Previous modeling studies indicate that even though marine cloud brightening under a susceptibility-based strategy is effective in reducing the global average surface temperature, it triggers a La Niña-like sea-surface temperature response with cooling mostly confined within lower latitudes. Here we explore a different cloud seeding strategy involving seeding of regions with low susceptibility. Simulations with the Community Earth System Model, version 2 (CESM2) reveal that because the regional forcing is weaker and more widespread, cooling is more evenly distributed over the globe. This new strategy also does not result in the La Niña-like state seen in the other strategies.

Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are overall not significantly improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

The efficiency of marine cloud brightening in cooling Earth’s surface temperature is investigated by using a medium ensemble of simulations with the Community Earth System Model version 2 (CESM2). Various cloud seeding schemes based on susceptibility are examined to determine what area extent will be required to induce 1oC cooling under SSP2-4.5. The results indicate that cloud seeding over 5% of the ocean area is capable of achieving this goal. Under this seeding scheme, cloud seeding is mainly deployed over lower latitudes where strong surface temperature and precipitation responses are induced. The simulations also reveal that the 5% cloud seeding scheme induces an overall reduction in global precipitation, with an increase over land and a decrease over the ocean.

To address the effect of stratiform latent heating on meso- to large scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. This enhancement is partly due to the scheme’s refinement of Madden-Julian Oscillation (MJO) spectra, achieving better alignment with reanalysis data by intensifying MJO events and maintaining their eastward propagation after passing the Maritime Continent. However, the scheme also increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation but also lessens the improvements of seasonal cycle in India. Spatially correlated biases highlight the necessity for advancements beyond deterministic approaches to align MCSP with environmental conditions.

This work documents version two of the Department of Energy’s Energy Exascale Earth System Model (E3SM). E3SM version 2 (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 by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate is generally realistic, 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 Programme (WCRP) assessment. However, E3SMv2 significantly underestimates the global mean surface temperature in the second half of the historical record. 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.

Mesoscale organization of convection is typically not represented in global circulation models, and hence its influence on the global circulation is not accounted for. A parameterization aiming at representing the dynamical and physical effects of the circulation associated with organized convection, referred to as the multiscale coherent structure parameterization (MCSP), is implemented in the Energy Exascale Earth System Model version 1 (E3SMv1). Simulations are conducted to assess its impact on the simulated climate. Besides E3SMv1 simulations, we performed high-resolution (1 km) simulations using the Weather Research and Forecasting (WRF) Model to determine the temperature tendencies induced by mesoscale convective systems embedded in deep convection. We tuned the free parameters of the MCSP based on the WRF simulations. We found that the MCSP enhances Kevin wave spectra in E3SMv1, improves the representation of the Madden-Julian Oscillation, and reduces model precipitation biases over the tropical Pacific.

Clouds are one of the most critical yet uncertain aspects of weather and climate prediction. The complex nature of sub-grid scale cloud processes makes traceable simulation of clouds across scales difficult (or impossible). Often models and measurements are used to develop empirical relationships for large-scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the warm rain formation process in a General Circulation Model (GCM) with a detailed treatment from a bin microphysical model that causes a 400\% slowdown in the GCM. We analyze the changes in climate that result from the use of the bin microphysical calculation and find improvements in the rain onset and frequency of light rain compared to detailed models and observations. We also find a resulting change in the cloud feedback response of the model to warming, which will significantly impact the climate sensitivity. We then emulate this process with an emulator consisting of multiple neural networks that predict whether specific tendencies will be nonzero and the magnitude of the nonzero tendencies. We describe the risks of over-fitting, extrapolation, and linearization of a non-linear problem by using perfect model experiments with and without the emulator and show we can recover the solutions with the emulators in almost all respects, and recover nearly all the speed to get simulations that perform as the detailed model, but with the computational cost of the control simulation.