Sub-Seasonal Predictability of Heatwave Onset using Coupled Ocean-Atmosphere Teleconnections
Abstract
Heatwaves are among the deadliest climate extremes, and their impacts on human health, agriculture, energy demand, and infrastructure are strongly dependent on the amount of advance warning available to decision-makers. Weather forecasts provide reliable skill only to about 10–14 days, while seasonal forecasts operate at climatological, not event-scale, resolution, leaving a critical "sub-seasonal" gap of roughly two to six weeks in which actionable heatwave-onset prediction is most needed but least mature. This paper investigates the sub-seasonal predictability of heatwave onset by examining coupled ocean-atmosphere teleconnection patterns, including the El Niño–Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and mid-latitude atmospheric blocking, as sources of extended-range predictive skill. The study reviews observational and reanalysis-based evidence linking specific MJO phases and ENSO states to anomalous ridging, soil-moisture feedbacks, and Rossby wave train propagation associated with heatwave initiation over North America, Europe, and East Asia. A composite and lagged-correlation methodology is described using ERA5 reanalysis, NOAA Optimum Interpolation Sea Surface Temperature (OISST) data, and the Real-time Multivariate MJO (RMM) index, alongside statistical and machine-learning-based predictive frameworks (logistic regression, random forest, and LSTM architectures) applied in the subseasonal-to-seasonal (S2S) prediction literature. Comparative skill metrics reported across studies indicate that heatwave predictability at 3–4 week lead times is substantially enhanced when MJO phase and ENSO state are used as joint predictors relative to climatology alone, though skill remains regionally heterogeneous and highly sensitive to the underlying dynamical driver. The paper concludes by identifying persistent challenges in operational S2S heatwave prediction, including model bias in MJO teleconnection representation, land-atmosphere coupling errors, and the need for regionally tailored predictor combinations.
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Introduction
Heatwaves rank among the most damaging climate extremes globally, causing more weather-related mortality in many regions than any other hazard, alongside substantial agricultural losses, electricity grid stress, and wildfire risk amplification [1]. Unlike hurricanes or floods, heatwaves are frequently under-anticipated because they build gradually from persistent atmospheric ridging rather than a single, rapidly evolving synoptic system, which historically limited the lead time at which their onset could be reliably forecast [2].
Numerical weather prediction (NWP) systems provide useful deterministic skill only out to approximately 10–14 days, beyond which chaotic error growth dominates individual trajectories [3]. Seasonal forecasting systems, by contrast, provide skillful information on monthly-to-seasonal mean anomalies but cannot resolve the timing of individual extreme events within a season. This leaves a forecasting gap spanning roughly two to six weeks, formally designated the sub-seasonal-to-seasonal (S2S) range, in which skill must be derived not from initial-condition memory of the atmosphere alone but from slowly evolving boundary forcings such as sea surface temperature (SST) anomalies, soil moisture, and large-scale tropical convective organization [4].
Conclusion
This study has reviewed the physical mechanisms and empirical evidence underlying the sub-seasonal predictability of heatwave onset through coupled ocean-atmosphere teleconnections. The Madden-Julian Oscillation, ENSO, and atmospheric blocking together form a two-step causal pathway—tropical and interannual forcing modulating blocking persistence, which
in turn drives regional heatwave onset—that provides a physically coherent basis for extended-range prediction beyond the deterministic weather forecast horizon [5], [7], [8]. Reported skill metrics indicate that combined multi-teleconnection statistical and machine-learning frameworks consistently outperform single-predictor approaches and, in several documented cases, outperform current dynamical S2S model reforecasts, primarily due to persistent model bias in MJO teleconnection representation [10], [12], [15].
While meaningful predictability exists at the three-to-four week lead time most relevant for public health and infrastructure preparedness, this skill remains regionally heterogeneous and sensitive to the specific teleconnection pathway operating in a given basin, underscoring the need for regionally tailored, physically informed prediction frameworks rather than a single universal model. The gap between observed teleconnection-based predictability and operational dynamical model skill represents the primary barrier to translating scientific understanding into improved early-warning systems.
References
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