SDM meets Isotopes
Modelling isotopic signatures in precipitation using particle-based cloud microphysics
We model the evolution of isotopic composition of water droplets and ambient vapour. Our focus is on stable water isotopologues: H2O, HDO, H218O, H217O. Among considered processes are the diffusional and collisional growth/breakage of droplets and the concurrent isotopic kinetic fractionation.
We use the Lagrangian particle-based approach for modelling cloud microphysics. Particle-resolved formulation, using the so-called super-droplet Monte-Carlo method, offers a uniquely suitable framework for simulating the isotopic fractionation in clouds and precipitation. This is due to the ability to represent, with high fidelity, the dynamics of particles in a multidimensional space of attributes.
Among the attributes of super-particles are such extensive quantities as mass of water per droplet, and the multiplicity, i.e. the number of real-world particles represented by a computational super-particle. In this project, the set of attributes is extended to include: moles of deuterium (D), oxygen-17 (17O), and oxygen-18 (18O). The evolution in time of a droplet water isotopic composition is driven by kinetically limited diffusion processes (condensation, evaporation, deposition, sublimation), and is further modulated by collision-triggered coalescence and breakage. Isotopic composition in the vapour changes due to advection and diffusional growth. It is represented by molality in dry air, which is the number of the moles of a given heavy isotope over the mass of dry air. This parameter is chosen to be consistent with the advection logic of dynamical cores advecting mixing ratios (i.e. with the dry-air flow assumed to act as a carrier flow for trace constituents).
Defining and prototyping a new model of the above phenomena, we use PySDM - a Python particle-based cloud microphysics package developed and maintained in our group.
Our objective is to create a novel tool enabling:
- usage of isotopic composition data to constrain the unknown aspects of cloud microphysical processes such as evolution of supersaturation, turbulent mixing, and formation of hydrometeors;
- improvements in methods for inferring atmospheric thermodynamic state from meteoric water isotopic composition data (e.g., from ice cores).
Project publications
🎓 Student project opportunities
- Extending PySDM to explicitly resolve droplet heat budget and thus capture non-equilibrium drop temperature (as an alternative to the current equilibrium approximation).
Acknowledgement
This project received support from Polish National Science Centre (SONATA grant no. 2020/39/D/ST10/01220) and the Excellence Initiative – Research University programme at the AGH University of Krakow (priority research area: Water-Energy-Climate).
Contributors from our team
Agnieszka Żaba
