Cloud macro-physical properties in Saharan-dust-laden and dust-free North Atlantic trade wind regimes: a lidar case study

Gutleben, Manuel; Groß, Silke; Wirth, Martin

doi:10.5194/acp-19-10659-2019

Cited by 22 publications

(27 citation statements)

References 61 publications

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“…On average, longwave cooling is stronger than shortwave heating, such that net heating rates are largely negative from the surface up to 10 km, with a median value around -1 K/day. Additional local minima in longwave heating are observed around 3 and 5 km between the 5% and https://doi.org/10.5194/essd-2020-269 25% quantiles, which could correspond to the radiative effect of moisture reaching these higher levels, albeit less frequently (Stevens et al, 2017;Wood et al, 2018a, b;O et al, 2018;Gutleben et al, 2019).…”

Section: Variability Across Soundingsmentioning

confidence: 98%

Atmospheric radiative profiles during EUREC<sup>4</sup>A

Albright¹,

Fildier²,

Pincus³

et al. 2020

Preprint

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Abstract. The couplings among clouds, convection, and circulation in trade-wind regimes remain a fundamental puzzle that limits our ability to constrain future climate change. Radiative heating plays an important role in these couplings. Here we calculate the clear-sky radiative profiles from 2001 in-situ soundings (978 dropsondes and 1023 radiosondes) collected during the EUREC4A field campaign, which took place south and east of Barbados in January–February 2020. We describe the method used to calculate these radiative profiles and present preliminary results sampling variability at multiple scales, from the variability across all soundings to groupings by diurnal cycle and mesoscale organization state, as well as individual soundings associated with elevated moisture layers. This clear-sky radiative profiles data set can provide important missing detail to what can be learned from calculations based on passive remote sensing and help in investigating the role of radiation in dynamic and thermodynamic variability in trade-wind regimes. All data are archived and freely available for public access on AERIS (Albright et al. (2020), https://doi.org/10.25326/78).

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Section: Variability Across Soundingsmentioning

confidence: 98%

Atmospheric radiative profiles during EUREC<sup>4</sup>A

Albright¹,

Fildier²,

Pincus³

et al. 2020

Preprint

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“…Particle extinction at 532 nm as well as particle linear depolarization and particle backscatter at 532 and 1064 nm can be retrieved. The high vertical resolution of 15 m allows detecting cloud top heights with high vertical accuracy (Gutleben et al, 2019). WALES data with 5 Hz temporal resolution corresponding to a horizontal resolution of approximately 170 m at typical aircraft speed are used in this study.…”

Section: Walesmentioning

confidence: 99%

“…According to Gutleben et al (2019), the along-track cloud top height z cth is determined using a threshold of 20 in vertical profiles of backscatter ratio. The Cloud Geometrical Thickness z cgt can then be calculated using…”

Section: Macrophysicsmentioning

confidence: 99%

Synergy of Active- and Passive Remote Sensing: An Approach to Reconstruct Three-Dimensional Cloud Macro- and Microphysics

Höppler¹,

Gödde²,

Gutleben

et al. 2020

Preprint

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Abstract. This paper presents a method to retrieve three-dimensional cumulus cloud macro- and microphysics measured by remote sensing instruments on the German research aircraft HALO. This is achieved by combining our hyper-spectral pushbroom spectrometer specMACS with active and passive remote sensing instruments, such as a lidar, a microwave radiometer, a radar and dropsondes. Two-dimensional cloud information such as cloud size, optical thickness, effective radius and thermodynamic phase are retrieved by specMACS with established remote sensing methods. Information of the other active and passive remote sensing instruments with a smaller field-of-view are mapped to the wider specMACS swath following Barker et al. (2011). The combination of specMACS with passive and active remote sensing quantities, for example, the Cloud Top Height from lidar measurements, allows new possibilities: three-dimensional cloud macrophysics can be reconstructed. Applying a sub-adiabatic microphysical model constrained with measurements allows to extend the measured quantities to a three-dimensional representation of microphysics. A consistency check by means of a three-dimensional radiative transfer simulation of the specMACS observations of these derived three-dimensional cloud fields shows good agreement.

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“…However, the aerosol signal is much smaller than the hydrometeor signal. Therefore, we follow Gutleben et al (2019) and use a threshold of backscatter ratio (BSR) > 20 in the 532 nm channel to differentiate cloudy scenes from clear-sky or dusty scenes. As hydrometeors attenuate the lidar signal strongly, the WALES lidar is used only to detect cloud top height using that threshold.…”

Section: Lidarmentioning

confidence: 99%

“…Gutleben et al (2019) is applied to clearly distinguish between backscatter from dust aerosols and cloud droplets. A comparison of the airborne measurements to ground-based radar records reveals a reliable radar reflectivity detection threshold of −20 dBZ for the airborne radar over the full column.…”

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confidence: 99%

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

Jacob

Kollias

Ament

et al. 2020

Preprint

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Airborne remote sensing observations over the tropical Atlantic Ocean upstream of Barbados are used to characterize trade wind shallow cumulus clouds and to benchmark two cloud-resolving ICON (ICOsahedral Nonhydrostatic) model simulations at kilo-and hectometer scales. The clouds were observed by an airborne nadir pointing backscatter lidar, a cloud radar, and a microwave radiometer in the tropical dry winter season during daytime. For the model benchmark, forward operators convert the model data into the observational space for considering instrument specific cloud detection thresholds. The forward 5 simulations reveal the different detection limits of the lidar and radar observations, i.e., most clouds with cloud liquid water content greater than 10 −7 kg kg −1 are detectable by the lidar, whereas the radar is primarily sensitive to the "rain"-category hydrometeors in the models and can detect even low amounts of rain.The observations reveal two prominent modes of cumulus cloud top heights separating the clouds into two layers. The lower mode relates to boundary layer convection with tops closely above the lifted condensation level, which is at about 700 m above 10 sea level. The upper mode is driven by shallow moist convection, also contains shallow outflow anvils, and is closely related to the trade inversion at about 2.3 km above sea level. The two cumulus modes are reflected differently by the lidar and the radar observations and under different liquid water path (LWP) conditions. The storm-resolving model (SRM) at kilometer scale reproduces the cloud modes barely and shows the most cloud tops slightly above the observed lower mode. The large-eddy model (LEM) at hectometer scale reproduces better the observed cloudiness distribution with a clear bimodal separation. We 15 hypothesize that slight differences in the autoconversion parametrizations could have caused the different cloud development in the models. Neither model seems to account for in-cloud drizzle particles that do not precipitate down to the surface but generate a stronger radar signal even in scenes with low LWP. Our findings suggest that even if the SRM is a step forward for better cloud representation in climate research, the LEM can better reproduce the observed shallow cumulus convection and should therefore in principle represent cloud radiative effects and water cycle better. The representation of low-level oceanic clouds contributes largely to differences between climate models in terms of equilibrium climate sensitivity (Schneider et al., 2017). Global atmospheric models with kilometer-scale resolution are considered as the way forward in forecasting future climate scenarios (Bony and Dufresne, 2005; Satoh et al., 2019). The increased model 25 resolution and better matching scales with measurements allow for a more direct observational assessment by comparing the present day representation in the models with atmospheric measurements and thus anchoring models to reality. Recently, Stevens et al. (2020) demonstrated the general advantage...

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Cloud macro-physical properties in Saharan-dust-laden and dust-free North Atlantic trade wind regimes: a lidar case study

Cited by 22 publications

References 61 publications

Atmospheric radiative profiles during EUREC<sup>4</sup>A

Atmospheric radiative profiles during EUREC<sup>4</sup>A

Synergy of Active- and Passive Remote Sensing: An Approach to Reconstruct Three-Dimensional Cloud Macro- and Microphysics

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

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Cloud macro-physical properties in Saharan-dust-laden and dust-free North Atlantic trade wind regimes: a lidar case study

Cited by 22 publications

References 61 publications

Atmospheric radiative profiles during EUREC&lt;sup&gt;4&lt;/sup&gt;A

Atmospheric radiative profiles during EUREC&lt;sup&gt;4&lt;/sup&gt;A

Synergy of Active- and Passive Remote Sensing: An Approach to Reconstruct Three-Dimensional Cloud Macro- and Microphysics

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

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Product

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Atmospheric radiative profiles during EUREC<sup>4</sup>A

Atmospheric radiative profiles during EUREC<sup>4</sup>A