Comparisons of cirrus clouds and their linkages to meteorology at the SACOL and the SGP sites

Dong, Zixiang; Ge, Jinming; Gao, Ang; Zhu, Zeen; Yan, Jialin; Mu, Qingyu; Jing, Su; Yang, Xuan; Hu, Xiaobin

doi:10.1016/j.atmosres.2022.106467

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Other cloud types are relatively less abundant because they require additional conditions. For example, deep convective clouds not only require moisture and unstable conditions but also need lifting mechanisms (Henkes et al, 2021); Stratus and altocumulus typically form under relatively stable atmospheric conditions (Shahnaz et al, 2023); Cumulus relies on upward convection and reaching a height above the condensation level (Barrett, 1936); Various factors influence the formation of cirrus (Dong et al, 2023).…”

Section: Analysis Of Spatiotemporal Variationsmentioning

confidence: 99%

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Bao,

Letu,

Shang

et al. 2024

Geophysical Research Letters

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The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds.

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Section: Analysis Of Spatiotemporal Variationsmentioning

confidence: 99%

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Bao,

Letu,

Shang

et al. 2024

Geophysical Research Letters

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Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site

Li,

Ge,

et al. 2024

JGR Atmospheres

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Low clouds significantly influence Earth's energy budget by reflecting solar radiation. Consequently, inadequate representation of these clouds in models introduces the largest uncertainty in predicting future climate change. This study investigates low cloud cover (LCC) variation using 6 years (2014–2019) of high‐precision ground‐based Ka‐band Zenith Radar (KAZR) observations at the Semi‐Arid Climate and Environment Observatory of Lanzhou University (SACOL). We analyze the relationship between observed low cloud properties and four large‐scale meteorological factors: 700 hPa relative humidity, estimated inversion strength, low‐level wind shear, and 700 hPa vertical velocity. These factors are identified as key parameters influencing low cloud evolution over this semi‐arid region. We utilize principal component analysis to integrate these parameters into a single meteorological predictor (PC1) and establish a robust linkage between meteorological conditions and low cloud properties. By comparing LCC fluctuations derived from the meteorological factors with those directly simulated by models over the same period, we assess the projected LCC trends under various carbon emission scenarios. Contrary to the declining LCC projected by CMIP6 models outcomes, the LCC form PC1 shows a rising tendency by 2100 under global warming. This discrepancy implies that CMIP6 models may exaggerate the extent of future warming at the SACOL site. Our approach can be applied to a broader global distribution of low clouds to examine the differences between low cloud variations constrained by meteorological fields and those from direct model simulations. This will enhance our understanding of low cloud feedback on future climate change.

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Climatology of Cirrus Clouds over Observatory of Haute-Provence (France) Using Multivariate Analyses on Lidar Profiles

Mandija,

Keckhut,

Alraddawi

et al. 2024

Atmosphere

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This study aims to achieve the classification of the cirrus clouds over the Observatory of Haute-Provence (OHP) in France. Rayleigh–Mie–Raman lidar measurements, in conjunction with the ERA5 dataset, are analyzed to provide geometrical morphology and optical cirrus properties over the site. The method of cirrus cloud climatology presented here is based on a threefold classification scheme based on the cirrus geometrical and optical properties and their formation history. Principal component analysis (PCA) and subsequent clustering provide four morphological cirrus classes, three optical groups, and two origin-related categories. Cirrus clouds occur approximately 37% of the time, with most being single-layered (66.7%). The mean cloud optical depth (COD) is 0.39 ± 0.46, and the mean heights range around 10.8 ± 1.35 km. Thicker tropospheric cirrus are observed under higher temperature and humidity conditions than cirrus observed in the vicinity of the tropopause level. Monthly cirrus occurrences fluctuate irregularly, whereas seasonal patterns peak in spring. Concerning the mechanism of the formation, it is found that the majority of cirrus clouds are of in situ origin. The liquid-origin cirrus category consists nearly entirely of thick cirrus. Overall results suggest that in situ origin thin cirrus, located in the upper tropospheric and tropopause regions, have the most noteworthy occurrence over the site.

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Comparisons of cirrus clouds and their linkages to meteorology at the SACOL and the SGP sites

Cited by 3 publications

References 48 publications

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site

Climatology of Cirrus Clouds over Observatory of Haute-Provence (France) Using Multivariate Analyses on Lidar Profiles

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