Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy, which is largely controlled by boundary-layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere-only and ocean-atmosphere coupled models. The diurnal cycles of convection in both simulations are significantly improved with small changes to their mean states. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at subdiurnal time scales. Relaxed quasi-equilibrium for convective available potential energy holds in significant measure as a result. Future model improvement will focus on the remaining biases in the diurnal cycle, which may be further reduced by including stochastic entrainment and cold pools.

The higher-order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near-surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB’s stability-driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB-computed zonal and meridional wind speed tendencies. Our 30-year present-day climate simulations (1980-2010) show that static coupling overestimates 10-m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10-m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind-speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near-surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.

This study investigates how climate sensitivity depends upon the spatial pattern of radiative forcing. Sensitivity experiments using a coupled ocean-atmosphere model were conducted by adding anomalous incoming solar radiation over the entire globe, Northern Hemisphere mid-latitudes, Southern Ocean, and tropics, respectively, with both positive and negative perturbation considered. The varied forcing patterns led to highly divergent climate sensitivities, with extratropical forcing inducing significantly more global-mean temperature change compared to tropical forcing. This dependence is particularly strong over the Southern Hemisphere, where the climate is nearly twice as sensitive to Southern Ocean forcing as tropical forcing. This dependence of climate sensitivity on the location of radiative forcing stems from covariations between lapse rate feedback, cloud feedback and tropospheric stability. These results contrast with the conventional SST-pattern effect in which tropical surface temperature changes regulate the climate sensitivity, and has important implications for geoengineering and understanding the mechanisms of paleoclimate change.

The atmospheric Green’s function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green’s functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere-only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming vs. cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green’s function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.

High-latitude winter warming was observed following strong tropical volcanism, which has long been believed to be due to the volcanic-induced positive NAO phase. However, recent works argue that this warming is caused by ENSO variability. Moreover, controversies exist about the sensitivity of ENSO to volcanism. Therefore, to better resolve the controversies related to post-volcanic ENSO and NAO sensitivity and associated high-latitude winter warming, we conducted ENSO-preconditioned volcanic experiments using GFDL-CM2.1 coupled model. Our simulations strongly suggest that the post-eruption Eurasian winter warming is caused by a post-eruption positive NAO phase and not by coexisting ENSO-preconditioned variability. Additionally, we find that the El Niño-preconditioned volcanic eruption enhances the El Niño phase; however, the neutral and La Niña-preconditioned volcanisms do not lead to ENSO–like response. These findings are helpful to better understand volcanic-induced circulation impacts and have important implications for the interpretation of model results and for post-volcanic prediction.

We describe the model performance and simulation characteristics of a new global coupled climate model configuration, CM4-MG2. Beginning with the Geophysical Fluid Dynamics Laboratory’s fourth-generation physical climate model (CM4.0), we incorporate a two-moment Morrison-Gettelman bulk cloud microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature-dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere-ocean-land-sea ice simulations, following Coupled Model Intercomparison Project Phase 6 (CMIP6) protocols. CM4-MG2 generally captures CM4.0’s baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward “double” Intertropical Convergence Zones south of the equator, and a stronger Madden-Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger-than-observed El Nino-Southern Oscillation (ENSO). Compared to CM4.0, the climate sensitivity is reduced by about 10\% in CM4-MG2.

The newest atmospheric climate model at GFDL, AM4, succeeded at significantly reducing the toa radiative flux biases as compared to CERES observations. Despite a relatively low top-of-atmosphere sensitivity to uniform warming of SSTs (Cess warming experiments), the corresponding coupled climate model, CM4, has high transient and equilibrium climate sensitivities. We will present a systematic picture of the modeled clouds across a hierarchy of model configurations which utilize this atmospheric model. This hierarchy includes the CFMIP Aquaplanet and AMIP experiments, fully coupled model experiments (using GFDL’s CM4 model) as well as additional AMIP-like experiments with particular SST patterns. This demonstrates the large range of sensitivities that are possible from a single atmospheric climate model. Looking at the global mean radiative feedbacks across the different model configurations as well as in the context of CMIP5 and CMIP6 models will allow us to assess to what extent the cloud feedbacks in the idealized experiments relate to the fully coupled experiments and to observed clouds.

We describe the baseline model configuration and simulation characteristics of GFDL’s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon-chemistry-climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL’s AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 km to ~100 km) with revised dynamics and physics from GFDL’s previous-generation AM3 atmospheric chemistry-climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land-atmosphere interactions, comprehensive land-atmosphere-ocean cycling of dust and iron, and interactive ocean-atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0’s baseline simulations characteristics and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.

Key Points: • The concept of precipitation efficiency is broad, and can be related to many proposed cloud feedback mechanisms • Microphysical precipitation efficiency of tropical clouds likely increases with warming, but bulk precipitation efficiency and precipitation efficiency of midlatitude clouds could decrease • The impacts of precipitation efficiency on clouds and feedbacks deserve further study and require better evaluation against observations A number of studies have demonstrated strong relationships between precipitation efficiency, particularly its changes under warming, and climate sensitivity. In this chapter, we review the evidence for these relationships, including how they depend on the definition of precipitation efficiency. We identify six mechanisms by which changes in precipitation efficiency may affect Earth’s net climate feedback, and also discuss evidence for an inverse relationship between present-day precipitation efficiency and climate sensitivity based on several perturbed physics ensembles. This inverse relationship hints at the possibility of developing emergent constraints on climate sensitivity using precipitation efficiency, though it is put in doubt by studies varying convective entrainment rates, which have found the opposite relationship. More work is required to refine our understanding of the mechanisms linking changes in precipitation efficiency to climate sensitivity and more observational data is needed to validate model results. In particular, the precipitation efficiency of mid-latitude clouds has been relatively understudied, but deserves more attention in light of the importance of extratropical cloud feedbacks for the high climate sensitivities of CMIP6 models.