The “winter predictability barrier” for IOD events and its error growth dynamics: Results from a fully coupled GCM

Abstract

Within the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) coupled model, we find that the winter predictability barrier (WPB) exists in both the growing and decaying phases of positive Indian Ocean dipole (IOD) events, due to the effects of initial errors. The physical mechanism of the WPB, in which the initial errors show a significant seasonal-dependent evolution with the fastest error growth in winter, is explored from the dynamical and thermodynamical viewpoints. In terms of dynamics, in the growing phase of pure positive IOD events, the vertical temperature advection associated with the reference state IOD events plays a dominant role in advancing the fastest error growth in winter; in terms of thermodynamics, the latent heat flux error and the shortwave radiation error lead to the fastest error growth in winter and favor the occurrence of the WPB. In the decaying phase of pure positive IOD events, the occurrence of the WPB is mainly due to the latent heat flux error since the dynamics play an insignificant role in advancing the fast error growth in winter. For positive IOD events accompanied by El Niño-Southern Oscillation (ENSO), the physical mechanism of the WPB is similar to that for pure positive IOD events in both the growing and decaying phases, except that the shortwave radiation error has a different effect on the error growth in winter, which may be closely related to the perturbed atmospheric circulation in the tropical Indian Ocean associated with ENSO.

Summer Predictability Barrier of Indian Ocean Dipole Events and Corresponding Error Growth Dynamics

“…The WPB for IOD predictions was reported by previous studies (Feng & Duan, ; Feng et al, ; Luo et al, ). Feng et al () showed that the seasonality of vertical temperature advection, latent heat flux, and shortwave radiation favor the season‐dependent evolution of prediction errors for IOD events.…”

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

“…Generally speaking, these additional observations would be assimilated by a data assimilation system to provide the numerical model a more reliable initial state. The idea of the target observation has been applied to some weather and climate events forecasting, such as Fronts and Atlantic Storm-Track Experiment (FASTEX; Synder 1996), North Pacific Experiment (NORPEX; Langland et al 1999), tropical cyclone (TC; Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011), Kuroshio large meander (KLM; Wang et al 2013), India Ocean Dipole (IOD; Feng et al 2014), ENSO Hu and Duan 2016), etc.…”

“…Recently, the nonlinear approach of CNOP has been successfully used to determine the sensitive areas for targeting in TC, IOD, KLM forecasting (Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011;Feng et al 2014;Wang et al 2013). For the TC forecasting, the CNOP is computed case by case and the sensitive areas for targeting observation are case dependent (Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011).…”

Impacts of the IOD-associated temperature and salinity anomalies on the intermittent equatorial undercurrent anomalies

“…During the positive IOD events, strong negative subsurface temperature anomalies exist in the eastern Indian Ocean, with negative surface salinity anomalies in the central and eastern Indian Ocean, resulting in a large pressure gradient force to drive EUC during the August-November. Further, 2011;Ummenhofer et al 2009;Hashizume et al 2012;Feng et al 2014;Guo et al 2015;Yao et al 2016).…”