School of Marine and Atmospheric Sciences Stony Brook University Stony Brook NY USA
Department of Meteorology and Atmospheric Science Pennsylvania State University University Park PA USA, Now at Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USA
Department of Atmospheric Sciences University of Illinois at Urbana–Champaign Urbana IL USA
NASA Goddard Institute for Space Studies New York NY USA
Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.
Knopf, Daniel A., et al. "A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed‐Phase Arctic Stratus Cloud Layer." Journal of Advances in Modeling Earth Systems, vol. 15, no. 10, Oct. 2023. https://doi.org/10.1029/2023MS003663
Knopf, Daniel A., Silber, Israel, Riemer, Nicole, Fridlind, Ann M., & Ackerman, Andrew S. (2023). A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed‐Phase Arctic Stratus Cloud Layer. Journal of Advances in Modeling Earth Systems, 15(10). https://doi.org/10.1029/2023MS003663
Knopf, Daniel A., Silber, Israel, Riemer, Nicole, et al., "A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed‐Phase Arctic Stratus Cloud Layer," Journal of Advances in Modeling Earth Systems 15, no. 10 (2023), https://doi.org/10.1029/2023MS003663
@article{osti_2067645,
author = {Knopf, Daniel A. and Silber, Israel and Riemer, Nicole and Fridlind, Ann M. and Ackerman, Andrew S.},
title = {A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed‐Phase Arctic Stratus Cloud Layer},
annote = {Abstract Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.},
doi = {10.1029/2023MS003663},
url = {https://www.osti.gov/biblio/2067645},
journal = {Journal of Advances in Modeling Earth Systems},
issn = {ISSN 1942-2466},
number = {10},
volume = {15},
place = {United States},
publisher = {American Geophysical Union (AGU)},
year = {2023},
month = {10}}