Relationship Between the Global Distribution of Double Tropopause From Radio Occultation Data and Climate Indices

Abstract Double tropopauses (DTs) are a key but underutilized diagnostic of upper troposphere–lower stratosphere (UTLS) dynamics and stratosphere–troposphere exchange. Here we provide a global, observation‐based assessment of DT variability and its coupling to large‐scale climate modes using 14 years (2007–2020) of Radio Occultation Global Navigation Satellite System (RO‐GNSS) temperature profiles. DT occurrence is quantified as the relative frequency of double to single tropopauses following the World Meteorological Organization lapse‐rate definition. The global DT distribution exhibits robust midlatitude maxima and pronounced hemispheric asymmetries, including persistent enhancement over South America. A clustering framework reveals spatially coherent regimes of DT variability, highlighting stronger heterogeneity in the Northern Hemisphere and organized latitudinal structures linked to large‐scale circulation boundaries. We then establish a systematic framework to diagnose DT–climate relationships by combining linear correlation (accounting for temporal autocorrelation) with nonlinear dependence via normalized mutual information (NMI), including lead–lag analysis within ±12 months. DT variability shows statistically significant, spatially coherent associations with multiple climate indices, including ENSO‐ and annular‐mode variability. These relationships frequently organize into contiguous regions with consistent lags, indicating physically meaningful teleconnections. Near‐zero lags reflect rapid atmospheric adjustment, while positive and negative lags point to delayed responses and potential precursor behavior, respectively. Nonlinear dependencies reveal regime‐dependent dynamics that are not captured by linear metrics alone. Our results identify DTs as a sensitive, globally consistent tracer of UTLS dynamical variability and demonstrate their potential to provide new constraints on atmosphere–ocean coupling and predictive diagnostics of climate variability.