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Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales.

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Title: Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales.
Authors: Li, Yize1 (AUTHOR), Ge, Jinming1,2 (AUTHOR) gejm@lzu.edu.cn, Hu, Yue1 (AUTHOR), Xu, Ziyang1,2 (AUTHOR), Du, Jiajing2 (AUTHOR), Mu, Qingyu1 (AUTHOR)
Source: Remote Sensing. Dec2025, Vol. 17 Issue 24, p4045. 21p.
Subjects: Stratocumulus clouds, Climate feedbacks, Trend analysis, Cloud dynamics, El Niño, Timescale number, Weather
Abstract: Highlights: What are the main findings? Low cloud variability was analyzed using ISCCP-H observations and the Ensemble Empirical Mode Decomposition method, revealing distinct cloud–meteorology relationships over land and ocean. Low clouds exhibit nonlinear trends; stratocumulus and cumulus are primarily sensitive to temperature changes, while stratus responds to mid-level humidity over ocean and surface sensible heat flux over land. What are the implications of the main findings? Timescale-dependent analysis shows low cloud feedback cannot be fully captured by linear frameworks, calling for reconsideration of cloud–climate coupling in current models. These results provide observational constraints to improve cloud parameterization and climate projections. Low clouds significantly influence Earth's radiation budget, but their climate feedback remains highly uncertain due to complex interactions with meteorological conditions across spatial and temporal scales. The cloud controlling factor framework is widely used to link meteorological variables with cloud properties. However, most studies assume a static, linear relationship, potentially obscuring the timescale-dependent responses. In this study, we apply the Ensemble Empirical Mode Decomposition method to ISCCP-H cloud observations and ERA5 data (1987–2016) to isolate low cloud amount across multiple intrinsic timescales and trends over global land and ocean. The trends show a nonlinear increase in stratocumulus (Sc) and a significant nonlinear decline in cumulus (Cu), while stratus (St) exhibits weaker trends. We categorize timescales short (≤1 year) for annual variations, medium (1–8 years) for interannual variability such as ENSO, and long (>8 years) for decadal and longer-term climate changes. It is found that Sc and Cu over land are primarily influenced by near-surface heating, while sea surface temperature and surface sensible heat flux (SHF) dominate over ocean at short timescales. SHF becomes dominant over land at medium timescales, largely reflecting ENSO-related induced surface anomalies. At long timescales, atmospheric stability and wind speed influence continental clouds, while SHF remains dominant over ocean. Trend components reveal that Sc and Cu are most sensitive to temperature changes, whereas St responds to mid-level humidity over ocean and SHF over land. These findings underscore the importance of timescale-dependent cloud–meteorology relationships to improve cloud parameterizations and reduce climate projection uncertainties. Overall, our results demonstrate that low cloud variability and trends cannot be explained by a single linear mechanism but instead arise from distinct meteorological controls that change across timescales, cloud types, and surface regimes. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Yize%22">Li, Yize</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ge%2C+Jinming%22">Ge, Jinming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gejm@lzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Yue%22">Hu, Yue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Ziyang%22">Xu, Ziyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Du%2C+Jiajing%22">Du, Jiajing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Qingyu%22">Mu, Qingyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Dec2025, Vol. 17 Issue 24, p4045. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Stratocumulus+clouds%22">Stratocumulus clouds</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+feedbacks%22">Climate feedbacks</searchLink><br /><searchLink fieldCode="DE" term="%22Trend+analysis%22">Trend analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+dynamics%22">Cloud dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22El+Niño%22">El Niño</searchLink><br /><searchLink fieldCode="DE" term="%22Timescale+number%22">Timescale number</searchLink><br /><searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Low cloud variability was analyzed using ISCCP-H observations and the Ensemble Empirical Mode Decomposition method, revealing distinct cloud–meteorology relationships over land and ocean. Low clouds exhibit nonlinear trends; stratocumulus and cumulus are primarily sensitive to temperature changes, while stratus responds to mid-level humidity over ocean and surface sensible heat flux over land. What are the implications of the main findings? Timescale-dependent analysis shows low cloud feedback cannot be fully captured by linear frameworks, calling for reconsideration of cloud–climate coupling in current models. These results provide observational constraints to improve cloud parameterization and climate projections. Low clouds significantly influence Earth's radiation budget, but their climate feedback remains highly uncertain due to complex interactions with meteorological conditions across spatial and temporal scales. The cloud controlling factor framework is widely used to link meteorological variables with cloud properties. However, most studies assume a static, linear relationship, potentially obscuring the timescale-dependent responses. In this study, we apply the Ensemble Empirical Mode Decomposition method to ISCCP-H cloud observations and ERA5 data (1987–2016) to isolate low cloud amount across multiple intrinsic timescales and trends over global land and ocean. The trends show a nonlinear increase in stratocumulus (Sc) and a significant nonlinear decline in cumulus (Cu), while stratus (St) exhibits weaker trends. We categorize timescales short (≤1 year) for annual variations, medium (1–8 years) for interannual variability such as ENSO, and long (>8 years) for decadal and longer-term climate changes. It is found that Sc and Cu over land are primarily influenced by near-surface heating, while sea surface temperature and surface sensible heat flux (SHF) dominate over ocean at short timescales. SHF becomes dominant over land at medium timescales, largely reflecting ENSO-related induced surface anomalies. At long timescales, atmospheric stability and wind speed influence continental clouds, while SHF remains dominant over ocean. Trend components reveal that Sc and Cu are most sensitive to temperature changes, whereas St responds to mid-level humidity over ocean and SHF over land. These findings underscore the importance of timescale-dependent cloud–meteorology relationships to improve cloud parameterizations and reduce climate projection uncertainties. Overall, our results demonstrate that low cloud variability and trends cannot be explained by a single linear mechanism but instead arise from distinct meteorological controls that change across timescales, cloud types, and surface regimes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17244045
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 4045
    Subjects:
      – SubjectFull: Stratocumulus clouds
        Type: general
      – SubjectFull: Climate feedbacks
        Type: general
      – SubjectFull: Trend analysis
        Type: general
      – SubjectFull: Cloud dynamics
        Type: general
      – SubjectFull: El Niño
        Type: general
      – SubjectFull: Timescale number
        Type: general
      – SubjectFull: Weather
        Type: general
    Titles:
      – TitleFull: Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales.
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            NameFull: Li, Yize
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              Text: Dec2025
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