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Mean-state biases and interannual variability affect perceived sensitivities of the Madden-Julian oscillation to air-sea coupling

Klingaman, N. P. ORCID: and Demott, C. A. (2020) Mean-state biases and interannual variability affect perceived sensitivities of the Madden-Julian oscillation to air-sea coupling. Journal of Advances in Modeling Earth Systems, 12 (2). e2019MS001799. ISSN 1942-2466

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To link to this item DOI: 10.1029/2019MS001799


Atmosphere–ocean feedbacks often improve the Madden–Julian oscillation (MJO) in climate models, but these improvements are balanced by mean-state biases that can degrade the MJO through changing the basic state on which the MJO operates. The Super-Parameterized Community Atmospheric Model (SPCAM3) produces perhaps the best representation of the MJO among contemporary models, which improves further in a coupled configuration (SPCCSM3) despite considerable mean-state biases in tropical sea-surface temperatures and rainfall. We implement an atmosphere–ocean-mixed-layer configuration of SPCAM3 (SPCAM3-KPP) and use a flux-correction technique to isolate the effects of coupling and mean-state biases on the MJO. When constrained to the observed ocean mean state, air–sea coupling does not substantially alter the MJO in SPCAM3, in contrast to previous studies. When constrained to the SPCCSM ocean mean state, SPCAM3-KPP fails to produce an MJO, in stark contrast to the strong MJO in SPCCSM3. Further KPP simulations demonstrate that the MJO in SPCCSM3 arises from an overly strong sensitivity to El Niño–Southern Oscillation (ENSO) events. Our results show that simulated inter-annual variability and coupled-model mean-state biases affect the perceived response of the MJO to coupling. This is particularly concerning in the context of internal variability in coupled models, as many MJO sensitivity studies in coupled models use relatively short (20–50 year) simulations that undersample interannual–decadal variability. Diagnosing the effects of coupling on the MJO requires simulations that carefully control for mean-state biases and interannual variability.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:88035
Publisher:American Geophysical Union


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