Summer Forecast Skill for the Indian Ocean Basin Mode Rises and Falls With ENSO’s Reach

Every summer, the tropical Indian Ocean quietly hands the atmosphere a memory of the winter just past. When El Niño peaks around the turn of the year, the tropical Indian Ocean absorbs heat like a charging capacitor, warming across its entire basin. That stored warmth then shapes the Asian summer monsoon, strengthens the South Asian High, and steers circulation over the western North Pacific long after El Niño itself has faded. This basin-wide swing in sea surface temperature, known as the Indian Ocean Basin Mode, or IOBM, is one of the most consequential climate signals in the Indo-Pacific region, driving both flooding rains over the Yangtze River valley and punishing heat waves across southern China. A new study published in Climate Dynamics now reveals a troubling twist: our ability to forecast this pivotal mode of variability more than a season ahead is not a fixed property of the climate system, but one that has swung dramatically over the past 75 years.

Chuyue Xu and Yanling Wu of Hohai University set out to answer a question that operational forecasting centers have largely been unable to tackle: how has the predictability of the boreal summer IOBM changed across different climate regimes? The obstacle has always been data. State-of-the-art dynamical prediction systems, such as the North American Multi-Model Ensemble, typically only produce hindcasts beginning around 1980, because running coupled ocean-atmosphere models backward through decades of history is computationally prohibitive. That short record makes it nearly impossible to detect whether forecast skill itself rises and falls from decade to decade. To get around this bottleneck, the researchers turned to a technique called the Model-based Analog Forecast, or MAF, method, which borrows its power from a library of long climate model simulations rather than from expensive new computations.

The logic behind the MAF method is elegantly simple. It rests on the assumption that sufficiently similar climate states tend to follow similar evolutionary pathways. For any observed initial state of sea surface temperature anomalies across the Indo-Pacific, the method searches a vast library of coupled general circulation model simulations for the twenty closest analogs, and then simply follows what happened next in those simulated worlds. The library in this study was assembled from the last 500 years of pre-industrial control simulations from 29 CMIP6 models, concatenated into a 14,500-year multimodel time series. Because the forecasts unfold entirely within the model’s own phase space, the method sidesteps the initial shock that plagues assimilation-initialized dynamical forecasts, in which observations are abruptly injected into a model and generate spurious transient adjustments.

Sensitivity tests shaped the final design of the forecasting system. The researchers found that matching analogs over the full Indo-Pacific domain, spanning 20 degrees south to 20 degrees north and 40 degrees east to 90 degrees west, produced substantially better and more stable skill than restricting the search to the Indian Ocean alone, a clear indication that Pacific ENSO signals are essential for initializing the Indian Ocean’s subsequent evolution. Ensemble size also mattered: skill improved rapidly as members were added and saturated at twenty analogs, beyond which additional members offered no statistically significant gain. The forecasts were verified against the Hadley Centre’s HadISST observational dataset, with wind and precipitation benchmarks drawn from ERA5, and all fields were interpolated onto a common two-degree grid with linear trends removed, so that the reported skill reflects only internal variability rather than external forcing.

The validation results were striking. Across all calendar months, the MAF method outperformed the majority of NMME dynamical models, and for the boreal summer IOBM index specifically, it achieved anomaly correlation coefficients above 0.6 at all lead times from one to twelve months, comparable to or even exceeding individual state-of-the-art dynamical models. Skill remained highest in the southwestern Indian Ocean, where downwelling Rossby waves deepen the thermocline and preserve persistent subsurface heat anomalies, and in the North Indian Ocean, where strong air-sea coupling anchored by monsoon-related feedbacks extends predictability. With the method’s credibility established, the team could finally ask the question that had been out of reach: does the predictability of the summer IOBM itself change over time?

The answer is an emphatic yes. Using a 21-year sliding window to filter out interannual noise while preserving low-frequency signals, and applying a change-point detection algorithm, the researchers identified statistically significant transitions in forecast skill at 1978 and 2000. Three distinct regimes emerged. Before 1980, useful forecasts extended only about four months ahead. From 1980 to 2000, skill surged: anomaly correlations consistently exceeded 0.6, and normalized errors stayed below one standard deviation of the observed index for lead times up to a full year, with correlations above 0.8 at most lead times. After 2002, skill collapsed back to levels resembling the pre-1980 era. Crucially, a simple persistence forecast, though far less skillful overall, showed the same decadal rise and fall, confirming that the regime shifts reflect genuine changes in the IOBM’s intrinsic predictability rather than artifacts of the analog method.

What drives these swings? The strongest clue came from the strength of the teleconnection between ENSO and the IOBM, measured as the correlation between the summer basin index and the preceding winter’s Niño3.4 index. The correspondence was remarkable: the time series of forecast skill correlated at 0.92 with the strength of this ENSO-IOBM link. During the high-skill epoch of 1980 to 2000, El Niño events were strong enough to leave a deep imprint on the Indian Ocean. The winter El Niño forced anticyclonic wind anomalies over the southeastern Indian Ocean, exciting westward-propagating downwelling Rossby waves that warmed the southwestern Indian Ocean substantially. That warming then activated the wind-evaporation-SST feedback, a self-reinforcing loop in which weakened surface winds suppress evaporative cooling, allowing the basin-wide warming to persist through the summer monsoon onset and into boreal summer, delivering a strong, predictable signal to forecasters.

During the low-skill periods, by contrast, the chain of events broke down early. Weaker ENSO forcing failed to generate significant southwestern Indian Ocean warming or a well-defined wind-evaporation-SST feedback. The characteristic C-shaped wind anomaly over the tropical Indian Ocean never materialized in spring, the basin-wide warming terminated prematurely, and no significant anticyclonic anomaly formed over the Northwest Pacific in the following summer. The recent low-skill period from 2002 to 2022 followed essentially the same script as the 1958 to 1978 era. The researchers also examined alternative explanations, including decadal changes in ENSO intensity, the depth of the southwestern Indian Ocean thermocline, and the amplitude of the IOBM itself. While the high-skill epoch coincided with stronger ENSO events and a shallower thermocline, neither factor correlated significantly with the skill transitions across the full 75-year record, and the Atlantic capacitor effect, in which the North Tropical Atlantic stores and relays ENSO’s influence, proved negligible once the ENSO signal was removed.

The implications reach well beyond the Indian Ocean. Because the summer IOBM anchors atmospheric anomalies that govern monsoon rainfall and heat extremes across densely populated South and East Asia, the finding that its predictability is nonstationary carries a direct warning for forecast users: the current background climate appears to have reverted to a regime in which summer IOBM signals are intrinsically harder to capture at long lead times, and overconfidence in contemporary seasonal forecasts could prove costly. The study also delivers a constructive message for model developers. Accurately representing the ENSO-IOBM teleconnection, particularly the wind-evaporation-SST feedback that sustains Indian Ocean warming into summer, is essential for advancing forecast skill, and climate models that mishandle this interbasin link will systematically misjudge their own reliability. The MAF framework itself, computationally cheap and free of initialization shock, offers forecasting centers a practical complement to dynamical ensembles and a unique window into how predictability has waxed and waned across three-quarters of a century of climate history, though its authors note it has so far been applied only in hindcast mode and not yet implemented for real-time operational forecasting.

Subject of Research: Decadal variability in seasonal predictability of the boreal summer Indian Ocean Basin Mode and its link to the ENSO teleconnection

Article Title: Decadal change in seasonal prediction skills of the Indian Ocean Basin Mode during boreal summer

Article References: Xu, C., & Wu, Y. (2026). Decadal change in seasonal prediction skills of the Indian Ocean Basin Mode during boreal summer. Climate Dynamics, 64(10), Article 441. https://doi.org/10.1007/s00382-026-08397-5

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08397-5

Keywords: Indian Ocean Basin Mode, ENSO, seasonal prediction, predictability, analog forecasting, CMIP6, wind-evaporation-SST feedback, air-sea interaction, Asian summer monsoon, Indian Ocean, climate dynamics, decadal variability