The core of the problem lies in how scientists reconstruct sea level before the satellite era. Direct global measurements from satellite altimetry only began in late 1992, so everything we know about earlier decades comes from tide gauges, instruments that are fixed to the coast and measure the sea right at the shoreline. The most widely used reconstruction technique, known as reduced space optimal interpolation, infers empirical orthogonal functions, or EOFs, from satellite data and then fits a subset of these spatial patterns to tide gauge records to estimate how each pattern waxed and waned through time. Because long-term trends are otherwise poorly captured, a spatially uniform pattern, often called EOF0, is added to the mix, following an approach first proposed by John Church and colleagues in 2004.

That addition comes at a steep price. Previous work has shown that while EOF0 dramatically improves the estimate of the underlying long-term trend, it cripples the reconstruction’s ability to capture interannual to decadal variability. The scale of the failure is striking. When the researchers compared the detrended, de-seasoned global mean sea level from the gridded CMEMS altimetry product with the widely used Church and White 2011 reconstruction over the period 1994 to 2013, the two series showed almost no resemblance, with a correlation of minus 0.19. Worse, the reconstruction overestimated the variability by more than a factor of two, with a standard deviation of 0.45 centimeters against just 0.21 centimeters for the observed altimetry average.

Getting this variability right matters far more than academic tidiness. The year-to-year ups and downs of global sea level carry information about the global hydrological cycle, revealing how much water temporarily shifts between the ocean and the land. They also influence estimates of the long-term trend and its acceleration, the numbers that matter most for climate policy. Earlier studies found that both the warming-driven expansion of seawater and the addition of meltwater from land ice contribute roughly equally to this variability, and both are strongly tied to the El Niño-Southern Oscillation. Sea level variability is also increasingly significant in its own right, contributing to coastal flooding and erosion as the ocean warms.

The key clue came from earlier numerical experiments by Calafat and colleagues, who showed that the low skill of EOF reconstructions is largely due to differences between tide gauge records and the altimetry data at the nearest offshore points. Satellite altimetry degrades within roughly 10 to 20 kilometers of the coast, and coastal waters host real physical signals that a nadir-looking radar struggles to resolve. When the researchers replaced tide gauges with these altimetry points, effectively using them as virtual tide gauges, the reconstruction captured global variability almost perfectly, achieving a correlation of 0.98 with the true altimetry average. The differences between the two instruments, which the team calls Signal Differences, were clearly the dominant source of error, not the sparseness of the tide gauge network.

Crucially, the new study shows that these Signal Differences are not random noise. Their standard deviations range from nearly zero to 4.5 centimeters, with clusters of high values along continental coastlines, and they display strong regional coherence. Sites along the west coast of North America, the Bay of Biscay and English Channel, northeast Australia and the Río de la Plata basin each correlate strongly with their regional averages, with mean correlations between 0.62 and 0.74. A control test in which the differences were replaced with random values and the analysis repeated 100,000 times yielded a mean correlation of only 0.37, far below what the real data show. The differences are geophysical, not instrumental.

When the researchers probed what these differences actually represent, a telling pattern emerged. At 146 of 229 analyzed locations, the Signal Differences correlated significantly with the tide gauge records themselves, but at only 45 locations did they correlate with altimetry. This means the discrepancies are mostly caused by real oceanographic signals that the tide gauges detect but the altimeter does not, rather than by errors in either instrument. The likely physical mechanism is wind-driven coastal trapped waves, which propagate sea level signals over long distances along continental shelves around the world, from Europe and Australia to the United States. These waves have cross-shelf scales comparable to the shelf width, so their effects are confined to the coastal zone and may not be fully captured by altimetry.

Because the differences have a geophysical origin, they can be modeled. The team correlated the Signal Differences with the zonal and meridional components of 10-meter winds from the ERA5 reanalysis and with the Southern Oscillation Index, a normalized pressure difference between Tahiti and Darwin that tracks the El Niño-Southern Oscillation. Strong, coherent correlation patterns emerged along the Pacific coast of the United States, Australia, the United Kingdom and Japan, with absolute correlations of 0.5 or more at many sites. Building on this, the researchers constructed three multiple linear regression models at each tide gauge, progressively adding wind components, the Southern Oscillation Index and the tide gauge record itself as predictors. The full model achieved a globally averaged correlation of 0.67 with the Signal Differences and explained 49 percent of their variance, with wind alone accounting for 26 percent.

The payoff came when the best-performing model was used to correct the tide gauge data before running the reconstruction. The team derived the regression parameters over the satellite era from 1994 to 2020, then extrapolated the corrections back to 1941, extending the improvement deep into the pre-altimetry past. The corrected reconstruction’s standard deviation of variability dropped from 0.30 to 0.25 centimeters for the satellite period, matching the true altimetry value exactly, and its correlation with the observed record doubled from 0.20 to 0.40. The difference between reconstruction and truth shrank by 23 percent. Importantly, the long-term trends were statistically unchanged, at around 2.0 millimeters per year for the full corrected record, confirming that the correction refines the variability without distorting the trend.

The improvement was most visible during the strongest El Niño events. Uncertainties in the corrected reconstruction shrank during the very strong El Niños of 1997-1998 and 2015-2016, and the corrected curve tracked the true altimetry record more closely through both episodes, evidence that uncorrected reconstructions had systematically mishandled the ENSO signature. The authors caution that some Signal Differences may still reflect poor-quality altimetry or tide gauge data rather than genuine ocean physics, and they note that non-linear vertical land motion at some sites remains a stubborn complication; correcting for it using contemporary gravitational, rotational and deformation data did not further improve the result. Future refinements may incorporate river discharge effects, the angle of wind approach to the coastline, and targeted testing of which low-difference sites actually need correction. For now, the study offers something the sea level community has lacked: a physically grounded way to make the pre-satellite record of our planet’s rising oceans finally tell the whole story.

Subject of Research: Improving short-term variability in Global Mean Sea Level reconstructions by correcting wind-driven tide gauge and altimetry differences

Article Title: An improvement to short term variability in Global Mean Sea Level reconstruction

Article References: Shaw, A. G. P., Jevrejeva, S., & Calafat, F. M. (2026). An improvement to short term variability in Global Mean Sea Level reconstruction. Ocean Science, 22(5), 2673-2689. https://doi.org/10.5194/os-22-2673-2026

Image Credits: AI Generated

DOI: 10.5194/os-22-2673-2026

Keywords: global mean sea level, sea level reconstruction, tide gauges, satellite altimetry, empirical orthogonal functions, coastal trapped winds, ENSO, Southern Oscillation Index, coastal trapped waves, sea level budget, climate variability, Ocean Science

The research, led by Wanxiong Zhang of Southwest Forestry University and Zhuoya Zhang, with colleagues from the Chinese Academy of Sciences, Fujian Normal University and the Gaoligongshan National Nature Reserve, focused on the cellulose locked inside the tree’s annual growth rings. Cellulose, the structural polymer of plant cell walls, is laid down each growing season and incorporates oxygen from the water the tree takes up and from the leaf water that evaporates through its stomata. Because the heavy isotope oxygen-18 behaves differently from the lighter oxygen-16 during evaporation and condensation, the ratio of these isotopes in tree-ring cellulose acts as a natural archive of the humidity conditions under which each ring formed. In humid air, less water evaporates from the leaf and less isotopic enrichment occurs; in dry air, the opposite happens. The result is a proxy that can be read like a barometer of past atmospheric moisture.

Sampling such a rare and protected species demanded extraordinary care. Working with the reserve administrations of Baoshan and Nujiang, the team obtained cores from living trees without harming them, then cross-dated the rings using standard dendrochronological quality-control procedures to assign each ring a precise calendar year. From the resulting 221-year chronology, spanning 1802 to 2022, the researchers reconstructed May-to-November relative humidity, the period covering the monsoon growing season. The calibration against instrumental records from 1960 to 2022 explained 36.3 percent of the observed variance in relative humidity, a respectable figure for a single-species isotope chronology in a topographically complex mountain environment.

The reconstructed record tells a clear story of long-term change. Wet years cluster predominantly before 1900, while the twentieth century brought a general drying trend that has continued into the present. This finding echoes results from other Himalayan isotope studies, including a 223-year tree-ring oxygen isotope chronology from Nepal that documented increasing aridity over a similar interval. What makes the new record particularly valuable is its location: the Gaoligong Mountains sit at a climatic crossroads where Indian monsoon circulation, Tibetan Plateau dynamics and local orographic effects interact, making the region both hydrologically sensitive and poorly covered by long instrumental observations.

To confirm that the chronology reflects genuine regional climate rather than local noise, the team performed spatial correlation analyses. The reconstructed relative humidity series showed coherent relationships with observed relative humidity, vapor pressure deficit, the standardized precipitation-evapotranspiration index and precipitation across southwestern China and adjacent monsoon-influenced regions. Vapor pressure deficit, the gap between how much moisture the air holds and how much it could hold at saturation, is a key driver of plant water stress, and rising deficits have been implicated in increasing tree mortality across tropical forests worldwide. The fact that the rhododendron isotopes track these regional moisture fields so consistently supports the record’s use as a regional hydroclimatic archive.

A central technical question in tree-ring isotope research is what exactly the signal represents. Oxygen isotopes in cellulose are shaped both by the isotopic composition of the source water taken up by the roots and by evaporative enrichment in the leaves, which is strongly modulated by ambient humidity. Using modeled precipitation isotope data alongside the humidity-related atmospheric variables, the researchers quantified the relative contributions of each pathway. Both made important contributions to the cellulose isotope variability, leading the team to interpret the chronology as an integrated, relative-humidity-sensitive hydroclimatic record that captures atmospheric evaporative demand and source-water isotope changes together. This dual sensitivity, they argue, is precisely what makes the proxy robust: it responds to the full moisture environment of the tree rather than to a single variable in isolation.

The record also carries the fingerprints of the planet’s great ocean-atmosphere oscillations. Spectral analysis of the reconstruction, combined with examinations of sea-surface temperatures and large-scale atmospheric circulation patterns, suggests that May-to-November relative humidity in the Gaoligong region is intermittently modulated by the El Niño-Southern Oscillation and the Walker circulation, the vast east-west overturning of air across the tropical Pacific that shifts rainfall patterns during El Niño and La Niña events. The influence of the Indian Ocean Dipole, by contrast, appears more conditional, depending on the background climate state. This kind of intermittent teleconnection is consistent with earlier work showing that ENSO events disrupt Indian summer monsoon rainfall and that dipole events have been linked to drought episodes in southwestern China, including the severe droughts of 2006 and 2011.

Perhaps the most consequential part of the study looks forward. The team analyzed climate model simulations from the sixth phase of the Coupled Model Intercomparison Project, the model ensemble underpinning the most recent IPCC assessment, under a range of future emissions scenarios. The models consistently indicate an overall decline in May-to-November relative humidity in the region during the twenty-first century, with stronger decreases under higher radiative-forcing scenarios. In other words, the drying trend that the rhododendrons have quietly recorded since 1900 is projected to intensify as greenhouse gas concentrations rise, with the magnitude of future aridification depending directly on humanity’s emissions choices.

For Rhododendron protistum var. giganteum itself, the implications are alarming. The species is already classified as endangered, restricted to fragmented habitat on these mountains, and a companion study by several of the same authors found that rising temperatures have reshaped the tree’s climate-growth relationships and increased its vulnerability. Declining relative humidity and rising vapor pressure deficit compound the physiological stress on a species whose enormous leaves, while spectacular, present a large surface area for water loss. Global syntheses have shown that tropical and subtropical tree mortality rises with atmospheric water stress, and hydraulically vulnerable species have been killed outright by catastrophic droughts elsewhere in the world’s mountains and tropics. A long-term drying trajectory, superimposed on warming, leaves this botanical giant with shrinking margins for survival.

Beyond its significance for conservation, the study demonstrates the power of oxygen isotope dendroclimatology in regions where conventional ring-width records fall short. In humid, montane forests, tree growth is often limited less by water availability than by temperature or light, muting the drought signal in ring widths. Isotope ratios, by contrast, record the evaporative environment directly, regardless of how much the tree grows in a given year. By extending this approach to a new species and a new corner of the monsoon domain, the researchers have added a crucial piece to the puzzle of Asian hydroclimate variability. The full dataset and reconstruction code have been made publicly available in the Zenodo repository, allowing other scientists to scrutinize, reuse and extend the record. As climate change accelerates across the world’s mountains, the silent testimony of these ancient rhododendrons, and the 221-year moisture history they preserve, may prove essential for anticipating what the coming century holds for the water supplies, forests and extraordinary biodiversity of southwestern China.

Subject of Research: Tree-ring oxygen isotope reconstruction of 221 years of relative humidity variability in the Gaoligong Mountains, China

Article Title: Tree-ring oxygen isotopes reveal a 221-year hydroclimatic history for the endangered Rhododendron protistum var. giganteum in the Gaoligong Mountains

Article References: Zhang, W., Ge, H., He, Y., Xu, C., Wang, J., Fang, K., An, W., & Zhang, Z. (2026). Tree-ring oxygen isotopes reveal a 221-year hydroclimatic history for the endangered Rhododendron protistum var. giganteum in the Gaoligong Mountains. Climate Dynamics, 64(11), Article 458. https://doi.org/10.1007/s00382-026-08425-4

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08425-4

Keywords: tree rings, oxygen isotopes, paleoclimate, relative humidity, drought, Rhododendron protistum var. giganteum, Gaoligong Mountains, monsoon, ENSO, Indian Ocean Dipole, CMIP6, climate change

Mark Collier of CSIRO Environment in Melbourne, together with Dylan Harries of the South Australian Health and Medical Research Institute and Terence O’Kane of CSIRO Environment in Hobart, applied a technique known as Bayesian structure learning to decades of climate data. Rather than simply measuring correlations between climate patterns, their method infers directed networks of Granger-causal relationships, in which one time series is said to cause another if knowledge of its past improves predictions of the other. The result is a dynamic Bayesian network: a graph in which the major modes of climate variability appear as nodes, and the most probable lagged influences between them appear as arrows pointing forward in time. Because the approach is Bayesian, it does not deliver a single definitive diagram but a probability distribution over possible networks, allowing the researchers to quantify exactly how confident they are in each inferred connection.

The team drew on the ERA5 reanalysis, the European Centre for Medium-Range Weather Forecasts’ state-of-the-art reconstruction of the historical atmosphere, which blends observations with numerical weather prediction to provide a physically consistent record back to 1940. From this dataset they computed a battery of standard climate indices: the multivariate ENSO index capturing El Niño-Southern Oscillation variability in the tropical Pacific, the Indian Ocean Dipole measuring east-west sea surface temperature gradients in the Indian Ocean, the Madden-Julian Oscillation indices RMM1 and RMM2 tracking the eastward-propagating pulse of tropical convection, and a suite of extratropical atmospheric patterns including the North Atlantic Oscillation, the Pacific North American pattern, the Arctic Oscillation, the Southern Annular Mode, the Pacific South American patterns, and Scandinavian blocking. The IPO itself was represented by the Tripole Index, which contrasts sea surface temperature anomalies in the central tropical Pacific against those in the northern and southern subtropics.

The observational record poses a stubborn problem for anyone studying multidecadal variability: it contains only one clear positive IPO phase, from roughly 1977 to 1998, and one clear negative phase, from 1948 to 1976, long enough for reliable statistical fitting. To overcome this limitation, the researchers turned to an ensemble of historical simulations from the ACCESS-CM2 coupled climate model, run under CMIP6 protocols from 1850 to 2014. Because each simulation evolves its own internal variability, the ensemble provides multiple independent realizations of IPO phase transitions, giving the team a much richer sample of background states than the single observed transition. The trade-off was that daily model output needed to compute the MJO indices was unavailable for the model runs, so the researchers ran parallel analyses of the reanalysis data with and without the MJO to make fair comparisons.

The headline result is that the causal architecture of the climate system looks strikingly different depending on which way the IPO is leaning. During the negative IPO phase, which is characterized by cooler tropical Pacific waters, the IPO index shows strong autocorrelation persisting out to six-month lags, and the learned networks reveal an enhanced role for extratropical teleconnections acting on the tropics. Long-lagged influences from the Pacific North American pattern, the Southern Annular Mode, and the IPO itself become prominent. During the positive IPO phase, by contrast, the picture flips: IPO autocorrelation shrinks to about two months, while the Indian Ocean Dipole becomes far more persistent, and the Madden-Julian Oscillation emerges as a powerful conduit linking tropical convection to both the IOD and ENSO. This is consistent with the enhanced Walker circulation that prevails when tropical Pacific waters are warm, and with the observation that El Niño events are more frequent and longer-lived during positive IPO periods.

Perhaps the most striking discovery concerns the humble Madden-Julian Oscillation, an eastward-moving envelope of clouds and rain that circles the tropics every one to two months. When the researchers removed the MJO indices from their analysis, the learned networks underwent a dramatic collapse. Crucial pathways connecting the Northern Hemisphere’s tropospheric modes, including the North Atlantic Oscillation and the Pacific North American pattern, to equatorial sea surface temperatures vanished, while posterior edge weights between ENSO, the IPO, and the IOD swelled to compensate. In other words, the MJO is not merely an intraseasonal curiosity confined to the Maritime Continent; it is the key intermediary that transmits extratropical variability down into the tropical ocean-atmosphere system and helps sustain the very persistence of ENSO and the IPO themselves. Without it, the tropics and extratropics become largely strangers to one another in the inferred graphs.

The directionality of the learned edges also carried physical meaning that aligns with independent lines of evidence. The networks recovered a robust one-month-lagged influence of the negative phase of the North Atlantic Oscillation on the second MJO index, meaning that a strong negative NAO tends to precede and shape subsequent MJO behavior. This dovetails with earlier studies showing that seasonal prediction models initialize MJO forecasts more skillfully when the NAO state is strong and negative, and with proposed mechanisms involving extratropical Rossby wave propagation into the tropical upper atmosphere. Similarly, the analysis found that when the IPO is included as a node, the Southern Annular Mode exerts a statistically significant influence on ENSO at six-month lags, a relationship that paleoclimate reconstructions had hinted at but that the instrumental record had been too short to pin down.

Comparisons with the ACCESS-CM2 model ensemble revealed both the promise and the limits of current climate models. The simulated networks showed considerable diversity across the three IPO transition cases examined, including one in which a nineteen-year neutral period separated the negative and positive phases, and the extratropical-to-tropical connections were systematically stronger than in the reanalysis. The model also exhibited known biases, with an ENSO-like sea surface temperature pattern extending too far west and weakened anomalies over the tropical Indian Ocean. Notably, the observed IOD-to-ENSO connection present during the negative IPO phase did not appear in any of the modeled networks. These discrepancies matter, the authors argue, because the current generation of models often shows large systematic errors in autocorrelations and dependencies, and causal network diagnostics offer a sharp new tool for identifying exactly where those errors originate.

The implications reach well beyond academic diagram-making. The IPO has been linked to periods of accelerated global warming and to the early-century warming hiatus, to Australian rainfall and flood risk, and to the modulation of the Walker and Hadley circulations that shape weather across the Indo-Pacific region. If the strength and even the direction of teleconnections depend on the IPO phase, then seasonal-to-decadal prediction systems may need to condition their forecasts on the background state, and the intrinsic predictability of modes like ENSO may itself be regime-dependent. The authors caution that their directed graphs indicate the probability that causal relationships exist, and that verifying the underlying mechanisms requires detailed follow-up investigation. But the message is clear: the climate system’s wiring diagram is not fixed. It is redrawn, decade by decade, by the slow breathing of the Pacific.

Subject of Research: The influence of Interdecadal Pacific Oscillation phase on tropical-extratropical climate teleconnection dependencies

Article Title: Inferring the role of Interdecadal Pacific Oscillation phase on tropical-extratropical teleconnection dependencies

Article References: Collier, M. A., Harries, D., & O'Kane, T. J. (2026). Inferring the role of Interdecadal Pacific Oscillation phase on tropical-extratropical teleconnection dependencies. Nonlinear Processes in Geophysics, 33(1), 103-122. https://doi.org/10.5194/npg-33-103-2026

Image Credits: AI Generated

DOI: 10.5194/npg-33-103-2026

Keywords: Interdecadal Pacific Oscillation, ENSO, Madden-Julian Oscillation, Indian Ocean Dipole, teleconnections, Bayesian structure learning, causal inference, ERA5 reanalysis, climate variability, Walker circulation, North Atlantic Oscillation, CMIP6

The events in question are westerly wind bursts, or WWBs. These are episodes lasting a week or two, spanning roughly a thousand kilometres of the equatorial Pacific, in which the normally reliable easterly trade winds reverse or weaken dramatically. They are not rare curiosities. According to the observational record, westerly wind bursts have preceded and amplified every major El Niño event ever documented. The mechanism is physically direct: a burst of westerly wind pushes warm surface water eastward and excites oceanic Kelvin waves, waves that travel along the equatorial thermocline and accelerate warming in the eastern Pacific near the coast of South America. The monster El Niños of 1997 and 2015, two of the strongest in the instrumental record, were both accompanied by multiple powerful wind bursts.

Crucially, these bursts are not purely random weather. Observational analyses have shown that westerly wind bursts tend to occur when equatorial sea surface temperatures begin to warm, and they occur more frequently during the active phase of the Madden–Julian Oscillation, a large-scale pulse of tropical atmospheric variability. This state-dependency creates a feedback loop: warming water makes bursts more likely, and the bursts amplify the warming. In simplified models of ENSO, this behaviour has traditionally been encoded as multiplicative Gaussian noise, a stochastic forcing whose amplitude grows with the sea surface temperature anomaly itself. This formulation fits the observed spectrum and probability distribution of ENSO remarkably well, which is precisely why it became the consensus choice.

But fitting the bulk statistics is not the same as capturing the dynamics. In the new work, Georg Gottwald of the University of Sydney, Eli Tziperman of Harvard University, and Alexey Fedorov of Yale University examined what the wind-burst forcing actually looks like when measured in a global climate model. They computed a time-integrated measure of wind stress from bursts in the Community Earth System Model, combining burst duration and wind speed into a single quantity that reflects the ocean’s exposure to each event. The resulting time series is striking: a quiet background punctuated by sporadic, high-amplitude peaks. That shape, the authors realised, looks nothing like smoothed Gaussian noise. It looks instead like a class of stochastic processes known as correlated additive and multiplicative noise, or CAM noise, which has previously been used to describe non-Gaussian sea surface temperature anomalies and atmospheric variability.

The mathematical distinction matters. When a Gaussian process such as the Ornstein–Uhlenbeck process, the workhorse of stochastic climate modelling, is integrated over time, the central limit theorem guarantees that the result is Brownian motion: continuous, well-behaved, and Gaussian. CAM noise behaves differently. Under certain parameter regimes, its time integral converges to an alpha-stable Lévy process, a random walk punctuated by abrupt jumps of potentially enormous size. The stability parameter alpha controls how large these jumps can be, while a skewness parameter beta controls whether they favour one direction. With parameters chosen so that only positive jumps occur, the integrated CAM noise produces exactly the kind of sporadic, one-sided jolts that a westerly wind burst delivers to the ocean. In the recharge oscillator framework, a positive wind stress anomaly shoals the western Pacific thermocline and amplifies eastern Pacific sea surface temperatures, so one-sided positive jumps translate directly into sudden warming kicks.

The recharge oscillator model itself, introduced by Fei-Fei Jin in 1997, reduces ENSO to two coupled variables: the eastern Pacific sea surface temperature anomaly and the western Pacific thermocline depth. Warm water in the east drives the winds, the winds adjust the thermocline, and the thermocline feeds back on the temperature through the Bjerknes feedback, producing a self-sustained or damped oscillation depending on the parameters. The model is crude, but it has proved remarkably durable as a conceptual laboratory for ENSO theory, and recent reviews have reaffirmed its central role. The question the new study poses is deceptively simple: what kind of random forcing should be injected into this oscillator to represent the winds realistically?

The authors compared three answers. The first was the conventional choice, multiplicative Gaussian Ornstein–Uhlenbeck noise whose amplitude scales with the temperature anomaly. The second was pure additive CAM noise, with its intermittent unbounded peaks. The third, and the study’s central contribution, was a conditional model the authors call CON: when the sea surface temperature anomaly is negative, the forcing is ordinary Gaussian noise, but once the anomaly turns positive and persists over a three-month average, the forcing switches to CAM noise. The rationale is physical. Over a cool equatorial ocean, wind variability may indeed be well described by Gaussian statistics, but as the water warms and El Niño begins to develop, the wind field reorganises, and the burst-like, non-Gaussian character of the forcing takes over.

Each model was calibrated against 153 years of observed NiNO3 index data, from 1870 to 2026, matching the empirical histogram, the power spectrum, and the variance and skewness of the temperature record. On these bulk statistics, all three models performed comparably well. Kolmogorov–Smirnov tests and Wasserstein distances between modelled and observed distributions showed the conventional Gaussian model and the conditional model in a statistical dead heat, with pure CAM noise, as expected, producing too many extreme events and a heavier tail. But the decisive test came from a finer-grained dynamical signature: the number of strong wind bursts occurring in the twelve months before an El Niño peak. In the real climate system, the largest El Niño events are preceded by a cluster of bursts, a signature of the feedback between warming water and burst occurrence. When the researchers counted large noise events preceding the strongest fifth of El Niño events in million-month-long simulations, the CAM and conditional models reproduced this clustering dramatically, while the multiplicative Gaussian model captured it only weakly.

The conditional model also achieved something that normally requires deliberate engineering: it generated the well-known asymmetry between El Niño and La Niña, in which warm events tend to be stronger than cold events, without any deterministic nonlinear terms in the temperature equation. The skewness of the simulated temperature distribution emerged entirely from the skewed, jump-rich structure of the noise itself, echoing earlier work by Martinez-Villalobos and colleagues who showed that a linear model driven by CAM noise can capture the observed asymmetry. The authors did find that including an asymmetric response of sea surface temperature to thermocline depth, with a stronger effect for positive than negative thermocline anomalies, was still needed to match the full observed histogram, but the burst clustering survived even with a symmetric response, confirming that it is the conditional noise, not the nonlinearity, doing the dynamical work.

The implications extend beyond a toy model. If extreme El Niño events are genuinely the product of a sustained sequence of wind bursts rather than a smooth random walk of forcing, then forecasts and projections that rely on Gaussian stochastic parameterisations may systematically underestimate the likelihood and potential intensity of the largest events. The authors suggest that their conditional scheme, which reproduces both the quiet background statistics of ENSO and the burst-driven dynamics of its extremes, offers a better template, and they point toward testing it in more realistic coupled climate models as the natural next step. In a warming world, where the stakes of predicting the next 1997-style event keep rising, the difference between smooth Gaussian noise and a process built from sudden jumps may turn out to be one of the most consequential details in climate mathematics.

Subject of Research: Stochastic modelling of westerly wind bursts in the recharge oscillator model of the El Niño–Southern Oscillation

Article Title: An improved noise model for representing westerly wind bursts in the recharge oscillator model of ENSO

Article References: Gottwald, G. A., Tziperman, E., & Fedorov, A. (2026). An improved noise model for representing westerly wind bursts in the recharge oscillator model of ENSO. Nonlinear Processes in Geophysics, 33(3), 373-383. https://doi.org/10.5194/npg-33-373-2026

Image Credits: AI Generated

DOI: 10.5194/npg-33-373-2026

Keywords: El Niño, ENSO, westerly wind bursts, recharge oscillator, CAM noise, Lévy processes, stochastic forcing, non-Gaussian noise, tropical Pacific, climate modelling, sea surface temperature, Madden–Julian Oscillation

The team’s central finding is quantitative and unambiguous. Before the early 1980s, only 51.8 percent of mature eastern-pole cooling events went on to develop into a full, two-poled dipole pattern. After the early 1980s, that success rate jumped to 82.1 percent during the period from 1981 to 2023. In other words, over the past four decades, the eastern and western halves of the Indian Ocean have become far more tightly coupled, so that a cooling anomaly near Sumatra is now much more likely to be mirrored by warming near Africa. This is not a trivial bookkeeping detail. The spatial shape of the IOD determines where its climatic fingerprints land, from East African long rains to East Asian summer monsoon rainfall, so knowing whether a dipole will fully form is essential for seasonal prediction.

To understand the mechanism behind this shift, the researchers turned to a mixed-layer heat budget analysis, a diagnostic technique that tracks every major term contributing to temperature change in the upper ocean. Their analysis singled out one player as decisive: the strength of zonal wind anomalies along the equatorial Indian Ocean. When easterly wind anomalies blow from east to west along the equator, they do two things at once. They drive the local Bjerknes feedback that amplifies the dipole by reinforcing the east-west temperature contrast, and, crucially, they trigger downwelling oceanic Rossby waves that propagate westward toward the thermocline ridge region of the western Indian Ocean. These waves deepen the local thermocline, trap warm water in the subsurface, and set the stage for the warm western pole that completes the dipole.

The logic of the mechanism is elegant. Eastern-pole cooling, often initiated by coastal upwelling off Sumatra driven by southeasterly winds, is only the first act of the drama. The second act depends on whether the equatorial easterlies are strong enough to launch the Rossby waves that warm the west. In the decades before the early 1980s, those winds were frequently too weak, so the eastern cooling fizzled into a monopole rather than a dipole. Since the early 1980s, stronger and more persistent easterly anomalies have made the full two-poled pattern the norm rather than the exception. The dipole, in this view, is not a self-contained Indian Ocean phenomenon but the end product of a chain of interactions that spans the entire tropical belt.

So what strengthened the winds? The study points to two remote drivers operating through the tropical atmosphere. The first is the El Niño-Southern Oscillation, the dominant mode of Pacific climate variability. During El Niño events, shifts in deep convection over the Pacific excite atmospheric circulation anomalies that extend across the Maritime Continent into the Indian Ocean basin, a pathway long described in the literature as the atmospheric bridge. After the early 1980s, the linkage between ENSO and the IOD strengthened, meaning that El Niño events more effectively excite the easterly wind anomalies over the equatorial Indian Ocean that are needed to warm the western subsurface. A stronger ENSO handshake, in effect, has been pulling the Indian Ocean’s two poles into tighter synchronization.

The second driver comes from an ocean basin that most people would not suspect: the tropical Atlantic. The researchers found that sea surface temperature anomalies in the tropical North Atlantic influence the winds over the equatorial Indian Ocean through Gill-Matsuno-type atmospheric responses, in which heating or cooling over the Atlantic generates Rossby and Kelvin wave patterns in the atmosphere that reach across Africa and the Indian Ocean. Before the early 1980s, cold sea surface temperature anomalies in the tropical North Atlantic worked in the opposite direction to El Niño, suppressing the easterly wind anomalies over the equatorial Indian Ocean. This suppression inhibited the subsurface warming in the western Indian Ocean and impeded dipole development. After the early 1980s, with stronger ENSO forcing no longer being undercut by Atlantic cooling, the easterlies could do their work unimpeded, and full dipoles became far more common.

The evidence base for these conclusions is unusually broad. The team analyzed multiple independent sea surface temperature datasets, including the NOAA Extended Reconstructed Sea Surface Temperature version 5 and the Met Office Hadley Centre’s HadISST, together with atmospheric reanalyses from the European Centre for Medium-Range Weather Forecasts, including ERA-20C and ERA5, and ocean reanalysis products such as SODA and GODAS. Triangulating across datasets matters enormously in this field, because the Indian Ocean’s observational record before the satellite era is sparse, and spurious trends in any single product can masquerade as interdecadal change. The authors also conducted model simulations using the GFDL-CM4 coupled climate model to test the causal chain, complementing the observational diagnostics with controlled numerical experiments.

The findings add an important nuance to a lively scientific debate. Previous studies have documented changes in the ENSO-IOD relationship over recent decades, with some analyses suggesting a weakening of the coupling and others pointing to enhanced ENSO influence on the dipole under greenhouse warming. This study reframes the question: what matters for the dipole’s spatial completeness is not simply whether ENSO and the IOD co-occur, but whether the cross-basin forcing is strong enough to activate the oceanic pathway, the Rossby waves and thermocline deepening, that builds the western pole. It also echoes and extends earlier work showing that the influence of north tropical Atlantic sea surface temperatures on the IOD strengthened after the mid-1980s, placing that result within a unified cross-basin framework that includes the Pacific as well.

There are practical consequences for climate prediction. Seasonal forecast systems and the coupled models used for climate projection often struggle to reproduce the IOD’s amplitude and spatial pattern, and a recent companion study by overlapping authors traced some of those biases to the models’ representation of dipole dynamics. If the probability that an eastern-pole event becomes a full dipole depends on interdecadal states of the Pacific and Atlantic, then forecast systems need to capture those cross-basin links to predict the dipole’s shape, not just its existence. For the roughly two billion people living around the Indian Ocean rim, whose agriculture, fisheries and flood defenses respond to IOD-driven rainfall anomalies, the difference between a monopole and a full dipole can be the difference between a manageable season and a disaster.

The study also carries a cautionary message for the future. As greenhouse warming continues to alter the mean states of all three tropical oceans, the cross-basin interactions identified here will not remain static. Research has already suggested that the frequency of extreme positive IOD events may increase under warming, while other work indicates that long-term projections of dipole variability remain deeply uncertain. What this new analysis makes clear is that the Indian Ocean Dipole cannot be understood, modeled, or predicted in isolation. It is a node in a planetary network of tropical ocean-atmosphere interactions, and its behavior over the past four decades has been quietly rewritten by forces originating thousands of kilometers away in the Pacific and Atlantic. Recognizing that connectivity, the authors argue, is a key step toward improving the predictability of the IOD’s spatial pattern and, by extension, the climate of the regions that depend on it.

Subject of Research: Interdecadal strengthening of the coupling between the eastern and western poles of the Indian Ocean Dipole through tropical cross-basin interactions with ENSO and the tropical Atlantic

Article Title: Strengthened coupling between the eastern and western poles of the Indian Ocean Dipole since the early 1980s: roles of tropical cross-basin interactions

Article References: Ju, X., Chen, S., Chen, L., Cao, X., Wang, Z., Wu, R., & Chen, W. (2026). Strengthened coupling between the eastern and western poles of the Indian Ocean Dipole since the early 1980s: roles of tropical cross-basin interactions. Climate Dynamics, 64(11), Article 452. https://doi.org/10.1007/s00382-026-08412-9

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08412-9

Keywords: Indian Ocean Dipole, ENSO, tropical Atlantic, cross-basin interactions, sea surface temperature, interdecadal variability, oceanic Rossby waves, zonal wind anomalies, climate prediction, Climate Dynamics, thermocline, atmospheric bridge

The two patterns at the heart of the study are the monsoon’s fingerprints of the El Niño-Southern Oscillation, or ENSO, in the Pacific, and the Indian Ocean Dipole, or IOD, in the Indian Ocean. El Niño events, marked by unusually warm sea surface temperatures in the equatorial eastern Pacific, have long been known to suppress Indian rainfall, while La Niña conditions tend to enhance it. The IOD, a see-saw of sea surface temperatures between the western and eastern Indian Ocean, similarly modulates the monsoon, with positive dipole events generally favouring above-normal rain. What has remained poorly understood is how these two influences combine, particularly during the recent decades of accelerating ocean warming.

To disentangle the two signals without imposing any preconceived filter on the data, the team applied a data-adaptive statistical technique called multi-channel singular spectrum analysis, or MSSA. The method decomposes daily rainfall and satellite-measured outgoing longwave radiation into space-time patterns, extracting oscillations and persisting structures directly from the observations. Because the rainfall data cover only India’s land area, the researchers first identified the seasonally persisting modes in outgoing longwave radiation, which spans both land and ocean, and then projected the century-long rainfall record onto those modes. This procedure yielded two clean components, dubbed M-ENSO and M-IOD, each retaining the same sign of anomaly through most of the June-to-September season but varying from year to year.

The results are striking. The sum of the two modes correlates at 0.83 with the observed seasonal rainfall anomaly across the full 1901 to 2021 period, meaning that the combined influence of the Pacific and Indian oceans explains the bulk of the year-to-year variation in India’s summer rain. Individually, the ENSO-related mode correlates at 0.55 and the IOD-related mode at 0.6 with the total anomaly. Crucially, the two modes sometimes reinforce one another and sometimes cancel out. In 1983, both modes were positive and the season ended with a strong rainfall anomaly of 0.89 millimetres per day; in 1972, both were negative, producing a weak monsoon at minus 1.62 millimetres per day. In 1989 and 1997, the modes opposed each other and the seasonal rainfall came out near normal.

The year 1997 stands out as a dramatic illustration of this destructive interference. That year brought the strongest El Niño of the twentieth century, an event that on its own would likely have produced severe drought across India. Instead, a coincident positive IOD event counteracted the Pacific influence, and the seasonal rainfall anomaly finished at just minus 0.08 millimetres per day, essentially normal. Over the full record, the two modes were jointly positive in 32 years, yielding an average anomaly of 0.48 millimetres per day, and jointly negative in 37 years, averaging minus 0.68 millimetres per day. Years of opposing phases, 52 in total, produced near-normal seasons regardless of which mode dominated.

Beneath this interannual drama, the study uncovers a longer-term reorganisation of the monsoon system. Splitting the record into two epochs, 1901 to 1960 and 1961 to 2021, the researchers found that sea surface temperatures in both basins showed little or no warming trend in the first epoch but steep warming in the second. The Indian Ocean warmed faster than the Pacific, consistent with the assessment of the Intergovernmental Panel on Climate Change that the Indian Ocean and western Pacific have warmed faster than the global average. The variance of the Niño-3.4 index, a standard measure of ENSO, increased from 0.29 to 0.42 square kelvin between the epochs, while the variance of the dipole mode index rose from 0.08 to 0.12 square kelvin, signalling more energetic variability in both oceans.

The rainfall itself followed suit. Nonlinear trend analysis, performed with ensemble empirical mode decomposition and independently confirmed by the MSSA method, reveals that the all-India monsoon rainfall was dominated by strong positive anomalies before 1960 and by predominantly negative anomalies with an almost flat trend after 1960. The two ocean-driven modes shifted in opposite directions: the ENSO-related rainfall mode, which had an increasing trend in the first epoch, settled into a near-flat but positive regime in the second, while the IOD-related mode flipped from positive anomalies in the first epoch to a pronounced negative trend in the second. The relative strength of the two modes, rather than the behaviour of either alone, therefore largely determines the long-term character of the monsoon.

The spatial fingerprints of the two epochs also differ in revealing ways. Correlation maps show that the ENSO-monsoon relationship has remained essentially stable across the century, with the familiar Pacific pattern of sea surface temperature anomalies linked to Indian rainfall in both epochs. The IOD-monsoon relationship, by contrast, was weak and ill-defined before 1960 but became stronger and better organised after 1960, particularly when the IOD and ENSO influences opposed each other. In the second epoch, positive rainfall anomalies became associated with a clear positive dipole pattern in the Indian Ocean, whereas in the first epoch the corresponding dipole signature was weakly negative. The authors attribute this reorganisation to the anomalous warming of the Indian Ocean, which has significantly altered the intensity and phases of the IOD mode and its associated rainfall over the past six decades.

The implications reach well beyond the archive of historical gauges. India has issued seasonal monsoon forecasts for more than a century, first with statistical regression models that ultimately showed little skill and more recently with dynamical global models that still perform poorly. The authors argue that a central reason for this shortfall is the inadequate representation of the oceans in prediction systems. Their findings suggest that reliable seasonal forecasts and long-term projections demand coupled ocean-atmosphere models capable of correctly simulating sea surface temperatures over both the Pacific and the Indian Ocean, including the Arabian Sea, and of capturing the constructive and destructive interference between the two basins’ influences.

The study also leaves open a compelling question for future research: why did the epochal shift occur around 1960, and how will the monsoon respond as the differential warming of the two oceans continues through the twenty-first century? With ocean heat content expected to keep rising, and with the livelihoods of more than a billion people hanging on the answer, the framework developed by Krishnamurthy and Shukla provides a unified way to read the monsoon’s past and a sharper lens for anticipating its future. What emerges is a monsoon not governed by a single oceanic puppeteer, but by a delicate duet between two warming basins, one whose harmony, and discord, may now be changing with the climate itself.

Subject of Research: Long-term variability of Indian summer monsoon rainfall linked to ENSO and the Indian Ocean Dipole under differential ocean warming

Article Title: Long-term variability of Indian monsoon rainfall related to warming of Indian and Pacific oceans

Article References: Krishnamurthy, V., & Shukla, J. (2026). Long-term variability of Indian monsoon rainfall related to warming of Indian and Pacific oceans. Climate Dynamics, 64(11), Article 451. https://doi.org/10.1007/s00382-026-08409-4

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08409-4

Keywords: Indian monsoon, ENSO, Indian Ocean Dipole, sea surface temperature, ocean warming, climate variability, seasonal prediction, Climate Dynamics, monsoon rainfall, Pacific Ocean, Indian Ocean, climate change

The research, led by Ingo Richter of the Japan Agency for Marine-Earth Science and Technology together with colleagues from institutions in Japan, the United States, Norway, and Brazil, rests on an unusually broad foundation of evidence. The team compared simulations of a pre-industrial climate with projections of a high-emissions future, known as scenario ssp585, drawn from 23 state-of-the-art climate models participating in the Coupled Model Intercomparison Project Phase 6, the same modeling framework that underpins the assessments of the Intergovernmental Panel on Climate Change. By contrasting these two worlds, one without human interference and one heated by sustained emissions, the researchers could isolate how the machinery of Atlantic variability itself changes when the planet warms.

The headline finding confirms a trend that earlier studies had already hinted at: the variability of sea-surface temperatures along the equatorial Atlantic, the signature of the Atlantic Zonal Mode, weakens under global warming. In plain terms, the Atlantic Niño becomes less intense, swinging less dramatically between warm and cold phases. Off the equator, by contrast, the picture is different, with sea-surface temperature variability tending to increase slightly in the flanking regions of the tropical Atlantic. That split result matters, because the equatorial mode is the one most tightly linked to the seasonal rains that farmers and water managers from Senegal to Angola depend upon, and its weakening has been flagged in previous work as a potential source of forecast uncertainty.

What makes the new analysis distinctive is its dissection of why the weakening occurs. The Atlantic Niño, like its Pacific counterpart, is sustained by the Bjerknes feedback, a self-reinforcing loop in which a warm anomaly in the eastern equatorial ocean weakens the prevailing easterly trade winds, which in turn suppresses the upwelling of cold subsurface water, further warming the surface. The study finds that this feedback is losing its grip. Contrary to some earlier projections, the team found that the mean-state equatorial Atlantic thermocline, the sharp vertical boundary between warm surface water and cold deep water, actually shoals slightly under radiative forcing, a change that should, on its own, strengthen the mode by making the surface more sensitive to subsurface dynamics. The decline of the Atlantic Niño therefore cannot be blamed on a deepening thermocline. Instead, the culprits are a weakening of the mean upwelling that supplies cold water to the surface and a reduced sensitivity of surface winds to sea-surface temperature anomalies, both of which blunt the Bjerknes feedback at its most critical links.

The second major discovery concerns the changing character of the mode’s forcing. In the pre-industrial simulations, the Atlantic Zonal Mode is driven primarily by dynamic forcing, meaning anomalies in surface wind stress that stir the ocean and rearrange its heat content. In the high-emissions future, the composites of Atlantic Niño events show that this dynamic pathway diminishes, while thermodynamic forcing takes on a more prominent role. Thermodynamic forcing operates through the exchange of heat at the ocean surface, particularly through latent heat flux, the energy carried away by evaporation, and through shortwave radiation, the sunlight that warms the upper ocean. In a warmer world, the Atlantic Niño becomes less a story of winds pushing water around and more a story of clouds, evaporation, and radiant heat reshaping the sea surface.

That shift in mechanism is tied to a striking change in the Atlantic’s relationship with the Pacific. The analysis reveals that the influence of the El Niño-Southern Oscillation on the Atlantic Zonal Mode strengthens under global warming, to the point that a positive correlation emerges between the two phenomena, with Pacific events preceding their Atlantic counterparts by roughly half a year. In today’s climate, the connection between El Niño and the Atlantic Niño has long been described as inconsistent and fragile, appearing in some decades and vanishing in others, a puzzle that has occupied tropical climate scientists for years. The new projections suggest that the greenhouse-warmed atmosphere will knit the two basins together more tightly, transmitting Pacific signals across Central America and the tropical atmosphere with greater reliability.

Two processes appear to explain this tightening bond. First, El Niño itself grows stronger in the high-emissions simulations, and a more powerful Pacific oscillator naturally broadcasts a louder signal into neighboring basins. Second, and perhaps more intriguingly, the weakening of the coupled air-sea feedbacks within the equatorial Atlantic leaves that ocean more susceptible to external forcing. A system whose internal feedbacks have gone quiet is one that listens more attentively to remote voices. When the Atlantic’s own Bjerknes feedback can no longer dominate its variability, the Pacific’s influence, arriving through atmospheric bridges such as shifts in the Walker circulation and changes in tropical tropospheric temperature, finds less resistance and leaves a clearer imprint on Atlantic sea-surface temperatures.

Paradoxically, this foreign domination may carry a silver lining for forecasters. A simple linear analysis performed by the team indicates that, in some of the models, the association of the Atlantic Zonal Mode with El Niño makes the Atlantic mode more predictable, even as its amplitude fades. The logic is straightforward: if a large fraction of Atlantic variability can be traced back to a Pacific precursor that emerges about six months in advance, then forecast systems that skillfully predict El Niño gain, for free, a measure of skill in predicting the Atlantic Niño. Seasonal prediction centers in Africa, Europe, and the Americas could eventually exploit this teleconnection to extend the useful lead time of rainfall outlooks for the Sahel, the Guinea Coast, and northeastern Brazil, regions where the Atlantic mode’s influence on the West African monsoon and coastal precipitation is well documented.

The study also underscores how much remains uncertain. The 23 models do not speak with one voice; the emergence of the ENSO-Atlantic correlation is clear in the ensemble but varies in strength from model to model, and tropical Atlantic simulations remain haunted by persistent mean-state biases that have plagued coupled models for decades. The researchers cross-checked their projections against observational and reanalysis products, including the ERA5 atmospheric reanalysis, the ORAS5 ocean reanalysis, and the HadISST sea-surface temperature dataset, all of which are publicly available, but the fundamental limits of simulating a basin as small and as seasonally locked as the equatorial Atlantic still apply. Whether the real ocean will follow the models’ script is a question that only the coming decades of observation can answer.

Still, the implications are hard to ignore. A tropical Atlantic that dances increasingly to the Pacific’s tune would alter the risk landscape for coastal fisheries, which are disrupted when the cold tongue fails to deliver its usual nutrient-rich upwelling, and for agricultural planners who rely on the statistical rhythms of Atlantic variability. It would also complicate attribution studies, since an Atlantic event that once looked like a local fluke may in the future be the distant echo of an El Niño half a world away. The research, published in Climate Dynamics as volume 64, article 450, offers both a warning and a tool: the Atlantic Niño of the future may be weaker and less self-reliant, but by borrowing predictability from the Pacific, it may also become a signal that humanity can see coming with more confidence than ever before.

Subject of Research: Projected changes in tropical Atlantic sea-surface temperature variability and its coupling to ENSO under global warming

Article Title: Strengthened tropical Pacific influence on tropical Atlantic variability in global warming projections

Article References: Richter, I., Chang, P., Kataoka, T., Kido, S., Keenlyside, N., Kosaka, Y., Okumura, Y., Tokinaga, H., Tozuka, T., & Vilela, I. (2026). Strengthened tropical Pacific influence on tropical Atlantic variability in global warming projections. Climate Dynamics, 64(11), Article 450. https://doi.org/10.1007/s00382-026-08390-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08390-y

Keywords: Atlantic Niño, Atlantic Zonal Mode, ENSO, CMIP6, Bjerknes feedback, tropical Atlantic, sea-surface temperature, climate change, thermocline, seasonal prediction, Walker circulation, Climate Dynamics

Lead author Julia Cole, professor and chair of the University of Michigan Department of Earth and Environmental Sciences, emphasized that the comparison between past and present is strikingly unambiguous. According to Cole, there is no period in the reconstructed record where El Niños were as strong as they are today, and the strength of El Niño appears to change in parallel with the warming of global temperatures. The findings, she noted, demonstrate that the large El Niño events of the last 40 years are not normal within the context of the last thousand years. This conclusion carries significant weight because El Niño is one of the most influential sources of year-to-year climate variability on the planet, capable of reshaping weather patterns across entire continents and driving droughts, floods, wildfires, and disease outbreaks far from its tropical Pacific origins.

Understanding why the researchers turned to corals requires an appreciation of both the phenomenon itself and the remarkable natural recorders that grow in the waters surrounding the Galápagos. El Niño is a natural climate oscillation that, every few years, causes the tropical Pacific to become warmer than usual. Under normal conditions, trade winds blow steadily along the equator from east to west, pushing sun-warmed surface water from the coast of South America toward Australia and Indonesia. When these trade winds weaken, the warm water sloshes back eastward toward South America, setting an El Niño event in motion. The atmosphere responds in turn: strong rainfall shifts from the Indonesian region into the central Pacific, leaving the western Pacific in drought, which further weakens the trade winds and locks in El Niño conditions that can persist for one to two years.

Although these temperature and precipitation fluctuations originate in the tropical Pacific, their consequences ripple across the entire globe. Storm tracks over the United States shift southward, bringing more rain to the desert Southwest and less to the Pacific Northwest. Similar circulation changes around the world produce droughts and flooding that contribute to crop failure, wildfires, waterborne disease, and other crises affecting human health and well-being. The Galápagos Islands sit at a geographic sweet spot for recording these shifts, a location where El Niño exerts its largest influence. The archipelago is famous for its astounding biodiversity, a product of high nutrient availability and the mixing of cool and warm waters, and its corals grow in an environment that faithfully registers every swing of the eastern Pacific climate system.

To reconstruct the history of El Niño intensity, Cole and her colleagues sampled cores from 13 corals, drawing on both living colonies and boulders of ancient coral collected from the Galápagos. Corals grow by secreting layers of calcium carbonate at a rate of roughly one to two centimeters per year, and the chemistry of each layer preserves a record of the seawater temperature in which the coral grew. Because El Niño extremes occur every few years, the team focused on core samples spanning at least 20 years, ensuring that each record captured multiple events. Sampling the cores a millimeter at a time, the researchers measured two independent aspects of the skeleton’s chemistry, building a robust proxy archive of past ocean conditions.

The first measurement targeted the ratio of strontium to calcium in the coral skeleton, a geochemical parameter that depends directly on the temperature at which the coral formed its skeleton. The team supplemented these results with an analysis of oxygen isotope ratios, which at this particular site also serve as a reliable measure of temperature. Together, these two proxies yielded a detailed history of temperature variability in the Galápagos region. The pattern that emerged was remarkably consistent: strong El Niños appeared throughout the last 40 to 50 years, whereas the El Niños recorded prior to that period displayed a steady pattern of lower intensity. Cole described the experience of adding record after record and expecting the story to grow more complicated, only to find that it did not—the signal was that clear.

A critical next step was to rule out the possibility that natural processes alone could have produced a strengthening of this magnitude. The researchers turned to climate models that simulate the last thousand years using reconstructions of volcanic eruptions and solar variability, the two dominant natural forcings of pre-industrial climate. When the models were run with these natural drivers, they produced no large shifts in El Niño behavior that could account for the magnitude of change documented in the coral records. This absence of a natural explanation, combined with the timing of the intensification after about 1850, points toward human-driven global warming as the most plausible cause of the observed strengthening, a conclusion consistent with the finding that El Niño intensity tracks rising global temperatures.

The timing of the study’s release gives its conclusions particular urgency. As a powerful El Niño develops atop an already warmed climate system, Cole noted that the relevant question is not whether the event will happen but how severe it will become and how damaging its impacts will be. Because the event is superimposed on global warming, it is likely to supercharge the temperature increase that would normally be expected from greenhouse gases alone. Forecasts cited in connection with the study suggest global temperatures could reach as much as 1.7 or 1.8 degrees Celsius above pre-industrial levels during this period, which would be considerably higher than the current record. The research was supported by the U.S. National Science Foundation and the United Kingdom Natural Environmental Research Council, with additional support from the Galápagos National Park and the Charles Darwin Research Station.

The implications extend well beyond the immediate forecast. El Niño is a major source of climate extremes, and if the phenomenon is intensifying, then its impacts intensify with it: droughts, floods, wildfire, and food insecurity. Changes in the hydrologic cycle also translate into damage to infrastructure, including floods that destroy homes, highways, and railroads, as well as public health consequences such as outbreaks of diseases like cholera. Cole and her colleagues argue that global warming is supercharging El Niño, and if that is correct, stronger climate extremes should be expected to amplify ecological, infrastructural, and human losses. No country, she cautioned, has the resources to be fully protected from these impacts, a reality that makes the strengthening of El Niño a global concern rather than a regional one.

For the research team, the ultimate takeaway is a call for mitigation. The coral archives of the Galápagos have delivered one of the clearest long-term pictures yet of how a major climate oscillation is responding to human influence, and the message is that the recent strengthening of El Niño represents one more reason to move away from fossil fuels, which the researchers identify as the root cause of the problem. The study’s co-authors include University of Michigan researchers Kelsey Dyez, Cameron Tripp, Jonathan Overpeck, and alumnus Jake Okun; University of Arizona researchers Diane Thompson and Marcus Lofverstrom; Samantha Stevenson-Karl of the University of California, Santa Barbara; Sandy Tudhope of the University of Edinburgh; Allison Lawman of Colorado College; Jessica Conroy of the University of Illinois; Gloria Jimenez of Moody’s Risk Management Services; and R. Lawrence Edwards of the University of Minnesota. Together, their work transforms a string of tropical islands into a millennium-scale warning system, one that indicates the powerful El Niños of recent decades stand alone in a thousand years of climate history.

Subject of Research: Unprecedented strengthening of El Niño events over the last four decades reconstructed from Galápagos coral paleoclimate records

Article Title: El Niños more intense over last 40 years than previous 1,000, according to U-M study

Article References: El Niños more intense over last 40 years than previous 1,000, according to U-M study. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: El Niño, ENSO, coral proxies, paleoclimatology, Galápagos Islands, climate change, global warming, tropical Pacific, strontium-to-calcium ratio, oxygen isotopes, climate models, University of Michigan

Dry spells, defined as stretches of consecutive days with negligible rainfall, are among the most consequential yet underappreciated features of semi-arid hydroclimatology. Unlike droughts, which are usually assessed over months or seasons, dry spells operate on the scale of days and weeks, precisely the timescale at which crops experience water stress. For a deltaic district like Thanjavur, where paddy cultivation depends on both monsoon rainfall and canal irrigation from the Cauvery system, the duration and frequency of rainless runs determine how much supplementary water must be drawn from canals or groundwater. The researchers argue that a robust climatology of dry spell behavior is therefore a prerequisite for sustainable irrigation planning and drought vulnerability assessment in the region.

The study’s methodology unfolded in four phases. First, the team assembled a long-term climatology baseline of dry spell characteristics at each of the 13 stations. Second, they developed a descriptive, percentile-based classification scheme to sort dry spells into distinct regimes, allowing comparisons across seasons and locations. Third, they examined temporal variability and long-term trends using two widely trusted non-parametric tools: the Mann-Kendall test, which detects monotonic trends in time series without assuming any particular distribution, and Sen’s Slope estimator, which quantifies the magnitude of such trends. Finally, they turned to unsupervised and supervised machine learning to confirm that their classification reflected real structure in the data rather than arbitrary thresholds.

The machine learning confirmation relied on three complementary techniques. Principal Component Analysis (PCA) reduced the dataset to its essential dimensions, revealing that the first components captured 79.3 percent of the total variance, a strong indication that the underlying dataset has a compact and interpretable structure. K-means clustering and Agglomerative Hierarchical clustering were then applied to group stations with similar dry spell behavior. The hierarchical approach produced a dendrogram, a tree-like diagram of station similarity, that delivered one of the study’s most striking findings: the Thiruvaiyaru station stands apart from all other stations in the district, exhibiting a distinctly higher level of dry spell severity than its neighbors.

This spatial heterogeneity matters for practical water management. If dry spell risk were uniform across Thanjavur, a single district-wide irrigation schedule might suffice. Instead, the clustering results show that stations just tens of kilometers apart can belong to fundamentally different dry spell regimes, meaning that water allocation, canal scheduling, and groundwater extraction plans may need to be tailored station by station. The identification of Thiruvaiyaru as an outlier gives local authorities a concrete geographic priority for drought monitoring and for investments in storage or conveyance infrastructure that can buffer longer rainless periods.

On the temporal side, the trend analysis produced a genuinely encouraging result. Across the 37-year record, the annual dry spell duration shows a declining trend, and the regional analysis reveals that the strongest decreasing trend occurs in Thanjavur itself. In other words, over nearly four decades, the longest and most punishing rainless stretches have, on average, been shortening in this deltaic district. The authors link dry spell behavior to large-scale climate teleconnections, including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole-related variability they refer to as INO, both of which are known to modulate Indian monsoon rainfall. Understanding these links opens the door to seasonal forecasting: if a developing El Niño or Indian Ocean event can be tied statistically to dry spell risk, water managers could anticipate stress months before it materializes.

The supervised learning phase of the study tested whether the dry spell classification could be predicted from hydroclimatic features alone. Two gradient- and ensemble-based algorithms, Random Forest (RF) and XGBoost, both staples of modern applied machine learning, achieved classification accuracy above 93 percent. That level of performance demonstrates that dry spell regimes in the Cauvery Delta are not stochastic noise but emerge from identifiable patterns in the hydroclimatic record. Even more valuable is the feature importance analysis: the single most powerful predictor of dry spell classification turned out to be the annual number of rainfall days. This is an intuitively satisfying result, since a landscape that receives rain on more days per year has fewer opportunities for long consecutive dry runs, but quantifying that relationship gives forecasters a simple, measurable indicator to track.

The broader scientific context makes this work timely. Across India and globally, researchers have documented shifting precipitation structures under a warming climate, with changes in the frequency and intensity of both wet and dry extremes. Studies of Indian meteorological subdivisions, the Indo-Gangetic Plains, and Mediterranean basins have all highlighted the value of classifying dry spells rather than treating rainfall as a single aggregate quantity. What distinguishes the Thanjavur study is its integration of classical climatology, rigorous non-parametric trend testing, and machine learning validation within a single framework applied at the district scale, where the results can feed directly into local decision-making rather than remaining at the level of regional generalization.

For the Cauvery Delta, where disputes over water sharing and the pressures of growing demand have made every cubic meter consequential, the study provides what the authors describe as a robust scientific basis for drought monitoring, irrigation planning, water resource management, and climate adaptation strategies. The declining trend in annual dry spell duration offers a measure of reassurance, but the pronounced spatial contrasts, exemplified by Thiruvaiyaru’s outlier status, and the demonstrated influence of ENSO and Indian Ocean variability caution against complacency. A single strong teleconnection event could still deliver a season of extended dry spells, and the percentile-based classification gives planners a common vocabulary for describing how severe such a season would be relative to the 37-year baseline.

The methodological template is arguably as important as the regional findings. By showing that PCA can compress 37 years of multi-station data into a few dominant dimensions, that clustering can expose meaningful geographic structure, and that Random Forest and XGBoost can classify dry spell regimes with over 93 percent accuracy while identifying annual rainfall days as the key predictor, the study offers a replicable workflow for any monsoon-dependent region facing similar questions. As climate variability intensifies and semi-arid agricultural zones worldwide confront longer, less predictable rainless periods, the ability to characterize, classify, and anticipate dry spells at the district scale may prove to be one of the most practical tools in the adaptation toolkit. For the farmers of Thanjavur, whose fields have depended on the rhythm of the Cauvery for generations, that rhythm has now been measured with unprecedented statistical clarity.

Subject of Research: Characterization and machine learning classification of dry spells in the Cauvery Delta district of Thanjavur, India

Article Title: The characterization of dry spell through classification and clustering analysis for cauvery river deltaic district of thanjavur: a 37-year hydroclimatic assessment

Article References: Priyanka, K., & Suribabu, C. R. (2026). The characterization of dry spell through classification and clustering analysis for cauvery river deltaic district of thanjavur: a 37-year hydroclimatic assessment. Theoretical and Applied Climatology, 157(10), Article 627. https://doi.org/10.1007/s00704-026-06554-8

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06554-8

Keywords: dry spells, Thanjavur, Cauvery Delta, drought, Mann-Kendall test, Principal Component Analysis, K-means clustering, Random Forest, XGBoost, ENSO, monsoon, irrigation planning

The research, conducted by Hak-Jun Lee of GeoSystem Research Corporation, Sang-Wook Yeh of Ewha Womans University, and Myong-In Lee of Ulsan National Institute of Science and Technology, focuses on a Pacific sea surface temperature pattern known as a tripole. This pattern, familiar to climate scientists from studies of the Interdecadal Pacific Oscillation, consists of anomalies in three key basins: the Northwest Pacific, the central equatorial Pacific, and the Southwest Pacific. When one region is warmer than average while the other two are cooler, and vice versa, the ocean surface takes on a three-poled structure that can persist for years and influence atmospheric circulation far beyond the tropics.

The challenge the researchers faced is that this tripole pattern does not exist in isolation. El Niño and its cold counterpart La Niña, together comprising the El Niño and Southern Oscillation, or ENSO, imprint their own signature on Pacific sea surface temperatures and dominate the tripole signal. To isolate the part of the variability that operates independently, the team constructed what they call a linear ENSO-independent tripole index, abbreviated EITI. The method is statistically elegant: they computed the tripole index from sea surface temperature anomalies averaged over the three Pacific regions during boreal summer, then subtracted the portion of that index that could be linearly explained by the simultaneous Niño3.4 index, the standard yardstick of ENSO strength measured in the central equatorial Pacific. What remains is a clean measure of Pacific variability that has nothing to do with concurrent ENSO conditions.

Using observed sea surface temperatures from the NOAA Extended Reconstructed Sea Surface Temperature version 5 dataset, together with precipitation data from the Global Precipitation Climatology Project and atmospheric reanalysis from the Copernicus ERA5 product, the researchers examined what happens during the positive phase of the EITI, when the tripole pattern reaches a particular configuration. The results were striking. A positive EITI phase is associated with enhanced precipitation along the East Asian monsoon rainband, meaning wetter conditions across the densely populated monsoon region. The atmospheric machinery behind this response involves two well-known components of the summer circulation: the subtropical jet stream and the western North Pacific subtropical high.

During positive EITI phases, the team found that the subtropical jet shifts equatorward, toward the equator, while the western North Pacific subtropical high strengthens. Both changes matter enormously for the monsoon. The subtropical jet acts as a guide rail for storm tracks and is intimately tied to the position of the meiyu-baiu rainband that stretches from eastern China through Korea and Japan; a shift in the jet translates directly into a shift in where the heaviest summer rains fall. The subtropical high, meanwhile, is the great anticyclonic circulation over the western Pacific that steers moist air masses toward East Asia and controls the timing and intensity of the monsoon onset. When the high strengthens, it pumps more moisture-laden air toward the rainband, amplifying rainfall. The EITI thus provides a single oceanic index that captures the coordinated behavior of these two circulation systems.

But the most consequential finding of the study concerns predictability rather than mechanism. The researchers turned to the Decadal Climate Prediction Project component of the sixth Coupled Model Intercomparison Project, known as CMIP6 DCPP. This international effort coordinates hindcast experiments in which state-of-the-art climate models are initialized with observed ocean and atmosphere conditions and then run forward for several years, allowing scientists to test how far ahead different climate features can actually be predicted. The team evaluated how well the models predicted the winter Niño3.4 index and the summer EITI at various lead times, comparing the forecasts against observations.

The comparison revealed a sharp contrast between the two indices. The boreal winter Niño3.4 index, the classic ENSO measure, is skillfully predicted at a lead time of one year, but its prediction skill declines rapidly as the lead time increases. This mirrors the well-documented spring predictability barrier and the fundamental limits of ENSO forecasting, which rarely extends useful skill beyond a year. The summer EITI, by contrast, remains skillfully predicted up to a lead time of four years. Because the EITI is independent of ENSO, its persistence reflects the slower, longer-lived dynamics of the broader Pacific climate system, including the decadal-scale ocean memory that underlies patterns such as the Pacific Decadal Oscillation and the Interdecadal Pacific Oscillation.

Even more importantly, the link between the EITI and the East Asian summer monsoon survives the forecasting test. The researchers found that the EITI and EASM relationship is most robustly reproduced in the multi-model ensemble, the combined output of many different climate models, and that this relationship remains statistically significant at lead times of up to three years. In other words, when the models collectively predict the state of the ENSO-independent Pacific tripole several years ahead, that prediction carries genuine information about the likely behavior of the East Asian summer monsoon. This is a remarkable result for a climate feature that has historically resisted prediction beyond a single season, and it suggests that the multi-model ensemble approach, which averages out the idiosyncratic errors of individual models, is particularly effective at capturing this slow ocean-atmosphere coupling.

The implications extend well beyond the academic literature. Multi-year lead forecasts of monsoon rainfall would transform water resource management across East Asia, allowing reservoir operators, agricultural planners, and disaster preparedness agencies to anticipate prolonged wet or dry phases years in advance rather than reacting to seasonal forecasts issued only months ahead. The study also adds to a growing body of work showing that components of the climate system other than ENSO, including the Indian Ocean basin mode, the tropical Atlantic, and Pacific decadal variability, contribute substantially to East Asian climate. By explicitly removing the ENSO contribution, the new EITI framework clarifies how much of the monsoon’s variability is governed by these slower, more predictable oceanic patterns.

Caveats remain, as they always do in climate science. The analysis relies on linear regression to strip out the ENSO signal, and nonlinear interactions between ENSO and the tripole pattern could complicate the picture in ways a linear framework does not capture. The robustness of the EITI and EASM relationship also varies among individual models, appearing most reliably only in the multi-model ensemble, a reminder that model biases in simulating Pacific sea surface temperatures and monsoon dynamics still limit how confidently these results can be translated into operational forecasting. Nevertheless, the core message of the study is clear and potentially transformative: the Pacific Ocean stores climate information that persists for years, and by learning to read the ENSO-independent part of that signal, scientists may finally extend the horizon of East Asian summer monsoon prediction from months to years, offering societies across the monsoon domain a far longer window of preparation for the rains that shape their lives.

Subject of Research: ENSO-independent Pacific sea surface temperature variability and its multi-year predictability for the East Asian summer monsoon

Article Title: The influence of linear ENSO-independent Pacific variability on the East Asian summer monsoon and its predictability

Article References: Lee, H.-J., Yeh, S.-W., & Lee, M.-I. (2026). The influence of linear ENSO-independent Pacific variability on the East Asian summer monsoon and its predictability. Climate Dynamics, 64(11), Article 445. https://doi.org/10.1007/s00382-026-08398-4

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08398-4

Keywords: East Asian summer monsoon, ENSO, Pacific tripole, sea surface temperature, CMIP6, decadal climate prediction, subtropical jet, western North Pacific subtropical high, multi-year predictability, Climate Dynamics, Pacific Decadal Oscillation, climate modeling