Predicting decadal trends in cloud droplet number concentration
using reanalysis and satellite data

McCoy, Daniel; Bender, Frida A.‐M.; Grosvenor, Daniel P.; Mohrmann, Johannes; Hartmann, Dennis L.; Wood, Robert; Field, Paul R.

doi:10.5194/acp-2017-811

Cited by 6 publications

(7 citation statements)

References 45 publications

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“…Three regions generally exhibit obvious decreasing trends in CDNC, AOD, AE, and AI on both annual and seasonal timescales (Figures 3 and S3–S5). For EUS and WEU regions, negative trends in CDNC and AOD are consistent with previous studies (Bennartz & Rausch, 2017; Li et al, 2018; Mao et al, 2014; Pozzer et al, 2015; Yoon et al, 2014; Zhao et al, 2017) and are closely related to a persistent decline in air pollutant emissions in the eastern United States and Western Europe (Krotkov et al, 2016; McCoy et al, 2018; Pozzer et al, 2015; Xing et al, 2013; Zhao et al, 2017). For annual time series of aerosol properties over EC (Figure 3), we find a peak value in 2005 rather than 2007 when a turning point is found from recent studies (Alfaro‐Contreras et al, 2017; de Leeuw et al, 2018; Zhang et al, 2017; Zhao et al, 2017).…”

Section: Resultssupporting

confidence: 89%

“…These satellite‐based AOD trends are generally consistent with previous studies based on atmospheric visibility (Che et al, 2007; Hu et al, 2017; Li et al, 2016) or ground‐based aerosol properties, such as PM 2.5 (Hasheminassab et al, 2014; Kim et al, 2015; Rattigan et al, 2016; Yang et al, 2018). Long‐term trends in cloud properties, such as liquid water path (LWP), cloud effective radius (CER), cloud optical thickness (COT), and cloud droplet number concentration (CDNC), have also been examined (e.g., Bennartz & Rausch, 2017; Elsaesser et al, 2017; Li et al, 2018; Manaster et al, 2017; McCoy et al, 2018; Mishra, 2019; Sreekanth, 2016; Stengel et al, 2017; Zhao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…AI, defined by AI = AOD × AE, is a product that weights AOD by particle size and is considered as a suitable proxy for aerosol number concentration and thus CCN (Nakajima et al, 2001). Some recent studies (e.g., Li et al, 2018; McCoy et al, 2018) have examined trends in CDNC based on satellite observations, but the observed trends of CDNC are explained using aerosol derived from reanalysis data only and have not examined synergistic trends of CDNC and aerosol properties from satellite observations. Here, we examine the synergistic trends in aerosol and cloud properties derived from both satellite observations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

et al. 2020

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Decadal‐scale trends in aerosol and cloud properties provide important ways for understanding aerosol‐cloud interactions. In this paper, by using MODIS products (2003–2017), we analyze synergetic trends in aerosol properties and warm cloud properties over global ocean. Cloud droplet number concentration (CDNC) and aerosol parameters (aerosol optical depth, angstrom exponent, and aerosol index) show consistent decreasing trend over East Coast of the United States (EUS), west coast of Europe (WEU), and east coast of China (EC), and no significant trend in liquid water path is found over these regions during the period 2003–2017. Over regions with significant long‐term trends of aerosol loading and CDNC (e.g., EUS and WEU), the sensitivity of CDNC to aerosol loading based on the long‐term trend is closer to those derived from ground and aircraft observations and larger than those derived from instantaneous satellite observations, providing an alternative way for quantifying aerosol‐cloud interactions. A clear shift in the normalized probability density function of CDNC between the first 5 years (2003–2007) and the last 5 years (2013–2017) is found, with a decrease of around 50% in the occurrence frequency of high CDNC (>400 cm−3) over EUS and WEU. The relative variances of cloud droplet effective radius generally decrease with decreasing aerosol loading, providing large‐scale evidence for the effects of anthropogenic aerosols on the dispersion of cloud droplet size distribution. The long‐term satellite data sets provide great opportunities for quantifying aerosol‐cloud interactions and further confronting these interactions in climate models in the future.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

et al. 2020

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“…Constructs such as the albedo susceptibility (Platnick & Twomey, ) or precipitation susceptibility (Sorooshian et al, ) are useful in that they survey globally the regions of the Earth that have the potential to generate large responses to aerosol perturbations while controlling for key meteorologically driven variables. Progress in accounting for spurious correlations (i.e., correlations that do not imply a causal aerosol effect on the respective cloud property) has been made using statistical techniques (Gryspeerdt et al, ), careful sampling (Christensen et al, ) and through combination with reanalysis data (Koren, Feingold, & Remer, ; McCoy, Bender et al, ).…”

Section: Conceptual Model and Lines And Evidencementioning

confidence: 99%

Bounding Global Aerosol Radiative Forcing of Climate Change

Bellouin

Quaas

Gryspeerdt

et al. 2020

Reviews of Geophysics

Self Cite

551

348

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Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth's radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This review provides a new range of aerosol radiative forcing over the industrial era based on multiple, traceable, and arguable lines of evidence, including modeling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes constrain the forcing from aerosol‐radiation interactions. A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol‐driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction is less clear, and the influence on mixed‐phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to a 68% confidence interval for the total aerosol effective radiative forcing of ‐1.6 to ‐0.6 W m−2, or ‐2.0 to ‐0.4 W m−2 with a 90% likelihood. Those intervals are of similar width to the last Intergovernmental Panel on Climate Change assessment but shifted toward more negative values. The uncertainty will narrow in the future by continuing to critically combine multiple lines of evidence, especially those addressing industrial‐era changes in aerosol sources and aerosol effects on liquid cloud amount and on ice clouds.

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“…Combinations of multiple tools can take the form of constraining model behavior to observations (e.g., [52,110,148]) or of using a combination of observations and reanalysis to address the problem of concurrently observing aerosol and cloud from satellite (e.g., [26,90,91]). (We discuss [110] and [148] in greater detail in the context of emergent constraints; see "Improving Observational Constraints on GCMs").…”

Section: Current Erf Aci Estimatesmentioning

confidence: 99%

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

Mülmenstädt

Feingold

2018

Curr Clim Change Rep

112

101

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This article discusses some of the challenges that have limited progress in quantifying the radiative forcing associated with aerosol-cloud interactions (ACI) in warm (liquid-water) clouds. It reviews recent progress and suggests new ways of viewing the problem that might accelerate progress. It calls for much greater attention to the scale problem, both in terms of aerosol-cloud process representation in models and comparison with observations. It suggests careful consideration of the balance in detail with which processes are represented, and proposes a merging of detailed process understanding and system-wide behavior. In this spirit, it advocates tackling the problem with models of varying complexity that consider both the depth and breadth of the complex dynamical system. Finally, it considers shifting attention from untangling aerosol and meteorological effects on cloud systems towards understanding the co-variability of key aerosol and meteorological drivers of cloud systems.

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Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

Cited by 6 publications

References 45 publications

Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

Bounding Global Aerosol Radiative Forcing of Climate Change

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

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