Investigating ENSO and its teleconnections under climate change in an ensemble view – a new perspective

Abstract

Abstract. The changes in the El Niño–Southern Oscillation (ENSO) phenomenon and its precipitation-related teleconnections over the globe under climate change are investigated in the Community Earth System Model Large Ensemble from 1950 to 2100. For the investigation, a recently developed ensemble-based method, the snapshot empirical orthogonal function (SEOF) analysis, is used. The instantaneous ENSO pattern is defined as the leading mode of the SEOF analysis carried out at a given time instant over the ensemble. The corresponding principal components (PC1s) characterize the ENSO phases. By considering sea surface temperature (SST) regression maps, we find that the largest changes in the typical amplitude of SST fluctuations occur in the June–July–August–September (JJAS) season, in the Niño3–Niño3.4 (5∘ N–5∘ S, 170–90∘ W; NOAA Climate Prediction Center) region, and the western part of the Pacific Ocean; however, the increase is also considerable along the Equator in December–January–February (DJF). The Niño3 amplitude also shows an increase of about 20 % and 10 % in JJAS and DJF, respectively. The strength of the precipitation-related teleconnections of the ENSO is found to be nonstationary, as well. For example, the anticorrelation with precipitation in Australia in JJAS and the positive correlation in central and northern Africa in DJF are predicted to be more pronounced by the end of the 21th century. Half-year-lagged correlations, aiming to predict precipitation conditions from ENSO phases, are also studied. The Australian and Indonesian precipitation and that of the eastern part of Africa in both JJAS and DJF seem to be well predictable based on the ENSO phase, while the southern Indian precipitation relates to the half-year previous ENSO phase only in DJF. The strength of these connections increases, especially from the African region to the Arabian Peninsula.

Climate change in a conceptual atmosphere–phytoplankton model

“…Such an ensemble approach was shown to be the only method providing reliable statistical predictions in systems with underlying nonpredictable dynamics (since in this class the traditional approach based on a single time series is known to provide seriously biased results). A number of papers illustrate these statements within the physics literature (see, e.g., Romeiras et al, 1990;Lai, 1999;Serquina et al, 2008), as well as in low-order climate models (Chekroun et al, 2011;Bódai et al, 2011;Bódai and Tél, 2012;Bódai et al, 2013;Drótos et al, 2015), in general circulation models (Haszpra and Herein, 2019;Kaszás et al, 2019;Pierini et al, 2018Pierini et al, , 2016Drótos et al, 2017;Herein et al, 2017;Bódai et al, 2020;Haszpra et al, 2020b, a), and also in experimental situations (Vincze, 2016;Vincze et al, 2017).…”

Nonlinear Forced Change and Nonergodicity: The Case of ENSO-Indian Monsoon and Global Precipitation Teleconnections

“…They consider, in particular, ENSO-driven precipitation anomalies in tropical regions around the globe, and assess them jointly with changes of mean precipitation. Haszpra et al (2020a) have evaluated only the trend in the strength of ENSO-precipitation teleconnection, however, not in a multimodel ensemble but the so-called "single model initial condition large ensemble" (SMILE) CESM1-LE (Kay et al, 2015). Working with a SMILE has the advantages that the response to forcing is correctly represented in that model at least (Bódai and Tél, 2012;Drótos et al, 2015;Tél et al, 2019), and seeking a physical interpretation of changes is not faced with confusion at the outset, even if the physics depicted in that model is inaccurate or unrealistic.…”

“…The anomaly can correspond to a simple spatial (mean of a temporal) mean (Bódai et al, 2020b), but also the PC of an EOF concerning variability across the ensemble, called a snapshot EOF (SEOF) (Haszpra et al, 2020c). Haszpra et al (2020a) take the latter approach; however, this can be extended to obtaining anomalies observing the "mutual variability," e.g., in the sense of Maximal Covariance Analysis (MCA) (Storch and Zwiers, 1999) or Canonical Correlation Analysis (CCA) (Storch and Zwiers, 1999;Härdle and Simar, 2007). That is, with an interest in a teleconnection and its forced response, MCA and CCA-just like EOF analysis-can also be pursued concerning the variability across the ensemble, whereby we can refer to these methods as SMCA and SCCA.…”

The Impact of Applying Individually Perturbed Parametrization Tendency Scheme on the Simulated El Niño-Southern Oscillation in the Community Earth System Model

“…A variety of studies have shown that coupled atmospheric-ocean general circulation models (CGCMs) have been an important tool for ENSO prediction (Paolino et al, 2012;Levine and Jin, 2015;Wu et al, 2015;Zhang et al, 2015;Kumar et al, 2016;Barnston et al, 2019;Wang et al, 2020). Despite the fact that the model improves during the last few decades, there remain systemic errors and other shortcomings in ENSO simulation even with state-of-the-art climate models (Bauer et al, 2015;Kumar et al, 2016;Vega-Westhoff and Sriver, 2017;Haszpra et al, 2020;Watterson et al, 2020). Despite using the data assimilation technique to initialize the model, fully coupled models still have deficiencies and are marginally better than the mathematical models in the ENSO process and intensity prediction.…”