Journal of Advances in Modeling Earth Systems (Oct 2024)

Enhancing Global Simulation of Smoke Injection Height for Intense Pyro‐Convection Through Coupling an Improved One‐Dimensional Plume Rise Model in CAM‐chem

  • Chaoqun Ma,
  • Ruijing Ni,
  • Hang Su,
  • Yafang Cheng

DOI
https://doi.org/10.1029/2023MS004127
Journal volume & issue
Vol. 16, no. 10
pp. n/a – n/a

Abstract

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Abstract The impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.

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