South Atlantic Forced Multidecadal Teleconnection to the Midlatitude South Indian Ocean

Abstract

Sea surface temperature (SST) in the midlatitude south Indian Ocean (MSIO) exhibits prominent multidecadal fluctuations that have profound climate impacts for regions around the Indian Ocean. Observational analysis suggests that these multidecadal fluctuations can be explained by remote forcing from South Atlantic multidecadal variability. A suite of Atlantic Pacemaker experiments performs well in reproducing the observed MSIO SST multidecadal variation and its association with the South Atlantic. This transbasin covariability can be described by an atmospheric bridge mechanism, in which South Atlantic multidecadal variability‐related SST warming weakens regional meridional circulation over the South Atlantic, suppressing convection over tropical Africa. The reduced diabatic heating then drives a cold atmospheric Kelvin wave that penetrates into the tropical Indian Ocean, leading to an anomalous cyclonic circulation there that weakens the westerlies over the MSIO and subsequently triggers the warming of MSIO. This initial warming is further amplified by local SST‐water vapor positive feedback over the MSIO.

Reconciling Roles of External Forcing and Internal Variability in Indian Ocean Decadal Variability Since 1920

“…However, remote influences from other regions may also play a role (Krishnamurthy & Krishnamurthy, 2016). For example, the Southern Ocean (Zhang et al., 2021), South Atlantic (Xue et al., 2018), extratropical Pacific (Krishnamurthy & Krishnamurthy, 2016), and the North Atlantic Oscillation (Xie et al., 2021) may contribute to the Indian Ocean multidecadal variability. We also notice that current climate models (e.g., CESM1) tend to overestimate the magnitude of the SST variability in the southeastern Indian Ocean, which implies that the models may not realistically simulate the decadal to multidecadal variations (Kravtsov et al., 2018; Mann et al., 2020) or inter‐basin teleconnection (Cai et al., 2019; Li et al., 2016) for the Indian Ocean.…”

Multidecadal variation of northern hemisphere summer monsoon forced by the SST inter-hemispheric dipole

“…where N is the sample size, ρ XX (j) and ρ YY (j) denote the autocorrelations of time series X and Y at time lag j, respectively (Sun et al 2017, Xue et al 2018b, 2018c.…”

ENSO–IOD Inter‐Basin Connection Is Controlled by the Atlantic Multidecadal Oscillation

“…The statistical significance for decadal‐scale linear regression and correlation is assessed based on a two‐tailed Student's t ‐test using the effective number of degrees of freedom N eff , which is given by the following approximation: $$$\frac{1}{{N}^{\text{eff}}}\approx \frac{1}{N}+\frac{2}{N}\sum\limits _{j=1}^{N}\frac{N-j}{N}{\rho }_{XX}(j){\rho }_{YY}(j),$$$ where N is the sample size, $${\rho }_{XX}(j)$$ and $${\rho }_{YY}(j)$$ separately represent the autocorrelations of time series $$X$$ and $$Y$$ at time lag $$j$$ (Pyper & Peterman, 1998; Xue et al., 2018, 2022).…”