Using Causal Discovery to Clarify Observed and Simulated Relationships Between ENSO and Other Ocean Basins

Observed sea-surface temperatures in various ocean basins are confounded by anthropogenic and natural radiative forcing and by teleconnections to modes of internal variability, especially the El Nino Southern Oscillation (ENSO). While confounding due to anthropogenic and natural forcing can be removed in coupled simulations, confounding due to ENSO is unavoidable. When not appropriately characterized and quantified, this confounding can obscure causal relationships between various ocean basins and atmospheric phenomena of huge humanitarian import, such as monsoon rainfall, with implications for attribution of past disasters and prediction of the future. These relationships have been difficult to characterize in part because observational data is limited and simulated data may not represent the observed climate system. This study uses causal discovery to examine the coupled relationships between ENSO and other ocean basins in simulations and observations. We begin by evaluating the (L)PCMCI(+) causal discovery algorithms under various conditions and assumptions on data generated by two continuous idealized models of ENSO: the classic Zebiak-Cane model and a simple stochastic dynamical model proposed by Thual, Majda, Chen, and Stechmann. We then apply the causal discovery algorithms to seasonally and spatially-averaged sea surface temperature (SST) indices for ENSO and other ocean basins in preindustrial control simulations from the Coupled Model Intercomparison Project Phase 6. We discuss the robustness of the results, and the differences between the causal relationships in different General Circulation Models. Finally, we apply the causal learning algorithm to observed SST, and discuss to what extent simulated relationships can be used to learn about the observed climate system. We additionally demonstrate the implications of this study for other scientific questions, specifically for understanding variability in Sahel Monsoon rainfall.