We describe the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high‐quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

A new coupled chemistry-carbon-climate Earth system model has been developed at the Geophysical Fluid Dynamics Laboratory. This model unifies component advances in chemistry, carbon, and ecosystem comprehensiveness within a single coupled climate framework. This model features much improved climate mean patterns and variability from previous chemistry and carbon coupled models.

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Changes in concentrations of greenhouse gases lead to changes in radiative fluxes throughout the atmosphere. The value of this change, the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. Clear-sky instantaneous forcing by greenhouse gases is computed using a set of 100 profiles, selected from a reanalysis of present-day conditions, that represent the global annual mean forcing from preindustrial times to the present day with sampling errors of less than 0.01 W m −2. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 W m −2 while even recently developed parameterizations have typical errors 4 or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily identifiable. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are estimated from climate model simulations, and the adjustment due to stratospheric temperature changes estimated by assuming fixed dynamical heating. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest nonlinearity. The models participating in the previous phase of CMIP translated prescribed changes in atmospheric composition into a relatively wide range of effective radiative forcing, much of which remains even when model-specific adjustments are accounted for (e.g., Chung & Soden, 2015); initial results (Smith et al., 2020