Abstract. Aerosol-cloud interactions are one of the most uncertain processes in climate models due to their nonlinear complexity. A key complexity arises from the possibility that clouds can respond to perturbed aerosols in two opposite ways, as characterized by the traditional "cloud lifetime" hypothesis and more recent "buffered system" hypothesis. Their importance in climate simulations remains poorly understood. Here we investigate the response of the liquid water path (LWP) to aerosol perturbations for warm clouds from the perspective of general circulation model (GCM) and ATrain remote sensing, through process-oriented model evaluations. A systematic difference is found in the LWP response between the model results and observations. The model results indicate a near-global uniform increase of LWP with increasing aerosol loading, while the sign of the response of the LWP from the A-Train varies from region to region. The satellite-observed response of the LWP is closely related to meteorological and/or macrophysical factors, in addition to the microphysics. The model does not reproduce this variability of cloud susceptibility (i.e., sensitivity of LWP to perturbed aerosols) because the parameterization of the autoconversion process assumes only suppression of rain formation in response to increased cloud droplet number, and does not consider macrophysical aspects that serve as a mechanism for the negative responses of the LWP via enhancements of evaporation and precipitation. Model biases are also found in the precipitation microphysics, which suggests that the model generates rainwater readily even when little cloud water is present. This essentially causes projections of unrealistically frequent and light rain, with high cloud susceptibilities to aerosol perturbations.

Aerosols affect climate by modifying cloud properties through their role as cloud condensation nuclei or ice nuclei, called aerosol–cloud interactions. In most global climate models (GCMs), the aerosol–cloud interactions are represented by empirical parameterisations, in which the mass of cloud liquid water (LWP) is assumed to increase monotonically with increasing aerosol loading. Recent satellite observations, however, have yielded contradictory results: LWP can decrease with increasing aerosol loading. This difference implies that GCMs overestimate the aerosol effect, but the reasons for the difference are not obvious. Here, we reproduce satellite-observed LWP responses using a global simulation with explicit representations of cloud microphysics, instead of the parameterisations. Our analyses reveal that the decrease in LWP originates from the response of evaporation and condensation processes to aerosol perturbations, which are not represented in GCMs. The explicit representation of cloud microphysics in global scale modelling reduces the uncertainty of climate prediction.

A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.

We examined the performance of autoconversion (mass transfer from cloud water to rainwater by the coalescence of cloud droplets) schemes in warm rain, which are commonly used in general circulation models. To exclude biases in the different treatment of the aerosol‐cloud‐precipitation‐radiation interaction other than that of the autoconversion process, sensitivity experiments were conducted within a single model framework using an aerosol‐climate model, MIROC‐SPRINTARS. The liquid water path (LWP) and cloud optical thickness have a particularly high sensitivity to the autoconversion schemes, and their sensitivity is of the same magnitude as model biases. In addition, the ratio of accretion to autoconversion (Acc/Aut ratio), a key parameter in the examination of the balance of microphysical conversion processes, also has a high sensitivity globally depending on the scheme used. Although the Acc/Aut ratio monotonically increases with increasing LWP, significantly lower ratio is observed in Kessler‐type schemes. Compared to satellite observations, a poor representation of cloud macrophysical structure and optically thicker low cloud are found in simulations with any autoconversion scheme. As a result of the cloud‐radiation interaction, the difference in the global mean net cloud radiative forcing (NetCRF) among the schemes reaches 10 Wm−2. The discrepancy between the observed and simulated NetCRF is especially large with a high LWP. The potential uncertainty in the parameterization of the autoconversion process is nonnegligible, and no formulation significantly improves the bias in the cloud radiative effect yet. This means that more fundamental errors are still left in other processes of the model.

Global climate models (GCMs) have been found to share the common too-frequent bias in the warm rain formation process. In this study, five different autoconversion schemes are incorporated into a single GCM, to systematically evaluate the warm rain formation processes in comparison with satellite observations and investigate their effects on the aerosol indirect effect (AIE). It is found that some schemes generate warm rain less efficiently under polluted conditions in the manner closer to satellite observations, while the others generate warm rain too frequently. Large differences in AIE are found among these schemes. It is remarkable that the schemes with more observation-like warm rain formation processes exhibit larger AIEs that far exceed the uncertainty range reported in IPCC AR5, to an extent that can cancel much of the warming trend in the past century, whereas schemes with too-frequent rain formations yield AIEs that are well bounded by the reported range. The power-law dependence of the autoconversion rate on the cloud droplet number concentration β is found to affect substantially the susceptibility of rain formation to aerosols: the more negative β is, the more difficult it is for rain to be triggered in polluted clouds, leading to larger AIE through substantial contributions from the wet scavenging feedback. The appropriate use of a droplet size threshold can mitigate the effect of a less negative β. The role of the warm rain formation process on AIE in this particular model has broad implications for others that share the too-frequent rain-formation bias.