CCSM CAM3 Climate Simulation Sensitivity. to Changes in Horizontal Resolution

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1 CCSM CAM3 Climate Simulation Sensitivity to Changes in Horizontal Resolution James J. Hack, Julie M. Caron, G. Danabasoglu, Keith W. Oleson, Cecilia Bitz, and John E. Truesdale National Center for Atmospheric Research, Boulder, Colorado University of Washington, Seattle Washington Submitted to the Journal of Climate CCSM Special Issue NCAR, P.O. Box 3000, Boulder, CO , 1

2 Abstract The latest version of the Community Climate System Model (CCSM) Community Atmosphere Model, Version 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 of horizontal resolutions through modifications to adjustable coefficients in the parameterized treatment of clouds and precipitation. Coefficients are modified to provide similar cloud radiative forcing characteristics for each resolution. Simulations with the CAM3 show robust systematic improvements in a variety of features, most notably associated with the large-scale dynamical circulation. We will focus on simulation differences between the two principal configurations of the CAM3, which differ by a factor of two in their horizontal resolution. 1. Introduction Solution of the continuous nonlinear differential equations governing atmospheric motions requires the use of a discrete approximation, most frequently utilizing finite difference or spectral methods. The horizontal and vertical resolution at which global climate applications are numerically integrated has historically, and continues to be, chosen on the basis of computational expense, generally weighed against gross measures of solution accuracy. One measure of accuracy is the smallest feature that can be correctly represented by the discretization. In a linear application, issues of accuracy can be ambiguous for finite-difference approximations due to the complex nature of their response functions, particularly in spherical geometry. In a spectral model, however, the smallest feature size that can be accurately represented is unambiguously defined by the spectral truncation. In 2

3 two-dimensional spherical geometry, the wavenumber truncation denotes both the type and magnitude of the truncation. A triangular truncation of wavenumber space (denoted with a T) is most commonly used in global spectral models, for which the main advantage is an isotropic representation of scalar information. To place any confidence in numerical simulations of climate, the horizontal and vertical resolution must be fine enough to accurately represent the phenomenological scales of motion of most importance to the climate system. A common spectral truncation used in global climate models is a 42-wave triangular truncation (T42), which can very accurately treat features and their horizontal derivatives down to approximately 950 km. Motion scales below this truncation limit must be treated in some other way, and generally enter the solution in the form of a forcing term. In a spectral model these subgrid-scale terms are evaluated on a transform grid whose grid intervals are directly related to the spectral truncation. In a T42 model the transform grid interval is approximately 300 km at the equator. These terms are almost always evaluated using parameterization techniques, which can be highly nonlinear and are generally functions of the explicitly resolved atmospheric state variables. Ideally, one would select a horizontal resolution for which the solutions are in a convergent regime; i.e., where additional increases in resolution would not greatly alter the solutions. Under such circumstances it might also be expected that the behavior of parameterized forcing terms would not significantly change with additional increases in resolution. Exploration of global atmospheric simulation sensitivity to horizontal resolution goes back more than 30 years (Manabe et al. 1970), and has continued sporadically in the intervening years (e.g., Manabe et al. 1979; Boer and Lazare 1988; Boville 1991; Kiehl and Williamson 1991; Chen and Tribbia 1993). Most investigations have identified some systematic improvements related to increases in horizontal resolution. In an earlier version of the CAM, Williamson et al. (1995) showed that many statistics used to characterize 3

4 climate properties began to converge in the range of a T63 spectral truncation for midlatitudes. They also showed that scales of motion included at T63 and higher resolutions were needed to capture the nonlinear processes which drive some larger scale circulations. The more discouraging outcome of that investigation was that they were unable to demonstrate convergence for many other quantities, even at a T106 truncation. In this paper we will explore simulation sensitivity to horizontal resolution in the most recent version of the Community Climate System Model (CCSM) Community Atmosphere Model, Version 3 (CAM3). This model is the latest in a succession of atmospheric general circulation models that have been made widely available to the scientific community, originating with the NCAR Community Climate Model (CCM). The CAM3 incorporates a significant number of changes to the dynamical formulation, the treatment of cloud and precipitation processes, radiation processes, and atmospheric aerosols, and is described in Collins et al. (2005b). One of the unique design goals for the CAM3 was to provide simulations with comparable large-scale fidelity over a range of horizontal resolutions. This is accomplished through modifications to adjustable coefficients in the parameterized physics package associated with clouds and precipitation. The standard configuration of the CAM3 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 of the CAM3 are distributed, including horizontal spectral truncations of T31 ( 3.75 transform grid), T42 ( 2.8 transform grid), and T85 ( 1.4 transform grid). The discussion that follows will focus on simulation differences between the T85L26 and T42L26 CAM3 configurations, which we anticipate will be the most commonly used versions. Twenty-two year uncoupled simulations, using observed sea surface temperatures and observed sea ice, are used to characterize features of the simulated climate at the two resolutions. These simulation characteristics are then contrasted 4

5 with simulation properties obtained from the fully-coupled CCSM3 (Collins et al. 2005a). Characteristics of the T31L26 simulation are discussed in Yeager et al. (2005) for both uncoupled applications, and when coupled to a nominal 3 resolution ocean model. 2. Tuning of the physical parameterization package Changes to horizontal resolution in a global atmospheric model directly affect the scales of motion available for the explicit solution of the governing equations. For example, doubling the horizontal resolution from T42 to T85 means that motion scales that did not exist in the T42 model are now explicitly resolved in the solution of the large-scale equations of motion. A familiar adjustment to compensate for such changes is the need to maintain reasonable energy characteristics for the smallest resolved scales. In the case of the CAM3 spectral model, this requires that the coefficient on the bi-harmonic diffusion operator vary with resolution. This variation is determined experimentally so that, in the mid to upper troposphere, the two-dimensional kinetic energy spectra have a reasonably well behaved distribution as a function of the high-order wavenumbers (i.e., near the truncation limit), an approach discussed in detail by Boville (1991). Similarly, the behavior of the parameterized treatment of physical processes also changes with resolution, for a variety of reasons. One example is shown in Figure 1 which illustrates the time series of the resolved 3-dimensional advective tendency of temperature over the warm pool ( 1 N, 155 E) for the T42 and T85 CAM3 configurations, along with an observationally-derived version of the same field. We note that the T85 time series has been spatially averaged to the equivalent T42 area average for a fair comparison. This quantity is one of the primary destabilizing resolved-scale terms seen by the parameterized treatment of moist convection. It is clear from simple inspection that the fundamental character of this term changes with resolution, in terms of vertical structure, temporal behavior, and 5

6 the amplitude of the deviations from the long-term mean. The high-resolution time series is much more consistent with observational estimates of this quantity. This is admittedly a convolved product of both the additional scales of explicitly resolved motions and their interactions with the parameterized physics. But as shown in Hack and Caron (2005) the more realistic behavior of the resolved motion field at higher resolution is very robust and largely determined by the internal behavior of the dynamical motion field. As might be expected, the temporal characteristics of parameterized processes like precipitation or cloud water (e.g., their PDFs) differ significantly, even for cases where the time-mean properties are very similar. A second example of how changes in resolution can affect the behavior of parameterized physics is shown in Figure 2. This figure shows the difference in zonally and annuallyaveraged temperature between the T85 and T42 CAM3 simulations. The figure shows a systematic warming of the troposphere, with the largest signals at high-latitudes. The enhanced warming associated with higher resolution is a desirable signal when compared with observational estimates, particularly at high latitudes. It also is a very robust signal that has a weak dependence on the formulation of the parameterized physics package and is presumed to be attributable to improved accuracy in the treatment of the large-scale motion field. Systematic changes like this warming can have a significant impact on the treatment of parameterized processes. For example, the CAM3 exploits relative humidity thresholds in the treatment of cloud formation. Systematic changes in the temperature field accompanying changes in horizontal resolution require that the selection of these thresholds be revisited in order to maintain a similar cloud field and similar cloud radiative properties. Changing free parameters in parameterized physics formulations, such as relative humidity thresholds or cloud water autoconversion thresholds, in pursuit of a specific simulation goal is most frequently, and often disparagingly, referred to as tuning. Tuning 6

7 generally involves the exploration of simulation sensitivity to a limited number of loosely constrained coefficients in the parameterized physics. The most common goal is to identify a parameterization configuration which yields simulation results that best agree with observations on some arbitrary combination of time and space scales. Generally, these time and space scales involve zonally-averaged seasonal means of quantities like cloud radiative forcing. In the case of CAM3, adjustments are made to spatially and temporally invariant coefficients incorporated in the physical parameterization package. For the CAM3 these parameters include large-scale relative humidity thresholds on cloud formation, rainfall evaporation efficiencies in stratiform and convective precipitation processes, adjustment time scales associated with moist convection, and autoconversion thresholds for transforming cloud and ice water to rain water and snow respectively. The objective of modifying the physics package as a function of horizontal resolution in the CAM3 is to ensure that the top-of-atmosphere (TOA) energy budget remains as close to observational estimates as possible, at all resolutions. This nonlinear exercise is done experimentally, exploiting expert knowledge of the way in which the various physical processes are formulated and are likely to interact. The CAM3 was initially developed at the T42 resolution, and the T31 and T85 configurations were developed as derivatives. A selected set of global annual climate metrics for the T42 and T85 CAM3 configurations are shown in Table 1. The top-of-model (TOM) annual energy balance remains well within 0.5 Wm within 1 Wm for both resolutions, where the component fluxes in the energy budget are both between the two resolutions, and well within the observational uncertainty. It s reasonable to ask how these quantities would compare without changes to the physics parameterizations. Using the T42 physics parameters, a T85 configuration would have a TOM and surface energy imbalance of -3.6 Wm, principally due to differences in the longwave portion of the energy budget. This would render the configuration unsuitable for 7

8 coupled simulation applications. Most other measures in Table 1 are virtually unchanged with the exception of the vertical distribution of cloud, the clear-sky TOM longwave flux, the all-sky net surface longwave flux, and the surface sensible heat flux. The change in the cloud distribution is what is required to keep the all-sky top-of-model radiative fluxes relatively unchanged. The changes in the clear-sky longwave flux are attributable to subtle changes in the vertical distribution of water vapor and changes to the tropospheric static stability. Generally speaking, the T42 and T85 configurations are very similar to each other in terms of the large-scale global annual energy and water cycle budget. However, as we will show, the details of how this balance is maintained can be quite different depending on the spatial and temporal averaging procedures. An important check on the changes made to the cloud and precipitation processes is to examine the response of the cloud field to anomalies in Sea Surface Temperature (SST). One approach exploits the observed linear correlation between longwave cloud forcing (LWCF) and shortwave cloud forcing (SWCF) as discussed in Kiehl and Ramanathan (1990), Ramanathan and Collins (1991), and Kiehl (1994). Figure 3 shows this relationship over the Western Pacific Warm Pool (10 S - 20 N, 110 E E) for both ERBE and the T42 and T85 configurations of CAM3. The ERBE data show a strong linear correlation between the SWCF and LWCF, most closely approximated by the T42 CAM3. The T85 configuration exhibits a nonlinear correlation between the cloud radiative forcing (CRF) anomalies, with a much greater range in the magnitude of the CRF anomalies. This unusual behavior of the cloud scheme in the T85 configuration will require additional research at the process level to better understand and improve the relationship between SWCF and LWCF. More importantly, it does indicate that the radiative behavior of clouds is likely to be different at different resolutions due to the changes made to achieve global energy balance. This may explain, in part, why the CAM3 exhibits slightly different climate sensitivity as 8

9 a function of horizontal resolution (Kiehl et al. 2005). 3. Simulation differences: Uncoupled atmosphere In many respects, the large-scale simulation properties of the T42 and T85 CAM3 configurations are very similar, exhibiting analogous biases with respect to observational data. Fields like surface temperature generally show changes that would be expected from differences in elevation associated with changes in horizontal resolution. One of the larger and more obvious systematic changes to the simulation is a general warming of the troposphere (see Fig. 2), with a relatively widespread warming of the tropopause at virtually all latitudes. The T85 simulation also shows a modest drying of the atmosphere outside of the deep tropics, most notably over Northern Hemisphere land areas. There are also a collection of other significant simulation differences that are of importance to coupling the atmosphere to other component models. These differences fall into three categories worthy of discussion: differences in radiative forcing, differences in the low-level dynamical circulation, and differences in surface water exchange processes, principally attributable to the precipitation component. As shown in Table 1, the T42 and T85 CAM3 configurations exhibit very similar energy budget properties on global annual time scales. There is, however, a redistribution of energy in the system at T85, which is easiest to discuss in terms of zonal means. The T85 model shows a reduction in outgoing longwave radiation (OLR) of approximately 3 Wm in the deep tropics, and an increase in OLR on the order of 4 Wm Similarly, the absorbed solar radiation shows a reduction of 8 Wm poleward of 30. in the deep tropics and an increase in the extratropics maximizing in the storm tracks around 8 Wm. From a cloud radiative forcing point of view, CRF is enhanced at low latitudes, and decreased at high latitudes, most notably in the storm tracks. The largest differences in the tropical 9

10 radiation budget are generally confined to the western Pacific and Indian Oceans, where both the longwave and shortwave budgets show large spatially coherent increases in cloud forcing (see Fig. 4). These regions are convectively active, and show significant increases in liquid and ice water loading at higher resolution, consistent with the increases in CRF. Generally speaking, cloud radiative forcing, and the associated condensed water loading, appears to be biased high when compared to available observational data. Despite the systematic biases in the longwave and shortwave radiation budgets over the Pacific and Indian Oceans, the radiative response to ENSO is generally improved for the higher resolution configuration. This is especially true for the shortwave response. Figure 5 shows the spatial pattern of the anomaly response of monthly-averaged differences in absorbed solar and outgoing longwave radiation between a specific warm and cold event. This way of looking at the radiative response has the advantage of amplifying the response to the ENSO cycle. Figure 5 shows the monthly-averaged shortwave (left panels) and longwave (right panels) difference between November 1984 (warm phase) and October 1989 (cold phase) as seen by ERBE and as simulated by CAM3. Generally speaking, the T85 response is much stronger than the T42 response, and more consistent with the observed response. The most extreme difference is seen in the shortwave response, for which the T42 configuration exhibits an extremely weak response in the western Pacific. The longwave response is also weaker than observed, where the strongest response is positioned well to the east of the observed maximum. This raises additional questions about the unusual way in longwave and shortwave cloud forcing compensate for each other, as shown in Figure 3. Changes in the simulated cloud field and the top-of-atmosphere cloud radiative forcing, introduce relatively large local differences to the net surface energy budget (see Fig. 6). Some of these differences can be characterized as local reflections of changes to the cloud radiative forcing, a consequence of tuning cloud and precipitation processes to achieve 10

11 global energy balance. Others are non-local changes, associated with differences in the low-level dynamical circulation affecting properties like surface latent heat fluxes in the subtropics. Several of the differences that can be seen in the radiative forcing of the climate system strongly motivated an interest in higher resolution. A persistent simulation deficiency for most global atmospheric climate models is the simulation of stratocumulus clouds along the coasts of the eastern ocean basins. Figure 7 shows the change in annually-averaged absorbed solar radiation off the coasts of Baja, Peru, and Namibia. Note the significant reductions in absorbed solar radiation immediately along the coast, attributable to a much improved representation of stratocumulus cloud cover. Unlike the changes to the radiation budget in the convectively-active deep tropics, these signals are entirely associated with higher horizontal resolution since they have a very weak dependence on the cloud radiative budget tuning. The east-west dipole structure in the Southern Hemisphere plots of absorbed shortwave radiation reflects the migration of stratus clouds toward the coast where they should be located. Previous coupled models exhibited large warm SST biases in these regions, attributed in part to deficiencies in the surface radiation budget. Local reductions of more than 40 Wm represent a significant local improvement to the simulation. We will discuss the impact of these radiative changes on the coupled simulation in later sections. Another area where the T85 configuration exhibits systematic improvements when compared to the T42 configuration is with respect to the low-level dynamical circulation. Generally speaking, the differences in the low-level circulation represent significant simulation improvements, although some biases continue to exist. We begin by examining features of the surface wind stress in the eastern ocean stratocumulus regimes. The upper panel in Figure 8 shows the annually averaged surface wind stress off the west coast of Peru as simulated by the T42 CAM3 configuration and as estimated from Earth Remote Sens- 11

12 ing (ERS) scatterometer retrievals, along with their difference. The surface height field, in geopotential meters as represented by the T42 truncation, is also illustrated using color contours in the top left panel. As can be seen, there is virtually no equatorward stress on the ocean surface immediately off the coast in the T42 model. This wind stress is responsible for the upwelling of cold ocean water in these regions, and its absence was another simulation deficiency attributed to warmer than observed sea surface temperatures in this region in coupled applications of the CCSM. The lower panel shows the simulated T85 and T42 surface wind stress (along with their respective surface geopotential structures) and the difference in the oceanic stress. A clear improvement in the low-level wind field can be seen in this figure, although the upwelling wind component immediately along the coast continues to be slightly deficient. Nevertheless, this improvement in circulation is a very robust simulation feature that is entirely attributable to changes in resolution. Two other areas at low latitudes benefit from circulation changes associated with higher horizontal resolution. Figure 9 shows surface wind stress over the Pacific Basin for the T85 and T42 configurations along with ERS scatterometer estimates. Two features that have plagued T42 versions of the CAM include excessively strong trades in the subtropical Pacific, and a very weak westerly wind stress on the equator. The T85 configuration pushes both of these biases toward observational estimates, with significantly enhanced westerly equatorial wind stress in the central Pacific, and a marked decrease in the subtropical Pacific trades. This latter difference is associated with comparably large reductions in surface latent heat flux. One area where the low-level circulation degrades is in the Indian Ocean which exhibits an enhanced and anomalous cross equatorial flow. One of the most reliable simulation signals associated with higher horizontal resolution in the atmosphere is the location of the Southern Hemisphere storm track. Figure 10 shows the surface wind stress over the Southern Hemisphere (SH) for the T42 and T85 configu- 12

13 rations of the CAM3, along with their difference. This figure clearly shows the poleward migration of the SH storm track, which is in much better agreement with observational analyses. This response is one of the signals that monotonically improved with higher resolution as shown in Williamson et al. (1995). Finally, improving simulation errors in the low-level wind field over the Arctic ocean has been of interest because of systematic errors in the distribution of sea ice in coupled integrations using earlier T42 versions of the CAM. The Arctic surface wind simulation changes significantly when the CAM3 resolution is increased to T85. Although there continue to be notable differences with analyses, there are large reductions in circulation biases, where the most prominent difference is a reduction in an anomalous polar summer anticyclone as seen in Figure 11. This anomalous flow pattern at T42 has been thought to be a major contributor to the simulated Arctic sea ice distribution, which exhibits ice that is too thick off the Siberian coast and too thin along the Canadian coast. We will briefly touch on this when we discuss coupled simulation results. The final area in which there are conspicuous large-scale simulation differences between the T42 and T85 models involves fresh water exchange with the surface. Although changes to the low-level dynamical circulation introduce desirable improvements in the evaporation of water (e.g., central Pacific subtropics), differences in the net exchange of water are more often dominated by changes in precipitation. Both configurations of the model continue to reproduce the major features of the hydrological cycle, but the T85 configuration exhibits marked redistributions of precipitation at low latitudes, as seen in Figure 12. The Pacific ITCZ sharpens in the meridional direction at higher resolution, particularly during Boreal summer (Fig. 13). There are similar improvements to the representation of the Atlantic ITCZ, historically a difficult feature to reproduce. The improved defini- 13

14 tion of the Pacific ITCZ is manifested primarily in the form of reductions in subtropical precipitation, with very modest increases in ITCZ precipitation along the equator. Some of the more important differences are seen in the tropical western Pacific which exhibits a substantial increase in precipitation rate maximizing northeast of New Guinea and north of the Solomon Islands. Overall, there is a tendency to move precipitation closer to the equator, and in some cases into the equatorial Northern Hemisphere, as seen in the Indian Ocean. If we use the CPC Merged Analysis of Precipitation (CMAP) product as our standard observed precipitation climatology (Xie and Arkin 1997) these changes can be viewed as improvements. Reductions in precipitation rates over Indonesia and the Indian peninsula represent desirable simulation improvements, as are the reductions in precipitation rate over the Western Arabian Sea and Gulf of Oman (not shown). The Boreal winter simulation shows a significant and realistic enhancement of the South Pacific Convergence Zone (SPCZ), as well as a reduction of precipitation in the Northern Indian Ocean (not shown). The improvements to the SPCZ are only weakly seen in the annual mean plots shown in Figure 12, and arise from a weakening of the double-itcz-like structure extending across the Pacific just south of the equator, and an enhancement of precipitation in the southern extratropical extension of the SPCZ. Changes to the precipitation distribution represent significant alterations to the net water exchange between the atmosphere and ocean. The increase in precipitation rate in the tropical western Pacific alters the T42 fresh water budget by approximately 20% and is another area where we might expect to see large simulation differences when these atmospheres are coupled to a fully interactive ocean component model. 14

15 4. Simulation Differences: Coupled Configuration In this section we provide an overview of the simulation differences as a function of resolution in CCSM3 coupled configurations with an emphasis on the simulation results obtained from the land, ocean, and sea ice components. All simulations employ the CAM3 as the atmospheric component, either at T42 or T85 resolution. The land surface model is discretized on the same horizontal transform grid as the atmosphere; 2.8 at T42, and 1.4 at T85. The ocean and sea-ice models make use of a nominal 1 horizontal finitedifference discretization for all the coupled simulations to be discussed. We will make use of the nomenclature T85x1 to refer to the T85 atmosphere coupled to the 1 ocean model, and T42x1 to refer to the T42 atmosphere coupled to the 1 ocean. The T85x1 configuration of the coupled model is what has been used to document the CCSM3 simulations for international climate-change assessment purposes (see Collins et al. 2005a). a. Atmosphere: In the previous section we discussed a broad class of atmospheric simulation differences, including a warming and drying of the simulated atmosphere at high resolution, along with three specific classes of simulation differences that would affect coupled component models: local radiation budget differences, local dynamical circulation differences, and localized changes in the fresh water budget. The differences in global annual measures of the coupled atmospheric simulation at the two different resolutions are remarkably similar to the differences documented in Table 1 for the uncoupled simulation. Other large-scale simulation differences also carry over to the coupled framework, including the tendency for a slightly warmer and drier simulation at high resolution. The resolution differences in the vertical distribution of water, in both 15

16 condensed and vapor phases, are also in qualitative agreement with the uncoupled solutions. Radiation biases are very similar, generally confined to convectively active regions in the the Western Pacific and Indian Oceans as in the uncoupled model. Interestingly, the shortwave absorption anomalies in the eastern ocean stratocumulus regimes persist in the coupled framework, but are not apparent in the coupled net surface energy budget differences. This is due to adjustments in the other components of the surface energy budget in response to differences in SST (e.g., a warmer ocean) and local changes in the low-level dynamical circulation. Generally speaking, differences in the net surface energy budget as a function of resolution are considerably more complex than shown in Figure 6, largely attributable to complicated low-latitude meridional structures in convection, clouds, and the associated dynamical circulation. Improvements in the dynamical circulation due to higher resolution are generally similar to what is seen in the uncoupled framework. Flow along the eastern ocean coastlines is improved, excessively strong trades are reduced, the SH storm track moves poleward in agreement with observations, and seasonal anomalies in the low-level Arctic circulations are reduced. The major exception includes the equatorial Indian Ocean where the low-level surface circulation improves along with the distribution of diabatic heating. A portion of this improvement appears to be associated with the coupled atmosphere-ocean configuration (e.g., see Hack et al. 2005), and further improves at higher resolution. As noted in other investigations of the CCSM coupled simulation quality, many global annual atmospheric measures of the energy and water cycles are essentially identical to the uncoupled simulations. There are, however, some notable local differences in the coupled and uncoupled configuration. One of the most egregious biases in the coupled framework is associated with the behavior of the simulated hydrological cycle, which exhibits significant anomalies in the exchange and storage of water in the atmosphere when compared 16

17 to the uncoupled CAM3 configuration. One feature is the shift in the surface exchange of water from the Northern to Southern Hemisphere tropics, producing a significant and unrealistic change to the freshwater budget over the tropical oceans, most notably during the Boreal winter. This shift is largely attributable to differences in the precipitation distribution. Although precipitation anomalies appear in both the Atlantic and Pacific basins, the zonal mean anomaly is dominated by changes over the Pacific. This takes the form of an unrealistic enhancement of a southern and more vigorous branch of ITCZ convection extending across the Pacific basin from the warm pool to the Ecuador coast. There are hints of this tendency in the uncoupled model (see Fig. 12), a tendency which is amplified in the coupled configuration. The change to the precipitation distribution is symptomatic of the so-called double-itzc problem that plagues many coupled models (e.g., see Davey et al. 2002). Figure 14 shows the coupled model precipitation distribution for the T42x1 and T85x1 configurations along with their difference. This figure serves to illustrate an important and more general observation about the role of horizontal resolution in determining many large-scale systematic biases in the coupled CCSM3. As can be seen, the double- ITCZ problem is qualitatively present at both resolutions where the localized precipitation differences are consistent with the resolution biases seen in the uncoupled model (e.g., poleward extension of the SPCZ at high resolution). This result is emblematic of many other important large-scale systematic simulation biases, such as measures of internal variability, which also show little if any sensitivity to changes in horizontal resolution. b. Ocean: In this section we present a brief summary of the ocean model solutions from T42x1 and T85x1 and will refer to other papers in this volume for additional detail. The ocean component in both configurations is identical (for model details see Danabasoglu et al. 2005), 17

18 and is initialized with January-mean climatological potential temperature ( ) and salinity ( ) (Levitus et al. 1998; Steele et al. 2001) at state of rest. For consistency, our analysis of the mean states is based on the same 30-year time-mean period (years ) as in Large and Danabasoglu (2005, hereafter LD). Both the T42x1 and T85x1 ocean simulations show modest, linear cooling trends after the first 50 years of integration. We compute these average trends as and W m heat losses at the surface between years in T42x1 and T85x1, respectively. The annual- and global-mean values are 3.46 and 3.41 C at year 600, compared to the initial mean value of 3.64 C. In contrast, the initial global-mean value of psu is well preserved in both the T85x1 and T42x1 integrations. Indeed, at year 600, the annualand global-mean differs by less than psu between the two cases. The time- and horizontal-mean global difference profiles from observations (Levitus et al. 1998; Steele et al. 2001) for and are plotted in Figure 15. The T42x1 profile is warmer between m and colder elsewhere when compared to the T85x1 profile. These warming and cooling regions are very similar in all major ocean basins except the deep Pacific where T42x1 is uniformly warmer by about 0.1 C below 2000 m, in better agreement with observations (not shown). The two regions where the T42x1 profile represents an improvement over the T85x1 profile when compared with observations are the upper 250 m and between about m. Below 2250 m, T42x1 is colder than T85x1 by as much as 0.15 C, further from observational estimates. Although the global-mean is essentially the same, the profiles (Fig. 15) show that it is redistributed in the vertical in the two configurations. In particular, the T42x1 profile is saltier than the T85x1 profile in the upper 1500 m and fresher below this depth. All major ocean basin profiles show the same behavior as seen in the global profiles. With the exception of two small depth ranges, i.e. between m and between m, the T42x1 profile matches 18

19 observations better than the T85x1 profile. This is especially so below 2250 m where the differences from observations are reduced by one-half in T42x1. We also note that the colder and fresher abyssal redistributions in T42x1 are density compensating. Figure 16 shows the time-mean, vertically integrated mass transport (barotropic) streamfunction distribution from T42x1 and its difference from the T85x1 solution. A detailed discussion of the T85x1 circulation, including comparisons with observations, is presented in LD. In general, all gyre circulation patterns and magnitudes are very similar in the two configurations. There are at most order 5 10 Sv (1 Sv 10 m! s #" ) localized differences in the northern Gulf Stream, southern Agulhas, and equatorial Pacific gyre regions due to minor shifts in the circulation patterns. This similarity of the two solutions suggests that the wind stress curls are very similar in both resolutions of the atmospheric model. The exception to this, however, is the Southern Ocean where the Antarctic Circumpolar Current (ACC) is primarily driven by the zonal wind stress. In T85x1, the latitude of the maximum zonal-mean westerlies is in good agreement with observations, but the simulated winds are too strong in the latitude band of the ACC (Yeager et al. 2005, hereafter YHSL). The location of this maximum is shifted equatorward by about 5 in T42x1, accompanied by some reduction in the strength of the westerlies in the ACC latitude band. As a consequence, the core of the ACC is slightly shifted equatorward in T42x1, and its mean transport at the Drake Passage (177 Sv) is 15 Sv lower than in T85x1. This is still too large compared to the observational estimate of %$'&($ Sv (Whitworth III and Peterson 1985). The range of the interannual variability of the ACC transport in T42x1 ( Sv) is comparable to that of T85x1. Along the ACC, the transport differences between T42x1 and T85x1 exceed 35 Sv locally, e.g. southwest of Australia and the Malvinas confluence zone. The SST from T42x1 is differenced from observations (see Section 2.1 of LD for a full description) in Figure 17, showing that the error patterns and magnitudes are very similar 19

20 to T85x1 (LD). This is primarily due to the same circulation errors in each of the coupled configurations, leaving the associated large SST biases virtually unchanged. This is also true in the Southern Ocean where the equatorward shift of the ACC in T42x1 does not significantly change the SST errors. For example, the persistent negative bias southeast of New Zealand is slightly weaker in T42x1, but the region of positive bias to the southeast is slightly enhanced. The large positive biases off the west coasts of South America, South Africa, and Baja California, however, are more extensive and larger in amplitude (by about 1 C) in the T42x1 model. To quantify this warming, we use the observationally-derived differences shown in YHSL, where the spatially-averaged differences are computed for a 15 -wide strip immediately off these coasts. The T42x1 biases of 2.54 and 4.0 C represent approximately a 0.8 C warmer bias off the coasts of South America and South Africa respectively when compared to the T85x1 simulation. The Baja California biases are much more alike, exhibiting a 1.68 C in T42x1 and 1.61 C in T85x1. As discussed earlier, the regional changes in the surface winds and short-wave heat fluxes between the two atmospheric resolutions contribute to these differences. However, the source of these biases still remain an unresolved coupled problem, because subsurface ocean temperatures and upwelling patterns also exhibit differences in the T42x1 and T85x1 ocean solutions. In general, simulation differences in the sea surface salinity (SSS) in the tropical regions primarily reflect the changes in the precipitation fields. For example, excessive precipitation in T42x1 in the equatorial and southern tropical Atlantic results in reduced SSS compared to T85x1, thus further increasing the fresh bias by about 0.5 psu compared to observations (not shown). The average Eulerian-mean meridional overturning streamfunction (MOC) is very similar in the two configurations, as are the northward transports of heat and freshwater. Some minor MOC differences in T42x1 are a reduction of the Northern Hemisphere global max- 20

21 imum by about 2 Sv to 21.3 Sv, an increase in the circulation associated with the Antarctic Bottom Water by about 2 Sv to 17.9 Sv, a slight equatorward shift of the Eulerian-mean Deacon cell as a direct consequence of the equatorward shift in the westerlies, and shallower penetration depth of the circulation associated with the North Atlantic Deep Water formation by about m. Because this penetration is already too shallow compared to observations in T85x1, T42x1 has a larger bias. In contrast with the time-mean MOC, the amplitudes of decadal time scale variability differ substantially, with T85x1 showing about a factor of 2 larger amplitudes. Further details of the MOC comparisons, particularly for the Atlantic Ocean, are given in Bryan et al. (2005). Finally, in the eastern equatorial Pacific, both solutions have a semi-annual signal in the seasonal cycle of SST anomalies. The characteristics of ENSO variability are also very similar as documented by YHSL. Compared to T85x1, the T42x1 Equatorial Undercurrent maximum is weaker by about 10 cm s #", and the South Equatorial Current has an asymmetric structure with a stronger northern branch. While the former is a slight degradation, the latter is a definite improvement compared to observations (not shown). c. Land: From the perspective of the Community Land Model (CLM), there are three aspects of the simulation that change with increased resolution. First, because the land model currently runs on the same grid as that of the host atmospheric model, a finer scale representation of the land surface is required. Consequently, the underlying land surface can change in terms of the distribution of land cover and soil types. Second, the land surface is forced by and responds to changes in the near-surface atmosphere (e.g., precipitation, temperature, specific humidity) that may result from changes in circulation in the atmospheric model. Third, there may be feedback mechanisms between the land surface and the atmosphere 21

22 that may amplify or dampen the response of the land surface to the changes in near-surface forcing. There are no scaling or tuning modifications made within the land model itself to accommodate higher resolution. Attributing changes in land surface climatology to any of these mechanisms separately is difficult and outside the scope of this paper. However, the contribution of the finer scale land surface representation is likely to be small compared to other factors. Globally, the land cover types change by at most 0.2% (e.g., wetlands increase from 2.8% of the land surface in the T42x1 configuration to 3.0% at T85, C3 non-arctic grass decreases from 13.6% to 13.4%). Regional changes are larger but still small overall. In examining 32 geographically and climatically distinct regions, the largest changes in fractional cover were found to be at most 6%. Changes of this magnitude occurred in regions that have extensive coastline or where land cover is fragmented. For example, in the Alaskan Arctic ( N, W), bare ground decreased from 25% of the region in the T42x1 simulation to 19% in T85, due primarily to increases in needleleaf evergreen trees and C3 arctic grass. In the Sahel, bare ground decreased from 8% to 3%, due to increases in grasses and crops. Generally, it takes more substantive changes in land cover to affect CLM 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 maximum of 3% for land cover change and 2% for soil texture. Therefore, we simply address the issue of whether the increased spatial resolution significantly affects land surface climatology primarily from the viewpoint of the land model s response to changes in atmospheric radiative and hydrological forcing and subsequent feedbacks. We divide the land surface into continental-sized regions, with subdivisions into regions with known biases in the T42x1 simulation to assess whether higher spatial resolution has improved or degraded the simulation (Table 2). 22

23 Global land seasonal averages of precipitation, evapotranspiration, runoff, air temperature, net radiation, and the Bowen ratio are fairly similar at T85x1 and T42x1 resolutions (Table 2). In general, changes in the air temperature correspond to changes in radiative forcing and/or changes in the partitioning of net radiation into sensible and latent heat as captured by the Bowen ratio. There is about a 0.3K decrease and increase in T85x1 global surface temperature in Boreal winter and summer, respectively. These changes compare favorably to observations, and correspond to decreases and increases in net radiation. In Boreal winter, higher resolution results in cooler temperatures at low and middle latitudes of North America (0.9K and 0.2K cooling) and Europe (0.8K). A decrease in downward longwave radiation (not shown) and hence net radiation is likely to be partially responsible for this cooling. At low and middle latitudes in North America longwave radiation decreases by 7 and 10 Wm global net radiation reflects a 6 Wm, and in Europe by 9 Wm. The 4 Wm decrease in decrease in downward longwave radiation. Global absorbed solar radiation is virtually unchanged. However, there are changes in downward solar radiation and albedo in some of these regions that compensate somewhat for the decrease in longwave radiation and modulate the cooling. For example, at middle latitudes in North America, an increase in downward solar radiation along with a decrease in albedo increases absorbed solar radiation, which compensates somewhat for the decrease in longwave radiation. Similarly, changes in the partitioning of net radiation into sensible and latent heat also interact with changes in radiative forcing. At low latitudes in North America, a wet bias in precipitation increases with higher resolution resulting in a shift in the Bowen ratio toward larger latent heat fluxes that contribute to cooling. The cooling in Europe and at middle latitudes in North America reduces the T42x1 warm bias in these regions. However, a cold bias at low latitudes in Asia is enhanced (not shown) and a cold bias is introduced at low latitudes in North America. 23

24 Warming in the high latitudes of North America and Asia offsets some of the Boreal winter global cooling described above. The 3K warm bias in the T42x1 simulation in these regions increases by about 1.5K at higher resolution. This does not appear to have much to do with the atmospheric radiative forcing. Absorbed solar and downward longwave radiation change by less than 2 W m. The decrease in net radiation in these regions is primarily a consequence of greater longwave loss to the atmosphere due to higher surface temperatures. Changes in atmospheric circulation may be responsible for the warming in these regions (Dickinson et al. 2005, this issue). In Boreal summer, the warming in global temperature appears to be primarily driven by the response of the land surface to increases in net radiation caused by increases in absorbed solar radiation (not shown). In particular, there are increases in absorbed solar radiation of 22 Wm Europe (1.1K), and 10 and 16 Wm at mid latitudes in North America (1.4K warming), 12 Wm at mid and high latitudes in Asia (1.5K and 1.6K). in Changes in albedo are minimal at these scales () 0.01), with the exception of high latitudes in Asia. Snow cover in high latitude Asia is lower in the T85x1 model, which results in lower albedo and contributes to increased absorbed solar radiation. The warming in Boreal summer reduces cold biases in all regions except Europe where a warm bias is introduced at higher resolution. In North Africa, land air temperature is somewhat cooler in the T85x1 simulation in both seasons. In summer, air temperature is 0.5K cooler. This appears to be due in part to an increase in precipitation and evapotranspiration and decrease in sensible heat that lowers the Bowen ratio. Radiative forcing of the surface is also lower due to less incoming solar radiation. These changes contribute to a year-round cold bias in this region. The global average hydrologic cycle is not strongly affected by these changes in horizontal resolution. Precipitation and runoff are biased high and low in Boreal winter and 24

25 summer, respectively, in both simulations. However, there are noteworthy changes with resolution at smaller scales. There is an overactive hydrological cycle at northern high latitudes year-round in the T42x1 simulation. This appears to be slightly enhanced at higher resolution. In particular, winter precipitation and snow depth (not shown) at high latitudes in North America are overestimated. Consequently, snowmelt season is delayed and snow persists into early summer, which likely contributes to the cold summer bias. This problem is less severe in high latitude Asia because of smaller biases in precipitation. However, as noted previously, the cold bias in summer in this region appears to be improved at higher resolution because there are fairly large increases in incoming solar radiation in spring and summer that compensate for the increase in snow depth in the winter. Snow appears to melt back at about the same rate as the T42x1 simulation. Summer runoff at high latitudes in North America increases by 50% at higher resolution and is about double what the observations suggest. Much of this is due to a much stronger runoff peak in June due to melting of the deeper snow pack. In high latitude Asia, despite a small increase in winter precipitation and snow depth, the runoff in summer is actually lower in the T85x1 simulation. This is because warmer temperatures and increased solar radiation combine to melt snow sooner and create a runoff peak that occurs in May in the T85x1 simulation as compared to June in the T42x1 simulation. However, the runoff peak in May is still substantially higher than in the observations. On the other hand, other regions that have significant biases in hydrology in the T42x1 simulation show improvement at higher resolution. Summer precipitation in Europe and North Africa have improved slightly. In the Amazonia region, higher resolution increases dry season precipitation by 67% resulting in favorable increases in evapotranspiration and runoff and cooler temperatures. However, improvements in precipitation do not necessarily translate to improvements in the simulation of runoff in other regions. For example, sum- 25