Abstract
We study the forced response of the teleconnection between the El Niño–Southern Oscillation (ENSO) and the Indian summer monsoon (IM) in the Max Planck Institute Grand Ensemble, a set of Earth system ensemble simulations under historical and Representative Concentration Pathway (RCP) forcing. The forced response of the teleconnection, or a characteristic of it, is defined as the time dependence of a correlation coefficient evaluated over the ensemble. We consider the temporal variability of spatial averages and that with respect to dominant spatial modes in the sense of Maximal Covariance Analysis, Canonical Correlation Analysis and Empirical Orthogonal Function analysis across the ensemble. A further representation of the teleconnection that we define here takes the point of view of the predictability of the spatiotemporal variability of the Indian summer monsoon. We find that the strengthening of the ENSO-IM teleconnection is robustly or consistently featured in view of various teleconnection representations, whether sea surface temperature (SST) or sea level pressure (SLP) is used to characterize ENSO, and both in the historical period and under the RCP8.5 forcing scenario. It is found to be associated dominantly with the principal mode of ENSO variability. Concerning representations that involve an autonomous characterisation of the Pacific, in terms of a linear regression model, the main contributor to the strengthening is the regression coefficient, which can outcompete even a declining ENSO variability when it is represented by SLP. We also find that the forced change of the teleconnection is typically nonlinear by 1) formally rejecting the hypothesis that ergodicity holds, i.e., that expected values of temporal correlation coefficients with respect to the ensemble equal the ensemble-wise correlation coefficient itself, and also showing that 2) the trivial contributions of the forced changes in means and standard deviations are insignificant here. We also provide, in terms of the test statistics, global maps of the degree of nonlinearity/nonergodicity of the forced change of the teleconnection between local precipitation and ENSO.
Highlights
ENSO teleconnections are widely studied, but their changes resulting from external forcing, such as an increasing concentration of greenhouse gases, remain to be further explored and understood. Power and Delage (2018) provide a multi-model assessment of ENSO-precipitation teleconnection changes based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5) archive
Nonlinearity is implied in our case by a nonmonotonic change in the ENSO variability: after a seemingly monotonic change, a decline follows in the second half of the 21st c. under RCP8.5.4 Given that the ensemble size is finite and not so large from the point of view of teleconnections, we develop here a statistical test whereby we can detect nonergodicity, and, subsequently, map out regions of the world where such a nonergodicity can be detected in the MPI-GE in the context of the relationship of local precipitation with ENSO
We show in the Supplementary Material that forced changes of spatial patterns do not necessarily have robust implications for the change of the teleconnection strength: r of Canonical Correlation Analysis (CCA)-all-Indian summer monsoon rainfall (AISMR) may be dominated by r of EOF1–AISMR even if the spatial pattern of CCA-AISMR in the Pacific has a large weight from EOFi, i > 1, thanks to the small variability that is associated with that given EOFi compared to EOF1
Summary
ENSO teleconnections are widely studied, but their changes resulting from external forcing, such as an increasing concentration of greenhouse gases, remain to be further explored and understood. Power and Delage (2018) provide a multi-model assessment of ENSO-precipitation teleconnection changes based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5) archive. In a previous publication (Bódai et al, 2020b) we examined the forced response of the ENSO-Indian monsoon teleconnection in the Max Planck Institute Grand Ensemble (MPI-GE) (Maher et al, 2019) representing the monsoon by the average JJAS precipitation over India and the ENSO by either the gridpointand SLP-based SOI (Southern Oscillation Index), or the areal mean of the SST in some extended area in the Equatorial Pacific. Standard choices for the latter are the Niño, Niño, and Niño3.4 regions.
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