Developing a Plume‐in‐Grid Model for Plume Evolution in the Stratosphere

Abstract

Stratospheric emissions from aircraft or rockets are important sources of chemical perturbations. Small‐radius high‐aspect‐ratio plumes from stratospheric emissions are smaller than global Eulerian models' grid cells. To help global Eulerian models resolve subgrid plumes in the stratosphere, a Lagrangian plume model, comprising a Lagrangian trajectory model and an adaptive‐grid plume model with a sequence of plume cross‐section representations (from a highly resolved 2‐D grid to a simplified 1‐D grid based on a tradeoff between the accuracy and computational cost), is created and embedded into a global Eulerian (i.e., GEOS‐Chem) model to establish a multiscale Plume‐in‐Grid (PiG) model. We compare this PiG model to the GEOS‐Chem model based on a 1‐month simulation of continuous inert tracer emissions by aircraft in the stratosphere. In the PiG results, the final injected tracer is more concentrated and approximately 1/3 of the tracer is at concentrations 2–4 orders of magnitude larger compared to the GEOS‐Chem results. The entropy of injected tracer in the PiG results is 6% lower than the GEOS‐Chem results, indicating less tracer mixing. The total product mass from a hypothetical second‐order process (applied to the injected tracer) in the PiG results is 2 orders of magnitude larger than the GEOS‐Chem results. Increasing the GEOS‐Chem model's horizontal resolution 4‐fold is insufficient to resolve this product difference, while requiring over seven times the computational resources of the PiG model. This paper describes the PiG model framework and parameterization of plume physical processes. Chemical and aerosol processes will be introduced in the future.

Opinion: The Scientific and Community-Building Roles of the Geoengineering Model Intercomparison Project (GeoMIP) - Past, Present, and Future

“…This is uncharacteristic of diffusion processes or mixing forced by an aircraft jet. Additionally, many relevant that can be embedded in ESM grid boxes (Sun et al, 2022) is underway, but it needs further testing and has not been widely implemented, so using this capability in GeoMIP is premature. Dynamical downscaling, either using a regional model or a regionally refined global model, is a standard technique for gaining finer-scale information.…”

Exploring ship track spreading rates with a physics-informed Langevin particle parameterization

“…Previous attempts to constrain ship track spreading rates from satellite images aimed to estimate average lateral plume spreading behavior (Durkee et al, 2000;Patel and Shand, 2022), but assuming a constant rate of plume spreading may result in subgrid plume-fraction imprecision that leads to compounding, non-linear errors on the resolved-scale properties as prior LES modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers (Prabhakaran et al, 2023), or wind shear (Berner et al, 2015). Recent efforts to represent subgrid plumes, such as the Plume-in-grid (PIG) method with adaptive grids (Sun et al, 2022), allow for time-dependent changes in the horizontal spreading rate as a function of wind shear but require grid refinements in the presence of plumes. The associated increase in computational demand may be a bottleneck for the assessment of large-scale injection strategies.…”

Reply on RC2

“…Previous attempts to constrain ship track spreading rates from satellite images aimed to estimate average lateral plume spreading behavior (Durkee et al, 2000;Patel and Shand, 2022), but assuming a constant rate of plume spreading may result in subgrid plume-fraction imprecision that leads to compounding, non-linear errors on the resolved-scale properties as prior LES modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers (Prabhakaran et al, 2023), or wind shear (Berner et al, 2015). Recent efforts to represent subgrid plumes, such as the Plume-in-grid (PIG) method with adaptive grids (Sun et al, 2022), allow for time-dependent changes in the horizontal spreading rate as a function of wind shear but require grid refinements in the presence of plumes. The associated increase in computational demand may be a bottleneck for the assessment of large-scale injection strategies.…”