Articles | Volume 17, issue 15
https://doi.org/10.5194/acp-17-9535-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-9535-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Aug 2017
Research article |
| 08 Aug 2017
Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks
Hendrik Andersen
CORRESPONDING AUTHOR
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Jan Cermak
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Julia Fuchs
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Reto Knutti
ETH Zürich, Institute of Atmospheric and Climate Science, Zurich, Switzerland
Ulrike Lohmann
ETH Zürich, Institute of Atmospheric and Climate Science, Zurich, Switzerland
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Cited
36 citations as recorded by crossref.
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- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
- Constraining the aerosol influence on cloud liquid water path E. Gryspeerdt et al. 10.5194/acp-19-5331-2019
- Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations H. Andersen et al. 10.5194/acp-23-10775-2023
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- Using a 10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A Probabilistic Machine Learning Approach L. Foresti et al. 10.1175/WAF-D-18-0206.1
- Analysis of the Thermodynamic Phase Transition of Tracked Convective Clouds Based on Geostationary Satellite Observations Q. Coopman et al. 10.1029/2019JD032146
- Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
- Assessing the representational accuracy of data-driven models: The case of the effect of urban green infrastructure on temperature M. Zumwald et al. 10.1016/j.envsoft.2021.105048
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. 10.5194/acp-20-3415-2020
- Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean C. Wall et al. 10.1073/pnas.2210481119
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. 10.16993/tellusb.1868
- On the relationship between convective precipitation and aerosol pollution in North China Plain during autumn and winter Z. Xiao et al. 10.1016/j.atmosres.2022.106120
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
- Global observations of aerosol indirect effects from marine liquid clouds C. Wall et al. 10.5194/acp-23-13125-2023
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- Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover Y. Chen et al. 10.1038/s41561-022-00991-6
- Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data D. Neubauer et al. 10.5194/acp-17-13165-2017
- Untangling causality in midlatitude aerosol–cloud adjustments D. McCoy et al. 10.5194/acp-20-4085-2020
- Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H. Andersen et al. 10.1080/16000889.2021.1971925
- Surprising similarities in model and observational aerosol radiative forcing estimates E. Gryspeerdt et al. 10.5194/acp-20-613-2020
- Deconvolution of boundary layer depth and aerosol constraints on cloud water path in subtropical stratocumulus decks A. Possner et al. 10.5194/acp-20-3609-2020
- Applying big data beyond small problems in climate research B. Knüsel et al. 10.1038/s41558-019-0404-1
- A seasonal analysis of aerosol-cloud-radiation interaction over Indian region during 2000–2017 S. Kant et al. 10.1016/j.atmosenv.2018.12.044
- Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors H. Andersen et al. 10.1029/2021GL096498
- Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach E. Fons et al. 10.1038/s41612-023-00452-w
- On the Influence of Air Mass Origin on Low‐Cloud Properties in the Southeast Atlantic J. Fuchs et al. 10.1002/2017JD027184
- Understanding climate phenomena with data-driven models B. Knüsel & C. Baumberger 10.1016/j.shpsa.2020.08.003
- Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. 10.1126/science.aav0566
- Separating radiative forcing by aerosol–cloud interactions and rapid cloud adjustments in the ECHAM–HAMMOZ aerosol–climate model using the method of partial radiative perturbations J. Mülmenstädt et al. 10.5194/acp-19-15415-2019
- Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations L. Zipfel et al. 10.3390/atmos13040586
- Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds I. Glenn et al. 10.1175/JAS-D-19-0269.1
- The Response of Cloud-Precipitation Recycling in China to Global Warming Q. Guo et al. 10.3390/rs13081601
- Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean K. Mansour et al. 10.1029/2021JD036355
36 citations as recorded by crossref.
- MODIS Retrievals of Cloud Effective Radius in Marine Stratocumulus Exhibit No Significant Bias M. Witte et al. 10.1029/2018GL079325
- First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib H. Andersen & J. Cermak 10.5194/amt-11-5461-2018
- Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning J. Fuchs et al. 10.5194/acp-18-16537-2018
- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
- Constraining the aerosol influence on cloud liquid water path E. Gryspeerdt et al. 10.5194/acp-19-5331-2019
- Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations H. Andersen et al. 10.5194/acp-23-10775-2023
- A satellite-observation based study on responses of clouds to aerosols over South Asia during IOD events of south-west monsoon season J. Panda et al. 10.1016/j.apr.2023.101861
- Using a 10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A Probabilistic Machine Learning Approach L. Foresti et al. 10.1175/WAF-D-18-0206.1
- Analysis of the Thermodynamic Phase Transition of Tracked Convective Clouds Based on Geostationary Satellite Observations Q. Coopman et al. 10.1029/2019JD032146
- Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
- Assessing the representational accuracy of data-driven models: The case of the effect of urban green infrastructure on temperature M. Zumwald et al. 10.1016/j.envsoft.2021.105048
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. 10.5194/acp-20-3415-2020
- Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean C. Wall et al. 10.1073/pnas.2210481119
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. 10.16993/tellusb.1868
- On the relationship between convective precipitation and aerosol pollution in North China Plain during autumn and winter Z. Xiao et al. 10.1016/j.atmosres.2022.106120
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
- Global observations of aerosol indirect effects from marine liquid clouds C. Wall et al. 10.5194/acp-23-13125-2023
- Strong Aerosol Effects on Cloud Amount Based on Long‐Term Satellite Observations Over the East Coast of the United States Y. Cao et al. 10.1029/2020GL091275
- Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover Y. Chen et al. 10.1038/s41561-022-00991-6
- Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data D. Neubauer et al. 10.5194/acp-17-13165-2017
- Untangling causality in midlatitude aerosol–cloud adjustments D. McCoy et al. 10.5194/acp-20-4085-2020
- Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H. Andersen et al. 10.1080/16000889.2021.1971925
- Surprising similarities in model and observational aerosol radiative forcing estimates E. Gryspeerdt et al. 10.5194/acp-20-613-2020
- Deconvolution of boundary layer depth and aerosol constraints on cloud water path in subtropical stratocumulus decks A. Possner et al. 10.5194/acp-20-3609-2020
- Applying big data beyond small problems in climate research B. Knüsel et al. 10.1038/s41558-019-0404-1
- A seasonal analysis of aerosol-cloud-radiation interaction over Indian region during 2000–2017 S. Kant et al. 10.1016/j.atmosenv.2018.12.044
- Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors H. Andersen et al. 10.1029/2021GL096498
- Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach E. Fons et al. 10.1038/s41612-023-00452-w
- On the Influence of Air Mass Origin on Low‐Cloud Properties in the Southeast Atlantic J. Fuchs et al. 10.1002/2017JD027184
- Understanding climate phenomena with data-driven models B. Knüsel & C. Baumberger 10.1016/j.shpsa.2020.08.003
- Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. 10.1126/science.aav0566
- Separating radiative forcing by aerosol–cloud interactions and rapid cloud adjustments in the ECHAM–HAMMOZ aerosol–climate model using the method of partial radiative perturbations J. Mülmenstädt et al. 10.5194/acp-19-15415-2019
- Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations L. Zipfel et al. 10.3390/atmos13040586
- Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds I. Glenn et al. 10.1175/JAS-D-19-0269.1
- The Response of Cloud-Precipitation Recycling in China to Global Warming Q. Guo et al. 10.3390/rs13081601
- Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean K. Mansour et al. 10.1029/2021JD036355
Latest update: 19 Apr 2024
Short summary
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system understanding. In this study, we use near-global satellite and reanalysis data sets to predict marine liquid-water clouds by means of artificial neural networks. We show that on the system scale, lower-tropospheric stability and boundary layer height are the main determinants of liquid-water clouds. Aerosols show the expected impact on clouds but are less relevant than some meteorological factors.
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system...
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