The Climate Sensitivity of the Community Climate System Model: CCSM3. Jeffrey T. Kiehl*, Christine A. Shields, James J. Hack and William D.

Transcription

1 For JCLI CCSM Special Issue The Climate Sensitivity of the Community Climate System Model: CCSM3 Jeffrey T. Kiehl*, Christine A. Shields, James J. Hack and William D. Collins National Center for Atmospheric Research January 3, 2005 Corresponding author,

2 Abstract The climate sensitivity of the Community Climate System model is described in terms of the equilibrium change in surface temperature due to a doubling of carbon dioxide in a slab ocean version of the Community Atmosphere Model (CAM3) and the transient climate response, which is the surface temperature change at the point of doubling of carbon dioxide in a 1% per year CO 2 simulation with the fully coupled CCSM3. The dependence of these sensitivities on horizontal resolution is explored. The equilibrium sensitivity of the high resolution version of CCSM3 is 2.7 C, while the transient climate response is 1.5 C. Limitations of a single global metric of climate sensitivity are also discussed by considering differences in regional climate response. 2

3 1. Introduction: Climate sensitivity is one of the main descriptors of the climate system (e.g. Cubasch et al., 2001). Much attention has focused on the difference in climate sensitivity among climate models used to project future climate change. Climate sensitivity is usually defined as the model simulated equilibrium change in global surface temperature due to a doubling of carbon dioxide. For practical reasons, an equilibrium solution is obtainable by using a slab ocean model, rather than a fully interactive ocean model. It is important to point out that this is only one measure of climate sensitivity, which has definite limitations given the global long time average nature of the metric (e.g. Boer and Yu, 2003). Models may agree in terms of the global climate sensitivity, but have very different regional responses to increased greenhouse gases. This study explores and documents the climate sensitivity of the most recent version of the Community Climate System Model, CCSM3 (Collins et al. 2005a). The CCSM3 is a fully coupled atmosphere, ocean, land and sea-ice climate system model. A version of the CCSM3 employing the same atmosphere, land and thermodynamic components coupled to a slab ocean model is used to obtain the equilibrium climate sensitivity. CCSM3 and CAM3 have been designed to realistic simulations at a variety of resolutions (section 3). Unless otherwise noted, results from CCSM3 and CAM3 are from the high resolution version used for the IPCC 4th assessment report (AR4). The CCSM has evolved over the past decade with major improvements to virtually all aspects of the climate system components (Boville and Gent, 1998; Kiehl and Gent, 2004; Collins et al. 2005a). The first version of the model CSM1 was released to the 3

4 community in 1994 and had a climate sensitivity of 2.1 C (Boville et al. 2001). The next version of the model, CCSM2 (Kiehl and Gent, 2004), was released to the community in 2003 and had a climate sensitivity, which was slightly higher than that of CSM1, i.e. 2.2 C. The current version of the model, CCSM3, has an even higher climate sensitivity of 2.7 C. Reasons for this progressive increase in climate sensitivity in the evolution of CCSM versions is discussed in this study, not surprisingly much of the change is related to the treatment of clouds in the various model versions. The present study is organized as follows, a brief description of the model and simulations is given in section 2, section 3 explores the equilibrium climate sensitivity using the slab ocean version of the model, section 4 describes the response of the fully coupled CCSM3 to a 1% per year increase in carbon dioxide and is thus a measure of the transient climate sensitivity. Finally, section 5 summarizes the findings of the study and discusses future directions for climate sensitivity studies using CCSM3. 2. Model Description and Simulations: A comprehensive description of the CCSM3 is given in Collins et al. (2005a). Only a brief description of aspects of the model germane to climate sensitivity is provided here. The atmospheric component of the CCSM3 is the Community Atmosphere Model (CAM3) (Collins et al., 2005b). This version of the CAM includes extensive changes to the cloud parameterizations including a new formulation of the prognostic cloud water scheme that explicitly accounts for the cloud ice phase and solid precipitate (Boville et al. 2005). A reformulation for the diagnoses of convective cloud fraction in terms of the cumulus mass flux leads to a marked change in mid level cloud fraction. In CAM3 the 4

5 detrainment of cloud water from shallow convection acts as a source of condensate for the large scale prognostic cloud scheme. In the radiation scheme, the longwave parameterization for the water vapor continuum has been modified to fit the latest understanding of continuum absorption. The shortwave scheme in CAM3 accounts for the realistic spatial distribution of aerosols. The ocean component of CCSM3 (Large and Danabasoglu, 2005) is the Parallel Ocean Program with the physical parameterizations developed at NCAR, including a revision to the boundary layer scheme, inclusion of solar absorption by a prescribed climatology of ocean color. The Community Land Model (Dickinson et al., 2005) is a completely new land model that includes a new formulation for the determination of snow covered vegetation, which impacts the overall climate sensitivity. The equilibrium simulations used for the analysis of climate sensitivity are based on slab ocean model (SOM) versions of CSM1 (Meehl et al., 2000, Dai et al., 2001), CCSM2 (Kiehl and Gent, 2004) and CCSM3. The slab ocean model specifies the spatial, monthly mean distribution of ocean heat transport, Q flx, which is obtained from the net surface energy budget, F net, of a control integration of the atmospheric model, the climatological annual mean ocean mixed layer depths, h, and observed sea surface temperatures, SST, using the following expression,! o c p h "SST "t = F net + Q flx (1.1) where ρ o is density of water and c p is the ocean heat capacity. The sea-ice model is purely thermodynamic and neglects sea-ice dynamics. The CAM3 version of the slab ocean model employs the thermodynamic sea-ice component of the CCSM3 model. The slab 5

6 ocean version of the CCSM is run in a control configuration to simulate the present climate and then run with twice the present CO 2 concentration (370ppmv) until the model asymptotes to a steady state, which typically requires about 40 to 50 years of model integration. Climate statistics from the SOM simulations are based on 20 year averages from the end of the simulations. Gregory et al.(2004) have shown that the forcing due to increased CO 2 in these types of models can be obtained by plotting the global annual mean top of model net energy flux versus the change in global annual mean surface temperature for each year of the SOM integration. Figure 1 shows this type of scatter plot for the T85 SOM version of CAM3 for the first 5 years of a 50 year integration. Extrapolating the linear regression of the net flux dependence on change in surface temperature change to ΔT s = 0 yields the CO 2 forcing estimate, which for CAM3 yields a forcing of 3.5 Wm -2 for a doubling of CO 2 and is identical to the forcing value obtained by Gregory et al. (2004). The transient climate sensitivity is based on fully coupled simulations of CSM1.4, CCSM2 and CCSM3 where a 1% per year increase in CO 2 mixing ratio is initiated at some point in a long (i.e. multiple centuries) control integration. Given this rate of increase the CO 2 mixing ratio, for any year T(years) is given by, CO 2 (T ) = (1.01) T CO 2 (0) (1.2) where CO 2 (0) is the control CO 2 concentration. Eq (1.2) implies that a doubling of CO 2 occurs at year 70 and a quadrupling of CO 2 occurs at year 140. Note that the CSM1 was not run to a point of quadrupling, but both CCSM2 and CCSM3 have been run past 6

7 the point of 4XCO 2 (0). Climate statistics at the point of doubling for this study are based on 20 year averages centered at year 70 from the transient simulations. All model output used in this study are available to the public (see 3. Equilibrium Simulations: The equilibrium change in globally averaged surface air temperature is shown in figure 2a for CCM3, CAM2 and CAM3, which are the atmospheric components to CSM1, CCSM2 and CCSM3. It is apparent that the climate sensitivity has monotonically increased with each version of the atmospheric component of CCSM. The current high resolution version of CCSM3 has an equilibrium sensitivity of 2.7 C a 35% increase over that of CSM1. For the same horizontal resolution of T42 the CAM3 has a climate sensitivity of 2.5 C, which is close to the sensitivity of the CAM2 sensitivity. One reason for the difference in sensitivity is that the increase in shortwave cloud forcing in CAM2 is larger than that in CAM3 as shown in figure 2b. A more negative shortwave cloud forcing will lead to less warming in the model. The change in zonal annual mean equilibrium surface temperature is shown in figure 3. The CCM3 change in temperature is lowest at all latitudes except in the southern hemisphere. The warming in CAM2 is less than that in CAM3 at all latitudes. Between 30 S and 60 S the CAM3 warming is significantly larger than that in CAM2. These differences in surface temperature are strongly correlated with differences in the change in low cloud cover between CAM2 and CAM3, as shown in figure 4a. Between 30 S and 60 S there is a significant decrease in low cloud cover, which leads to a significant weakening of shortwave cloud forcing (i.e. a positive change in shortwave cloud forcing) as shown in figure 4b. The larger warming 7

8 over Antarctica in CAM3 is due to a larger ice and snow albedo feedback in CAM3 compared to CAM2. The exact causes for the differences in cloud response between CAM2 and CAM3 are difficult to diagnose. The differences between the cloud water and cloud fraction parameterizations in CAM2 and CAM3 are extensive. Diagnosis of simulations of model development versions between the CAM2 and CAM3 is currently underway to shed more light on the causal mechanisms for the changes in cloud forcing. There are three versions of the CCSM3 defined by the horizontal resolution of the atmosphere and ocean models. The lowest resolution version of the CCSM (Yeager et al. 2005) employs a T31 (3.75 x3.75 ) spectral truncation for the CAM3 and a nominal 3 resolution for the ocean model. The second version of CCSM3 employs a T42 (2.8 x2.8 ) spectral truncation for the CAM3 and a nominal 1 ocean model, while the third version of CCSM3 used for the IPCC scenario simulations, employs a T85 (1.4 x1.4 ) spectral truncation in CAM3 and the 1 ocean model. The three versions of the CAM3 (T31, T42 and T85) have been coupled to slab ocean models of equivalent horizontal resolutions and control and doubled CO 2 simulations have been carried out. The climate sensitivity of these three versions of the CAM3 are 2.3 C, 2.5 C and 2.7 C, respectively (see figure 5a). The monotonic increase in climate sensitivity with increased horizontal resolution is strongly correlated with the change in shortwave cloud forcing (see figure 5b). The shortwave cloud forcing in the T31 version of the CAM3 has the largest increase of all models resolutions. This increase in shortwave cloud forcing acts to suppress warming in the model compared to the T42 and T85 versions of the models. Note the changes in longwave cloud forcing (figure 5b) are considerably smaller than the 8

9 shortwave component such that the net cloud forcing is similar in magnitude to the shortwave cloud forcing. Although the global mean metrics convey information on climate sensitivity they say nothing about the regional structure of the climate response to a given forcing (e.g. Boer and Yu, 2003). Two models can agree in their global climate sensitivity yet differ significantly in their regional response. The geographic distribution of the change in the equilibrium annual mean surface temperature for the T42 and T85 versions of CCSM3 is shown in figure 6a. The global mean change in surface temperatures agrees to within 8%, but there are large regional differences in response between these two versions of CCSM3. For example, in the Eastern equatorial Pacific the T42 surface warming is 2 C, while in the T85 version the warming is more than twice this amount. The surface warming over many of the continental regions is larger at the higher horizontal resolution version of the CAM3. There is a strong correlation between these regional changes in surface temperature and the changes in low cloud (see figure 6b). There is a ~15% decrease in Eastern tropical Pacific low cloud cover in the T85 model compared to a ~2% decrease in the T42 model. The larger decrease in low cloud cover at T85 results in more solar radiation reaching the ocean surface and hence a larger surface warming than at T42. Similarly, the larger response in surface warming in western Africa is also associated with a larger cloud decrease in this region at the T85 horizontal resolution. An analogous anti-correlation between low cloud and surface warming occurs over western Australia. This difference in cloud response could be related to either a difference in local moisture availability (e.g. surface evaporation) and/or differences in moisture transport 9

10 into specific regions. This difference may also be due to the tuning that was required to bring the three model versions into global energy balance. 4. Transient Simulations: Although the slab ocean models are useful for considering the equilibrium warming due to increased carbon dioxide, these types of models use a very simplified representation for ocean processes. The real world experiences transient forcings on multiple time scales. To understand the transient response of the climate system to time evolving climate forcings (e.g. greenhouse gases, aerosols, natural forcing) requires the use of a fully dynamic ocean component. The climate sensitivity of fully coupled climate system models is usually defined in terms of transient climate response (TCR) due to a 1% per year increase in CO 2 (Cubasch et al. 2001). The TCR is defined as the change in surface air temperature at a doubling of CO 2. Note that using the TCR as a measure of global climate sensitivity introduces another important factor into the measure of climate response, which is the efficiency of ocean heat uptake. For slab ocean models the climate sensitivity is determined to a large degree (excluding surface feedbacks) by atmospheric processes, the ocean itself does not effect climate sensitivity. But for fully coupled models the magnitude of surface warming at the time of doubling of CO 2 is affected by the rate at which surface energy forcing is taken down into the ocean. Given this additional effect on climate response there is no a priori guarantee that the TCR and equilibrium sensitivity will scale with one another across model versions or horizontal resolution. 10

11 The time evolution of surface warming for CSM1, CCSM2 and CCSM3 is shown in figure 7a. All three models are in close agreement up to about year 70, i.e. the point of CO 2 doubling. Beyond this point the CCSM2 consistently produces a lower warming than either the CSM1 or the CCSM3. Thus, in spite of the fact that there is a monotonic increase in equilibrium climate sensitivity with newer CCSM versions, there is no such behavior with the TCR indicating that ocean heat uptake is compensating for the differences in atmospheric feedbacks for the three model versions. The somewhat lower warming response of CCSM2 may be linked to shortwave cloud response in this version of the CCSM2. The time evolution of the changes in longwave and shortwave cloud forcing from the three coupled model simulations is shown in figure 7b. Note that the changes in shortwave cloud forcing are in general two to three times larger in magnitude than the changes in longwave cloud forcing. Thus, the net effect of clouds on the simulated climate system is to reflect more energy to space as the climate system warms. This feedback in the system is strongest in CCSM2, which suppresses surface warming more in this version of the CCSM compared to either the CSM1 or CCSM3. To gain better insight into this process the change in tropical vertical cloud structure is shown in figure 8 for the coupled models. There are significant differences in the vertical cloud changes among these models. The cloud changes in CCSM2 indicate large increases in cloud fraction between the surface and 700 hpa compared to either CSM1 or CCSM3. These clouds lead to a larger increase in shortwave cloud forcing compared to the other models. Note the large differences in cloud structure between CCSM2 and CCSM3 in the middle and upper troposphere, where CCSM3 shows a more complex layering structure in cloud response to the CO 2 warming. 11

12 Associated with these changes in cloud cover there is also more structure to the specific and relative humidity (not shown), which is due to the changes in the role of evaporation to the moisture budget in CAM3 compared to CAM2. The focus up to this point has been on changes in cloud properties. in different versions of the CCSM. Changes in surface properties also contribute to climate sensitivity. Over the oceans reduction in sea ice area due to greenhouse forcing leads to enhanced warming, which is the well known sea-ice albedo feedack. Over high latitude land regions reduction in snow cover also leads to an analogous positive feedback in the climate system. One measure of the strength of surface feedback is to consider the change in surface albedo for the 1% transient simulations, which is shown in figure 9. The most significant difference occurs for CSM1.4, where the change in surface albedo is lower compared to either CCSM2 or CCSM3. The reason for this difference appears in figure 10, which shows the change in sea ice area for the three model simulations. The CSM1.4 simulated northern hemisphere sea ice change is much smaller than the other versions of CCSM. These changes are quite large even in the global mean. For example, the global mean change in clear sky surface flux at the time of doubling for CCSM3 is 5.9 Wm -2. Assessing cloud feedbacks in fully coupled models can be computationally expensive. It is therefore worth exploring the question as to what degree the slab ocean model cloud response agrees with the results from the fully coupled simulations. One approach is to compare the changes in cloud properties from a doubled CO 2 SOM simulation with the analogous changes from a 1% coupled integration at the time of doubling. Given the thermal inertia of the fully interactive ocean the warming in the 12

13 transient simulation will be less than the equilibrium SOM simulation. Thus, to accommodate the comparison of SOM and fully coupled model simulations the changes in cloud properties have been normalized by the respective changes in global annual mean surface temperature. Thus, the SOM cloud fields are divided by the global climate sensitivity while the fully coupled CCSM3 changes in cloud properties are normalized by the TCR. The zonal mean change in annual mean shortwave and longwave cloud forcing is shown in figure 11 for both SOM and transient simulations. There is remarkable agreement between the two model simulations for all latitudes except in the polar regions. The cause for the differences at the poles is the differences in sea-ice albedo feedback between the SOM and fully coupled models, since the fully coupled model employs a more comprehensive sea ice model (i.e. it includes sea ice dynamics). The differences in tropical longwave cloud forcing are mainly related to the presence of a double Inter- Tropical Convergence Zone (ITCZ) in the fully coupled model, while the specification of ocean heat flux in the SOM prevents the occurrence of a spurious double ITCZ. Figure 11a indicates a general weakening of shortwave cloud forcing (positive change) in the extra-tropical latitudes, while shortwave cloud forcing strengthens in the subtropics. A more detailed consideration of the changes in zonal annual mean cloud fraction for the two model simulations appears in figure 12. As for cloud forcing the latitudinal changes in low, middle and high cloud cover produced by the SOM and fully coupled models are in good agreement. For the low cloud field there are decreases in extratropical cloud cover and increases in subtropical clouds, which correlate well with the changes in shortwave cloud forcing. Changes in middle and high cloud cover also support 13

14 the changes in cloud radiative forcing, although the changes in tropical clouds in the fully coupled simulations are modified due to the double ITCZ in this model. In summary, it appears that studies of cloud feedbacks in slab ocean model simulations are quite relevant to understanding the same feedback mechanisms in fully coupled models. The advantages of this are that SOM simulations are more computationally efficient to carry out and that the statistics from equilibrium simulations are more stable than fully coupled transient simulations. 5. Summary: The present study has explored the climate sensitivity of the latest version of the Community Climate System Model, CCSM3. The equilibrium climate sensitivity of the T85 version of CCM3 is 2.7 C, while the transient climate response of this model is 1.5 C. Exploration of the dependence of the equilibrium climate sensitivity on horizontal resolution of the Community Atmosphere Model (CAM3) indicates that the lowest resolution version (T31) has the lowest sensitivity of 2.2 C, while the highest resolution version (T85) has the highest sensitivity of 2.7 C. Examination of differences in the geographic response of clouds among the various resolutions indicates that low cloud response can have marked differences among the three resolution versions of CAM3. The transient model simulations exhibit a similar dependence of cloud response on horizontal resolution. The causal reason for this dependence is not clear. 14

15 The purpose of the present study is to document the climate sensitivity of the CCSM3 and compare this sensitivity to previous versions of the CCSM and various horizontal resolution versions of the model. Diagnosis of these simulations indicates that cloud processes in the various versions of the CAM contribute to differences in the equilibrium climate sensitivity. Further studies are underway to elucidate the physical mechanisms that produce these differences in sensitivity. For example, development versions of the CAM exist with limited changes to the cloud scheme that may help in dissecting the model behavior. It has also been shown that the slab ocean model can be used to explore cloud feedbacks. Additional studies using specified changes in sea surface temperature from either SOM or transient runs indicate that using structured SST perturbations in the CAM can be used to study regional cloud feedback processes, which provides a very computationally efficient method to understand model processes. 15

16 Acknowledgments We would like to recognize that computational facilities have been provided by the National Center for Atmospheric Research (NCAR). NCAR is supported by the National Science Foundation. We would also like to recognize that the Department of Energy's Office of Science supports the CCSM program through its Biological and Environmental Research Program and the use of high performance computing as part of its Advanced Scientific Computing Research (ASCR). ASCR provides computing at the National Energy Research Center and at the Oak Ridge National Laboratory Center for Computational Science. 16

17 References Boer, G.J. and B. Yu, 2003: Dynamical aspects of climate sensitivity. Geophys. Res. Lett., 30, 1135, doi: /2002gl Boville, B.A., J.T. Kiehl, P.J. Rasch, and F.O. Bryan, 2001: Improvements to the NCAR CSM-1 for transient climate simulations. J. Climate, 14, Boville, B.,A., P.J. Rasch, J.J. Hack, and J.M. McCaa, 2005: The impact of changing moist physical processes on the general circulation of CAM: sensitivity studies. Submitted to J. Climate. Collins, W.D., C.M. Bitz, M. Blackmon, G.B. Bonan, C.S. Bretherton, J.A. Carton, P. Chang, S. Doney, J.J. Hack, T. Henderson, J.T. Kiehl, W.G. Large, D. McKenna, B.D. Santer, and R. Smith, 2005a: The Community Climate System Model, Version 3. Submitted to J. Climate. Collins, W.D., P.J. Rasch, B.A. Boville, J.J. Hack, J.M. McCaa, D.L. Williamson, B. Briegleb, C.M. Bitz, S.-J. Lin, and M. Zhang, 2005b: The formulation and atmospheric simulation of the Community Atmosphere Model: CAM3. Submitted to J. Climate. Cubasch, U. et al., 2001: Projections of future climate change. In Climate Change 2001, Cambridge University Press, Cambridge, England. Dai, A., T.M.L. Wigley, B.A. Boville, J.T. Kiehl, and L.E. Buja, 2001: Climates of the twentieth and twenty-first centuries simulated by the NCAR Climate System Model. J. Climate, 14, Dickinson, R.E., K.W. Oleson, G.B. Bonan, F. Hoffman, P. Thorton, M. Vertenstein, Z.-L. Yang, and X. Zeng, 2005: The community land model and its climate statistics as a component of the community climate system model. Submitted to J. Climate. 17

18 Gregory, J.M., W.J. Ingram, M.A. Palmer, G.S. Jones, P.A. Stott, R.B. Thorpe, J.A. Lowe, T.C. Johns, and K.D. Williams,2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi: /2003gl018747,. Kiehl, J.T. and P.R. Gent, 2004: The Community Climate System Model, Version 2. J. Climate, 17, Large, W. and G. Danabasoglu, 2005: Attribution and impacts of the upper ocean biases in CCSM3. Submitted to J. Climate. Meehl, G.A, W.D. Collins, B.A. Boville, J.T. Kiehl, T.M.L. Wigley, and J.M. Arblaster, 2000: Response of the NCAR climate system model to increased CO2 and the role of physical processes. J. Climate, 13, Yeager, S., J.J. Hack, C. Shields, and W. Large, 2005: The low resolution CCSM3. Submitted to J. Climate. 18

19 Figure Captions Figure 1. Change in net forcing (Wm -2 ) at the model top versus change in surface temperature ( K) from the CAM3 slab ocean model simulation for doubled CO 2. Each data point is the annual mean value from the first 5 years of the simulation. Figure 2a. Equilibrium surface temperature change ( C) due to doubled CO 2 from the CCM3, CAM2 and CAM3 slab ocean models. Figure 2b. Change in net cloud forcing, shortwave cloud forcing and longwave cloud forcing (Wm -2 ) due to doubled CO2 for the CAM2 and CAM3 slab ocean models. Figure 3. Change in zonal annual mean surface temperature ( K) due to doubled CO 2 from the CCM3 ( ), CAM2 (------) and CAM3 ( ) slab ocean models. Figure 4a. Change in zonal annual mean low cloud fraction due to doubled CO 2 from the CAM2 (-----) and CAM3 ( ) slab ocean models. Figure 4b. Change in zonal annual mean shortwave cloud forcing due to doubled CO 2 from the CAM2 (-----) and CAM3 ( ) slab ocean models. Figure 5a. Change in global annual mean surface temperature ( K) due to doubled CO 2 in the T31, T42 and T85 CAM3 slab ocean models. 19

20 Figure 5b. Change in global annual mean shortwave and longwave cloud forcing (Wm -2 ) due to doubled CO 2 in the T31, T42 and T85 CAM3 slab ocean models. Figure 6a. Geographic distribution in the change in annual mean surface temperature ( K) due to doubled CO 2 from a) the T42 and b) T85 CAM3. Figure 6b. Geographic distribution in the change in annual mean low cloud fraction due to doubled CO 2 from a) the T42 and b) T85 CAM3. Figure 7a. Time Series of change in surface temperature ( K) in CSM1 ( ), CCSM2( ), CCSM3( ) due to 1% per year increase in CO2 mixing ratio. Doubling of CO 2 occurs at year 70, a quadrupling of CO 2 occurs at year 140. Figure 7b. Time Series in the change in cloud forcing (Wm -2 ) CSM1 ( ), CCSM2( ), CCSM3( ) due to 1% per year increase in CO2 mixing ratio. Doubling of CO 2 occurs at year 70, a quadrupling of CO 2 occurs at year 140. Figure 8. Change in tropical cloud fraction due to a 1% per year increase in CO 2 from CCSM3, CCSM2 and CSM

21 Figure 9. Change in surface albedo due to a 1% per year increase in CO 2 for the CSM1.4 ( ), CCSM2 ( ), and CCSM3 ( ). Figure 10. Change in sea-ice area due to a 1% per year increase in CO 2 for the CSM1.4 ( ), CCSM2 ( ), and CCSM3 ( ). a) Northern hemisphere ice area (10 6 km 2 ), b) Southern hemisphere ice area (10 6 km 2 ). Figure 11. Normalized Change in a) SWCF, b) LWCF (Wm -2 ) from the CAM3 SOM and the CCSM3 1% transient simulation. Change in cloud forcing is normalized by the global annual mean in surface temperature. Figure 12. Normalized change in a) low, b) middle and c) high cloud fraction from the CAM3 SOM and the CCSM3 1% transient simulation. Change in cloud fraction is normalized by the global annual mean in surface temperature. 21

22 3 2.8 NET Flux (Wm -2 ) y = x R= !Ts Figure 1. Change in net forcing (Wm -2 ) at the model top versus change in surface temperature ( K) from the CAM3 slab ocean model simulation for doubled CO 2. Each data point is the annual mean value from the first 5 years of the simulation. 22

23 3 2.5 Climate Sensitivity ( C) CCM3 CAM2 CAM3 Atmospheric Verison of CCSM Figure 2a. Equilibrium surface temperature change ( C) due to doubled CO 2 from the CCM3, CAM2 and CAM3 slab ocean models. 23

24 Figure 2b. Change in net cloud forcing, shortwave cloud forcing and longwave cloud forcing (Wm -2 ) due to doubled CO2 for the CAM2 and CAM3 slab ocean models. 24

25 Figure 3. Change in zonal annual mean surface temperature ( K) due to doubled CO 2 from the CCM3 ( ), CAM2 (------) and CAM3 ( ) slab ocean models. 25

26 Figure 4a. Change in zonal annual mean low cloud fraction due to doubled CO 2 from the CAM2 (-----) and CAM3 ( ) slab ocean models. 26

27 Figure 4b. Change in zonal annual mean shortwave cloud forcing due to doubled CO 2 from the CAM2 (-----) and CAM3 ( ) slab ocean models. 27

28 Figure 5a. Change in global annual mean surface temperature ( K) due to doubled CO 2 in the T31, T42 and T85 CAM3 slab ocean models. 28

29 Figure 5b. Change in global annual mean shortwave and longwave cloud forcing (Wm -2 ) due to doubled CO 2 in the T31, T42 and T85 CAM3 slab ocean models. 29

30 Figure 6a. Geographic distribution in the change in annual mean surface temperature ( K) due to doubled CO 2 from a) the T42 and b) T85 CAM3. 30

31 Figure 6b. Geographic distribution in the change in annual mean low cloud fraction due to doubled CO 2 from a) the T42 and b) T85 CAM3. 31

32 Figure 7a. Time Series of change in surface temperature ( K) in CSM1 ( ), CCSM2( ), CCSM3( ) due to 1% per year increase in CO2 mixing ratio. Doubling of CO 2 occurs at year 70, a quadrupling of CO 2 occurs at year

33 Figure 7b. Time Series in the change in cloud forcing (Wm -2 ) CSM1 ( ), CCSM2( ), CCSM3( ) due to 1% per year increase in CO2 mixing ratio. Doubling of CO 2 occurs at year 70, a quadrupling of CO 2 occurs at year

34 Figure 8. Change in tropical cloud fraction due to a 1% per year increase in CO 2 from CCSM3, CCSM2 and CSM

35 Figure 9. Change in surface albedo due to a 1% per year increase in CO 2 for the CSM1.4 ( ), CCSM2 ( ), and CCSM3 ( ). 35

36 Figure 10. Change in sea-ice area due to a 1% per year increase in CO 2 for the CSM1.4 ( ), CCSM2 ( ), and CCSM3 ( ). a) Northern hemisphere ice area (10 6 km 2 ), b) Southern hemisphere ice area (10 6 km 2 ). 36

37 a) 37

38 b) Figure 11. Normalized Change in a) SWCF, b) LWCF (Wm -2 ) from the CAM3 SOM and the CCSM3 1% transient simulation. Change in cloud forcing is normalized by the global annual mean in surface temperature. 38

39 a) b) c) 39

40 Figure 12. Normalized change in a) low, b) middle and c) high cloud fraction from the CAM3 SOM and the CCSM3 1% transient simulation. Change in cloud fraction is normalized by the global annual mean in surface temperature. 40