Changing Effects of External Forcing on Atlantic–Pacific Interactions
Machine learning methodology involving PCMCI+ causal discovery is used to better understand the processes and mechanisms that connect the Pacific and Atlantic Ocean basins on decadal timescales. In particular, the role of external forcing on these interactions is not clear from previous studies. Here we apply a novel machine learning causal discovery regime-oriented algorithm to observational, CMIP6, and pacemaker data to provide new insights into these interactions.
We apply a novel causal discovery regime-oriented analysis to show that external forcing most likely contributed to the strengthening of the Walker Circulation with easterly surface wind anomalies over the tropical Pacific, thus favoring La Niña-like negative Interdecadal Pacific Oscillation pattern in the Pacific during the most recent decades. This is the first time these effects of external forcing have been demonstrated, and the results have implications for what is needed for skillful Earth system predictions in the Pacific and Atlantic regions.
We used machine learning regime-oriented causal discovery to show that CMIP6 models exhibit varying skill in simulating the observed causal fingerprints governing the interactions between the modes of decadal climate variability over the Pacific and Atlantic basins. We also found that, on average, the models simulate better the connections when SSTs have opposite signs between the tropical Atlantic and tropical Pacific. Analyzing the effects of external forcing, we found that externally forced warming in the North Atlantic likely contributed to the cooling of the tropical Pacific by inducing easterly wind anomalies there that strengthened the Pacific Walker Circulation to favor the negative phase of the Interdecadal Pacific Oscillation in the early 2000s.