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Excessive precipitation over the southeastern tropical Pacific is a major common bias that persists through generations of global climate models. While recent studies suggest an overly warm Southern Ocean as the cause, models disagree on the quantitative importance of this remote mechanism in light of ocean circulation feedback. Here, using a multimodel experiment in which the Southern Ocean is radiatively cooled, we show a teleconnection from the Southern Ocean to the tropical Pacific that is mediated by a shortwave subtropical cloud feedback. Cooling the Southern Ocean preferentially cools the southeastern tropical Pacific, thereby shifting the eastern tropical Pacific rainbelt northward with the reduced precipitation bias. Regional cloud locking experiments confirm that the teleconnection efficiency depends on subtropical stratocumulus cloud feedback. This subtropical cloud feedback is too weak in most climate models, suggesting that teleconnections from the Southern Ocean to the tropical Pacific are stronger than widely thought.

In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models. Plain Language Summary Low clouds impose a strong radiative cooling effect on Earth's climate. Predicting low‐cloud fraction (LCF) is, however, challenging in global climate models (GCMs), partly due to some deficiencies in cloud parameterization schemes. Machine learning (ML) models might fill this gap as it is recognized as an efficient, economical, and accurate method to make predictions. In this study, we find that ML models (XGBoost) exhibit superior proficiency in predicting LCF regarding root mean squared errors and correlation coefficients compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. This improvement helps address one important issue of “too few” low clouds in GCMs. Furthermore, ML models demonstrate marked improvement in representing LCF variations when stratocumulus clouds transition to cumulus clouds, as opposed to too rapid decreases in LCF simulated by two CAMs and ERA5. Such findings testify to the unique role of ML models in refining the parameterization of LCF within GCMs. Key Points Machine learning (ML) models substantially improve “too few” low‐cloud problems in the subtropics compared to traditional cloud schemes They also show marked improvement in representing low‐cloud fraction (LCF) variations during the stratocumulus‐to‐cumulus transition Including the effect of moisture source in ML models is crucial to representing spatiotemporal variations in LCF in the midlatitudes

From July through October, smoke from biomass-burning (BB) fires on the southern African subcontinent is transported westward through the free troposphere over one of the largest stratocumulus cloud decks on our planet (Fig. 1). BB aerosol (smoke) absorbs shortwave radiation efficiently. This fundamental property implicates smoke within myriad small-scale processes with potential large-scale impacts on climate that are not yet well understood. A coordinated, international team of scientists from the United States, United Kingdom, France, South Africa, and Namibia will provide an unprecedented interrogation of this smoke-and-cloud regime from 2016 to 2018, using multiple aircraft and surface-based instrumentation suites to span much of the breadth of the southeast Atlantic

This article details corrections to: Schwarz, M, Savre, J, Sudhakar, D, Quaas, J and Ekman, AML. 2024. The Transition from Aerosol- to Updraft-Limited Susceptibility Regime in Large-Eddy Simulations with Bulk Microphysics. Tellus B: Chemical and Physical Meteorology, 76(1): 32–46. DOI: https://doi.org/10.16993/tellusb.94

We analyse the airborne measurements of above-cloud aerosols from the AErosol, RadiatiOn, and CLOuds in southern Africa (AEROCLO-sA) field campaign performed in Namibia during August and September 2017. The study aims to retrieve the aerosol above-cloud direct radiative effect (DRE) with well-defined uncertainties. To improve the retrieval of the aerosol and cloud properties, the airborne demonstrator of the Multi-Viewing, Multi-Channel, Multi-Polarization (3MI) satellite instrument, called the Observing System Including PolaRisation in the Solar Infrared Spectrum (OSIRIS), was deployed on-board the SAFIRE (Service des Avions Français Instrumentés pour la Rechercheen Environnement) Falcon 20 aircraft during 10 flights performed over land, over the ocean, and along the Namibian coast. The airborne instrument OSIRIS provides observations at high temporal and spatial resolutions for aerosol above clouds (AACs) and cloud properties. OSIRIS was supplemented with the Photomètre Léger Aéroporté pour la surveillance des Masses d'Air version 2 (PLASMA2). The combined airborne measurements allow, for the first time, the validation of AAC algorithms previously developed for satellite measurements. The variations in the aerosol properties are consistent with the different atmospheric circulation regimes observed during the deployment. Airborne observations typically show strong aerosol optical depth (AOD; up to 1.2 at 550 nm) of fine-mode particles from biomass burning (extinction Ãngström exponent varying between 1.6 and 2.2), transported above bright stratocumulus decks (mean cloud top around 1 km above mean sea level), with cloud optical thickness (COT) up to 35 at 550 nm. The above-cloud visible AOD retrieved with OSIRIS agrees within 10 % of the PLASMA2 sun photometer measurements in the same environment.

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large‐Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar behavior. Compared to observations, the simulated clouds are much more uniform. Analysis of the LES results suggests a connection to the local entrainment rate, so the poorly resolved entrainment interface in LES may be a cause of the uniformity. The uniformity of the large‐scale forcing could also be a factor.

The fundamental features of one kind of rarely known stratocumulus, which was termed as “Millipede Cloud,” occurred over the Eastern Pacific Ocean in 2017 were first documented by using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. These clouds had long and meandering “central axes” extending from several hundreds to thousands kilometers, and a number of “radical cloud arms” extending several tens of kilometers in its two sides. Total 59 “Millipede Clouds,” 4 and 55 of them, were formed over the Northern and the Southern Hemispheres, respectively. Their environmental backgrounds were analyzed by using ERA5 reanalysis data and MODIS sensor Level‐2 data. The cloud top pressures of these “Millipede Clouds” were between 850 and 800 hPa, and their top heights were about 1–2 km. There existed “inversion layer” of air temperature near the cloud tops at 800 hPa, which strongly suggested that these clouds were lower stratocumulus in essence. Plain Language Summary “Millipede Cloud,” one kind of rarely known stratocumulus which looks like “Millipede” shape, is termed for the first time in this paper. It has an obvious “central axis” and a number of well‐organized “radial cloud arms” in two sides of the “central axis” extending in several tens of kilometers length. This paper introduces the fundamental features of “Millipede Clouds” occurred over the Eastern Pacific Ocean in 2017 from the perspective of satellite image. Totally, 59 “Millipede Clouds” were found to occur over the Eastern Pacific Ocean. Their geographic distribution, cloud top features and vertical structure of one typical case on 16 July 2017 were documented. Key Points The fundamental features of “Millipede Clouds” over the Eastern Pacific Ocean in 2017 were documented by using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery The environmental backgrounds of these “Millipede Clouds” were analyzed by using ERA5 reanalysis data and MODIS data The cloud top pressures of these “Millipede Clouds” are between 850 and 800 hPa, and their top heights are about 1–2 km

This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2. Plain Language Summary Stratocumulus clouds have extensive coverage over the oceans and modulate the climate system by efficiently reflecting incoming solar radiation back to space. However, their simulations in climate models are challenging due to complex meteorological controls, in which temperature advection is one of the most uncertain controlling factors. To enhance our understanding, we examine the stratocumulus evolution influenced by cold‐advection (CADV) and warm‐advection (WADV) in the midlatitudes in a climate model, CESM2. A too rapid decrease in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) is erroneously simulated under CADV conditions, while an increase in CLWP is substantially overestimated under WADV conditions. Using an explainable machine learning approach, these errors are found to be caused by the amplified drying or moistening effects due to improper treatments of meteorological controls on clouds in CESM2. This study suggests that these misrepresentations of cloud physics in the midlatitudes should be imperatively improved to reduce climate prediction uncertainties. Key Points Community Earth System Model 2 simulates a too rapid decline in low‐cloud fraction and cloud liquid water path when clouds experience cold‐air advection It substantially overestimates increases in cloud liquid water path when clouds experience warm‐air advection Utilizing an explainable machine learning methodology, the effects of meteorological factors on cloud evolution are assessed

Turbulence models predict low droplet-collision rates in stratocumulus clouds, which should imply a narrow droplet-size distribution and little rain. Contrary to this expectation, rain is often observed in stratocumulus. In this paper we explore the hypothesis that some droplets can grow well above the average, because small-scale turbulence allows them to reside at cloud top for a time longer than the convective-eddy time t*. Long-resident droplets can grow larger because condensation due to long-wave radiative cooling and collisions have more time to enhance droplet growth. We investigate the trajectories of one billion Lagrangian droplets in direct numerical simulations of a cloudy mixed-layer configuration that is based on observations from the flight 11 from the VERDI campaign. High resolution is employed to represent a well-developed turbulent state at cloud top. Only one-way coupling is considered. We observe that 70% of the droplets spend less than 0.6t* at cloud top before leaving the cloud, while 15% of the droplets remain at least 0.9t* at cloud top. Besides, 0.2% of the droplets spend more than 2.5t* at cloud top and decouple from the large-scale convective eddies that brought them to the top, with the result that they become memoryless. Modeling collisions like a Poisson process leads to the conclusion that most rain droplets originate from those memoryless droplets. Furthermore, most long-resident droplets accumulate at the downdraft regions of the flow, which could be related to the closed-cell stratocumulus pattern. Finally, we see that condensation due to long-wave radiative cooling considerably broadens the cloud-top droplet-size distribution: 6.5% of the droplets double their mass due to radiation in their time at cloud top. This simulated droplet size distribution matches the flight measurements, confirming that condensation due to long-wave radiation can be an important mechanism for broadening the droplet-size-distribution in radiatively-driven stratocumulus.

The western North Pacific Subtropical High (WNPSH) is a key circulation system controlling the summer monsoon and typhoon activities over the western Pacific, but future projections of its changes remain hugely uncertain. Here we find two leading modes that account for nearly 80% intermodel spread in its future projection under a high emission scenario. They are linked to a cold-tongue-like bias in the central-eastern tropical Pacific and a warm bias beneath the marine stratocumulus, respectively. Observational constraints using sea surface temperature patterns reduce the uncertainties by 45% and indicate a robust intensification of the WNPSH due to suppressed warming in the western Pacific and enhanced land-sea thermal contrast, leading to 28% more rainfall projected in East China and 36% less rainfall in Southeast Asia than suggested by the multi-model mean. The intensification of the WNPSH implies more future monsoon rainfall and heatwaves but less typhoon landfalls over East Asia. Model biases and internal variability are a cause for uncertainties in climate projections. Here, the authors show that 45% of projected uncertainty in the western Pacific Subtropical High can be reduced by correcting sea surface temperature biases in the equatorial Pacific and beneath marine stratocumulus clouds.