Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

Thompson, David R.; Kahn, Brian H.; Green, Robert O.; Chien, Steve; Middleton, Elizabeth M.; Tran, Daniel

doi:10.5194/amt-11-1019-2018

Cited by 16 publications

(18 citation statements)

References 36 publications

(41 reference statements)

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“…The formation of O 3 is a very complicated process which is affected by many factors, including precursor emissions (e.g., NO X, CO, and VOCs), local climate conditions (e.g., temperature, relative humidity, solar radiation, and wind direction and speed), and atmospheric chemical processes [18][19][20][21]. Fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM 2.5 ) can scatter and/or absorb solar radiation and influence the surface radiation budget [22], and these scattering and/or absorbing properties are inherently related to its chemical composition. The carbonaceous components (e.g., OC and EC) and water-soluble ions (e.g., SO 4 2− , NO 3 − , and NH 4 + )…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Zhao

Zheng

2018

Sustainability

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This study analyzed the spatiotemporal variations in PM 2.5 and O 3 , and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM 2.5 and O 3 data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM 2.5 concentration and the lowest O 3 concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM 2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O 3 concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM 2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O 3 concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM 2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O 3 concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O 3 concentrations in winter.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Zhao

Zheng

2018

Sustainability

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“…The horizontal resolutions of the ICON-LEM (100 m) and the airborne observations (10 m (Kaur and Ganju, 2008;Li et al, 2003;Thompson et al, 2018). This raises the question of how much of the observed variability of I s is lost by horizontal averaging.…”

Section: Impact Of Spatial Resolutionmentioning

confidence: 99%

Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed by airborne remote sensing during a cold air outbreak and a warm air advection event

Ruiz-Donoso¹,

Ehrlich²,

Schäfer³

et al. 2019

Preprint

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The synergy between airborne lidar, radar, passive microwave, and passive imaging spectrometer measurements was used to characterize the vertical and small-scale (down to 10 m) horizontal distribution of the cloud thermodynamic phase.Two case studies of low-level Arctic clouds in a cold air outbreak and a warm air advection observed during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) were investigated. Both clouds exhibited the typical vertical mixed-phase structure with mostly liquid water droplets at cloud top and ice crystals in lower layers. The cloud top 5 horizontal small-scale variability observed during the cold air outbreak is dominated by the liquid water close to the cloud top and shows no indication of ice in lower cloud layers. Contrastingly, the cloud top variability of the case observed during a warm air advection showed some ice in areas of low reflectivity or cloud holes. Radiative transfer simulations considering homogeneous mixtures of liquid water droplets and ice crystals were able to reproduce the horizontal variability of this warm air advection. To account for more realistic vertical distributions of the thermodynamic phase, large eddy simulations (LES) 10 were performed to reconstruct the observed cloud properties and were used as input for radiative transfer simulations. The simulations of the cloud observed during the cold air outbreak, with mostly liquid water at cloud top, realistically reproduced the observations. For the warm air advection case, the simulated cloud field underestimated the ice water content (IWC).Nevertheless, it revealed the presence of ice particles close to the cloud top and confirmed the observed horizontal variability of the cloud field. It is concluded that the cloud top small-scale horizontal variability reacts to changes in the vertical distribution 15 of the cloud thermodynamic phase. Passive satellite-borne imaging spectrometer observations with pixel sizes larger than 100 m miss the small-scale cloud top structures, which limits their capabilities to provide indications about the cloud vertical structure.

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“…Unfortunately, many of the cloud tops in the Southern Ocean cannot be confidently classified as either liquid or ice by AIRS (Thompson et al, 2018). This makes it difficult to establish whether cyclones are transitioning to a more liquiddominated state.…”

Section: Changes In Lwp and Cloud-top Phasementioning

confidence: 99%

“…subtropics (Klein et al, 2017). Further clarity in the cloud phase information from AIRS may require additional algorithm development to exploit hyperspectral infrared radiances to classify supercooled liquid and mixed phase spectral signatures (e.g., Kahn et al (2011); Rowe et al (2013)), including the impacts of sub-pixel horizontal variations of liquid and ice phase mixtures (e.g., Thompson et al (2018)).…”

Section: Changes In Lwp and Cloud-top Phasementioning

confidence: 99%

Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations

McCoy¹,

Field²,

Elsaesser³

et al. 2018

Preprint

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<p><strong>Abstract.</strong> Extratropical cyclones provide a unique set of challenges and opportunities in understanding variability in cloudiness over the extratropics (poleward of 30&#176;). We can gain insight into the shortwave cloud feedback from examining cyclone variability. Here we contrast global climate models (GCMs) with horizontal resolutions from 7&#8201;km up to hundreds of kilometers with Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992&#8211;2015. We find that inter-cyclone variability in both observations and models is strongly driven by moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances liquid water path (LWP) within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. In the southern hemisphere (SH) oceans 28&#8211;42&#8201;% of the observed interannual variability in cyclone LWP may be explained by WCB moisture flux variability. This relationship is used to propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by Clausius-Clapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone; and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. We show that changes in moisture flux drive can explain the observed trend in Southern Ocean cyclone LWP over the last two decades. Transient warming simulations show that the majority of the change in cyclone LWP can be explained by changes in WCB moisture flux, as opposed to changes in cloud phase. The variability within cyclone composites is examined to understand what cyclonic regimes the mixed phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing SST in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones.</p>

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Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

Cited by 16 publications

References 36 publications

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed by airborne remote sensing during a cold air outbreak and a warm air advection event

Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations

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