Cirrus clouds of various thicknesses and radiative characteristics extend over much of the tropics, especially around deep convection. They can be difficult to observe due to their high altitude and sometimes small optical depths. They are also difficult to simulate in conventional global climate models, which have coarse grid spacings and simplified parameterizations of deep convection and cirrus formation. We investigate the representation of tropical cirrus in global storm-resolving models (GSRMs), which have higher spatial resolution and explicit convection and could more accurately represent cirrus cloud processes. This study uses GSRMs from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project. The aggregate life cycle of tropical cirrus is analyzed using joint albedo and outgoing longwave radiation (OLR) histograms to assess the fidelity of models in capturing the observed cirrus cloud populations over representative tropical ocean and land regions. The proportions of optically-thick deep convection, anvils, and cirrus vary across models and are reflected in the vertical distribution of cloud cover and top-of-atmosphere radiative fluxes. Model differences in cirrus populations, likely driven by subgrid processes such as ice microphysics, dominate over regional differences between convectively-active tropical land and ocean locations.

Marine heatwaves (MHWs) are events of abnormally warm sea surface temperatures (SSTs) that can have devastating impacts on marine ecosystems and coastal economies. The evolution of these events depends partially on the local atmospheric response, and how changes in clouds and surface heat fluxes in turn affect SSTs. Understanding the role of the atmosphere in MHWs is essential for modeling and forecasting these events. Here we use satellite data from 2001-2019 to identify MHWs and anomalous atmospheric variables- including radiative heat fluxes, turbulent heat fluxes, and cloud cover- associated with these events. We find robust patterns in SST-cloud and SST-heat flux relationships that show important geographical differences in atmosphere-ocean interactions during MHWs. Because of these regional differences, we don’t expect MHWs to evolve the same way in all regions. We also find that the cloud response observed during MHWs globally corresponds well with the cloud response to future warming, as identified in the Cloud Feedback Model Intercomparison Project (CFMIP) ensemble of global climate models. This suggests that MHWs can provide valuable insight to anomalous atmosphere-ocean interactions under future warming.

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Inter-model differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.