The standard CAM5 physics package , which is used in the control model for this paper, will be referred to as CAM5.3, which is the atmosphere component currently used in CESM version 1. These are the physical parameterizations that were used for the CESM CMIP5 submission in addition to the CESM large ensemble LE;. CAM5 represents a nearly complete overhaul in physical parameterization options from CAM4, with the exception of the deep convection scheme . The boundary layer scheme in CAM5 is based on downgradient diffusion of moist conserved variables UWMT;, the shallow convection scheme follows that of (UWSC), while cloud macrophysics is computed according to . The two-moment stratiform microphysics scheme for both liquid and ice is used in CAM5, using the ice closures as described in . Aerosols are predicted according to and linked to the microphysics through the parameterization of liquid and ice activation of cloud drops and crystals on aerosols .

We will also examine the coupled climate simulations for the first developmental version of CAM towards CAM6, known as CAM5.4. The purpose of CAM5.4 is to include physical upgrades to the CAM5 family and assess their climate effects, before CLUBB was turned on for the system. In addition, at the time of CAM5.4 development, it was unclear if CLUBB would be included into future versions of CAM as the default scheme, as CAM-CLUBB coupled simulations were still being evaluated. Changes from CAM5.3 to CAM5.4 include an upgrade from a diagnostic precipitation scheme to a prognostic precipitation scheme , a new ice nucleation scheme to better represent mixed-phase and cirrus ice nucleation, an upgrade from the three-mode Modal Aerosol Module (MAM3) to a four-mode version (MAM4) that includes the treatment of black carbon , the use of an additional two vertical layers near model top for consistent level treatment between CAM and the Whole Atmosphere Community Climate Model (WACCM), an improved treatment of the dust emissions size distributions and dust optical properties , a fix to the energy formulation in CAM , a change to the vertical remapping from energy to temperature in the finite volume dynamical core, and consistent topography files for the finite volume and spectral element dynamical cores.

The CAM5.5 version of the model uses all the upgrades developed for CAM5.4; however, the shallow convection, planetary boundary layer, and warm cloud macrophysics schemes are replaced with the CLUBB parameterization . Because CLUBB is currently a warm cloud parameterization, ice cloud fraction and coupling are closed using the current relative-humidity-based scheme in CAM, as described by and . Besides the changes to the physical parameterizations, CAM5.5 represents an inherently different coupling with the microphysics. For example, in CAM5.3 and CAM5.4, there are three separate microphysics schemes: the double-moment scheme for stratiform clouds, while each of the (ZM) deep convection scheme and shallow convection scheme contains its own simplified single-moment treatment of microphysics.

In CAM5.5, since CLUBB is a unified parameterization, the double-moment microphysics is applied for both the stratiform and shallow convection, although the simplified single-moment microphysics is retained for the ZM deep convection. Therefore, not only does CAM5.5 represent a more unified treatment of clouds and microphysics but also a more consistent treatment of cloud–aerosol interactions. In addition, CAM5.5 couples CLUBB and the microphysics together with the same time step, as opposed to the “sequentially split” method that is traditionally employed in CAM5.3, CAM5.4, and most other GCMs as described in. In other words, each time CLUBB is called with its 5 min time step, the microphysics is called and this loop continues until the 30 min CAM physics time step has expired. This is to ensure that cloud water is not entirely depleted in a single time step, which is often the case with the long time steps commonly employed with coarse-grid GCMs. Table  describes the differences in physical parameterizations between CAM5.3, CAM5.4, and CAM5.5.

It should be noted that CAM5.4 and CAM5.5 were tuned slightly differently in order to achieve top-of-atmosphere (TOA) radiation balances. For instance, CAM5.4 in its atmosphere-only tuned state produced a TOA radiation imbalance of -1.4 Wm-2 for the first 10 years of a pre-industrial control run. Therefore, a tuning decision had to be made, and to compensate for this imbalance a parameter that controls the autoconversion threshold of ice to snow in the double-moment microphysics (Dcs) was increased to produce more high clouds to help warm the climate system. However, when the CLUBB parameterization was run on top of an untuned coupled version of CAM5.4 the TOA radiation imbalance was +1.5 Wm-2; therefore, tuning decisions independent of those made in CAM5.4 had to be made.

CAM5.5 tuning involved both a decrease to the Dcs parameter to reduce high-level clouds as well as a modification to some CLUBB parameters to increase low cloud cover to cool the climate. Chiefly, the main CLUBB parameter that is tuned for radiation balance is the “γcoef parameter”, which influences the width of the individual Gaussian components of w relative to the width of the overall PDF of w . Decreasing the gamma parameter helps to decrease the skewness of vertical velocity and scalars, making the layer less cumuliform and more stratiform, with increased low-cloud cover. Implications of these tuning parameter decisions will be discussed in Sect. 4.

CAM5.4 and CAM5.5 were tuned in the development process in atmosphere-only simulations to achieve the best possible simulations in those configurations. However, in coupled mode, CAM5.4 and CAM5.5 were only tuned at this point to achieve (1) a TOA radiation imbalance of <|0.1| Wm-2, and (2) a stable (non-drifting) pre-industrial climate. Of the tuning parameters that are shared between CAM5.4 and CAM5.5, the two configurations only differ in their value of Dcs. CAM5.4 has a value of 250 µm while CAM5.5 has a value of 160 µm. For the sake of computational resources, a more comprehensive tuning will occur once the new component models have been integrated into CESM2 and is prepared for CMIP6 simulations.

The other component models used in this study are the same between the different configurations of atmosphere models used. The Community Land Model CLM; version 4 is used to represent terrestrial ecosystems in the climate system. The sea-ice component utilizes version 4 of the Los Alamos National Laboratory (LANL) Community Ice Code CICE4;, while the ocean component uses the LANL Parallel Ocean Program version 2 POP;.

Figure  displays the surface temperature evolution from the 1850 fully coupled pre-industrial control run for CESM-CAM5.3 (i.e., the control simulation used for the CESM LE), CESM-CAM5.4, and CESM-CAM5.5 for the first 200 years of integration. The CESM-CAM5.3 run reaches a reasonable equilibrium after about 90 years, whereas the CESM-CAM5.4 run stabilizes after about 60 years. The CESM-CAM5.3 run takes longer to stabilize because it was initialized from present-day Levitus observations. CESM-CAM5.5 has a longer spin-up period than CESM-CAM5.4 but appears to reach reasonable equilibrium after 100 years. All three simulations achieve a top-of-model radiation imbalance of <|0.1| Wm-2, which is what we strive for in these pre-industrial control runs. Both the CAM5.4 and CAM5.5 simulations appear to stabilize at a temperature slightly warmer than the CAM5.3 control runs.

First, we will explore the successive differences in each model version by focusing on the cloud radiation biases. The simulated shortwave cloud forcing (SWCF) biases, computed relative to the Clouds and the Earth's Radiant Energy System – Energy Balanced and Filled CERES-EBAF; can be seen in Fig. . These figures represent the 25-year climatological averages from a stable period in each simulation. The overall results for the analysis shown in this paper do not depend on the period selected for the averaging for any simulation, provided that period occurs after the model reaches a reasonable equilibrium (not shown). It is important to note that we are comparing pre-industrial model simulations to present-day observations. Thus, we expect there to be a bit of an offset between the two due to positive cloud feedbacks in a warmer world, concentrated in the Northern Hemisphere. With each successive model version, there is about a  2 Wm-2 reduction in the root mean squared error (RMSE) score for SWCF. In addition, CESM-CAM5.4 and CAM5.5 demonstrate modest improvements in the pattern correlation coefficient over CESM-CAM5.3. CESM-CAM5.3 contains large errors over the Southern Ocean, in the subtropical stratocumulus to cumulus transition areas, and over the tropical continents. These have all been longstanding biases in CESM and most previous generations of the CCSM.

With the introduction of CAM5.4, there is a 50 % reduction in the SWCF positive biases over the Southern Ocean, centered around 60∘ S. The bias in CAM5.3 exists primarily because low-level clouds contain insufficient amounts of supercooled liquid . This bias has been greatly ameliorated due to the new ice nucleation scheme as well as the new prognostic microphysics scheme . Previous work  suggests that an improvement in the Southern Ocean SWCF biases could potentially lead to an improvement in the simulated double Intertropical Convergence Zone (ITCZ) bias, which we will discuss further in this section.

Going to CAM5.5 physics we see a further reduction in the global SWCF RMSE. These improvements appear to come from the tropical continents where there is a reduction in the amount of reflected shortwave radiation. As will be shown later, it appears the reduction of these biases is due to a shift in timing of the most intense convection to later in the afternoon, when the Sun angle is lower. In addition, there are also improvements seen in the transition from stratocumulus to cumulus, whereas both CAM5.3 and CAM5.4 appear to transition a bit too abruptly, CAM5.5 tends to have a more gradual transition. This is generally in agreement with the prescribed SST results seen in ; however, we note that there are some differences compared to that work.

The simulated biases for the longwave cloud forcing (LWCF), also computed relative to CERES-EBAF observations, are displayed in Fig. . While there are modest improvements in RMSE for each successive configuration, the correlation coefficient is the same for each model configuration. CAM5.3 contains longstanding biases of an underestimate of LWCF in the midlatitudes and an overestimate in the tropics, which is partially due to biases related to the double ITCZ problem. CAM5.4 produces a global mean of LWCF that is most comparable to CERES-EBAF observations; however, this is mostly due to compensating errors in the regional biases. While CAM5.4 improves the midlatitude bias in the storm tracks, there is a large positive bias over the tropical oceans. This bias is largely due to the tuning of the autoconversion from ice to snow parameter (Dcs) in order to achieve a TOA radiation balance, as demonstrated in a series of experiments (not shown; Cecile Hannay, personal communication). With CAM5.5, the global mean LWCF is more comparable to observations than CAM5.3 but lower than CAM5.4. However, in this configuration, the large positive biases seen in the tropics in CAM5.4 are somewhat ameliorated in CAM5.5, which is responsible for the slightly lower RMSE score.

The zonal averages and differences from observations for SWCF and LWCF are illustrated in Fig. . Here, the reduction of the Southern Ocean SWCF biases near 60∘ S is evident for CAM5.4 and CAM5.5, as is the reduction of tropical SWCF biases for CAM5.5. The zonal mean differences for LWCF show reduced negative/positive biases compared to CAM5.3/CAM5.4 for CAM5.5. However, a negative SWCF bias emerges near 45∘ S for both CAM5.4 and CAM5.5, indicative of clouds that are too reflective at these latitudes.

Precipitation biases, computed relative to the Global Precipitation Climatology Project GPCP;, can be seen in Fig. . Unlike the cloud forcing biases, where the errors improved with each successive model version, the skill for precipitation remains generally unchanged for all models. This is also true for the pattern correlation coefficient between the three configurations. However, there are notable differences in regional biases between the three configurations. CESM-CAM5.3 has an obvious double ITCZ bias in the Southern Hemisphere tropical Pacific Ocean and this bias is worsened in the CAM5.4 version. This is interesting because  identified a potential link between Southern Ocean cloud biases and double ITCZ biases in CMIP5 models, with the idea being that a model with minimal Southern Ocean cloud biases would mitigate the double ITCZ due to global energy arguments. CESM-CAM5.4 shows a great improvement of the Southern Ocean SWCF biases (Fig. ); however, it also shows a worsened double ITCZ bias. show that a version of CAM5.3 with reduced Southern Ocean cloud biases did not result in an improved double ITCZ bias because the northward cross-equatorial heat transports reductions occurring primarily in the ocean and not the atmosphere.

The CESM-CAM5.5 configuration does show a modest reduction in the double ITCZ bias, both in the Atlantic and Pacific oceans, compared to the CAM5.4 and CAM5.3 versions of the model. We note that all tuning simulations we performed with CAM5.5 (not shown) resulted in a reduced double ITCZ bias of varying degrees when compared to CAM5.3 and CAM5.4. Thus, it does not appear that this was achieved simply due to happenstance. Other regions of improved precipitation in CAM5.5 can be found over the subtropics, the Atlantic deep convective regions, and Australia. There are also regional degradations in CAM5.5, such as over the tropical Pacific warm pool, the Indian Ocean, and the maritime continent.

The most notable bias for CAM5.5 is over tropical South America where precipitation is greatly reduced compared to observations and CAM5.3 and CAM5.4. It is interesting to note that the coupled simulation precipitation results for CAM5.5 are not dissimilar from those presented in the prescribed SST study of , with the exception being over the Amazon, hinting at a possible feedback between moisture transport and the Pacific and Atlantic SSTs with this region . However, an examination of the large-scale circulation over the area (not shown) provided no significant differences between the CAM5.3 and CAM5.5 simulations, suggesting that the difference in precipitation simulation may not be caused by biases induced to the large-scale circulation.

For a more in-depth look at the precipitation biases over the Amazon for CAM5.5, we examine the diurnal cycle of precipitation for CAM5.3 and CAM5.5 from the pre-industrial control runs. We note that although CAM5.4 is not included in this analysis, because sufficient output from the pre-industrial control run was not supplied, examination of the diurnal cycle of precipitation for CAM5.4 in shorter prescribed SST simulations has been performed and the behavior was shown to be nearly identical to that of CAM5.3. It is known that GCMs struggle to simulate the timing and intensity of precipitation . Fig  shows this is true for CESM-CAM5.3 over the tropical continents for December–January–February (DJF) and June–July–August (JJA), with the peak precipitation occurring around noon, whereas the Tropical Rainfall Measuring Mission (TRMM; Huffman et al., 2007) observations generally show a peak around 19:00 LT (local time). Thus, the CAM5.3 simulation of precipitation is tied too closely to the peak of solar insolation. The CAM5.5 representation, on the other hand, shows a large improvement when compared to CAM5.3, with the peak precipitation generally occurring around 17:00 LT. The reason for this improvement appears to be coming from the CLUBB parameterization, which is responsible for the growth of the boundary layer and mid-morning and early afternoon shallow convection. It is important to note that the improved simulation of the diurnal cycle has been a robust feature of CLUBB in every development coupled and atmosphere-only simulation. The CLUBB unified parameterization is able to successfully simulate a gradual transition of these regimes and prevent the deep convective scheme from firing off too early.

Figure  (bottom) shows the composite of the precipitation over Africa and the Amazon for the DJF season for CAM5.3 and CAM5.5. Both CAM5.3 and CAM5.5 underestimate the peak precipitation rate over the Amazon when compared to the observations; however, CAM5.3 begins to precipitate much too early in the day compared to the observed time. Therefore, CAM5.3 has a better mean state bias in the Amazon, but for the wrong reasons, since it begins to precipitate too early but ends at approximately the same time as CAM5.5. Improvements to CAM5.5 mean precipitation should therefore focus on increasing the intensity and duration of the precipitation, since both CAM5.3 and CAM5.5 stop precipitating too early. In addition, improvements to the JJA season precipitation for CAM5.5 will also help to ameliorate climatological biases in this region.

Results over tropical Africa are generally similar to those over the Amazon; however, CAM5.5 tends to simulate the maximum precipitation rate with better fidelity over this region than over the Amazon. Experiments and modifications to the deep convection scheme are currently underway. It is, however, encouraging that CAM5.5 is able to improve the diurnal cycle of precipitation over tropical land as this has been a longstanding bias in GCMs. Notably, this has been achieved without changing the deep convection scheme. Further improvements in the representation of the diurnal cycle of precipitation may be achieved by removing the conventional deep convection scheme and allowing CLUBB to also simulate this regime .

The SST biases, computed relative to the pre-industrial HadISST observation estimates , for the three models are shown in Fig. . Unlike other variables displayed in this section, which showed either an improved or static mean error and RMSE compared to the previous model iteration, the SST shows somewhat worsening error with each successive model iteration, with the difference in error from CESM-CAM5.3 to CESM-CAM5.5 being statistically significant. In a sense, this is not very surprising, as the CAM5.3 configuration, which was used for the CESM large ensemble project, was well tuned to achieve very good SSTs. Improvements to the simulation of SSTs in NCAR's next-generation climate model are being investigated as the new ocean, land, and sea-ice component models are being finalized and as final tunings to the model are iteratively performed.

With the introduction of CAM5.4 physics, a cold bias becomes present in the North Pacific and especially the North Atlantic Ocean. Likely this is due to the new ice nucleation scheme and upgraded microphysics, which is responsible for greater amounts of low-level liquid cloud that is more reflective at these latitudes. This is also the case in the Southern Ocean, where the cold bias in CAM5.4 and CAM5.5 can be explained by introduction of clouds that are too reflective near 45∘ S. While biases in the midlatitude regions between CAM5.4 and CAM5.5 are very similar, differences do exist in the tropics and subtropics. For example, CAM5.4 has a large positive SST bias in the southeastern tropical Pacific, which is ameliorated in CAM5.5 and one of the likely reasons for the reduced double ITCZ bias. Similar bias reductions are found in the tropical Atlantic Ocean. Not surprisingly, tropical SST biases in CAM5.5 are well correlated with the precipitation biases, namely over the tropical western Pacific and western Indian oceans.

Surface stress from the atmosphere is another important component to the coupled system and the surface stress biases, computed relative to the European Remote Sensing Satellite Scatterometer ERS; observations, can be seen in Fig . Similar to the SST biases, we see that the positive surface stress bias in the Southern Ocean increases by 20 % in CAM5.4 and CAM5.5 when compared to CAM5.3. We note that this increase in surface stress for CESM-CAM5.4 and CESM-CAM5.5 is very similar to that found in atmosphere-only simulations. This degradation is likely due to vast changes in low clouds over these regions, and reconciling these changes for an improved representation of surface stresses and SSTs is an area left for future work. The addition of CAM5.5 physics, CLUBB, neither improves nor degrades these biases, suggesting they are the result of the addition of the CAM5.4 physics. An examination of the surface stresses in the subtropics, where boundary layer clouds and trade wind cumulus are prevalent, shows that CAM5.5 reduces much of positive bias seen in the surface stress magnitude by 10 to 15 % in CAM5.3 and CAM5.4.

Figure  displays the biases for Arctic sea ice computed relative to the HadISST pre-industrial dataset. CESM-CAM5.3 generally has the best agreement with observations, and this is not surprising since the sea-ice model was not tuned at all in the CESM-CAM5.4 and CESM-CAM5.5 simulations. While the two new configurations of CAM tend to produce less sea ice in the Arctic and more in the North Atlantic and the Labrador Sea compared to the baseline CESM-CAM5.3 configuration, it appears that CESM-CAM5.4 produces the thinnest sea ice in the Arctic, while CESM-CAM5.5 is only marginally lower than the baseline CESM-CAM5.3 simulation. All three configurations use the same generation of the Community Ice CodE (CICE) and ocean model; thus, we speculate that differences may be due to differences in the atmospheric physics and their impact on the coupled system.

Figure  shows the global and Atlantic Ocean meridional overturning circulation (MOC) for CESM-CAM5.3 and CESM-CAM5.5. The maximum overturning in the Atlantic occurs near 35∘ N at a depth of 1 km for both CESM-CAM5.3 and CESM-CAM5.5. CESM-CAM5.5 is weaker at about 23 sverdrups (Sv = 106 m3s-1), compared to CESM-CAM5.3 at about 26 Sv. For comparison, CCSM3's maximum Atlantic MOC (AMOC) was about 20 Sv , whereas the maximum AMOC in CCSM4 was 24 Sv . However, whereas these configurations used different mixing parameterizations in the ocean model, the ocean models in CESM-CAM5.3 and CESM-CAM5.5 are largely the same. A possible reason for the differences between the two configurations is in the simulation of the surface wind stress (Fig. ) in the North Atlantic. Whereas CESM-CAM5.3 and CESM-CAM5.4 contain positive biases in the Labrador Sea, this has been largely reduced in the CESM-CAM5.5 simulations. It is also possible that differences in the simulated AMOC could arise from the simulation of surface wind stresses over the Southern Ocean . Overall, however, it does not appear that the inclusion of CLUBB degrades the simulation of AMOC in CESM-CAM5.5. Observational estimates generally show a maximum AMOC of 20 Sv , suggesting that most current and past configurations of CESM and CCSM potentially overestimate AMOC, though all configurations are in line with uncertainty estimates .

Of great importance for a climate model to represent with some fidelity is the ENSO, which is the strongest coupled mode of variability in the climate system. Teleconnections from the warming of the tropical waters in the eastern Pacific, associated with El Niño events, have significant impacts on weather and climate over much of the planet, which illustrates the importance to represent in a climate model. Previous versions of NCAR's climate model, namely CCSM3 , struggled to simulate ENSO, which was dominated by variability at the 2-year, rather than the 3- to 7-year, period from observations. The ENSO period was greatly improved upon with CCSM4 , with the introduction of changes made to the deep convection scheme ; however, the simulated ENSO in CCSM4 still had an unrealistically large amplitude.

Figure  displays the variance spectra of the Niño-3.4 monthly SST anomalies for observations (HadISST) and for CESM-CAM5.3, CESM-CAM5.4, and CESM-CAM5.5. Various 100-year samples are displayed from the long CESM-CAM5.3 control and it is quite clear there exists variability in the amplitude and periodicity of the simulated ENSO within this long control run. Therefore, caution must be exercised when evaluating the relatively short control simulations  of CESM-CAM5.4 and CESM-CAM5.5. However, it is also clear that the amplitude of the simulated ENSO from CESM-CAM5.4 is unrealistically large. Obviously, this simulation sparked concern about an inherent deficiency in the CAM5.4 physics causing a degradation in the simulation of ENSO. Sensitivity experiments revealed that the tuning of Dcs for radiation balance for CAM5.4 was the cause of the large-amplitude ENSO.

The ENSO simulation provided by CESM-CAM5.5 is much more reasonable than CAM5.4. While CESM-CAM5.5 simulates a better amplitude compared to CESM-CAM5.3, the periodicity is on the shorter end but still acceptable. It should be noted that the first 100 years simulated by CESM-CAM5.3 also had a 3-year periodicity but eventually settled into a 4- to 5-year periodicity, which is closer to observations. At this point, it is unclear if a longer simulation of CESM-CAM5.5 will result in slightly longer periodicity. However, it is worthwhile to note that it does not appear that the simulation of ENSO is significantly improved or degraded with the addition of the CAM5.5 physical parameterizations. The Niño-3.4 time series can be seen in Fig.  and demonstrates the ability of CESM-CAM5.5 to simulate, with reasonable fidelity, the variable cycles associated with ENSO, including the often observed 2-year La Niña events that follow a 1-year El Niño event.

Although the deep convection scheme has not changed in the evolution of CESM experiments shown in this paper, it is still important to assess the differences in the simulation of intra-annual seasonal tropical variability in the simulations with the new cloud and turbulence physics. In addition, several studies have found that changing the shallow convection scheme can greatly improve the simulation of the MJO . Figure  shows the composite of the 20- to 100-day bandpass-filtered daily anomalies of outgoing longwave radiation (OLR) and wind vectors at 850 hPa associated with the MJO for ERA, CESM-CAM5.3, and CESM-CAM5.5. The time periods displayed for the model simulations are the same as those shown for the results on climatology in Sect. 4.1.

The ERA analysis clearly shows eastward propagation of the OLR anomalies, associated with the MJO. CESM-CAM5.3 shows very little variability, characteristic of a model that struggles to simulate the MJO. While there is a slight improvement in the strength of the anomalies and signal of the propagation in the CESM-CAM5.5 simulation, it is still much weaker than in observations. These results are similar to those found in atmosphere-only simulations (not shown). While there are modest improvements in the simulation of the MJO with the addition of CLUBB, further improvements of the MJO may be achieved by allowing the CLUBB parameterization to also simulate the deep convective regime, such as the encouraging results presented in , or by modifications to the existing deep convection scheme in CAM5.5.

Figure  displays the time series of the globally averaged surface temperature anomaly for 1920 to 2005 from observations and the ensemble mean from 30 members of the CESM-CAM5.3 configuration. The model spread from the CESM-CAM5.3 ensemble is denoted by the shading. One realization from the CESM-CAM5.5 model is also displayed. Once again, it should be noted that a decision was made not to run the CESM-CAM5.4 model for the historical simulation since it was seen as an intermediate model version along the CAM development process. In addition, we stress that only a single realization from CESM-CAM5.5 is shown. Thus, the point of examining this is only to gauge the interplay between the climate sensitivity and aerosol interactions in CESM-CAM5.5 and how they may come into play for the 20th century simulation. The CESM-CAM5.5 run was started from year 150 of the pre-industrial control. Details on the specifics of the initialization of the CESM-CAM5.3 model can be found in .

The single realization of CESM-CAM5.5 stays mostly within the model spread of the 30-member CESM large ensemble, with some exceptions. The first is a period in 1930 that is much warmer than observations and the CESM-CAM5.3 model average. The second is that the CESM-CAM5.5 simulation ends cooler than the CESM-CAM5.3 average and about as cold as the coldest member. Although it is difficult to attribute these differences to either changes in the CAM physics or to internal variability, the fact that the CESM-CAM5.5 simulation ends colder than observations is worth investigation. We identify the three most likely reasons for this difference: (1) noise from internal variability that cannot be quantified from one ensemble member, (2) a relatively short pre-industrial control run in which the ocean may not be fully adjusted to the CAM5.5 physics, and (3) changes in the climate sensitivity and/or aerosol indirect forcing. Here, we focus on the third reason, since we can readily quantify these measures.

Various slab ocean model (SOM) experiments with doubled CO2 concentrations performed throughout the CAM-CLUBB development process have identified a climate sensitivity of 3.8 K associated with CAM5.5, which is slightly lower than the climate sensitivity of 4.1 K associated with CAM5.3 . These estimates are higher than the CMIP5 model mean 2×CO2 equilibrium climate sensitivity of 3.37 K . While the climate sensitivity associated with CAM5.5 is indeed slightly lower than CAM5.3, it is also necessary to investigate the compensating effect of aerosols on the climate system.

Table  documents the radiative flux perturbation (RFP), changes in SWCF and LWCF, and the aerosol indirect effect (AIE) for various aerosol perturbation experiments involving several versions of CAM throughout the development process. All experiments shown in Table  represent an aerosol perturbation calculation where each configuration used climatological SSTs and present-day (PD) forcing and was run twice: once with PD aerosol emissions and the other with pre-industrial (PI) aerosol emissions. Thus, the values shown in Table  are the differences between the simulations using PD aerosol emissions and PI emissions. The RFP  is defined as the difference in the top-of-model (TOM) radiation imbalance between the PD and PI aerosol emission simulations. The AIE is defined as AIE=ΔSWCF+ΔLWCF.

CAM5.3 has an RFP and AIE that are larger than satellite estimates presented in . Therefore, we can conclude that the successful CESM-CAM5.3 simulation of the 20th century is due to competing effects of a potentially large climate sensitivity and an AIE that is too strong. showed that the RFP and AIE are reduced by the implementation of the MG2 prognostic precipitation scheme (denoted by the CAM5.3+MG2 simulation in Table ). The reason for this is that precipitation processes are altered with more accretion relative to autoconversion in MG2. Accretion does not depend on cloud drop number, so the clouds are less sensitive to drop number. However, simulations of CAM5.3+MG2+CLUBB displayed an increase in the RFP and AIE when compared to the CAM5.3+MG2 simulations. This increase is due to the fact that since CLUBB is a unified parameterization of stratiform and shallow convective clouds and drives a single microphysics scheme, the aerosol indirect effect is now being considered in more cloud types than CAM5.3. While this physical consistency is desirable for a global model, it does subject the model to an increase in the sensitivity of cloud–aerosol interactions.

However, performing AIE experiments with CAM5.4 (which includes the MG2 prognostic precipitation scheme), we see that the forcing to aerosols has rebounded to CAM5.3 values. As expected, due to the AIE being considered in more cloud types than CAM5.4, the sensitivity is even higher for CAM5.5. Puzzled why CAM5.4 has an aerosol sensitivity so much higher than CAM5.3+MG2, the authors investigated and found that the increased sensitivity was due to increased lifetime of SO2, which was due to the new MAM4 aerosol model. Nevertheless, we now gain a deeper understanding for why CESM-CAM5.5 ended the 20th century simulation colder than observations and CESM-CAM5.3; this was due to compensating effects of a lower climate sensitivity and higher aerosol sensitivity when compared to CESM-CAM5.3.

The higher aerosol sensitivity associated with CESM-CAM5.5 is a combined effect due to the increased lifetime of SO2 from CAM5.4 physics as well as the fact that the AIE is now being considered in more cloud types with CAM5.5. One potential solution, following  is to change the autoconversion and accretion process rates from the default used in the MG2 microphysics  to that of . has lower autoconversion rates for lower liquid water paths than  because it includes a hysteresis effect, whereby autoconversion in the absence of existing rain is delayed, thus damping the AIE in the shallow cloud regime that CLUBB and MG2 are now acting on in CAM5.5. Indeed, Table  shows that, in climatologically prescribed SST simulations, the CAM5.5 runs using the  autoconversion and accretion physics reduce the AIE and RFP. The authors are currently investigating coupled simulations of CESM-CAM5.5 with the new process rate calculations that will be included in a future version of CAM6.