Atmospheric blocking occurs when a persistent anticyclone, or high-pressure ridge, parks itself over a region and deflects the usual eastward march of weather systems. Blocks are among the most consequential features of the extratropical circulation, yet predicting them more than two weeks ahead has long been considered near the edge of what is possible. The challenge is especially acute during transitional seasons, when the large-scale circulation is shifting rapidly between winter and summer regimes and the background flow is unusually volatile. The May–June 2023 sequence offered the researchers a rare natural experiment: three successive blocking episodes over Canada, the Ural Mountains, and Europe, each tied to a distinct surface disaster.

To untangle the dynamics, the team combined ERA5 reanalysis data, the Tibaldi–Molteni blocking index for identifying large-scale blocks, and a relatively new diagnostic technique based on the Hilbert transform. This method allows scientists to compute local wave parameters—phase speed, amplitude, and zonal wavenumber—at every grid point and every moment, rather than relying on hemispheric averages that wash out regional detail. They supplemented this with the Takaya–Nakamura wave activity flux, which traces the horizontal propagation of quasi-stationary Rossby wave energy, and with the stationary wavenumber, a theoretical reference scale at which waves tend to become quasi-stationary in a given background flow.

The diagnostics revealed a clear chain of causation in the wave energy budget. During the first episode, from 1 to 19 May, wave activity flux originating over the North Pacific converged over Canada, sustaining a strong quasi-stationary ridge there. When that Canadian blocking collapsed in mid-May, its energy did not simply dissipate; instead, it dispersed downstream toward the Ural Mountains and Europe, promoting the establishment of the ridges that dominated the second episode from 20 May to 5 June. In the third episode, from 6 to 20 June, wave energy propagating across the North America–North Atlantic sector split into two branches, one heading southeast toward the northwestern Atlantic and the other northeast via Greenland, and both converged over Europe, where a new blocking event formed between 11 and 16 June.

The local wave parameters told an equally striking story. All three blocks were dominated by planetary-scale Rossby waves with zonal wavenumbers of roughly 2 to 4, about one wavenumber lower than the climatological average. Because larger-scale disturbances feel the scale effect—broad waves propagate westward relative to the mean flow more readily than narrow ones—these anomalously low wavenumbers translated into exceptionally slow phase speeds, sometimes even westward motion. The result was a circulation that barely moved for weeks, baking Canada, funneling cold air southward into East Asia alongside the Ural ridge and the East Asian trough, and holding Europe under sustained subsidence and clear skies that marine heatwave feedbacks further amplified.

Perhaps most remarkable were the abrupt regime transitions the diagnostics exposed. At the onset of each block, the wave field flipped within just a few days from eastward-propagating, small-amplitude synoptic-scale waves—wavenumbers above six—to quasi-stationary or westward-propagating, large-amplitude planetary-scale waves. Phase speed dropped sharply, in the Canadian case from more than 3 meters per second to negative values, while amplitude surged past one standard deviation and the wavenumber fell below the stationary value. During decay, the sequence ran in reverse. Comparing the diagnosed wavenumber with the stationary wavenumber provided a useful rule of thumb: blocks stayed put when the two were similar, moved westward when the wavenumber was smaller, and resumed rapid eastward progression once it exceeded that threshold.

The second half of the study turned to forecast skill, evaluating the 50-member real-time ensemble of the ECMWF subseasonal-to-seasonal prediction system. The headline finding is genuinely encouraging: for all three episodes, forecasts initialized 15 to 19 days ahead showed high predictability, with 40 to 65 percent of members correctly predicting 500 hPa geopotential height anomalies exceeding one standard deviation and more than 80 percent predicting above-normal heights. Predictability dipped for forecasts made during the following week before recovering strongly at lead times of 2 to 5 days. The Ural episode proved the most skillful, with the best ensemble members reproducing both the intensity and duration of the ridge.

What separated successful forecasts from failures was not luck but wave dynamics. Comparing the ten strongest and ten weakest members for each episode, the researchers found that good forecasts captured the upstream quasi-stationary troughs—a trough over the North Pacific for the Canadian block, and one over the North Atlantic for the Ural and European blocks—along with the subsequent downstream energy dispersion that built the planetary-scale trough-ridge patterns. Poor forecasts, by contrast, let synoptic-scale waves run eastward along the subtropical or mid-latitude jet without ever amplifying into a blocking pattern, and their predictions drifted back toward climatology. Even the best members underestimated the amplification of wave amplitude and spatial scale, particularly for the Canadian and European blocks, a systematic shortfall that points to a concrete target for model improvement.

The study also uncovered a window of opportunity with real operational implications. Because the three blocks were dynamically chained, getting the first one right paid dividends downstream. In forecasts initialized on 17 April, members that correctly predicted the strong Canadian ridge went on to anticipate a stronger Ural ridge in late May, at a lead time exceeding 33 days, while members that missed the Canadian block also missed the Ural block. In other words, the collapse of the Canadian blocking and the associated shift toward a North Atlantic Oscillation-like regime acted as a predictable precursor, extending the horizon of useful guidance well beyond the conventional two-week barrier.

The authors are careful to note the limits of their analysis. The role of vertical planetary wave propagation, stratospheric processes, and lower-boundary forcing remains unexplored, and three case studies cannot establish how generally the wave-parameter transitions apply across seasons and regions. Future work, they suggest, should extend the Hilbert-transform diagnostics to larger blocking samples and probe why models consistently fail to amplify wave amplitude and scale to observed levels. Still, the central message is clear and actionable: the key to subseasonal prediction of persistent, high-impact blocking lies in correctly capturing upstream Rossby wave precursors and the quasi-stationary energy they dispatch downstream. For forecasters and disaster planners alike, the atmosphere’s worst weeks may be legible weeks in advance—if one knows where to look for the wave train.

Subject of Research: Subseasonal predictability and Rossby wave dynamics of atmospheric blocking events in May–June 2023

Article Title: Subseasonal predictability and Rossby wave dynamics of blocking high during transitional seasons: insights from three successive events in May–June 2023

Article References: Li, Z., Lu, J., & Liu, Y. (2026). Subseasonal predictability and Rossby wave dynamics of blocking high during transitional seasons: insights from three successive events in May–June 2023. Weather and Climate Dynamics, 7(3), 1821-1836. https://doi.org/10.5194/wcd-7-1821-2026

Image Credits: AI Generated

DOI: 10.5194/wcd-7-1821-2026

Keywords: atmospheric blocking, Rossby waves, subseasonal prediction, wave activity flux, Canadian wildfires, European heatwave, East Asian precipitation, ECMWF S2S, ERA5 reanalysis, phase speed, planetary waves, transitional seasons

The team focused on what forecasters call the subseasonal range, roughly two weeks to a month or more ahead, a notoriously awkward window that sits beyond the reach of weather forecasts but before the timescales where seasonal climate outlooks shine. To probe this gap, the researchers turned to the European Centre for Medium-Range Weather Forecasts Subseasonal to Seasonal, or S2S, ensemble reforecast dataset, a treasure trove of hindcast simulations produced by one of the world’s leading prediction systems. They compared these forecasts against high-quality sea surface temperature observations from the National Oceanic and Atmospheric Administration’s Optimum Interpolation Sea Surface Temperature product, evaluating performance across the tropical Indian Ocean for the twenty-year period from 2001 to 2020.

The headline finding is a hard limit on predictability: the effective sea surface temperature forecast lead time over the tropical Indian Ocean is about 31 days. Beyond roughly a month, the model’s temperature forecasts lose the skill needed to meaningfully constrain marine heatwave prediction. That number is more than a technical footnote. It defines the practical horizon within which fisheries managers, aquaculture operators, and conservation agencies could realistically act on a forecast before an oceanic heat event arrives, and it sets the benchmark against which future improvements to prediction systems will be measured.

But the study’s most striking results concern not just how far ahead forecasts work, but where and how they go wrong. When the researchers broke down the deterministic forecasts, meaning forecasts that give a single best estimate of future conditions, they found a sharp geographic split in the nature of the errors. In the near-equatorial region, the model systematically overestimated both the number of marine heatwave days and the cumulative intensity of events. The culprit was an excess of false positives, or false alarms, cases where the model predicted heatwave conditions that never actually materialized in the observations.

Off the equator, the story flipped. In these off-equatorial regions, the forecasts underestimated marine heatwave days and cumulative intensity, primarily because of false negatives, meaning misses where real heatwave events occurred but the model failed to flag them. These biases were not static; they grew steadily worse as the forecast lead time increased. At the maximum effective lead of 31 days, the overestimation of marine heatwave days in the near-equatorial region reached up to 82 percent, with cumulative intensity overestimated by as much as 62 percent. In the off-equatorial zones, the underestimation of days climbed to 47 percent and of cumulative intensity to 56 percent at the same lead time.

Why does the equator behave so differently from its surroundings in the model’s eyes? The researchers traced these regional differences closely to the spatial distribution of sea surface temperature anomaly forecast biases. In other words, where the forecast model systematically runs too warm or too cold relative to reality, the marine heatwave diagnostics inherit those errors. A warm bias near the equator pushes the model over the heatwave threshold too often, manufacturing spurious events, while cold biases elsewhere suppress the model’s ability to detect genuine warming episodes. This connection between background temperature bias and extreme event detection is a crucial insight, because it suggests that improving the mean state of the forecast should directly improve heatwave prediction.

There was one metric, however, where the errors were more uniform. The mean intensity of marine heatwaves, as opposed to their cumulative intensity or duration, was underestimated across the entire tropical Indian Ocean, regardless of latitude. This underestimation grew from about 10 percent at a one-day lead time to roughly 15 percent at 31 days. The implication is sobering: even when the model correctly identifies that a heatwave is underway, it tends to paint the event as milder than it truly is. For ecosystems sitting near their thermal tolerance limits, such as coral reefs where a single extra degree of sustained warmth can trigger mass bleaching, an intensity underestimate could translate into dangerously complacent risk assessments.

Deterministic forecasts tell only half the story, and the study’s probabilistic analysis offers a more encouraging picture. Rather than asking whether the model predicts a heatwave or not, probabilistic verification asks how well the forecast probabilities discriminate between events and non-events. Here the researchers used two standard tools: the Brier Skill Score, which measures whether a probabilistic forecast beats a naive reference forecast, and the Area Under the Curve, or AUC, which quantifies discrimination skill. The results showed that marine heatwave forecasting exhibits relatively high skill at lead times of one to seven days, with predominantly positive Brier Skill Scores and AUC values approaching 0.80, a level indicating strong ability to separate heatwave conditions from ordinary ones.

The practical stakes of this work are considerable. Marine heatwaves have been linked to devastating ecological outcomes worldwide, including coral bleaching, shifts in species distributions, and cascading effects on biodiversity and the ecosystem services that oceans provide. Their socioeconomic footprint is equally real, touching fisheries yields, aquaculture operations, and coastal livelihoods. In the Indian Ocean specifically, these events interact with the monsoon system and with climate modes such as the Indian Ocean Dipole and the Madden-Julian Oscillation, making the region both a hotspot for oceanic extremes and a critical piece of the global climate puzzle. A reliable early warning capability, even one limited to a few weeks of lead time, could allow fishers to adjust operations, aquaculture farms to deploy mitigation measures, and reef managers to prioritize interventions before the thermal stress peaks.

The study also maps out a clear agenda for improvement. Because the heatwave forecast errors track the underlying sea surface temperature anomaly biases, efforts to reduce those biases in subseasonal prediction models, whether through better ocean-atmosphere coupling, improved initialization, or higher resolution, should pay dividends for extreme event prediction. The finding that probabilistic skill remains respectable in the first week, while deterministic biases compound with lead time, suggests that forecast products for the tropical Indian Ocean may be most useful when they communicate uncertainty explicitly rather than offering a single deterministic answer. As global warming continues to push ocean temperatures upward and marine heatwaves grow longer, more frequent, and more intense, knowing precisely where our forecasting window ends, and where the blind spots lie within it, is an essential first step toward seeing the ocean’s next fever coming in time to act.

Subject of Research: Subseasonal forecast skill of marine heatwaves in the tropical Indian Ocean

Article Title: Evaluation of the subseasonal forecast skill of marine heatwaves in the tropical Indian ocean

Article References: Meng, L., Li, X., & Yan, Y. (2026). Evaluation of the subseasonal forecast skill of marine heatwaves in the tropical Indian ocean. Climate Dynamics, 64(11), Article 449. https://doi.org/10.1007/s00382-026-08374-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08374-y

Keywords: marine heatwaves, tropical Indian Ocean, subseasonal forecasting, sea surface temperature, ECMWF S2S, forecast verification, Brier Skill Score, SST anomaly bias, climate dynamics, coral bleaching, ocean warming, probabilistic forecasting