Polluted cloud lines in satellite snapshots and satellite climatologies

2021 ◽  
Author(s):  
Velle Toll ◽  
Heido Trofimov ◽  
Jorma Rahu ◽  
Piia Post
Keyword(s):  
Power Plants ◽  
Climate Models ◽  
Spatial Scales ◽  
Cloud Water ◽  
Global Climate Models ◽  
Anthropogenic Aerosols ◽  
Oil Refineries ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Cloud Response

<p>It is challenging to separate the cause from effect in aerosol-cloud interactions. Anomalous cloud lines polluted by anthropogenic aerosols help distinguish the cause from effect as properties of polluted clouds can be directly compared to nearby unpolluted clouds’ properties. Pollution tracks in clouds induced by localised aerosol emissions (Toll et al. 2019, Nature, https://doi.org/10.1038/s41586-019-1423-9)  are visually detectable ship-track-like quasi-linear polluted cloud features in satellite snapshots. We detected similar anomalous polluted cloud lines in the long-term average satellite data, where cloud response to aerosol over a long time is recorded. Polluted cloud tracks are induced by various aerosol sources like oil refineries, smelters, coal-fired power plants, smaller industry towns, ships, and volcanoes. We detected polluted cloud tracks at spatial scales varying from tens of kilometres to thousands of kilometres (Trofimov et al. 2020; JGR Atmospheres, https://doi.org/10.1029/2020JD032575).  </p><p> </p><p>Polluted cloud tracks detected in satellite snapshots are excellent for the process-level understanding of aerosol-cloud interactions. Polluted cloud tracks recorded in satellite climatologies are great for estimating the average cloud response to aerosols. MODIS snapshots of polluted cloud tracks show relatively weak cloud water response to aerosols at various spatial scales. High-resolution analysis of South-East Atlantic shipping corridor shows partial off-set of the Twomey effect by decreased cloud water. Cloud fraction sometimes increases in the polluted cloud tracks and sometimes decreases compared to the nearby unpolluted clouds. The temporal evolution of cloud responses in pollution tracks estimated from geostationary SEVIRI data and meteorological conditions favourable for pollution track occurrence is presented. We expect that the utilisation of these real-world laboratories of aerosol impacts on clouds helps to improve global climate models’ physical parameterisations.</p>

Real-world laboratories for studying anthropogenic aerosol impacts on clouds and Earth’s climate

2020 ◽  
Author(s):  
Velle Toll ◽  
Heido Trofimov ◽  
Jorma Rahu
Keyword(s):  
Real World ◽  
Power Plants ◽  
Satellite Images ◽  
Climate Models ◽  
Cloud Droplet ◽  
Cloud Water ◽  
Global Climate Models ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Oil Refineries

<p>The cooling of the Earth’s climate through the effects of anthropogenic aerosols on clouds offsets an unknown fraction of greenhouse gas warming. We discuss how causal relationship between aerosols and clouds can be derived from contrast between clouds polluted by anthropogenic aerosols and nearby unpolluted clouds. Ship tracks have been long considered to be real-world laboratories of aerosol-cloud interactions. More recently, polluted cloud tracks induced by aerosols emitted from volcanoes and wildfires and various industrial sources - such as oil refineries, smelters, coal-fired power plants, and cities have been analysed (Toll et al. 2019; Nature, https://doi.org/10.1038/s41586-019-1423-9). In this research, we extend satellite observations of polluted cloud tracks from Toll et al. (2019) with analysis of smaller and larger scale polluted cloud areas detected in satellite images.</p><p> </p><p>Polluted clouds are detected in MODIS and SEVIRI satellite images as areas with strongly increased cloud droplet number concentrations. Polluted cloud tracks can be utilized to study frequency and magnitude of anthropogenic cloud droplet number perturbations and subsequent cloud adjustments. Anthropogenic aerosol perturbations on liquid-water clouds are detected in various major global industrial areas. Both tens of kilometres wide ship-track-like polluted cloud tracks and hundreds by hundreds of kilometres wide polluted cloud areas show that cloud water can both increase and decrease in response to aerosols depending on meteorological conditions. On average, there is relatively weak decrease in cloud water. Polluted cloud tracks also show that cloud fraction can both increase and decrease compared to nearby less polluted clouds. Applicability of pollution tracks to study impact of absorbing aerosols situated above clouds on below-lying clouds is discussed. We expect that utilization of real-world laboratories of aerosol impacts on clouds will lead to improved physical parameterizations in global climate models and more reliable projections of the future climate.</p>

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 Reducing uncertainties in satellite estimates of aerosol-cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

2019 ◽  
Author(s):  
David Painemal ◽  
Fu-Lung Chang ◽  
Richard Ferrare ◽  
Sharon Burton ◽  
Zhujun Li ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Global Climate Models ◽  
Orthogonal Polarization ◽  
Near Surface ◽  
Aerosol Cloud ◽  
Error Characterization ◽  
Aerosol Cloud Interactions

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACI) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud-top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from Aqua-MODIS yield high correlations across a broad range of σBC values, with σBC quartile correlations > 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of less than 0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)−ln(σBC) (the standard method for quantifying ACI) is more physically meaningful than that derived from the Nd−AOD pair.

A synthesis of observations of aerosol-cloud interactions over the pristine, biologically active Southern Ocean and their implications for global climate model predictions

2021 ◽  
Author(s):  
Isabel L. McCoy ◽  
Daniel T. McCoy ◽  
Robert Wood ◽  
Christopher S. Bretherton ◽  
Leighton Regayre ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Biologically Active ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Industrial State

<div> <p>The change in planetary albedo due to aerosol-cloud interactions (aci) during the industrial era is the leading source of uncertainty in inferring Earth's climate sensitivity to increased greenhouse gases from the historical record. Examining pristine environments such as the Southern Ocean (SO) helps us to understand the pre-industrial state and constrain the change in cloud brightness over the industrial period associated with aci. This study presents two methods of utilizing observations of pristine environments to examine climate models and our understanding of the pre-industrial state.</p> </div><div> <p>First, cloud droplet number concentration (<em>N<sub>d</sub></em>) is used as an indicator of aci. Global climate models (GCMs) show that the hemispheric contrast in liquid cloud <em>N<sub>d</sub></em> between the pristine SO and the polluted Northern Hemisphere observed in the present-day can be used<strong> </strong>as a proxy for the increase in <em>N<sub>d</sub></em> from the pre-industrial. A hemispheric difference constraint developed from MODIS satellite observations indicates that pre-industrial <em>N<sub>d</sub></em> may have been higher than previously thought and provides an estimate of radiative forcing associated with aci between -1.2 and -0.6 Wm<sup>-2</sup>. Comparisons with MODIS <em>N<sub>d  </sub></em>highlight significant GCM discrepancies in pristine, biologically active regions.</p> </div><div> <p>Second, aerosol and cloud microphysical observations from a recent SO aircraft campaign are used to identify two potentially important mechanisms that are incomplete or missing in GCMs: i) production of new aerosol particles through synoptic uplift, and ii) buffering of <em>N<sub>d</sub></em> against precipitation removal by small, Aitken mode aerosols entrained from the free troposphere. The latter may significantly contribute to the high, summertime SO <em>N<sub>d</sub></em> levels which persist despite precipitation depletion associated with mid-latitude storm systems. Observational comparisons with nudged Community Atmosphere Model version 6 (CAM6) hindcasts show low-biased SO <em>N<sub>d  </sub></em>is linked to under-production of free-tropospheric Aitken aerosol which drives low-biases in cloud condensation nuclei number and likely discrepancies in composition. These results have important implications for the ability of current GCMs to capture aci in pristine environments.</p> </div>

scholarly journals The hemispheric contrast in cloud microphysical properties constrains aerosol forcing

2020 ◽  
Vol 117 (32) ◽  
pp. 18998-19006 ◽  
Author(s):  
Isabel L. McCoy ◽  
Daniel T. McCoy ◽  
Robert Wood ◽  
Leighton Regayre ◽  
Duncan Watson-Parris ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Climate Models ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Global Climate Models ◽  
Aerosol Cloud ◽  
Cloud Droplet Number Concentration ◽  
Aerosol Cloud Interactions

The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m−2to −0.6 W⋅m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

scholarly journals Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

2020 ◽  
Vol 20 (12) ◽  
pp. 7167-7177 ◽  
Author(s):  
David Painemal ◽  
Fu-Lung Chang ◽  
Richard Ferrare ◽  
Sharon Burton ◽  
Zhujun Li ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Global Climate Models ◽  
Orthogonal Polarization ◽  
Near Surface ◽  
Aerosol Cloud ◽  
Error Characterization ◽  
Aerosol Cloud Interactions

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol–cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from MODIS Aqua yield high correlations across a broad range of σBC values, with σBC quartile correlations ≥0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of 0.54–0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)–ln(σBC) (the standard method for quantifying ACIs) is more physically meaningful than that derived from the Nd–AOD pair.

scholarly journals Impacts of Cloud Microphysics Parameterizations on Simulated Aerosol–Cloud-Interactions for Deep Convective Clouds over Houston

2020 ◽  
Author(s):  
Yuwei Zhang ◽  
Jiwen Fan ◽  
Zhanqing Li ◽  
Daniel Rosenfeld
Keyword(s):  
Climate Models ◽  
Convective Cloud ◽  
Forecast Model ◽  
Cloud Microphysics ◽  
Anthropogenic Aerosols ◽  
Convective Clouds ◽  
Aerosol Cloud ◽  
Weather And Climate ◽  
Aerosol Cloud Interactions ◽  
Bin Microphysics

Abstract. Aerosol–cloud interactions remain largely uncertain in predicting their impacts on weather and climate. Cloud microphysics parameterization is one of the factors leading to the large uncertainty. Here we investigate the impacts of anthropogenic aerosols on the convective intensity and precipitation of a thunderstorm occurring on 19 June 2013 over Houston with the Chemistry version of Weather Research and Forecast model (WRF‐Chem) using the Morrison two-moment bulk scheme and spectral-bin microphysics (SBM) scheme. We find that the SBM predicts a deep convective cloud agreeing better with observations in terms of reflectivity and precipitation compared with the Morrison bulk scheme that has been used in many weather and climate models. With the SBM scheme, we see a significant invigoration effect on convective intensity and precipitation by anthropogenic aerosols mainly through enhanced condensation latent heating (i.e., the warm-phase invigoration). Whereas such effect is absent with the Morrison two-moment bulk microphysics, mainly due to limitations of the saturation adjustment approach for droplet condensation and evaporation calculation.

scholarly journals Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston

2021 ◽  
Vol 21 (4) ◽  
pp. 2363-2381
Author(s):  
Yuwei Zhang ◽  
Jiwen Fan ◽  
Zhanqing Li ◽  
Daniel Rosenfeld
Keyword(s):  
Climate Models ◽  
Convective Cloud ◽  
Forecast Model ◽  
Cloud Microphysics ◽  
Anthropogenic Aerosols ◽  
Convective Clouds ◽  
Aerosol Cloud ◽  
Weather And Climate ◽  
Aerosol Cloud Interactions ◽  
Bin Microphysics

Abstract. Aerosol–cloud interactions remain largely uncertain with respect to predicting their impacts on weather and climate. Cloud microphysics parameterization is one of the factors leading to large uncertainty. Here, we investigate the impacts of anthropogenic aerosols on the convective intensity and precipitation of a thunderstorm occurring on 19 June 2013 over Houston with the Chemistry version of Weather Research and Forecast model (WRF-Chem) using the Morrison two-moment bulk scheme and spectral bin microphysics (SBM) scheme. We find that the SBM predicts a deep convective cloud that shows better agreement with observations in terms of reflectivity and precipitation compared with the Morrison bulk scheme that has been used in many weather and climate models. With the SBM scheme, we see a significant invigoration effect on convective intensity and precipitation by anthropogenic aerosols, mainly through enhanced condensation latent heating. Such an effect is absent with the Morrison two-moment bulk microphysics, mainly because the saturation adjustment approach for droplet condensation and evaporation calculation limits the enhancement by aerosols in (1) condensation latent heat by removing the dependence of condensation on droplets and aerosols and (2) ice-related processes because the approach leads to stronger warm rain and weaker ice processes than the explicit supersaturation approach.

scholarly journals Large-scale industrial cloud perturbations confirm bidirectional cloud water responses to anthropogenic aerosols

2020 ◽  
Author(s):  
Heido Trofimov ◽  
Velle Toll
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Climate Models ◽  
Hot Spot ◽  
Cloud Water ◽  
Global Climate Models ◽  
Small Scale ◽  
Liquid Water Path ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol

<p>Aerosols offset poorly quantified fraction of anthropogenic greenhouse gas warming, whereas the aerosol impact on clouds is the most uncertain mechanism of anthropogenic climate forcing. In this research, we extend satellite observations of polluted cloud tracks from Toll et al. (2019, Nature, https://doi.org/10.1038/s41586-019-1423-9) with analysis of larger scale polluted cloud areas detected in MODerate-resolution Imaging Spectroradiometer satellite images. We demonstrate that large-scale anthropogenic aerosol-induced cloud perturbations exist at various major industrial aerosol source regions. The areal extent of the polluted cloud areas detected in MODIS satellite images extended to hundreds by hundreds of kilometres. Polluted clouds detected in satellite images in the global anthropogenic air pollution hot spot of Norilsk, Russia, and in other regions show close compensation between aerosol-induced cloud water increases and decreases. On average, there is relatively weak decrease in cloud water in the large areas with strong decreases in cloud droplet radii. This is in very good agreement with previous results based on small-scale polluted cloud tracks (Toll et al., 2019) and strongly disagrees with unidirectionally increased liquid water path in global climate models.</p>

scholarly journals Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements

2017 ◽  
Vol 17 (7) ◽  
pp. 4451-4475 ◽  
Author(s):  
Ilissa B. Ocko ◽  
Paul A. Ginoux
Keyword(s):  
Climate Models ◽  
Climate Modeling ◽  
Global Climate ◽  
Measurement Techniques ◽  
Global Climate Models ◽  
Orthogonal Polarization ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Multiple Model ◽  
Anthropogenic Aerosols ◽  
Aerosol Properties

Abstract. Anthropogenic aerosols are a key factor governing Earth's climate and play a central role in human-caused climate change. However, because of aerosols' complex physical, optical, and dynamical properties, aerosols are one of the most uncertain aspects of climate modeling. Fortunately, aerosol measurement networks over the past few decades have led to the establishment of long-term observations for numerous locations worldwide. Further, the availability of datasets from several different measurement techniques (such as ground-based and satellite instruments) can help scientists increasingly improve modeling efforts. This study explores the value of evaluating several model-simulated aerosol properties with data from spatially collocated instruments. We compare aerosol optical depth (AOD; total, scattering, and absorption), single-scattering albedo (SSA), Ångström exponent (α), and extinction vertical profiles in two prominent global climate models (Geophysical Fluid Dynamics Laboratory, GFDL, CM2.1 and CM3) to seasonal observations from collocated instruments (AErosol RObotic NETwork, AERONET, and Cloud–Aerosol Lidar with Orthogonal Polarization, CALIOP) at seven polluted and biomass burning regions worldwide. We find that a multi-parameter evaluation provides key insights on model biases, data from collocated instruments can reveal underlying aerosol-governing physics, column properties wash out important vertical distinctions, and improved models does not mean all aspects are improved. We conclude that it is important to make use of all available data (parameters and instruments) when evaluating aerosol properties derived by models.

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