Preprints
https://doi.org/10.5194/egusphere-2026-1153
https://doi.org/10.5194/egusphere-2026-1153
12 Mar 2026
 | 12 Mar 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Phenomena and Processes: A New MJO Diagnostic Framework using Moisture Mode Theory as the Testbed

Chun-Hao Chang, Kai-Chih Tseng, and Eric D. Maloney

Abstract. An unified diagnostic framework is proposed to bridge theoretical, phenomenological, and process-oriented approaches for investigating the Madden-Julian Oscillation (MJO). Building upon a physical theory (moisture mode theory in this study) and linear inverse modeling, the framework links the statistical behavior of observable indices to the underlying physical processes governing column moisture evolution. Applied to the MJO in ERA5 reanalysis and 15 CMIP6 models, the framework reveals that most models simulate an MJO that propagates too slowly eastward across the basins, and decays too rapidly, especially over the Maritime Continent. By projecting model biases in column-integrated water vapor-based MJO indices onto individual terms of the moisture budget, we diagnose the physical origins of their errors. Systematic biases are primarily tied to misrepresented horizontal moisture advection and compensating errors between vertical moisture transport and convective drying, while their relative importance varies across basins. This process-resolved perspective explains the inter-model diversity in MJO simulations and provides a physically interpretable bridge between dynamical theory, model evaluation, and observational constraints—offering a transferable framework for diagnosing variability in other climate phenomena.

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Chun-Hao Chang, Kai-Chih Tseng, and Eric D. Maloney

Status: open (until 07 May 2026)

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Chun-Hao Chang, Kai-Chih Tseng, and Eric D. Maloney
Chun-Hao Chang, Kai-Chih Tseng, and Eric D. Maloney
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Latest update: 12 Mar 2026
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Short summary
This study developed a unified Madden-Julian Oscillation (MJO) diagnostic framework that bridges the gap between two existed types of diagnostics (i.e. phenomenological diagnostics and process-oriented diagnostics). Utilizing this framework, we can attribute simulated MJO biases in general circulation models (GCMs) to specific physical processes.
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