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Stochastic and mesoscopic models for tropical convection
Andrew J Majda1, Boualem Khouider
1Courant Institute of Mathematical Sciences and Center for Atmosphere and Ocean Sciences, New York University, New York, NY 10012, USA. jonjon@cims.nyu.edu
Summary
This study introduces novel stochastic and mesoscopic models to better represent tropical convection, adapting statistical physics tools to capture complex atmospheric phenomena like convective inhibition and radiative equilibria.
Area of Science:
- Atmospheric Science
- Statistical Physics
- Mesoscopic Physics
Background:
- Tropical convection involves complex, unresolved processes crucial for climate modeling.
- Current parameterization schemes struggle to accurately capture features like convective inhibition and cloud feedbacks.
Purpose of the Study:
- To develop a new stochastic and mesoscopic modeling approach for tropical convection.
- To adapt tools from statistical physics and materials science for improved atmospheric modeling.
Main Methods:
- Utilized a "heat bath" model with stochastic spin flips and an external potential for convective inhibition.
- Developed both stochastic and deterministic mesoscopic parameterizations for tropical convection.
- Linked order parameter values to vertical mass flux in deep convection.
Main Results:
- Deterministic mesoscopic models revealed new phenomena, including multiple radiative equilibria.
- Initial numerical experiments qualitatively reproduced key features of convectively coupled tropical waves.
- The stochastic modeling strategy shows potential for capturing cloud-radiation feedbacks.
Conclusions:
- The proposed stochastic and mesoscopic modeling strategy offers a promising new framework for representing tropical convection.
- This approach enhances the modeling of unresolved convective processes and their impact on climate.
- Further research can explore its application to other complex tropical convection phenomena.