Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO

Cesana, G; Chepfer, Hélène; Winker, David M.; Getzewich, Brian; Cai, X.; Jourdan, Olivier; Mioche, Guillaume; Okamoto, Hajime; Hagihara, Yoshihiro; Noël, Vincent; Reverdy, Mathieu

doi:10.1002/2015jd024334

Cited by 73 publications

(73 citation statements)

References 83 publications

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“…Second, polar cloud phase is not well known, and the processes underlying phase transitions in current and warming climate are hard to observe and predict from first principles. Thus, CALIPSO cloud phase observations are especially important in polar regions [30] and have been vetted against in situ airborne measurements [31]. Third, polar hydrometeors cover surfaces with highly variable albedos and albedos that are changing in a warming world (e.g., as a consequence of sea ice loss).…”

Section: Observational Advances For Arctic Clouds Over the Last Decadementioning

confidence: 99%

Recent Advances in Arctic Cloud and Climate Research

Kay

L’Ecuyer

Chepfer

et al. 2016

Curr Clim Change Rep

143

114

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While the representation of clouds in climate models has become more sophisticated over the last 30+ years, the vertical and seasonal fingerprints of Arctic greenhouse warming have not changed. Are the models right? Observations in recent decades show the same fingerprints: surface amplified warming especially in late fall and winter. Recent observations show no summer cloud response to Arctic sea ice loss but increased cloud cover and a deepening atmospheric boundary layer in fall. Taken together, clouds appear to not affect the fingerprints of Arctic warming. Yet, the magnitude of warming depends strongly on the representation of clouds. Can we check the models? Having observations alone does not enable robust model evaluation and model improvement. Comparing models and observations is hard enough, but to improve models, one must both understand why models and observations differ and fix the parameterizations. It is all a tall order, but recent progress is summarized here.

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Section: Observational Advances For Arctic Clouds Over the Last Decadementioning

confidence: 99%

Recent Advances in Arctic Cloud and Climate Research

Kay

L’Ecuyer

Chepfer

et al. 2016

Curr Clim Change Rep

143

114

View full text Add to dashboard Cite

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“…Cesana and Chepfer (2013) indicated that these "undefined" samples account for about 10.3 % of cloudy pixels in 15 months of global statistics. In addition, because lidar cannot penetrate optically thick clouds (optical depth > 3, such as the supercooled liquid layer in the polar region) to detect ice crystals (Zhang et al, 2010), the CALIPSO-GOCCP cloud phase products possibly lead to a slight underestimation of ice clouds at the lowest levels at Arctic (Cesana et al, 2016).…”

Section: Cloud Phase Productmentioning

confidence: 99%

“…Choi et al (2010) has pointed out that this definition may lead to some overestimation of SCFs without considering horizontally oriented ice particles, which account for about 10 % of the uncertainty in their study. However, the impact of the oriented ice crystals on the determination of cloud phase is negligible after tilting the CALIOP to 3 • off-nadir (November 2007) Cesana et al, 2016).…”

Section: Cloud Phase Productmentioning

confidence: 99%

Effects of atmospheric dynamics and aerosols on the fraction of supercooled water clouds

Zhang

et al. 2017

Atmos. Chem. Phys.

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Abstract. Based on 8 years of (January 2008-December 2015 cloud phase information from the GCM-Oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP), aerosol products from CALIPSO and meteorological parameters from the ERA-Interim products, the present study investigates the effects of atmospheric dynamics on the supercooled liquid cloud fraction (SCF) during nighttime under different aerosol loadings at global scale to better understand the conditions of supercooled liquid water gradually transforming to ice phase.Statistical results indicate that aerosols' effect on nucleation cannot fully explain all SCF changes, especially in those regions where aerosols' effect on nucleation is not a first-order influence (e.g., due to low ice nuclei aerosol frequency). By performing the temporal and spatial correlations between SCFs and different meteorological factors, this study presents specifically the relationship between SCF and different meteorological parameters under different aerosol loadings on a global scale. We find that the SCFs almost decrease with increasing of aerosol loading, and the SCF variation is closely related to the meteorological parameters but their temporal relationship is not stable and varies with the different regions, seasons and isotherm levels. Obviously negative temporal correlations between SCFs versus vertical velocity and relative humidity indicate that the higher vertical velocity and relative humidity the smaller SCFs. However, the patterns of temporal correlation for lower-tropospheric static stability, skin temperature and horizontal wind are relatively more complex than those of vertical velocity and humidity. For example, their close correlations are predominantly located in middle and high latitudes and vary with latitude or surface type. Although these statistical correlations have not been used to establish a certain causal relationship, our results may provide a unique point of view on the phase change of mixed-phase cloud and have potential implications for further improving the parameterization of the cloud phase and determining the climate feedbacks.

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“…The high confidence criteria imposed to the CALIOP cloud phase (see below) shall, however, constrain the selected cloud and cloud profiles to high confidence liquid and high confidence ice clouds. Nevertheless, especially at high latitudes, an uncertainty remains due to the difficult cloud phase determination (Cesana et al, 2016).…”

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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Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO

Cited by 73 publications

References 83 publications

Recent Advances in Arctic Cloud and Climate Research

Recent Advances in Arctic Cloud and Climate Research

Effects of atmospheric dynamics and aerosols on the fraction of supercooled water clouds

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

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