Articles | Volume 16, issue 4
https://doi.org/10.5194/acp-16-2611-2016
© Author(s) 2016. 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-16-2611-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Understanding cirrus ice crystal number variability for different heterogeneous ice nucleation spectra
Sylvia C. Sullivan
Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Ricardo Morales Betancourt
Department of Civil and Environmental Engineering, University of Los Andes, Bogotá, Colombia
Donifan Barahona
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Athanasios Nenes
CORRESPONDING AUTHOR
Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Department of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
ICE-HT, Foundation for Research and Technology, Hellas, 26504 Patras, Greece
IERSD, National Observatory of Athens, Palea Penteli, 15236, Greece
Viewed
Total article views: 3,422 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Aug 2015)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
2,190 | 1,141 | 91 | 3,422 | 375 | 88 | 83 |
- HTML: 2,190
- PDF: 1,141
- XML: 91
- Total: 3,422
- Supplement: 375
- BibTeX: 88
- EndNote: 83
Total article views: 2,624 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 03 Mar 2016)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
1,776 | 783 | 65 | 2,624 | 238 | 66 | 66 |
- HTML: 1,776
- PDF: 783
- XML: 65
- Total: 2,624
- Supplement: 238
- BibTeX: 66
- EndNote: 66
Total article views: 798 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Aug 2015)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
414 | 358 | 26 | 798 | 137 | 22 | 17 |
- HTML: 414
- PDF: 358
- XML: 26
- Total: 798
- Supplement: 137
- BibTeX: 22
- EndNote: 17
Cited
12 citations as recorded by crossref.
- Cold cloud microphysical process rates in a global chemistry–climate model S. Bacer et al. 10.5194/acp-21-1485-2021
- Ice microphysical processes exert a strong control on the simulated radiative energy budget in the tropics S. Sullivan & A. Voigt 10.1038/s43247-021-00206-7
- Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53) S. Bacer et al. 10.5194/gmd-11-4021-2018
- How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00? S. Sullivan et al. 10.5194/gmd-16-3535-2023
- Role of updraft velocity in temporal variability of global cloud hydrometeor number S. Sullivan et al. 10.1073/pnas.1514039113
- Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system J. Seinfeld et al. 10.1073/pnas.1514043113
- Global Radiative Impacts of Black Carbon Acting as Ice Nucleating Particles Z. McGraw et al. 10.1029/2020GL089056
- Fluorescence lidar observations of wildfire smoke inside cirrus: a contribution to smoke–cirrus interaction research I. Veselovskii et al. 10.5194/acp-22-5209-2022
- The effect of secondary ice production parameterization on the simulation of a cold frontal rainband S. Sullivan et al. 10.5194/acp-18-16461-2018
- Comparison of Modeled and Measured Ice Nucleating Particle Composition in a Cirrus Cloud R. Ullrich et al. 10.1175/JAS-D-18-0034.1
- Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements E. Marinou et al. 10.5194/acp-19-11315-2019
- Airborne observations of the microphysical structure of two contrasting cirrus clouds S. O'Shea et al. 10.1002/2016JD025278
11 citations as recorded by crossref.
- Cold cloud microphysical process rates in a global chemistry–climate model S. Bacer et al. 10.5194/acp-21-1485-2021
- Ice microphysical processes exert a strong control on the simulated radiative energy budget in the tropics S. Sullivan & A. Voigt 10.1038/s43247-021-00206-7
- Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53) S. Bacer et al. 10.5194/gmd-11-4021-2018
- How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00? S. Sullivan et al. 10.5194/gmd-16-3535-2023
- Role of updraft velocity in temporal variability of global cloud hydrometeor number S. Sullivan et al. 10.1073/pnas.1514039113
- Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system J. Seinfeld et al. 10.1073/pnas.1514043113
- Global Radiative Impacts of Black Carbon Acting as Ice Nucleating Particles Z. McGraw et al. 10.1029/2020GL089056
- Fluorescence lidar observations of wildfire smoke inside cirrus: a contribution to smoke–cirrus interaction research I. Veselovskii et al. 10.5194/acp-22-5209-2022
- The effect of secondary ice production parameterization on the simulation of a cold frontal rainband S. Sullivan et al. 10.5194/acp-18-16461-2018
- Comparison of Modeled and Measured Ice Nucleating Particle Composition in a Cirrus Cloud R. Ullrich et al. 10.1175/JAS-D-18-0034.1
- Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements E. Marinou et al. 10.5194/acp-19-11315-2019
1 citations as recorded by crossref.
Saved (preprint)
Latest update: 23 Nov 2024
Short summary
We use the adjoint model of a cirrus parameterization to quantify sources of crystal variability for various ice-nucleating spectra and output from CAM5.
The sensitivities can be directly linked to nucleation regime and
efficiency of various INP.
The lab-based spectrum calculates much higher INP efficiencies than field-based ones, owing to aerosol surface properties.
The sensitivity to temperature tends to be low, due to the compensating effects of temperature on INP spectrum parameters.
We use the adjoint model of a cirrus parameterization to quantify sources of crystal variability...
Altmetrics
Final-revised paper
Preprint