We present the GFDL-CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary application for CM4X is to investigate ocean and sea ice physics as part of a realistic coupled Earth climate model. CM4X utilizes an updated MOM6 (Modular Ocean Model version 6) ocean physics package relative to CM4.0, and there are two members of the hierarchy: one that uses a horizontal grid spacing of $0.25^{\circ}$ (referred to as CM4X-p25) and the other that uses a $0.125^{\circ}$ grid (CM4X-p125). CM4X also refines its atmospheric grid from the nominally 100~km (cubed sphere C96) of CM4.0 to 50~km (C192). Finally, CM4X simplifies the land model to allow for a more focused study of the role of ocean changes to global mean climate.   CM4X-p125 reaches a global ocean area mean heat flux imbalance of $-0.02~\mbox{W}~\mbox{m}^{-2}$ within $\mathcal{O}(150)$ years in a pre-industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly $400~\mbox{ZJ}$ less ocean heat than present-day, which corresponds to estimates for anthropogenic ocean heat uptake between 1850 and present-day. CM4X-p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly $1100~\mbox{ZJ}$ {\it more} heat than its early 21st century ocean initial state. Furthermore, the root-mean-square sea surface temperature bias for historical simulations is roughly 20\% smaller in CM4X-p125 relative to CM4X-p25 (and CM4.0). We offer the {\it mesoscale dominance hypothesis} for why CM4X-p125 shows such favorable thermal equilibration properties.

This paper is Part II of a two-part paper that documents the CM4X (Climate Model version 4X) hierarchy of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory (GFDL). Part I of this paper is presented in \citeA{CM4X_partI}. Here we present a suite of case studies that examine ocean and sea ice features that are targeted for further research, which include sea level, eastern boundary upwelling, Arctic and Southern Ocean sea ice, Southern Ocean circulation, and North Atlantic circulation. The case studies are based on experiments that follow the protocol of version 6 from the Coupled Model Intercomparison Project (CMIP6). The analysis reveals a systematic improvement in the simulation fidelity of CM4X relative to its CM4.0 predecessor, as well as an improvement when refining the ocean/sea ice horizontal grid spacing from the $0.25^{\circ}$ of CM4X-p25 to the $0.125^{\circ}$ of CM4X-p125. Even so, there remain many outstanding biases, thus pointing to the need for further grid refinements, enhancements to numerical methods, and/or advances in parameterizations, each of which target long-standing model biases and limitations.

We demonstrate the use of information theory metrics, Shannon entropy and mutual information, for measuring internal and forced variability in general circulation coastal and global ocean models. These metrics have been applied on spatially and temporally averaged data. A combined metric reliably delineates intrinsic and extrinsic variability in a wider range of circumstances than previous approaches based on variance ratios that therefore assume Gaussian distributions. Shannon entropy and mutual information manage correlated fields, apply to any distribution, and are insensitive to outliers and a change of units or scale. Different metrics are used to quantify internal vs forced variability in (1) idealized Gaussian and uniformly distributed data, (2) an initial condition ensemble of a realistic coastal ocean model (OSOM), (3) the GFDL-ESM2M climate model large ensemble. A metric based on information theory partly agrees with the traditional variance-based metric and identifies regions where non-linear correlations might exist. Mutual information and Shannon entropy are used to quantify the impact of different boundary forcings in a coastal ocean model ensemble. Information theory enables ranking the potential impacts of improving boundary and forcing conditions across multiple predicted variables with different dimensions. The climate model ensemble application shows how information theory metrics are robust even in a highly skewed probability distribution (Arctic sea surface temperature) resulting from sharply non-linear behavior (freezing point).