Quoted from: https://ncar.github.io/CAM/doc/build/html/cam5_scientific_guide/introduction.html#cam4 

The CAM4 was the sixth generation of the NCAR atmospheric GCM and had again been developed through a collaborative process of users and developers in the Atmosphere Model Working Group (AMWG) with signficant input from the Chemistry Climate Working Group (Chem-Clim WG) and the Whole Atmosphere Model Working Group (WAMWG). The model had science enhancements from CAM3 and represented an intermediate release version as part of a staged and parallel process in atmospheric model development. In the CAM4 changes to the moist physical representations centered on enhancements to the existing [ZM95] deep convection parameterization. The calculation of Convective Available Potential Energy (CAPE) assumed an entraining plume to provide the in-cloud temperature and humidity profiles used to determine bouyancy and related cloud closure properties (chapter~ref{ssec:deep-convection}). The modification is based on the conservation of moist entropy and mixing methods of [RB86][RB92]. It replaced the standard undilute non-entraining plume method used in CAM3 and was employed to increase convection sensitivity to tropospheric moisture and reduce the amplitude of the diurnal cycle of precipitation over land. Sub-grid scale Convective Momentum Transports (CMT) were added to the deep convection scheme following [RR08] and the methodology of [GKI97] (chapter~:ref:ssec:Convection-CMT). CMT affects tropospheric climate mainly through changes to the Coriolis torque. These changes resulted in improvement of the Hadley circulation during northern Winter and it reduced many of the model biases. In an annual mean, the tropical easterly bias, subtropical westerly bias, and the excessive southern hemisphere mid-latitude jet were improved.

In combination these modifications to the deep-convection lead to significant improvements in the phase, amplitude and spacial anomaly patterns of the modeled El Ni~{n}o, as documented in [NRJ08]. The calculation of cloud fraction in polar climates was also modified for the CAM4.0. Due to the combination of a diagnostic cloud fraction and prognostic cloud water represntation it was possible to model unphysical extensive cloud decks with near zero in-cloud water in the CAM3. This was particularly pervasize in polar climates in Winter. These calculation inconsitencies and large cloud fractions are significantly reduced with modifications to the calculation of stratiform cloud following [VW08]. In the lower troposphere a ‘freeze-drying’ process is perfomed whereby cloud fractions were systematically reduced for very low water vaopr amounts. The low cloud reduction caused an Arctic-wide drop of 15 W m$^{-2}$ in surface cloud radiative forcing (CRF) during winter and about a 50% decrease in mean annual Arctic CRF. Consequently, wintertime surface temperatures fell by up to 4 K on land and 2 K over the Arctic Ocean, thus significantly reducing the CAM3 pronounced warm bias. More generally the radiation calculation was performed using inconsistent cloud fraction and condensate quantities in the CAM3. In CAM4 this was remedied with an updated cloud fraction calculation prior to the radiation call at each physics timestep. The coupled climate performance with the CAM4.0 physics changes was summarized in the horizontal resolution comparison study of [ent09].

For the dynamical core component of CAM4 the finite volume (FV) scheme was made the default due to its superior transport properties [LR96]. Modifications were made that upgraded the code version to a more recent NASA Goddard supported version. Other changes provided new horizontal grid discretizations (e.g., 1.9x2.5 deg and 0.9x1.25 deg) for optimal computational processor decompostion and polar filtering changes for noise reductions and more continuous (in latitude) filtering. In addition to the existing finite volume and spectral-based dynamical core a new option was also made available that represents the first scheme released with CAM that removes the computational scalability restrictions associated with a pole convergent latitude-longitude grid and the associated polar filtering requirements.

For the dynamical core component of CAM4 the finite volume (FV) scheme was made the default due to its superior transport properties (Lin and Rood 1996). Modifications were made that upgraded the code version to a more recent NASA Goddard supported version. Other changes provided new horizontal grid discretizations (e.g., 1.9x2.5 deg and 0.9x1.25 deg) for optimal computational processor decompostion and polar filtering changes for noise reductions and more continuous (in latitude) filtering. In addition to the existing finite volume and spectral-based dynamical core a new option was also made available that represents the first scheme released with CAM that removes the computational scalability restrictions associated with a pole convergent latitude-longitude grid and the associated polar filtering requirements.

Funded in part by the Department of Energy (DOE) Climate Change Prediction Program the scalable and efficient spectral-element-based atmospheric dynamical core uses the High Order Method Modeling Environment (HOMME) on a cubed sphere grid and was developed by members of the Computational Science Section and the Computational Numerics Group of NCAR’s Computational and Information Systems Laboratory (CISL). The finite element dynamical core (commonly referred to as the HOMME core) is fully integrated into CCSM coupling architecture and is invaluable for high resolution climate integrations on existing and upcoming massively parallel computing platforms.

Model flexibility was increased significantly from the CAM3, both within CAM and the CCSM system as a whole. The method for running thermodynamic sea-ice in CAM-only mode was moved to be maintained entirely within the CICE model of the CCSM4. The single-column version of CAM was given the flexibility to be built and run using the same infrastructure as the CAM build and run mechanism. The SCAM GUI run method was no longer supported. The increased coupling flexibility also allowed the introduction of a more consistant method for performing slab-ocean model (SOM) experiments. SOM experiments were, by default, now performed using forcing data from an existing CCSM coupled run. This had the advantage of having a closed temperature budget for both the ice and the ocean mixed layer from a coupled run. The methodology was therefore configured to reproduce the fully coupled CCSM climate as opposed to a reproduction of a psuedo-observed climate available with the CAM3-specific SOM method. The CAM3-specific SOM method was no longer made available. For more information regarding updated run methods see the CAM4.0 users guide of Eaton (2010).