Abstract A new data-driven method for the identification of causal interconnections between climate dynamics in different regions is suggested. It is based on assessing the improvements in the data-driven model prediction skills after inclusion of coupling between the regions. The key step of the method is constructing the optimal joint and isolated models of dynamics in two regions in the form of nonlinear stochastic dynamical systems. Bayesian approach is used for optimizations of the model structure. The method is applied to investigation of interactions of sea surface temperature (SST) anomalies between the Pacific and Indian oceans in the tropics. Both real climate data (ERSST reanalysis) and data from three CMIP-level Earth System Models (ESMs) are analyzed. We reveal a strong impact of the Pacific SST anomalies on the Indian ocean dynamics, especially, Indian Ocean Basin Mode and Indian Ocean Dipole with the time lag of 2-4 months. In the Pacific Ocean, the region of eastern El-Niño formation is sensitive to the Indian Ocean modes. Regarding the ESMs, our results demonstrate significant distinctions of interbasin connections both between the different models and between the models and reanalysis. Nevertheless, the general properties of interactions are shared across all the models and real data: a stronger and wider impact of interaction in the Indian Ocean and a narrow, equatorial impact in the Pacific Ocean. The suggested approach is recommended as a tool for evaluating the ESM regarding their capabilities in reproducing lagged teleconnections in the climate system.
Murzina et al. (Sun,) studied this question.
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