Nidheesh A. G., Lengaigne Matthieu, Vialard Jérôme, Izumo Takeshi, Unnikrishnan A. S., Krishnan R. (2019). Natural decadal sea-level variability in the Indian Ocean : lessons from CMIP models. Climate Dynamics, 53 (9-10), p. 5653-5673. ISSN 0930-7575.
Titre du document
Natural decadal sea-level variability in the Indian Ocean : lessons from CMIP models
Nidheesh A. G., Lengaigne Matthieu, Vialard Jérôme, Izumo Takeshi, Unnikrishnan A. S., Krishnan R.
Source
Climate Dynamics, 2019,
53 (9-10), p. 5653-5673 ISSN 0930-7575
Indian Ocean decadal sea-level variability is an active research area, with many unresolved questions due to inadequate observational coverage. In this study, we analyse 26 Coupled Model Intercomparison Project (CMIP) pre-industrial simulations and isolate two consistent modes of Indian Ocean variability, which collectively explain about 50% of the total decadal sea-level variance. With opposite sea-level signals in the southwestern and eastern Indian Ocean, the first mode is related to decadal modulation of the Indian Ocean Dipole (DIOD) and equatorial wind-driven dynamics. Though IOD is more independent of the El Nino-Southern Oscillation (ENSO) at decadal (r similar to 0.4) than interannual (r similar to 0.6) timescales, the DIOD-ENSO co-variability yields sea-level signals along the west coast of Australia, transmitted from the western Pacific via the Indonesian Throughflow. The second mode encompasses variability in the south Indian Ocean (SIODV), exhibiting a broad monopolar sea-level pattern east of Madagascar. In about half of the models, the SIODV is largely independent from DIOD (and decadal ENSO) and driven by south Indian Ocean wind-stress curl associated with meridional shifts in the Mascarene High (MH). In the other models, the SIODV lags the DIOD about 3 years. In those models, in addition to MH forcing, the DIOD-related alongshore wind stress off the northwest Australian coast triggers Rossby waves that also contribute to the SIODV, further west. The DIOD and MH forcing are mutually independent (r similar to 0.2). The results are broadly consistent with sea-level variations derived from the short altimeter data, despite an underestimation of the Oceanic bridge signals in CMIP models.
