We investigate positive subtropical low cloud feedback mechanisms in climate models which have performed the CMIP6/CFMIP-3 AMIP and AMIP uniform +4K experiments while saving CFMIP-3 process diagnostics on model levels. Our analysis focuses on the trade cumulus/stratocumulus transition region between California and Hawaii, where positive low cloud feedbacks are present in the JJA season. We introduce a methodology to} test various positive cloud feedback mechanisms proposed in the literature as primary explanations for the low cloud responses in the models. Causal hypotheses are tested by comparing their predictions with the models’ responses of clouds, cloud controlling factors, boundary layer depth and temperature/humidity tendencies to climate warming. Changes in boundary layer depth, relative humidity in the cloud layer and humidity advection at the top of the boundary layer are shown to distinguish among the hypotheses considered. For the cases examined, our approach rules out 4/5 of the mechanisms considered in half of the models and 3/5 in the remainder. We argue that unambiguously identifying the positive feedback mechanisms operating in models will in some cases require intervention experiments designed to test specific hypotheses.

Low cloud feedback in global warming projections by climate models is characterized by its positive sign, the mechanism of which is not well understood. Here we propose that the positive sign is primarily caused by the increase in upward longwave radiation from the sea surface. We devise numerical experiments that enable separation of the feedback into components coming from physically distinct causes. Results of these experiments with a climate model indicate that increases in upward longwave radiation from the sea surface cause warming and absolute drying in the boundary layer, leading to the positive low cloud feedback. The absolute drying results from decrease in surface evaporation, and also from decrease in inversion strength which enhances vertical mixing of drier free tropospheric air into the boundary layer. This mechanism is different from previously proposed understanding that positive low cloud feedback is caused by increases in surface evaporation or vertical moisture contrast.

In atmospheric models with kilometer-scale grids the resolution approaches the scale of convection. As a consequence the most energetic eddies in the atmosphere are partially resolved and partially unresolved. The modeling challenge to represent convection partially explicitly and partially as a subgrid process is called the convective gray zone problem. The gray zone issue has previously been discussed in the context of regional models, but the evolution in regional models is constrained by the lateral boundary conditions. Here we explore the convective gray zone starting from a defined global configuration of the Met Office Unified Model using initialized forecasts and comparing different model formulations to observations. The focus is on convection and turbulence, but some aspects of the model dynamics are also considered. The global model is run at nominal 5km resolution and thus contributions from both resolved and subgrid turbulent and convective fluxes are non-negligible. The main conclusion is that in the present assessment, the configurations which include scale-aware turbulence and a carefully reduced and simplified mass-flux convection scheme outperform both the configuration with fully parameterized convection as well as a configuration in which the subgrid convection parameterization is switched off completely. The results are more conclusive with regard to convective organization and tropical variability than extratropical predictability. The present study thus endorses the strategy to further develop scale-aware physics schemes and to pursue an operational implementation of the global 5km-resolution model to be used alongside other ensemble forecasts to allow researchers and forecasters to further assess these simulations.

The international field campaign for EUREC4A (Elucidating the role of clouds and circulation coupling in climate) gathered observations to better understand the links between trade-wind cumulus clouds, their organization, and larger scales, a large source of uncertainty in climate projections. A recent large-eddy simulation (LES) study showed a cloud transition that occurred during EUREC4A (2nd February 2020), where small shallow clouds developed into larger clouds with detrainment layers, was caused by an increase in mesoscale organization generated by a dynamical feedback in mesoscale vertical velocities. We show that kilometer-scale simulations with the Met Office Unified Model reproduce this increase in mesoscale organization and the process generating it, despite being much lower resolution. The simulations develop mesoscale organization stronger and earlier than the LES, more consistent with satellite observations. Sensitivity tests with a shorter spin-up time, to reduce initial organization, still have the same timing of development and sensitivity tests with cold pools suppressed show only a small effect on mesoscale organization. These results suggest that large-scale circulation, associated with an increased vertical velocity and moisture convergence, is driving the increase in mesoscale organization, as opposed to a threshold reached in cloud development. Mesoscale organization and clouds are sensitive to resolution, which affects changes in net radiation, and clouds still have substantial differences to observations. Therefore, while kilometer-scale simulations can be useful for understanding processes of mesoscale organization and links with large scales, including responses to climate change, simulations will still suffer from significant errors and uncertainties in radiative budgets.