Ocean–atmosphere coupling and the boreal winter MJO |
| |
| Authors: | Hye-Mi Kim Carlos D Hoyos Peter J Webster In-Sik Kang |
| |
| Institution: | (1) School of Earth and Atmospheric Science, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA 30332, USA;(2) School of Earth and Environmental Science, Seoul National University, Seoul, Korea |
| |
| Abstract: | The influence of ocean–atmosphere coupling on the simulation and prediction of the boreal winter Madden–Julian Oscillation
(MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model
(AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial
extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total
of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence
of coupled ocean–atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses
more realistic amplitude in the CGCM than in AGCM. In general, the ocean–atmosphere coupling acts to improve the simulation
of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST
over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship
between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship.
As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction
skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate
MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17–18
(15–16) days in the AGCM and 18–19 (16–17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and
AGCM. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
