Preprints
https://doi.org/10.5194/egusphere-2024-828
https://doi.org/10.5194/egusphere-2024-828
08 Apr 2024
 | 08 Apr 2024
Status: this preprint is open for discussion.

iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds

Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright

Abstract. Recent field campaigns and advances in observational techniques have yielded a wealth of observations of stable water isotopes in the atmosphere, but the heirarchy of isotope-enabled models is not well-placed to leverage these observation for improving constraints on parameterized physics in global models. Here, we introduce the isotope-enabled Python Cloud Large-Eddy Simulation model (iPyCLES) for mixed-phase clouds. Isotopic tracers are implemented in a parallel passive water cycle and experience all processes and phase changes that affect the model's prognostic total water variable. Isotopic fractionation occurs during cloud and precipitation processes as well as surface evaporation, with facilities for applying external forcing. In addition to isotopic tracers, we extend the two-moment warm cloud microphysics scheme to enable prognostic simulation of cloud liquid water and ice while eliminating dependence on saturation adjustment. Relative to a one-moment mixed-phase scheme with saturation adjustment, the new microphysical scheme yields substantial benefits in simulating phase partitioning and isotopic exchange in mixed-phase regions. The LES model is based on an energetically-consistent implementation of the anelastic equations and employs high-order weighted, essentially non-oscillatory numerics, and is therefore theoretically suitable for simulations spanning the gray zone of the convective spectrum. In this initial evaluation, we present the results of test cases for non-precipitating subtropical shallow cumulus, precipitating subtropical shallow cumulus, and precipitating Arctic mixed-phase stratocumulus clouds. The iPyCLES simulations agree well with available observations and previous model simulations in all three cases, with distinct signatures among the cases that highlight the added potential of isotopic tracers. The benefits of the revised microphysics scheme are especially evident in the Arctic mixed-phase cloud test case, with vapor-liquid-ice exchange within the cloud producing a conspicuous peak in deuterium excess near the top of the cloud. As an idealized testbed, the iPyCLES model can bridge gaps between cloud chamber experiments, real-world observations, and global and regional models, allowing information provided by water isotopes to be translated more effectively into observational constraints for cloud and boundary layer parameterizations.

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Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright

Status: open (until 03 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-828', lixian Zhang, 10 Apr 2024 reply
  • RC1: 'Comment on egusphere-2024-828', Anonymous Referee #1, 07 May 2024 reply
  • RC2: 'Comment on egusphere-2024-828', Franziska Aemisegger, 21 May 2024 reply
Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright

Data sets

Data for iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds Zizhan Hu and Jonathon S. Wright https://zenodo.org/doi/10.5281/zenodo.10911096

Model code and software

isotope-enabled Python Cloud Large Eddy Simulation model code Zizhan Hu https://github.com/huzizhan/ipycles/tree/isotopetracer

Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright

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Short summary
Clouds and precipitation are among the most difficult features of the climate system to simulate. Water isotopes provide valuable information about how clouds and precipitation develop and evolve, but most models that simulate water isotopes cannot resolve individual clouds. Here we introduce a new isotope-enabled model, iPyCLES, that simulates liquid and ice clouds on scales of 10 to 100 meters. This model can help to translate isotopic observations into constraints for larger-scale models.