The Madden-Julian Oscillation in a Coupled Data Assimilation System

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Wednesday, 7 January 2015: 4:15 PM
224A (Phoenix Convention Center - West and North Buildings)
Abhishek Chatterjee, NCAR, Boulder, CO; and J. Anderson, M. W. Moncrieff, N. Collins, G. Danabasoglu, T. J. Hoar, A. R. Karspeck, R. B. Neale, K. D. Raeder, and J. J. Tribbia

The Madden-Julian Oscillation (MJO) is the dominant component of sub-seasonal variability in the tropical atmosphere. Accurate and precise simulation of MJO features are becoming increasingly relevant due to their significant roles in modulating atmospheric and oceanic circulation as well as influencing a wide range of weather and climate phenomena (for e.g., tropical and extratropical precipitation, surface temperature around the global tropics and subtropics, the ENSO cycle). Current general circulation models exhibit considerable shortcoming in accurate and precise simulation of the MJO. This has been attributed to a variety of factors including deficient treatment of cumulus convection, improper phasing and representation of surface fluxes, an inaccurate model mean-state, or a combination thereof.

We present a quantitative evaluation of the simulated MJO in analyses produced with a coupled data assimilation (CDA) framework developed at the National Center for Atmosphere Research. This system is based on the Community Earth System Model (CESM; previously known as the Community Climate System Model -CCSM) interfaced to a community facility for ensemble data assimilation (Data Assimilation Research Testbed – DART). The system (multi-component CDA) assimilates data into each of the respective ocean/atmosphere/land model components during the assimilation step followed by an exchange of information between the model components during the forecast step. Note that this is an advance over many existing prototypes of coupled data assimilation systems, which typically assimilate observations only in one of the model components (i.e., single-component CDA). Even though the forecast step is coupled, the single-component setup results in limited impacts of the observations across the air-sea interface.

We will show that the more realistic treatment of air–sea interactions and improvements to the model mean state in the multi-component CDA recover many aspects of MJO representation, from its space–time structure and propagation to the governing relationships between precipitation and sea surface temperature on intra-seasonal scales. Standard qualitative and process-based diagnostics identified by the MJO Task Force (currently under the auspices of the Working Group on Numerical Experimentation) have been used to detect the MJO signals across a suite of coupled model experiments involving both multi-component and single-component DA experiments as well as a free run of the coupled CESM model (i.e., CMIP5 style without data assimilation). Short predictability experiments during the boreal winter will be used to demonstrate that the decay rates of the MJO convective anomalies are slower in the multi-component CDA system, which allows it to retain the MJO dynamics for a longer period. Finally, we will discuss possible mechanisms by which the multi-component CDA is able to simulate the MJO dynamics better. We anticipate that the knowledge gained through this study will not only enhance our understanding of the role of air-sea interactions on the MJO but also highlight the capability of coupled data assimilation systems for related tropical intraseasonal variability predictions.