Comparing results from a physical model with satellite and in situ observations to determine whether biomass burning aerosols over the Amazon brighten or burn off clouds

Hoeve, John E. Ten; Jacobson, Mark Z.; Remer, L. A.

doi:10.1029/2011jd016856

Cited by 49 publications

(63 citation statements)

References 166 publications

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“…The response of boundary layer turbulence and clouds may depend on the vertical orientation of the absorbing aerosols relative to the cloud layer, as well as on the response of the clouds themselves. For example, in the Amazon region, where deeper cumulus clouds are embedded in absorbing BC aerosols, the introduction of BC aerosols in a model experiment reduced TKE below the aerosol layer and increased TKE in a narrow layer above the aerosol layer (31). At low aerosol amounts, increasing aerosol led to taller clouds, whereas at high aerosol amounts, increasing aerosol led to reduced cloudiness.…”

Section: Discussionmentioning

confidence: 99%

Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer

Wilcox

Thomas

Praveen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

126

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The introduction of cloud condensation nuclei and radiative heating by sunlight-absorbing aerosols can modify the thickness and coverage of low clouds, yielding significant radiative forcing of climate. The magnitude and sign of changes in cloud coverage and depth in response to changing aerosols are impacted by turbulent dynamics of the cloudy atmosphere, but integrated measurements of aerosol solar absorption and turbulent fluxes have not been reported thus far. Here we report such integrated measurements made from unmanned aerial vehicles (UAVs) during the CARDEX (Cloud Aerosol Radiative Forcing and Dynamics Experiment) investigation conducted over the northern Indian Ocean. The UAV and surface data reveal a reduction in turbulent kinetic energy in the surface mixed layer at the base of the atmosphere concurrent with an increase in absorbing black carbon aerosols. Polluted conditions coincide with a warmer and shallower surface mixed layer because of aerosol radiative heating and reduced turbulence. The polluted surface mixed layer was also observed to be more humid with higher relative humidity. Greater humidity enhances cloud development, as evidenced by polluted clouds that penetrate higher above the top of the surface mixed layer. Reduced entrainment of dry air into the surface layer from above the inversion capping the surface mixed layer, due to weaker turbulence, may contribute to higher relative humidity in the surface layer during polluted conditions. Measurements of turbulence are important for studies of aerosol effects on clouds. Moreover, reduced turbulence can exacerbate both the human health impacts of high concentrations of fine particles and conditions favorable for low-visibility fog events.

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Section: Discussionmentioning

confidence: 99%

Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer

Wilcox

Thomas

Praveen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

126

View full text Add to dashboard Cite

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“…Accounting for aerosol absorption effects on clouds raises particularly challenging issues. These include effects on cloud heating by absorbing inclusions in droplets and of absorbing aerosol particles interstitially between droplets (21)(22)(23)(24)(25). (Note that the positive aerosol direct forcing contribution in Fig.…”

Section: Process-scale Modeling Of Aerosol−cloud Relationshipsmentioning

confidence: 99%

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Seinfeld

Bretherton

Carslaw

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

611

474

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The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.climate | aerosol−cloud effects | general circulation models | radiative forcing | satellite observations Clouds play a key role in Earth's radiation budget, and aerosols serve as the seeds upon which cloud droplets form. Anthropogenic activity has led to an increase in aerosol particle concentrations globally and an increase in those particles that act as cloud condensation nuclei (CCN) and ice nucleating particles (INP). The effect of an increase in aerosols on cloud optical properties, and associated radiative forcing, is the most uncertain component of historical radiative forcing of Earth's climate caused by greenhouse gases (GHGs) and aerosols. The Intergovernmental Panel on Climate Change (IPCC) AR5 assessment of climate forcing factors (Fig. S1) ascribes "high" confidence to the estimate of direct aerosol radiative forcing (mean

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“…Therefore we only acknowledge aerosol layers with an AOT > 0.2. These aerosols come mainly from desert dust (Weinzierl et al, 2011;Groß et al, 2015) but also from biomass burning (Rosário et al, 2011;Ten Hoeve et al, 2012) or, sometimes, sea salt (Toth et al, 2013). We assume that AOT ≤ 0.2 is a good approximation for the AOT of typical aerosol loads.…”

Section: Vertical Cloud-aerosol Structures From Caliopmentioning

confidence: 99%

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate largescale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary.In this paper the CiPS (Cirrus Properties from SE-VIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m −2 . For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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Comparing results from a physical model with satellite and in situ observations to determine whether biomass burning aerosols over the Amazon brighten or burn off clouds

Cited by 49 publications

References 166 publications

Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer

Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

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