scholarly journals 100 Years of Progress in Cloud Physics, Aerosols, and Aerosol Chemistry Research

2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Cloud Microphysics ◽  
Climate System ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Weather Modification ◽  
Theoretical Understanding ◽  
Aerosol Cloud ◽  
Chemistry Research ◽  
Aerosol Cloud Interactions

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.

scholarly journals Untangling causality in midlatitude aerosol-cloud adjustments

2019 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul Field ◽  
Hamish Gordon ◽  
Gregory S. Elsaesser ◽  
Daniel P. Grosvenor
Keyword(s):  
Radiative Forcing ◽  
Sensible Heat Flux ◽  
Cloud Droplet ◽  
Causal Link ◽  
Cloud Microphysics ◽  
Liquid Water Path ◽  
Aerosol Cloud ◽  
Causal Pathways ◽  
Model Adjustment ◽  
Aerosol Cloud Interactions

Abstract. Aerosol-cloud interactions represent the leading uncertainty in our ability to infer climate sensitivity from the observational record. The forcing from changes in cloud albedo driven by increases in cloud droplet number (Nd) (the first indirect effect) is confidently negative and has narrowed its probable range in the last decade, but the sign and strength of forcing associated with changes in cloud macrophysics in response to aerosol (aerosol-cloud adjustments) remain uncertain. This uncertainty reflects our inability to accurately quantify variability not associated with a causal link flowing from the cloud microphysical state to cloud macrophysical state. Once variability associated with meteorology has been removed, covariance between the liquid water path averaged across cloudy and clear regions (LWP, here, characterizing the macrophysical state) and Nd (characterizing the microphysical) is the sum of two causal pathways linking Nd to LWP: Nd altering LWP (adjustments) and precipitation scavenging aerosol and thus depleting Nd. Only the former term is relevant to constraining adjustments, but disentangling these terms in observations is challenging. We hypothesize that the diversity of constraints on aerosol-cloud adjustments in the literature may be partly due to not explicitly characterizing covariance flowing from cloud to aerosol, and aerosol to cloud. Here, we restrict our analysis to the regime of extratropical clouds outside of low-pressure centers associated with cyclonic activity. Observations from MAC-LWP, and MODIS are compared to simulations in the MetOffice Unified Model (UM) GA7.1 (the atmosphere model of HadGEM3-GC3.1 and UKESM1). The meteorological predictors of LWP are found to be similar between the model and observations. There is also agreement with previous literature on cloud-controlling factors finding that increasing stability, moisture, and sensible heat flux enhance LWP, while increasing subsidence, and sea surface temperature decrease it. A simulation where cloud microphysics are insensitive to changes in Nd is used to characterize covariance between Nd and LWP that is induced by factors other than aerosol-cloud adjustments. By removing variability associated with meteorology and scavenging we infer the sensitivity of LWP to changes in Nd. Application of this technique to UM GA7.1 simulations reproduces the true model adjustment strength. Observational constraints developed using simulated covariability not induced by adjustments and observed covariability between Nd and LWP predict a 25–30 % overestimate by the UM GA7.1 in LWP change and a 30–35% overestimate in associated radiative forcing.

Is there hope for reducing the uncertainty associated with aerosols in climate projections? 

2021 ◽  
Author(s):  
Ilona Riipinen ◽  
Annica Ekman ◽  
Matthew Salter ◽  
Karoliina Pulkkinen ◽  
Keyword(s):  
Knowledge Transfer ◽  
Radiative Forcing ◽  
Aerosol Particles ◽  
Scientific Understanding ◽  
Climate Projections ◽  
Earth System ◽  
Aerosol Cloud ◽  
The Earth ◽  
Aerosol Cloud Interactions ◽  
Aerosol Emissions

<p>Together with imminent climate action, building a sustainable future for the humanity requires striving for healthier environments. Atmospheric aerosol particles (also referred to as particulate matter, PM) play a key role in defining the air that the future generations will breathe but also the climates they live in, PM being an important short-lived climate forcer but also a key component of air quality and global environmental health hazard. The contribution of aerosol particles has been a key uncertainty in estimates of the Earth’s radiative forcing since the establishment of the Intergovernmental Panel for Climate Change (IPCC) and still remains as the single largest quoted source of uncertainty in the anthropogenic climate forcing during the industrial period. In the latest assessment by the IPCC, the radiative forcing by aerosol particles has been estimated to be -0.45 W m-2 (between -0.95 and 0.05 W m-2) for aerosol-radiation interactions (RFari) and -0.45 W m-2 (between -1.25 and 0 W m-2) for aerosol-cloud interactions (RFaci). Recent reviews indicate no significant reduction in the uncertainty. The large range of possible aerosol forcing values has serious consequences for climate projections and therefore developing strategies for reaching Paris agreement targets. It is currently not possible to say if a reduction in aerosol emissions due to air pollution mitigation and a phase-out of aerosol emissions will result in a noticeable increase in global mean temperature  or in a negligible climate effect. We will discuss the components and reasons of this uncertainty, focusing on those that are important for aerosol-cloud interactions. We will identify critical bottlenecks in 1) scientific understanding of fundamental aerosol and cloud microphysical processes; 2) method development for improving the understanding of aerosol, cloud, and aerosol-cloud processes as well as their representation in Earth System Models (ESMs); and 3) knowledge transfer within and between the relevant research communities. We will argue for key actions to overcome these bottlenecks, giving examples of good practices for breaking new ground in this long-standing problem that continues to intrigue the atmospheric and climate science communities. Besides enhancing the scientific understanding of the Earth system within the realm of natural science based on multiple lines of evidence, and developing novel (e.g. machine learning –based) methodologies for analyzing existing observational data and model output, we call for additional perspectives from social science and humanities on the communication and knowledge transfer practices within atmospheric and climate research. Making the relevant knowledge transfer pathways and processes transparent is urgently needed to enable systematic determination of the actions required to maximally utilize the existing knowledge, and to ensure effective implementation of new results that may help to narrow down the uncertainty associated with aerosols in climate projections. Improved understanding of the role of aerosols in the climate system will result in enhanced credibility of ESMs and hence also tighter constraints for policies aiming for simultaneous climate neutrality and zero pollution targets.</p>

scholarly journals Quantifying Albedo Susceptibility Biases in Shallow Clouds

2021 ◽  
Author(s):  
Graham Feingold ◽  
Tom Goren ◽  
Takanobu Yamaguchi
Keyword(s):  
Radiative Forcing ◽  
Radiation Effects ◽  
Spatial Aggregation ◽  
Climate Projections ◽  
Marine Stratocumulus ◽  
Aerosol Cloud ◽  
Large Eddy ◽  
Shallow Clouds ◽  
Highly Correlated ◽  
Aerosol Cloud Interactions

Abstract. The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.

scholarly journals Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges

2016 ◽  
Vol 73 (11) ◽  
pp. 4221-4252 ◽  
Author(s):  
Jiwen Fan ◽  
Yuan Wang ◽  
Daniel Rosenfeld ◽  
Xiaohong Liu
Keyword(s):  
Radiative Forcing ◽  
Large Scale ◽  
Dynamic Properties ◽  
Global Climate ◽  
Spatial Scales ◽  
Cloud Microphysics ◽  
Large Variability ◽  
Spatial And Temporal Scales ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract Over the past decade, the number of studies that investigate aerosol–cloud interactions has increased considerably. Although tremendous progress has been made to improve the understanding of basic physical mechanisms of aerosol–cloud interactions and reduce their uncertainties in climate forcing, there is still poor understanding of 1) some of the mechanisms that interact with each other over multiple spatial and temporal scales, 2) the feedbacks between microphysical and dynamical processes and between local-scale processes and large-scale circulations, and 3) the significance of cloud–aerosol interactions on weather systems as well as regional and global climate. This review focuses on recent theoretical studies and important mechanisms on aerosol–cloud interactions and discusses the significances of aerosol impacts on radiative forcing and precipitation extremes associated with different cloud systems. The authors summarize the main obstacles preventing the science from making a leap—for example, the lack of concurrent profile measurements of cloud dynamics, microphysics, and aerosols over a wide region on the observation side and the large variability of cloud microphysics parameterizations resulting in a large spread of modeling results on the modeling side. Therefore, large efforts are needed to escalate understanding. Future directions should focus on obtaining concurrent measurements of aerosol properties and cloud microphysical and dynamic properties over a range of temporal and spatial scales collected over typical climate regimes and closure studies, as well as improving understanding and parameterizations of cloud microphysics such as ice nucleation, mixed-phase properties, and hydrometeor size and fall speed.

scholarly journals Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas
Keyword(s):  
Radiative Forcing ◽  
Cloud Fraction ◽  
Future Climate Change ◽  
Satellite Measurements ◽  
Aerosol Cloud ◽  
Fine Mode ◽  
Sampling Biases ◽  
Aerosol Cloud Interactions ◽  
Significant Underestimation ◽  
Weak Satellite

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

scholarly journals Ocean Emission Effects on Aerosol-Cloud Interactions: Insights from Two Case Studies

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Armin Sorooshian ◽  
Hanh T. Duong
Keyword(s):  
Case Studies ◽  
Field Experiments ◽  
Remote Sensing Data ◽  
Anthropogenic Pollution ◽  
Cloud Microphysics ◽  
Marine Aerosols ◽  
Aerosol Cloud ◽  
First Case ◽  
Wide Range ◽  
Aerosol Cloud Interactions

Two case studies are discussed that evaluate the effect of ocean emissions on aerosol-cloud interactions. A review of the first case study from the eastern Pacific Ocean shows that simultaneous aircraft and space-borne observations are valuable in detecting links between ocean biota emissions and marine aerosols, but that the effect of the former on cloud microphysics is less clear owing to interference from background anthropogenic pollution and the difficulty with field experiments in obtaining a wide range of aerosol conditions to robustly quantify ocean effects on aerosol-cloud interactions. To address these limitations, a second case was investigated using remote sensing data over the less polluted Southern Ocean region. The results indicate that cloud drop size is reduced more for a fixed increase in aerosol particles during periods of higher ocean chlorophyll A. Potential biases in the results owing to statistical issues in the data analysis are discussed.

scholarly journals Aerosol effects on ice clouds: can the traditional concept of aerosol indirect effects be applied to aerosol-cloud interactions in cirrus clouds?

2010 ◽  
Vol 10 (21) ◽  
pp. 10345-10358 ◽  
Author(s):  
S. S. Lee ◽  
J. E. Penner
Keyword(s):  
Radiative Forcing ◽  
Critical Role ◽  
Cloud Microphysics ◽  
Cirrus Clouds ◽  
Ice Crystals ◽  
Radiation Budget ◽  
Traditional Concept ◽  
Substantial Impact ◽  
Double Moment ◽  
Aerosol Cloud Interactions

Abstract. Cirrus clouds cover approximately 20–25% of the globe and thus play an important role in the Earth's radiation budget. Therefore the effect of aerosols on cirrus clouds can have a substantial impact on global radiative forcing if either the ice-water path (IWP) and/or the cloud ice number concentration (CINC) changes. This study examines the aerosol indirect effect (AIE) through changes in the CINC and IWP for a cirrus cloud case. We use a cloud-system resolving model (CSRM) coupled with a double-moment representation of cloud microphysics. Intensified interactions among CINC, deposition and dynamics play a critical role in increasing the IWP as aerosols increase. Increased IWP leads to a smaller change in the outgoing LW radiation relative to that for the SW radiation for increasing aerosols. Increased aerosols lead to increased CINC, providing increased surface area for water vapor deposition. The increased deposition causes depositional heating which produces stronger updrafts, and leads to the increased IWP. The conversion of ice crystals to aggregates through autoconversion and accretion plays a negligible role in the IWP response to aerosols, and the sedimentation of aggregates is negligible. The sedimentation of ice crystals plays a more important role in the IWP response to aerosol increases than the sedimentation of aggregates, but not more than the interactions among the CINC, deposition and dynamics.

scholarly journals Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles

2021 ◽  
Author(s):  
Arshad Nair ◽  
Fangqun Yu ◽  
Pedro Campuzano Jost ◽  
Paul DeMott ◽  
Ezra Levin ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Radiative Forcing ◽  
Climate Models ◽  
Cloud Condensation Nuclei ◽  
Global Climate Models ◽  
Aerosol Composition ◽  
Airborne Measurements ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning/artificial intelligence model that quantifies CCN from variables of aerosol composition, atmospheric trace gases, and meteorology. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this machine learning model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. Machine learning extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust machine learning pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol–cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

scholarly journals Quantifying cloud adjustments and the radiative forcing due to aerosol–cloud interactions in satellite observations of warm marine clouds

2020 ◽  
Vol 20 (10) ◽  
pp. 6225-6241 ◽  
Author(s):  
Alyson Douglas ◽  
Tristan L'Ecuyer
Keyword(s):  
Radiative Forcing ◽  
Local Environment ◽  
Satellite Observations ◽  
Cloud Environment ◽  
Climate Scenarios ◽  
Liquid Water Path ◽  
Sources Of Uncertainty ◽  
Aerosol Cloud ◽  
Effective Radiative Forcing ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions and their resultant forcing remains one of the largest sources of uncertainty in future climate scenarios. The effective radiative forcing due to aerosol–cloud interactions (ERFaci) is a combination of two different effects, namely how aerosols modify cloud brightness (RFaci, intrinsic) and how cloud extent reacts to aerosol (cloud adjustments CA; extrinsic). Using satellite observations of warm clouds from the NASA A-Train constellation from 2007 to 2010 along with MERRA-2 Reanalysis and aerosol from the SPRINTARS model, we evaluate the ERFaci in warm, marine clouds and its components, the RFaciwarm and CAwarm, while accounting for the liquid water path and local environment. We estimate the ERFaciwarm to be -0.32±0.16 Wm−2. The RFaciwarm dominates the ERFaciwarm contributing 80 % (-0.21±0.15 Wm−2), while the CAwarm enhances this cooling by 20 % (-0.05±0.03 Wm−2). Both the RFaciwarm and CAwarm vary in magnitude and sign regionally and can lead to opposite, negating effects under certain environmental conditions. Without considering the two terms separately and without constraining cloud–environment interactions, weak regional ERFaciwarm signals may be erroneously attributed to a damped susceptibility to aerosol.

scholarly journals Snow-induced buffering in aerosol–cloud interactions

2020 ◽  
Author(s):  
Takuro Michibata ◽  
Kentaroh Suzuki ◽  
Toshihiko Takemura
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
State Of The Art ◽  
General Circulation Models ◽  
Regional Pattern ◽  
Cloud Droplets ◽  
Microphysical Process ◽  
Aerosol Cloud ◽  
Effective Radiative Forcing ◽  
Aerosol Cloud Interactions

Abstract. Complex aerosol–cloud–precipitation interactions lead to large differences in estimates of aerosol impacts on climate among general circulation models (GCMs) and satellite retrievals. Typically, precipitating hydrometeors are treated diagnostically in most GCMs, and their radiative effects are ignored. Here, we quantify how the treatment of precipitation influences the simulated effective radiative forcing due to aerosol–cloud interactions (ERFaci) using a state-of-the-art GCM with a two-moment prognostic precipitation scheme that incorporates the radiative effect of precipitating particles, and investigate how microphysical process representations are related to macroscopic climate effects. Prognostic precipitation substantially weakens the magnitude of ERFaci (by approximately 75 %) compared with the traditional diagnostic scheme, and this is the result of the increased longwave (warming) and weakened shortwave (cooling) components of ERFaci. The former is attributed to additional adjustment processes induced by falling snow, and the latter stems largely from riming of snow by collection of cloud droplets. The significant reduction in ERFaci does not occur without prognostic snow, which contributes mainly by buffering the cloud response to aerosol perturbations through depleting cloud water via collection. Prognostic precipitation also alters the regional pattern of ERFaci, particularly over northern mid-latitudes where snow is abundant. The treatment of precipitation is thus a highly influential controlling factor of ERFaci, contributing more than other uncertain tunable processes related to aerosol–cloud–precipitation interactions. This change in ERFaci caused by the treatment of precipitation is large enough to explain the existing difference in ERFaci between GCMs and observations.

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