providesa reasonable, albeit dry, representation of the analyzedzonal-mean structure. However, an examination of theglobal distribution of water vapor suggests the presenceof large local errors in the simulation.

Simulated cloud water path shows pronounced extra-tropical peaks associated with storm tracks, and a rel-atively flat and weak distribution at low latitudes. Low-latitude differences between the CCM3 and the Green-wald et al. (1995) and Weng and Grody (1996) satellite-derived climatologies are quite large, where the ITCZis a very weakly represented feature in the CCM3. Theglobal seasonal cycle of the CCM3 and Greenwald cloudwater path data is completely out of phase, even thoughthe CCM’s annual cycle in cloud forcing is consistentwith ERBE observational estimates. These differenceswarrant further study, particularly because of differ-ences in existing satellite climatologies of cloud water,which are presumably related to details in the retrievaltechniques.

Meridional distributions of precipitation and evapo-ration rates show systematic reductions when comparedto the CCM2, generally in the direction of observationalestimates. Annual and seasonal average evaporationrates exhibit substantial reductions over most of theglobe, particularly in the vicinity of deep tropical con-vection. Annual mean precipitation reductions are moreuniformally distributed with latitude, although CCM32 CCM2 differences in the seasonal amplitude of ITCZprecipitation are extremely large, where the CCM3 ismuch more consistent with observational estimates.These changes in evaporation and precipitation resultin significant changes in the distribution of the net waterexchange with the surface and the corresponding me-ridional transport of water by the simulated general cir-culation.

The CCM3’s precipitation response to ENSO forcingis highly realistic, both in structure and amplitude.Anomalies in the simulated precipitation field reproduceobserved patterns over the tropical Pacific, where thephase and amplitude of the response is well captured.Seasonal differences in precipitation for warm and coldevents are also very well simulated, where both thepattern and amplitude of the response agree with ob-servational estimates.

Despite many improvements in the simulated climate,there are a number of deficiencies in need of under-standing so that improvements can be made to the ap-propriate physical parameterization. The large-scalemoisture field exhibits a widespread lower-troposphericdry bias, which is manifested in significant local errorsof precipitable water. This bias is related to the CCM3changes in the parameterization of moist convection,and seriously affects the simulated moist static stabilityat the lower latitudes. There are also a number of im-portant errors in the tropical precipitation distribution,which have implications for anomalous forcing of thelocal surface climate and stationary wave structure. We

are continuing to develop a detailed understanding ofthese simulation biases in the context of the collectionof parameterization processes that contribute to the totaldiabatic forcing of the atmosphere. It is only with anintegrated approach to the problem that the underlyingcauses for existing biases will be completely understoodso that physically justifiable modifications to the modelformulation can be incorporated.

Acknowledgments. The authors would like to ac-knowledge members of the NCAR Climate ModelingSection, G. Bonan, B. Boville, D. Williamson, P. Rasch,J. Rosinski, T. Acker, J. Olson, J. Truesdale, and M.Vertenstein, for their respective contributions to the de-velopment of the CCM3. We would wish to thank ourcollaborators A. Holtslag (Utrecht University) and G.Zhang (Scripps Institution of Oceanography) for theirmodel development contributions. Thanks also to TomGreenwald for providing the satellite retrievals of cloudliquid water path used in this study. Finally, the firstauthor wishes to acknowledge Dave Randall for his sup-port of a collaborative leave at Colorado State Univer-sity, during which this manuscript was prepared.

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