Intraseasonal Equatorial Kelvin and Rossby Waves in Modern AI-ML Models
Shrutee Jalan, Jai Sukhatme

TL;DR
This study evaluates how well modern AI-ML weather models replicate large-scale equatorial Kelvin and Rossby waves, revealing strengths in Kelvin wave structure but notable deficiencies in Rossby wave representation and field consistency.
Contribution
It provides a comprehensive comparison of multiple AI-ML models' ability to simulate equatorial waves, highlighting specific successes and shortcomings in their structural representations.
Findings
Models accurately capture Rossby and Kelvin wave signatures in zonal winds.
Kelvin wave composites show correct convergence patterns and phase relations.
Models struggle with Rossby wave vertical structure and field inconsistencies.
Abstract
We examine the structure of large-scale convectively coupled Kelvin and Rossby waves in a suite of modern AI-ML models. In particular, multiple runs of PanguWeather, GraphCast, FourCastNet and Aurora are performed to assess the structure of the aforementioned waves. Wavenumber-frequency diagrams of zonal winds from all models show a clear signature of Rossby and Kelvin waves with equivalent depths that are in accord with observations and reanalysis. Composites of Kelvin waves show correct lower and upper troposphere horizontal convergence patterns, vertical tilts in temperature, humidity and vertical velocity as well as the phase relation between temperature and vertical velocity anomalies. Though, differences between models are notable such as smaller vertical tilts and incorrect surface temperature anomalies in GraphCast and relatively weak convergent flows in PanguWeather. The models…
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Taxonomy
TopicsOceanographic and Atmospheric Processes · Climate variability and models · Meteorological Phenomena and Simulations
