The NOW regional coupled model: Application to the tropical Indian Ocean climate and tropical cyclone activity

Abstract

This paper presents the NOW regional coupled ocean-atmosphere model built from the NEMO ocean and WRF atmospheric numerical models. This model is applied to the tropical Indian Ocean, with the oceanic and atmospheric components sharing a common 1 =4 horizontal grid. Long experiments are performed over the 1990-2009 period using the Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) cumulus parameterizations. Both simulations produce a realistic distribution of seasonal rainfall and a realistic northward seasonal migration of monsoon rainfall over the Indian subcontinent. At subseasonal time scales, the model reasonably reproduces summer monsoon active and break phases, although with underestimated rainfall and surface wind signals. Its relatively high resolution results in realistic spatial and seasonal distributions of tropical cyclones, but it fails to reproduce the strongest observed cyclone categories. At interannual time scales, the model reproduces the observed variability associated with the Indian Ocean Dipole (IOD) and the delayed basin-wide warming/cooling induced by the El Niño Southern Oscillation (ENSO). The timing of IOD occurrence in the model generally matches that of the observed events, confirming the influence of ENSO on the IOD development (through the effect of lateral boundary conditions in our simulations). Although the KF and BMJ simulations share a lot in common, KF strongly overestimates rainfall at all time scales. KF also overestimates the number of simulated cyclones by a factor two, while simulating stronger events (up to 55 m s 21) compared to BMJ (up to 40 m s 21). These results could be related to an overly active cumulus parameterization in KF.

Premonsoon/Postmonsoon Bay of Bengal Tropical Cyclones Intensity: Role of Air‐Sea Coupling and Large‐Scale Background State

“…While the overestimated TC count in our model strengthen the statistical significance of our results, it also suggests that the cumulus parameterization we use (Kain-Fritsch;Kain, 2004) may be overly active and/or too easily triggered by the environmental forcing when the atmospheric column is destabilized, as already discussed in Samson et al (2014). While the overestimated TC count in our model strengthen the statistical significance of our results, it also suggests that the cumulus parameterization we use (Kain-Fritsch;Kain, 2004) may be overly active and/or too easily triggered by the environmental forcing when the atmospheric column is destabilized, as already discussed in Samson et al (2014).…”

“…TCs from both simulations are tracked using the same methodology as in Samson et al (2014) and Lengaigne et al (2018) using criteria similar to those considered in previous studies (Jourdain et al, 2011;Jullien et al, 2014): TCs from both simulations are tracked using the same methodology as in Samson et al (2014) and Lengaigne et al (2018) using criteria similar to those considered in previous studies (Jourdain et al, 2011;Jullien et al, 2014):…”

Distinct Influence of Air–Sea Interactions Mediated by Mesoscale Sea Surface Temperature and Surface Current in the Arabian Sea

“…12i reaches that of the model domain (i.e., domain wavenumber 1), indicating that the emerging response is influenced by lateral boundary forcing data, which are identical in CTL and noU tot . Therefore, a more robust assessment of the low-frequency rectification effect should be made with larger-scale models (Seo et al 2009;Ratnam et al 2009;Samson et al 2014) or with global simulations (Izumo et al 2008). Given the highly nonlinear nature of the Findlater Jet, the downstream atmospheric response could also be model dependent.…”

Roles of land surface albedo and horizontal resolution on the Indian summer monsoon biases in a coupled ocean–atmosphere tropical-channel model

“…This allows to distinguish the effects of local versus remote 84 forcings on the ISM (Seo et al 2009, Samala et al 2013, to test the sensitivity of the 85 simulated ISM to different physical parameterizations (Mukhopadhyay et al 2010, 86 Srinivas et al 2013, Samson et al 2014 or to prescribe the orography in a more 87 realistic way (Ma et al 2014). But despite those specificities, significant biases still 88 exist in terms of precipitation and surface temperature (Lucas-Picher et al 2011), 89 which suggest that high resolution is not the unique missing ingredient in order to 90 improve ISM rainfall in current CGCMs and RCMs.…”