Complexity evaluation of Global climatic oscillations using Recurrence Quantification Analysis

Large scale climate oscillations (COs) are major explanatory drivers modulating climate systems and water resources across the Globe. Understanding the recurring patterns of Global climate oscillations (GCOs) is crucial for developing predictive models of hydro-climatic variables and management of water resources. In this study seven prominent GCOs of 1950-2025 period namely ElNino Southern Oscillations (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), Atlantic Multi-decadal Oscillation (AMO) Arctic Oscillation (AO) and Southern Oscillation Index (SOI) are subjected to Recurrence Quantification Analysis (RQA). Diverse set of RQ measures like laminarity determinism, trapping time, entropy and mean diagonal length are quantified for each of the time series considering the complete time spell. The complexity measures are further quantified for pre- and post- Global climate shifts of 1977-78 and 1998-99. Nino 3.4 Index is found to be the most deterministic and stable pattern irrespective of the time spell chosen for the analysis followed by AMO and PDO indices. IOD and AO indices of post-climatic shifts are showing more complex patterns, SOI is most sensitive to climatic shift while remaining indices showed stable patterns in the post-spells of both the climatic shifts. The insights gained from the study are helpful for proceeding with in-depth studies on selection of CO drivers in predictive modeling, multi-variate risk assessment and the synchronization studies of hydro-climatic extremes.