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Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.

Abstract. One of the challenges inrepresenting warm rain processes in global climate models (GCMs) is relatedto the representation of the subgrid variability of cloud properties, such ascloud water and cloud droplet number concentration (CDNC), and the effectthereof on individual precipitation processes such as autoconversion. Thiseffect is conventionally treated by multiplying the resolved-scale warm rainprocess rates by an enhancement factor (E_q) which is derived fromintegrating over an assumed subgrid cloud water distribution. The assumedsubgrid cloud distribution remains highly uncertain. In this study, we derivethe subgrid variations of liquid-phase cloud properties over the tropicalocean using the satellite remote sensing products from Moderate ResolutionImaging Spectroradiometer (MODIS) and investigate the correspondingenhancement factors for the GCM parameterization of autoconversion rate. Wefind that the conventional approach of using only subgrid variability ofcloud water is insufficient and that the subgrid variability of CDNC, as wellas the correlation between the two, is also important for correctlysimulating the autoconversion process in GCMs. Using the MODIS data whichhave near-global data coverage, we find that E_q shows a strongdependence on cloud regimes due to the fact that the subgrid variability ofcloud water and CDNC is regime dependent. Our analysis shows a significantincrease of E_q from the stratocumulus (Sc) to cumulus (Cu) regions.Furthermore, the enhancement factor E_N due to the subgrid variation ofCDNC is derived from satellite observation for the first time, and resultsreveal several regions downwind of biomass burning aerosols (e.g., Gulf ofGuinea, east coast of South Africa), air pollution (i.e., East China Sea),and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where theE_N is comparable to or even larger than E_q, suggesting an importantrole of aerosol in influencing the E_N. MODIS observations suggest thatthe subgrid variations of cloud liquid water path (LWP) and CDNC aregenerally positively correlated. As a result, the combined enhancementfactor, including the effect of LWP and CDNC correlation, is significantlysmaller than the simple product of E_q⋅E_N. Given the importanceof warm rain processes in understanding the Earth's system dynamics and watercycle, we conclude that more observational studies are needed to provide abetter constraint on the warm rain processes in GCMs.

Abstract. Satellite cloud observations have become an indispensable tool for evaluatinggeneral circulation models (GCMs). To facilitate the satellite and GCMcomparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project)Observation Simulator Package (COSP) has been developed and is nowincreasingly used in GCM evaluations. Real-world clouds and precipitation canhave significant sub-grid variations, which, however, are often ignored oroversimplified in the COSP simulation. In this study, we use COSP cloudsimulations from the Super-Parameterized Community Atmosphere Model (SPCAM5)and satellite observations from the Moderate Resolution ImagingSpectroradiometer (MODIS) and CloudSat to demonstrate the importance ofconsidering the sub-grid variability of cloud and precipitation when usingthe COSP to evaluate GCM simulations. We carry out two sensitivity tests:SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-gridcloud and precipitation properties from the embeddedcloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while inthe SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitationproperties (i.e., no sub-grid variations) are given to the COSP. We find thatthe warm rain signatures in the SPCAM5 COSP run agree with the MODIS andCloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSPrun which ignores the sub-grid cloud variations substantially overestimatesthe radar reflectivity and probability of precipitation compared to thesatellite observations, as well as the results from the SPCAM5 COSP run. Thesignificant differences between the two COSP runs demonstrate that it isimportant to take into account the sub-grid variations of cloud andprecipitation when using COSP to evaluate the GCM to avoid confusing andmisleading results.

Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing.