The stakes of extending forecast range are not abstract. In February 2021, three severe winter storms struck Texas, triggering the worst energy infrastructure failure in the state’s history. At least 246 people died as a result of the cascading disruptions, which cut off food, water, heat, and electricity across wide swaths of the state. According to the researchers, if energy officials had received reliable warnings weeks in advance, they could have taken concrete mitigating steps, such as arranging additional trucks of natural gas to serve disrupted areas before the crisis unfolded. The Texas disaster is a vivid illustration of why the subseasonal window, spanning roughly two to six weeks ahead, is considered a key planning horizon for emergency response, agriculture, and the energy sector.

To understand why forecasts decay so quickly, the researchers point to a foundational insight from MIT’s own history. In the 1970s, MIT Professor Edward Lorenz developed chaos theory, describing how infinitesimally small differences in the atmosphere’s initial conditions compound into completely different weather outcomes. Lorenz proposed a theoretical two-week limit beyond which deterministic weather prediction becomes impossible, because the noise in the system grows until it overwhelms any initial signal. That limit, says Judah Cohen, an MIT research scientist and co-author of the new study, exerted a strong influence on the field and may have discouraged researchers from even attempting forecasts beyond two weeks. Cohen, however, never fully accepted the barrier. While the atmosphere can in principle produce an infinite number of patterns, he argues, it tends to fall into favorite patterns repeatedly, so that once one pattern emerges, the sequence of subsequent dominoes becomes, to some degree, predictable.

Today’s best forecasts come from two main families of models. Dynamical models numerically simulate the physical evolution of the atmosphere and oceans over time, while artificial intelligence models learn to make predictions from vast archives of historical weather data. AI models have recently shown impressive gains at short ranges, but at longer time horizons they perform even worse than dynamical models. The gap between these regimes is what forecasters call the subseasonal range, and Cohen describes it bluntly as the predictability desert. In the short range, forecasters can exploit the initial conditions of the climate system and atmosphere; on seasonal and longer scales, slow-varying boundary forces such as El Niño provide leverage. The subseasonal range, caught in between, can take advantage of neither, and in most cases, Cohen says, forecasting there has been no different from rolling dice at a casino.

The breakthrough came from an idea credited to Cohen’s longtime collaborator Lester Mackey, a researcher at Microsoft: rather than trying to build a better forecasting model from scratch, use machine learning to quantify and remove the errors of existing ones. The team built what they describe as a wrapper, or postprocessing layer, that sits on top of an existing forecasting system. Unlike the more simplistic error-correction schemes used by other forecasting systems, the new layer uses AI to learn from a forecasting system’s historical mistakes and shifts its forecasts accordingly. Because the target range demands probabilistic forecasts, in which the likelihood of a range of outcomes is given rather than a single deterministic prediction, the correction operates on distributions of possibilities. Crucially, the model is adaptive: as more forecasts and observational data become available, it continuously refines its correction.

The researchers trained and developed the model using MIT Engaging, a supercomputing cluster hosted by MIT’s Office of Research Computing and Data, but the finished model does not require a supercomputer to run, a practical detail that lowers the barrier for meteorological services worldwide. To validate the approach, the team last year submitted three models incorporating their postprocessing layer to the European Center for Medium-Range Weather Forecasts, widely regarded as the best forecasting center in the world. ECMWF holds forecasting competitions between models each season, providing a rigorous, independent benchmark. The winning submission combined ECMWF’s dynamical forecasting model with another model and then applied the postprocessing correction on top. The result was a doubling of the original model’s predictive power in the subseasonal range and, for the first time, a demonstrated improvement over simply relying on long-term climatological averages.

That last achievement may be the most consequential. Until now, Cohen notes, no one had shown a model that predicts weather better than long-term averages for subseasonal forecasts; previous systems had essentially all been worse, to the point that flipping a coin could outperform them, and researchers had questioned whether investing in models for this range was worthwhile at all. The team’s models consistently beat climatology. Equally important, the approach produced a statistically significant improvement in predicting extreme weather events, such as flooding and cold spells. This is a notoriously difficult target. When ECMWF developed its own non-AI error correction, the corrected model actually got worse at predicting extremes. Cohen attributes that failure to the nature of traditional bias correction, which always pulls forecasts toward the mean, whereas extreme weather sits on the fringe of the probability distribution. The AI-driven correction, by contrast, learned to preserve and sharpen signals at the edges.

The technique’s versatility is another of its strengths. In the Nature paper, the team showed that the method could improve land forecasts across most regions of the globe. They also applied the bias-correction wrapper to other forecasting models and found it improved those as well, lifting even lesser models to parity with ECMWF’s forecasts. Cohen emphasizes that the model is an add-on wrapper rather than a replacement: the team used it with ECMWF’s dynamical model because that model was considered the best, but it can be applied to any model, and it even improves models that have already been bias-corrected using another AI algorithm. The improvement is robust, and the tool is open source and low cost, making it accessible to national weather services that lack the computational resources of the major forecasting centers.

Real-world adoption may already be beginning. Cohen says the meteorological office for the Republic of Kenya has reached out about implementing the model to improve forecasting of the annual monsoon season, with the goal of boosting agricultural yields. The potential applications extend across sectors: farmers could time fertilization around expected rain, protect crops from incoming freezes during winter harvests, and take steps to avoid drought losses. Energy and emergency management officials stand to benefit as well. Cohen recounts conversations with energy officials who explain that two days of warning before an ice storm is not enough time to prepare, but two weeks could be sufficient to make adequate arrangements, from securing fuel supplies to staging repair crews. The work was supported in part by the U.S. National Science Foundation, the Carlos Chagas Filho Foundation for Research Support, and the European Union, and the researchers hope that other groups around the world will build on the openly available model to push the predictability desert further back.

Subject of Research: Machine learning postprocessing to improve subseasonal weather forecasting accuracy from two to six weeks ahead

Article Title: Researchers improve weather forecasts looking two to six weeks out

Article References: Researchers improve weather forecasts looking two to six weeks out. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: subseasonal forecasting, machine learning, weather prediction, MIT, ECMWF, error correction, extreme weather, chaos theory, Nature, energy planning, emergency preparedness, climatology

The technique in question is data assimilation, the mathematical machinery by which numerical models are continuously corrected with real-world observations. The team focused on sea ice concentration, the fraction of ocean surface covered by ice, as measured by passive microwave satellite sensors and processed with the NASA Team algorithm at the National Snow and Ice Data Center. These observations were fed into the sea ice component of the Geophysical Fluid Dynamics Laboratory’s global ice-ocean model using the Data Assimilation Research Testbed, a community software facility. At the heart of the system sits an Ensemble Adjustment Kalman Filter, a statistical method that blends a spread of model simulations with observations, weighting each according to its uncertainty. With a horizontal localization radius of roughly 170 kilometers, the filter directly updated the concentration of each of the model’s five sea ice thickness categories, while the aggregate thicknesses of ice and snow adjusted implicitly in response. The result was a reanalyzed sea ice state, spanning 1992 to 2017, that reflects both the physics of the model and the reality captured from orbit.

To test whether this improved initial state actually translates into better forecasts, the researchers ran two parallel suites of reforecast experiments with the SPEAR_MED prediction system, part of GFDL’s Seamless System for Prediction and EArth System Research. SPEAR couples the Modular Ocean Model version 6, the Sea Ice Simulator version 2, an atmospheric model, and a land model, all sharing a nominal one-degree resolution for ocean and ice. The baseline suite, called SPEAR, was initialized the standard way: ocean conditions came from an ocean data assimilation experiment, while the atmosphere was nudged toward the NCEP Climate Forecast System Reanalysis and sea surface temperature toward NOAA’s Optimal Interpolation product. The second suite, called SICDA, was identical in every respect except that its sea ice initial conditions came from the new assimilation product. Each experiment comprised fifteen ensemble members, initialized on the first day of every month from 1992 through 2017 and integrated for a full year, with the analysis focused on the first 45 days.

The comparison of initial conditions alone was striking. The standard SPEAR initial state overestimated Antarctic sea ice extent in most seasons, with the largest positive biases in winter and spring across the Weddell, Amundsen-Bellingshausen, Indian, and West Pacific sectors, while underestimating summer extent in the West Pacific and Ross sectors. After assimilating sea ice concentration, these biases shrank substantially in nearly all months and sectors. The wintertime and springtime positive biases in the Weddell and Ross sectors, regions notorious for the open-water polynyas that the unperturbed model simulated too actively, were almost entirely eliminated. The assimilated state also tracked the observed year-to-year variability of regional sea ice extent more faithfully, showing higher detrended anomaly correlation coefficients, and it reduced the root-mean-square error of grid-cell-level concentration almost everywhere, with the greatest gains in the peripheral zone where ice grows and retreats seasonally.

An important and somewhat cautionary side effect emerged in the ice thickness fields. Because the underlying model carried a systematic positive bias in concentration, the assimilation tended to reduce concentration, and thickness followed in the same direction. The assimilated initial state was thinner than the standard one nearly everywhere, with exceptions in the interior Weddell and Ross sectors, where the ice actually thickened. Lacking direct observations of Antarctic sea ice thickness, the team benchmarked their results against two independent model-based references, the Global Ice-Ocean Modeling and Assimilation System and the Southern Ocean State Estimate, both of which reproduce the broad patterns of Antarctic ice volume despite their own biases. The comparison revealed that the four datasets disagree considerably on the magnitude and even the timing of the annual maximum in ice volume, underscoring how poorly constrained Southern Hemisphere ice thickness remains.

When the two forecast suites were evaluated against satellite observations, the assimilation paid clear dividends. Measured by the detrended anomaly correlation coefficient of sea ice concentration, SICDA outperformed SPEAR in nearly all initialization months, with the largest gains in austral spring, followed by winter and summer, and only minor differences in autumn. Both dynamical systems initially trailed a statistical benchmark, a damped anomaly persistence forecast that extrapolates the most recent observed anomaly while accounting for its expected decay, but they overtook it within the first week when averaged across the year. Notably, the skill advantage of the assimilated forecasts decayed gradually with lead time rather than vanishing immediately, and the magnitude of improvement was larger in the Antarctic than in parallel Arctic experiments, where the greatest benefit had previously been found in summer rather than spring.

The improvements extended to the sea ice edge, arguably the most operationally relevant forecast target for ships navigating ice-infested waters. The team quantified edge skill with the Spatial Probability Score, normalized against a trend climatology so that values below one indicate skill exceeding climatology. In winter and spring-initialized forecasts, the standard SPEAR system showed essentially no edge skill, with normalized scores at or above one throughout the 45-day window, reflecting large initial errors in edge position. SICDA transformed this picture: its scores were significantly lower than SPEAR’s for the entire 45 days in winter and about 40 days in spring, and it began beating the damped persistence benchmark around day 15 in winter. Averaged across all twelve initialization months, SICDA maintained significantly better edge skill than SPEAR for 35 days in the Antarctic, compared with only 22 days in the Arctic, and it outperformed the benchmark after roughly day 10 annually, a feat the standard system never achieved in the Southern Ocean.

The study is candid about its one notable failure mode. In forecasts initialized in December and January, SICDA initially led SPEAR but lost ground after about 20 days, with the degradation concentrated along the Antarctic coast and in the interior Weddell and Ross sectors. The mechanisms differ between these regions. Along the coast, the assimilated state featured lower concentration and thinner ice, which through the ice-albedo feedback absorbed more solar radiation, warmed the sea surface by up to 0.8 degrees Celsius relative to the standard forecasts, and accelerated bottom melt, compounding the initial deficit. In the interior Weddell and Ross sectors, by contrast, the assimilated state carried more extensive and thicker ice, which slowed an already sluggish model melt rate and allowed positive concentration biases to grow. The authors suggest that adjusting highly coupled fields such as concentration and thickness through assimilation can introduce inconsistencies that only surface at longer lead times, a caution relevant to any data assimilation system.

The broader implications reach beyond the Southern Ocean. Previous assessments of subseasonal prediction systems in the S2S database found that only one system outperformed simple benchmarks for Antarctic ice edge forecasts, and that all systems fared worse in the Antarctic than the Arctic, a gap attributed largely to deficient initial conditions. By showing that assimilating a single, widely available satellite observable can close much of that gap, the study offers a relatively inexpensive path to better polar forecasts, and the authors note that their assimilated system performs comparably to the leading European Centre for Medium-Range Weather Forecasts system in both hemispheres. The work also highlights a persistent observational deficit: without ground truth for ice thickness, it remains impossible to confirm whether the assimilation adjusts volume in the right direction, strengthening the case for expanded thickness measurements from satellites, aircraft, and ships. As Antarctic sea ice enters what may be a new, lower state, the ability to anticipate its fluctuations weeks in advance has shifted from an academic curiosity to a practical necessity, and this study shows that the tools to meet that challenge are already within reach.

Subject of Research: Improving subseasonal Antarctic sea ice forecasts through satellite sea ice concentration data assimilation

Article Title: Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic

Article References: Zhang, Y.-F., Bushuk, M., Winton, M., Gregory, W., Hurlin, B., Jia, L., & Lu, F. (2026). Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic. The Cryosphere, 20(10), 5867-5882. https://doi.org/10.5194/tc-20-5867-2026

Image Credits: AI Generated

DOI: 10.5194/tc-20-5867-2026

Keywords: Antarctic sea ice, data assimilation, subseasonal forecasting, sea ice concentration, sea ice edge, SPEAR model, GFDL, The Cryosphere, sea ice thickness, Kalman filter, Southern Ocean, polar prediction

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

The central puzzle the team confronted is one that has long frustrated the subseasonal-to-seasonal, or S2S, forecasting community. The skill of forecasts targeting East Asian winter cold surges does not vary smoothly or randomly; instead, it clusters. Some initialization dates yield forecasts that capture the timing and intensity of a surge with impressive accuracy, while others—initialized only days apart—miss the event entirely. Historically, the only way to know whether a forecast fell into a high-skill window was to wait several days after issuance, when verifying observations began to arrive. By then, of course, the forecast had already been delivered to energy planners, agricultural agencies, and disaster-preparedness officials, who had no way of knowing whether to lean on it or discount it. The new study asks a deceptively simple question: can those windows be recognized in advance, before the forecast is made, using precursor signals in the ocean?

To answer it, the researchers mined ECMWF hindcast archives covering the winters of 1997 through 2021, a quarter-century record that spans numerous El Niño and La Niña events and a wide range of Arctic sea-ice conditions. From this archive they systematically identified fourteen high-skill windows, encompassing fifty-six individual forecast cases in which week-three cold surge predictions performed exceptionally well. The first, and in some ways most sobering, finding was that the pre-forecast atmospheric circulation offered almost no help. When the team examined the state of the atmosphere in the days leading up to initialization, they could not reliably distinguish upcoming high-skill windows from ordinary periods, leaving a false alarm ratio of roughly nineteen percent. The atmosphere, in other words, does not announce when it is about to become predictable. The answer, it turned out, lay beneath the surface—in the sea.

Regardless of whether the tropical Pacific was in an El Niño or La Niña phase, every one of the fourteen high-skill windows was preceded by the same sea surface temperature pattern in the mid-latitude North Pacific: a warm anomaly over the western basin paired with a cold anomaly over the eastern basin, a configuration the authors describe as a warm-west/cold-east dipole. This dipole, averaged over the seven days before forecast initialization, emerged as a consistent precursor signal across all ENSO backgrounds. Its physical significance is considerable, because mid-latitude SST anomalies of this kind can reshape the baroclinic environment of the North Pacific storm track and excite Rossby wave trains—vast, undulating disturbances in the atmospheric flow that propagate downstream and can lock the wintertime circulation into persistent, high-impact patterns such as the Siberian high and Ural blocking.

Yet the study’s most important conclusion is that the North Pacific dipole, on its own, is not sufficient. A high-skill window materializes only when the dipole is joined by a matching sea surface temperature configuration in the other key ocean basins—and, remarkably, the state of those ancillary regions also determines which of two distinct dynamical pathways the atmosphere will follow. When warm SST anomalies occupy the Barents-Kara Seas, a condition widely regarded as an indicator of regional sea-ice loss, the Rossby wave train excited by the North Pacific dipole is favored to maintain itself and then to sustain the Ural blocking high, the anticyclonic anomaly over western Russia that acts as a gateway for Arctic air to spill into East Asia. In this pathway, the oceanic signal reinforces the blocking ridge, and the cold surge follows a well-teleconnected route from the polar reservoir southward.

The alternative pathway unfolds when cold anomalies instead dominate the Indian Ocean. In that case, the wave energy excited by the North Pacific dipole is confined to the North Pacific–polar sector rather than propagating through the Ural sector, and the circulation response takes the form of a meridional dipole that steers polar air directly southward into East Asia. Two different oceanic backgrounds, two different wave-guide behaviors, two different routes to the same destructive outcome. The practical implication is that forecasters cannot simply check one index or one basin; they must verify that the sea surface temperature anomalies in all the key regions—the mid-latitude North Pacific, the Barents-Kara Seas, the Indian Ocean, and the tropical Pacific—meet their respective thresholds at the same time. Only this simultaneous, basin-wide alignment marks a genuine high-skill window.

When the team applied this multi-region screening criterion to the hindcast archive, the results were dramatic. Forecasts selected by requiring all key-region SST thresholds to be satisfied simultaneously achieved hit rates of one hundred percent under La Niña-A conditions, ninety-five percent under La Niña-B conditions, and ninety percent under El Niño conditions. Those figures stand in stark contrast to the roughly nineteen percent false alarm ratio that prevailed when no such screening was applied. In effect, the oceanic precursor pattern functions as a reliability certificate: if the prescribed SST configuration is present at initialization, the week-three cold surge forecast can be issued with a degree of confidence that S2S prediction has rarely been able to claim, and if it is absent, users know to treat the forecast with caution.

Beyond its operational value, the study carries a deeper scientific message: forecast skill windows are not random accidents of chaos but physically traceable events, anchored in slow, predictable components of the climate system. The ocean evolves on timescales of weeks to months, far slower than the atmosphere, and its anomalies act as a kind of memory that conditions how atmospheric disturbances will amplify, propagate, and persist. By mapping which oceanic configurations render the atmosphere more predictable, the researchers have effectively converted an abstract question about ensemble spread and verification statistics into a concrete, observable checklist. The work also underscores the growing recognition that mid-latitude predictability is jointly governed by the tropics, the Arctic, and the mid-latitudes themselves, with the Barents-Kara Seas linking the story of Arctic sea-ice decline directly to the practical skill of winter forecasts thousands of kilometers away.

For the agencies that must act on winter forecasts—grid operators deciding when to pre-position fuel reserves, transportation authorities planning for ice and snow, and farmers protecting overwintering crops—the ability to know in advance that the coming two to three weeks are likely to be a high-skill period is of immediate practical value. It allows limited confidence to be spent where it is justified and withheld where it is not, sharpening decisions in energy dispatch and agricultural disaster prevention alike. As subseasonal prediction matures from a research frontier into an operational mainstay, studies of this kind suggest that the future of reliable week-three forecasting may depend less on faster supercomputers than on learning to read the ocean’s slow, patient signals before the atmosphere ever makes its move.

Subject of Research: Identifying sea surface temperature precursors that reveal high-skill windows in subseasonal forecasts of East Asian winter cold surges

Article Title: A "warm-west/cold-east" North Pacific sea surface temperature dipole, working in concert with anomalies in other key ocean regions, reveals when subseasonal forecasts of East Asian winter cold surges can be trusted

Article References: A "warm-west/cold-east" North Pacific sea surface temperature dipole, working in concert with anomalies in other key ocean regions, reveals when subseasonal forecasts of East Asian winter cold surges can be trusted. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: subseasonal forecasting, East Asian winter monsoon, cold surges, sea surface temperature, North Pacific dipole, El Niño, La Niña, Barents-Kara Seas, Ural blocking, Rossby wave train, ECMWF hindcasts, forecast skill windows