A century ago, Gilbert Walker noticed that pressure in the Pacific Ocean was linked to rainfall in India. Since then, El Niño has been considered the main predictor of the Indian monsoon. But the correlation between rainfall and Pacific sea surface temperature (the Niño 3.4 index) wanders from -0.4 to -0.8 across decades, and after the 1980s it seemed to weaken. This raised doubts: can the monsoon be predicted on long timescales at all? Nearby in the archive are studies on predicting El Niño a year and a half ahead, and on rainfall becoming less frequent but more intense. This work goes further: it shows that the problem was not the relationship itself, but an unfortunate choice of indicator.
Instead of surface temperature, the authors used the depth of the 20°C isotherm (D20) across the entire tropical belt from 30°S to 30°N and across all longitudes. They built correlation maps between D20 and monsoon rainfall for 1875–2010, kept only significant regions, and projected D20 anomalies onto them. The resulting index, Dp, captures signals from the Pacific, Indian, and Atlantic Oceans simultaneously. They then reconstructed the dynamics of the index and rainfall in phase space using Takens' method and measured joint recurrence of states with a joint recurrence plot. Additionally, they computed phase synchronization via the phase locking value and tested the robustness of the relationship on four rainfall datasets and four ocean temperature datasets.
The correlation between Dp and monsoon rainfall reaches 0.8 at an 18-month lead time and rises to 0.9 in recent decades, while the correlation with surface temperature drops to 0.2 at the same horizon. The sliding 21-year correlation of Dp with rainfall barely changes over time, unlike the Niño 3.4 index. Joint recurrence analysis showed statistically significant synchronization between rainfall and D20 at lags of 4–6 and 16–18 months, and the maximum diagonal length on the joint recurrence plot for D20 is several times larger than for surface temperature. The phase locking between rainfall and Dp at an 18-month lag is 0.75, corresponding to a lag synchronization regime. In CMIP6 models under RCP2.6 and RCP4.5 scenarios, the relationship remains strong and stable until 2100.
This work brings the concept of chaotic synchronization from dynamical systems theory into climatology. It shows that two chaotic oscillators, the ocean and the atmosphere, can resonate with a delay, and this resonance provides predictability beyond the usual limit of deterministic chaos. Unlike neighboring studies that seek statistical patterns or use deep learning, here a physical mechanism is found: the slow memory of the subsurface ocean synchronizes with the monsoon through multidecadal variability. This resolves the paradox of the weakening El Niño–monsoon link: the relationship was always stable, it was just measured with the wrong tool.
The method can be extended to other monsoon systems: Australian, East Asian, African, and American. For each, one can construct an analogous Dp and test for chaotic synchronization at long lags. An open question remains how exactly the off-equatorial thermocline accumulates multidecadal memory and why it filters climatic noise. Sensitive experiments with ocean models and longer observational records will be needed.
Seasonal rainfall forecasting in South Asia will gain a new reliable predictor that does not degrade over time. Agriculture, water resource management, and drought insurance can rely on forecasts a year and a half ahead.
Test whether modern climate models reproduce chaotic synchronization between D20 and monsoon rainfall. Construct similar indices for other monsoon regions and compare their predictive power.
In climatology, the nature of multidecadal monsoon variability and its connection to global warming remains an open question. This work shows that part of this variability may be an intrinsic property of the coupled ocean–atmosphere system, not a response to external forcing. This directly bears on the debate over how predictable the climate is decades ahead.
This section is addressed to the author of the paper and their colleagues. Neighbouring works were found by meaning-based search across our archive, not by word overlap. We do not review: we show the field around the work.
What the work does
The authors constructed a new global El Niño index based on the depth of the 20°C isotherm (D20) across all three tropical oceans and showed that it correlates with Indian monsoon rainfall at a level above 0.8 with an 18-month lead time, and this relationship is stable over time. To explain this predictability, they introduce a mechanism of chaotic synchronization between the ocean and atmosphere and test it using joint recurrence analysis and phase locking methods.
Where its strength is
The strength of this work lies in bringing the concept of chaotic synchronization into climatology: it identifies a physical mechanism that explains the long-term predictability of the monsoon and resolves the apparent weakening of its connection to El Niño.
Where it could go next
Worth trying
Why this may matter
If the proposed index and synchronization mechanism are confirmed by independent studies, this could substantially increase confidence in long-range monsoon forecasts, which is critical for agriculture and water resource management in South Asia under a changing climate.
Nearby works in our archive
The archive is sparse around this work: the closest review is only 0.53, against a usual 0.64. It looks like few have drilled nearby.
The depth of the 20°C isotherm in the tropics changes by tens of meters, and these slow waves remember the ocean state for years, while the surface forgets everything within a few months.