Depiction of the Madden-Julian Oscillation in the NCAR Community Earth System Model Coupled Data Assimilation System

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Thursday, 6 February 2014: 9:00 AM
Room C203 (The Georgia World Congress Center )
Abhishek Chatterjee, NCAR, Boulder, CO; and J. L. Anderson, N. Collins, G. Danabasoglu, T. J. Hoar, A. R. Karspeck, M. W. Moncrieff, K. D. Raeder, and J. J. Tribbia

The Madden-Julian oscillation is the dominant component of sub-seasonal variability in the tropical atmosphere. Characterized by active air-sea coupling, the MJO activity is commonly observed as "eastward propagating, equatorially trapped, baroclinic oscillations in the tropical wind field". Accurate and precise simulation of MJO features are becoming increasingly relevant due to their significant roles in modulating ocean momentum and heat exchanges as well as influencing a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather). Current global circulation models exhibit considerable shortcomings in capturing this phenomena, however, which can be attributed to a variety of factors ranging from the treatment of cumulus convection, treatment of air-sea interactions, proper phasing and representation of surface fluxes, initialization conditions, fidelity of the atmospheric mean-state, or combination thereof.

We present a quantitative evaluation of the simulated MJO within the context of a coupled ocean-atmosphere-land data assimilation framework (multi-component CDA), which has been developed recently 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 assimilates data into each of the respective ocean/atmosphere/land model components during the assimilation step followed by an exchange of the information between the model components during the forecast step. Note that this is an advancement over existing prototypes of coupled data assimilation systems worldwide, which typically tend to assimilate observations only in one of the model components even though the forecast step may be coupled (i.e., single-component CDA), thereby limiting the impact of observations across the air-sea interface. We expect the more realistic treatment of air–sea interactions and improvements to the model mean state in the coupled assimilation to improve 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. Ensembles of one-year long experiments are underway with this new system to develop an understanding of how the details of air-sea interaction and/or coupling configuration improve the MJO representation. Standard diagnostics identified by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group will be used to detect the MJO signals and the resultant variability and predictability across the different experiments. We anticipate that the knowledge gained through this study will improve our understanding of not only the MJO mechanisms (and aid its future prediction) but also the capability of CDA systems for long-term climate predictions.

Finally, work is ongoing to expand the multi-component CDA framework to allow for cross-component interactions. In the proposed cross-component framework, all model components are updated simultaneously at each assimilation step, i.e., observations of the atmosphere will immediately update the ocean state as part of the ensemble filter assimilation and vice versa. Depending on the maturity and level of implementation of the cross-component framework, preliminary simulations and comparison with results from the above multi-component framework may be discussed.