The research, led by Peng Gao and Bingliang Zhuang of Nanjing University together with colleagues, tackles a persistent weakness in climate modeling. When black carbon is freshly emitted, its particles are hydrophobic, repelling water and resisting removal from the air. Over time, through condensation of soluble vapors and coagulation with other particles, the soot becomes coated, hydrophilic, and internally mixed with other aerosol species. This transformation, known as aging, fundamentally changes everything that matters for climate: how the particles absorb and scatter light, how readily they act as cloud condensation nuclei, and how quickly they are rained out of the atmosphere. Many climate models, however, sidestep this complexity by assigning black carbon a fixed e-folding aging timescale, often around one day, regardless of where or when the particles find themselves.

That fixed assumption is demonstrably wrong in the real atmosphere. Chamber experiments in Beijing have measured aging timescales as short as 4.6 hours, while studies in Los Angeles and Mexico City found soot largely aged within about three hours. In clean, remote regions such as the Qinghai–Tibet Plateau, aging can stretch beyond a day. To capture this variability, the team incorporated a dynamic aging parameterization, derived from particle-resolved simulations of condensation and coagulation, into the regional climate and chemistry coupled model RegCM-Chem. Rather than a constant, the aging timescale in their scheme emerges from local conditions: it depends on the rate of condensable sulfate production and the total aerosol number concentration, meaning that soot ages fastest precisely where pollution is heaviest.

The simulations, spanning summers from 2008 to 2021 across a domain covering East, South and Southeast Asia at 60-kilometer resolution, revealed striking spatial contrasts. Over the North China Plain and the Sichuan Basin—two of the most heavily polluted regions on the planet—the black carbon aging timescale dropped below ten hours, with a minimum monthly mean of just 2.84 hours. Across most of eastern China and the Indian landmass, aging was consistently faster than the model’s default 27.6 hours. In remote areas such as the Tibetan Plateau and over the oceans, where condensable material is scarce, the process could take several days. The spatial pattern of aging, in effect, mirrors the map of anthropogenic emissions.

Accelerated aging reshaped the atmospheric budget of black carbon in counterintuitive ways. Because aged, hydrophilic particles are efficiently scavenged by rain, faster aging promoted wet deposition and reduced the regional mean black carbon column burden over East Asia by 0.12 milligrams per square meter. Yet at the surface, concentrations actually increased over eastern China, including the North China Plain and the southeastern coast, by more than 0.2 micrograms per cubic meter in places. The explanation lies in a compensating mechanism: hydrophilic particles, with their larger characteristic diameters and higher solubility, are less susceptible to dry deposition than their hydrophobic counterparts. Where summer precipitation is limited, this suppressed dry deposition dominates, leaving more soot lingering near the ground even as the total atmospheric burden shrinks.

These changes in loading and composition fed directly into the aerosol’s optical behavior. Black carbon aerosol optical depth increased significantly over eastern China, driven both by higher near-surface concentrations and by the greater extinction efficiency of aged particles, whose shortwave extinction coefficient is roughly 1.3 times that of fresh soot. The strengthened absorption amplified the instantaneous direct radiative forcing at the top of the atmosphere and deepened the dimming at the surface, where the regional mean forcing reached −3.00 watts per square meter. The additional atmospheric heating, exceeding 0.12 kelvin per day in localized layers, then set off a cascade of dynamical adjustments that rippled through the East Asian summer monsoon system.

The circulation response was pronounced. Anomalous southerly winds along the southeastern Chinese coast, coupled with a cyclonic circulation near 850 hectopascals over southern China, transported warm, moist air from the South China Sea inland, enhancing moisture convergence and raising cloud fraction there by one to two percent. These dynamics favor the development of the East Asian summer monsoon itself. Over northern China, the story inverted: compensating subsidence flanking the black carbon heating center near 35 degrees north, together with the semi-direct suppression of cloud formation, reduced cloud cover by two to three percent and dried the lower troposphere. The net result was a characteristic dipole—wetter, cloudier conditions in the south, drier and warmer conditions in the north.

Clouds responded to the aged soot through a second, microphysical pathway as well. With its enhanced hygroscopicity—assigned a value of 0.22 based on observational constraints—hydrophilic black carbon activates more readily as cloud condensation nuclei. Under the dynamic aging scheme, the mean cloud droplet number concentration in the lower troposphere over East Asia increased by 9.37 per cubic centimeter on top of the 36.98 per cubic centimeter induced by black carbon overall, shrinking droplet effective radii and thickening cloud optical depth. Sensitivity tests varying the assumed hygroscopicity between 0.11 and 0.34 showed that while droplet numbers were only modestly affected, the resulting cloud radiative and dynamical adjustments amplified the surface forcing response considerably, underscoring how small microphysical uncertainties can balloon into large climatic ones.

When all pathways were combined, the effective radiative forcing at the top of the atmosphere over East Asia reached +3.60 watts per square meter from black carbon–radiation interactions, −0.58 from black carbon–cloud interactions, and +0.90 for the total. Crucially, the total was not the simple sum of its parts. The team explicitly quantified a nonlinear interaction term and found that coupled circulation, moisture and cloud adjustments produced residuals comparable to, or larger than, the isolated pathway responses—for example, a −2.12 watts per square meter residual at the top of the atmosphere over northeastern China. In the total response, black carbon warmed northern China by about 0.044 kelvin while cooling the south by 0.042 kelvin, and produced a south-wet, north-dry precipitation pattern with regional anomalies of +0.13 and −0.09 millimeters per day.

The implications extend well beyond East Asia. Black carbon’s global effective radiative forcing spans −0.28 to +0.41 watts per square meter in the latest IPCC assessment, and its indirect forcing in liquid clouds ranges from −0.64 to +0.23 watts per square meter across models—uncertainties that this work traces, in part, to how aging is represented. The authors caution that their dynamic scheme did not fully resolve the persistent underestimation of black carbon concentrations in models, and that observational constraints on particle size distributions, mixing states and absorption enhancement remain scarce. Still, the message is clear: a process operating on timescales of mere hours, long treated as a fixed constant in climate models, exerts a first-order influence on regional radiative forcing, monsoon circulation and the distribution of floods and droughts. For a pollutant whose mitigation is among the most tractable climate interventions available, getting its life cycle right may be essential to predicting—and managing—the climate of the world’s most populous region.

Subject of Research: The sensitivity of black carbon aerosol climate effects in East Asia to dynamic aerosol aging processes in a regional climate model

Article Title: Sensitivity of dynamic aging on the climate effects of black carbon aerosols over East Asia in summer

Article References: Gao, P., Zhuang, B., Hu, Y., Zhou, Y., Zhao, R., Wang, Q., Li, S., Wang, T., Li, M., & Xie, M. (2026). Sensitivity of dynamic aging on the climate effects of black carbon aerosols over East Asia in summer. Atmospheric Chemistry and Physics, 26(19), 13861-13884. https://doi.org/10.5194/acp-26-13861-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-13861-2026

Keywords: black carbon, aerosol aging, East Asia, East Asian summer monsoon, radiative forcing, RegCM-Chem, aerosol-cloud interactions, wet deposition, cloud condensation nuclei, regional climate modeling, North China Plain, air pollution

The research, conducted by Stephen Cusack of Stormwise Ltd in the United Kingdom, draws on a recently developed set of historical storm reconstructions that were extensively validated against insurance loss data. Those reconstructions revealed that insured property damages across Europe, indexed to 2022 values, rose from roughly EUR 2.8 billion per year in 1960–1979 to EUR 6.7 billion in 1980–1999, before falling back to EUR 2.5 billion in 2000–2019. That threefold variation over just a few decades dwarfs anything that can be explained by gradual global warming alone, and understanding its causes could fundamentally change how this costly risk is managed by governments, reinsurers, and coastal communities.

To isolate the role of aerosols, the study turned to a specialized suite of experiments from the Detection and Attribution Model Intercomparison Project, or DAMIP, part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). In these experiments, climate models simulate the industrial period from 1850 to 2014 with one type of forcing set to its historical values while all others are held fixed at preindustrial levels. By comparing simulations driven only by anthropogenic aerosol forcing against control runs with no external forcing at all, researchers can extract the fingerprint of aerosols alone — a powerful technique for separating human influences from the background noise of natural climate variability.

The physical mechanism at work is subtle but increasingly well documented. Sulphate aerosols, produced mainly by burning coal and oil in Europe, Russia, and North America, reflect sunlight back to space and cool the surface beneath them. Because these emissions were concentrated in the northern mid-latitudes, they altered the latitudinal gradient of net radiation at the top of the atmosphere, which in turn modified the poleward transport of energy by both the atmosphere and the ocean. Previous studies have shown that this perturbation strengthened the North Atlantic storm track in the second half of the twentieth century, steepening meridional pressure gradients over Europe and intensifying the winter winds that sweep in from the Atlantic.

The scale of the historical aerosol burden is remarkable. Sulphates generated the largest multidecadal anomalies in shortwave radiative forcing of the entire industrial era, growing from a negligible influence at the start of the twentieth century to peak negative forcing values in the 1980s and 1990s — precisely the period when European windstorm losses hit their documented maximum. Since then, aggressive air quality legislation on both sides of the Atlantic has slashed sulphur emissions, and the aerosol burden has fallen dramatically. Crucially, research indicates that the northern hemisphere storm track is particularly sensitive to emissions from the mid-latitude source regions where most of that sulphur originated, making the timing of the rise and fall of aerosol pollution a plausible match for the rise and fall of storm damages.

Converting raw model winds into monetary losses required a well-established damage function first published by Klawa and Ulbrich in 2003 and since validated against insurance loss timeseries and widely adopted in the field. The method extracts daily maximum near-surface winds across a domain of sixteen northern and central European countries that experience the vast majority of insured wind losses, identifies the 98th percentile wind threshold for each grid cell, and cubes the excess of winds above that threshold — a nonlinear relationship that reflects how damage grows disproportionately with wind speed. Storm events of up to three days are identified, and losses are weighted by population density so that the resulting index tracks where people and property actually lie. By holding population fixed at a common year, the analysis ensures that all modelled loss anomalies reflect changes in storm climate alone, isolated from socio-economic factors.

The headline result emerged clearly from the multimodel mean of 62 simulations spanning six climate models. European windstorm losses in the aerosol-forced runs peaked at 45 percent above preindustrial control levels at the end of the twentieth century, a signal that remained more than four standard errors above zero throughout the final two decades of the century. The timing of this peak coincides closely with the maximum in aerosol forcing from northern mid-latitudes, strengthening the physical case for causation. Yet the spread between individual models was enormous: four of the six models produced statistically significant increases in storm losses, with two showing near-certain signals, while two models simulated no significant aerosol effect at all. The overall mean was driven largely by two particularly responsive models, CanESM5 and HadGEM3-GC31-LL.

The study did not stop at the raw model output. Recognizing the wide spread, Cusack assessed the credibility of the modelled storm responses using independent evidence from earlier research, focusing on two key diagnostics: the strength of the aerosol radiative forcing itself, and the response of the Atlantic Meridional Overturning Circulation (AMOC) to that forcing. The logic follows from the fact that aerosol-driven changes in poleward energy transport manifest as anomalies in both the AMOC and the storm track, so a model that gets the forcing and the ocean response right should also get the storm response right. The assessment found that the models’ global aerosol effective radiative forcing averaged about 13 percent weaker in magnitude than the best observational estimate, and that the models tended to simulate AMOC responses larger than reconstructions of historical ocean behaviour — both biases that would push modelled storm responses downward rather than upward.

That validation carries an intriguing implication: if the models systematically underestimate the aerosol influence, the true contribution of air pollution to late twentieth century storm losses may sit at the higher end of the zero-to-100 percent range. The circulation evidence supports this reading. In the multimodel mean, aerosol forcing produced meridional gradients of 500 hPa geopotential height anomalies over the North Atlantic of about 12 meters for the 1970–1999 period relative to 1940–1969 — roughly one third of the magnitude seen in the ERA5 reanalysis, mirroring the ratio found for the loss anomalies themselves. Models with steeper circulation gradients, such as CanESM5, also produced the largest loss anomalies, confirming that the modelled damage changes are physically connected to large-scale shifts in the atmospheric circulation rather than being statistical artifacts.

Significant uncertainties remain, and the study is careful to acknowledge them. Reliable observational records of the European storm climate span only a few decades, direct measurements of the AMOC began only in 2004 with the RAPID array, and pre-instrument estimates of ocean circulation trends vary so widely that even the sign of twentieth century changes is debated. The validation framework also operates at a zonal-mean scale that cannot resolve processes local to the Atlantic sector, and it relies partly on the behaviour of a single climate model. Nevertheless, the evidence points to a provocative conclusion: the cleanup of air pollution that has delivered enormous public health benefits since the 1990s may also have removed an unintended amplifier of European storminess. Because future aerosol emissions from northern mid-latitudes are relatively predictable compared with stochastic forcings such as major volcanic eruptions, accounting for this aerosol-storm link could sharpen forecasts of windstorm risk in the decades ahead — a finding with direct consequences for the insurance industry, infrastructure planners, and everyone who lives in the path of Europe’s winter gales.

Subject of Research: The influence of anthropogenic aerosol forcing on multidecadal variability of European windstorm losses in CMIP6 DAMIP climate model experiments

Article Title: Anthropogenic aerosol forcing of European windstorms in CMIP6 climate models

Article References: Cusack, S. (2026). Anthropogenic aerosol forcing of European windstorms in CMIP6 climate models. Natural Hazards and Earth System Sciences, 26(10), 4663-4673. https://doi.org/10.5194/nhess-26-4663-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4663-2026

Keywords: European windstorms, anthropogenic aerosols, CMIP6, DAMIP, storm losses, climate models, North Atlantic storm track, AMOC, sulphate emissions, radiative forcing, insurance losses, multidecadal variability

Attribution science, the branch of climate research that assigns observed changes to specific causes, has matured rapidly over the past decade. Earlier studies established methods for attributing a share of historical warming to individual companies, building on the Carbon Majors database, a comprehensive accounting of the emissions associated with the largest fossil fuel and cement producers in the world. Those efforts focused largely on the past and the present. Frierson and Henrie extended the approach forward in time, asking not only how much warming these entities have already caused, but how their historical emissions will continue to shape the climate system across coming centuries under different assumptions about humanity’s collective future.

To do this, the researchers turned to the Finite Amplitude Impulse Response model, or FaIR, a widely used reduced-complexity climate model that simulates the interactions between greenhouse gas emissions, atmospheric concentrations, radiative forcing, and global temperature. FaIR captures the essential physics of the carbon cycle and the atmospheric chemistry of methane and other greenhouse agents while remaining computationally efficient enough to run many simulations. The team ran the model out to the year 2500, an unusually long horizon for attribution work, and performed counterfactual experiments: simulations in which the emissions associated with the Carbon Majors were removed from the historical record, with the difference between the factual and counterfactual worlds representing the perturbation attributable to those entities.

The counterfactual technique is the analytical heart of the study. By comparing a simulated world that includes the emissions of the top 178 producers with an otherwise identical world that excludes them, the researchers isolated the corporate contribution to atmospheric carbon dioxide, methane, ozone, stratospheric water vapor, and ultimately global mean temperature. They repeated this comparison across several different future emissions scenarios, ranging from futures in which global emissions fall rapidly to futures in which they continue to climb. This design allowed them to test a question that has received little attention: does the warming legacy of past emissions look different depending on what the rest of the world does next?

The answer, perhaps unexpectedly, is yes—and in two opposing directions. For atmospheric carbon dioxide, the anomaly attributable to a company’s historical emissions is smaller, and carries less uncertainty across the model ensemble, in a low-emissions future than in a high-emissions one. The reason lies in carbon cycle feedbacks. In a high-emissions world, warming itself weakens the ability of oceans and land ecosystems to absorb carbon, so an initial pulse of carbon dioxide produces a larger and more variable atmospheric anomaly. In a cooler, lower-emissions world, those feedbacks are muted, and the extra carbon dioxide from historical emissions is drawn down more efficiently and more predictably.

Yet when the researchers translated those carbon dioxide anomalies into temperature, the picture reversed. The temperature change attributable to past emissions turned out to be larger in a lower-emissions future, even though the atmospheric carbon dioxide anomaly was smaller. The explanation is the logarithmic relationship between carbon dioxide concentration and radiative forcing: each additional part per million of carbon dioxide traps less additional heat than the part per million before it. In a high-emissions future, the background concentration is already so elevated that the corporate contribution sits on the flat, less sensitive portion of the forcing curve. In a low-emissions future, where concentrations are brought down substantially, the same corporate contribution occupies a steeper portion of the curve and therefore exerts a proportionally larger influence on temperature. In other words, the more successfully the world decarbonizes, the more visible the fingerprint of historical emitters becomes in the remaining warming.

Methane added a further twist. The simulations showed that after roughly 2080, the methane concentration anomaly attributable to the Carbon Majors turns negative, meaning the counterfactual world without their emissions actually contains more methane than the factual one. This counterintuitive result stems from atmospheric chemistry: methane’s lifetime in the atmosphere depends on temperature, and a warmer climate shortens the chemical lifetime of methane by accelerating its removal. The historical warming caused by corporate emissions therefore acts as a small negative feedback on methane concentrations in later centuries, partially offsetting—but nowhere near fully compensating for—the positive forcing from carbon dioxide.

Across every scenario the researchers examined, the net effect of the Carbon Majors’ historical emissions remained a warming one. The perturbations propagate through many linked components of the climate system: carbon dioxide in the atmosphere and in other reservoirs of the carbon cycle, methane, ozone, and stratospheric water vapor, the latter two arising as chemical byproducts of methane oxidation. When all of these contributions to radiative forcing are summed, the total stays positive throughout the simulations running to 2500, and positive forcing translates directly into positive temperature perturbations. There is no scenario in the study in which the corporate legacy simply cancels itself out.

The implications extend well beyond academic attribution. Courts, legislatures, and communities increasingly ask not just who caused climate change, but who bears responsibility for damages that have not yet occurred. This study demonstrates that such future damages are, in principle, calculable on timescales of centuries. Because the temperature perturbation attributable to each entity can be tracked through a physically consistent model under any given emissions pathway, the framework could in theory be coupled with damage functions and sea level projections to quantify ongoing liabilities. The researchers emphasize that the attributable warming is not a fixed historical quantity but a pathway-dependent one, which means that choices made in the coming decades will shape how the responsibility of past emitters is expressed in the climate of the future.

The study also carries a sobering message for the general public: the climate consequences of emissions already released are effectively locked in for many generations, regardless of how quickly the world now decarbonizes. Even in optimistic scenarios, the simulations show the Carbon Majors’ historical emissions continuing to raise global temperatures for centuries, interacting with carbon cycle feedbacks, atmospheric chemistry, and the logarithmic physics of radiative forcing in ways that keep their influence positive and quantifiable. As Frierson and Henrie conclude, in addition to the damages that have already occurred, these entities can be expected to be responsible for calculable climate damages for many centuries to come—a legacy written into the physics of the atmosphere itself.

Subject of Research: Attribution of centuries-long global warming to major fossil fuel and cement producers under future emissions scenarios

Article Title: Centuries of global heating from Carbon Majors: Dependence on future emissions pathways

Article References: Centuries of global heating from Carbon Majors: Dependence on future emissions pathways. (n.d.). https://doi.org/10.1371/journal.pclm.0001036

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0001036

Keywords: climate attribution, Carbon Majors, FaIR model, fossil fuel emissions, carbon cycle feedbacks, radiative forcing, methane lifetime, counterfactual simulations, PLOS Climate, global warming, emissions scenarios, climate damages

The mathematical backbone of the study is the Vector Autoregressive model, or VAR, a framework that treats a climate time series as the output of a random process whose present state depends on its own recent past. In its extended form, known as VARX, the model also includes exogenous inputs, meaning externally specified drivers such as greenhouse gas forcing, anthropogenic aerosols, volcanic and solar influences, and the seasonal cycle. In this picture, a climate simulation and an observational record are each realizations of a stochastic process characterized by three sets of parameters: autoregressive coefficients that capture memory and internal variability, transfer coefficients that describe how the system responds to external forcing, and a noise covariance matrix that describes the random fluctuations left over after these components are accounted for. If two time series come from the same underlying process, their parameters should be equal, and the question of model realism becomes a formal hypothesis test.

What makes the new work a genuine advance is what it refuses to assume. Earlier papers in the same series, published between 2020 and 2024, built up a hierarchy of tests for comparing climate time series, but all of them rested on the restriction that the noise covariance matrices of the two series being compared were identical. That assumption is convenient because it yields closed-form maximum likelihood estimates, but it is physically awkward. Noise variances in observations and simulations can differ by large factors, and in the study’s data the authors found differences of up to a factor of eight for a given spatial domain. Under such heterogeneity, pooling the data as if the noise were identical can distort the comparison. The new test removes the restriction entirely, extending earlier univariate results by Grant and Quinn to multivariate models with external forcing.

Dropping the equal-noise assumption comes at a mathematical price. Without it, the maximum likelihood estimates no longer have closed-form solutions, so the authors derived an iterative algorithm. The key insight is that under the constrained hypothesis, where a chosen subset of coefficients is forced to be equal across the two models, the shared coefficients satisfy a linear matrix equation whose solution can be written using Kronecker products. Because the noise covariance matrices themselves depend on those coefficients, the system is nonlinear and must be solved by iteration: estimate the coefficients, update the covariances, re-estimate the coefficients, and repeat. The authors report that the scheme converges rapidly, with the relative change in log-likelihood falling below one percent by the fourth iteration in 97 percent of all pairwise comparisons, allowing them to simply stop after four iterations.

The test statistic itself is a deviance, essentially a likelihood ratio measuring how much worse the data fit when the parameters are constrained to be equal. Under standard asymptotic theory, this statistic should follow a chi-squared distribution when the null hypothesis of parameter equality is true, with degrees of freedom determined by the number of constrained parameters. The authors also applied a Bartlett-style finite-sample correction, replacing raw sample sizes with residual degrees of freedom, which improved the agreement with theory. But Monte Carlo experiments revealed a subtlety that matters for anyone using the method: when the number of constrained parameters is small, the chi-squared approximation underestimates the upper tail of the true sampling distribution. In practice, this means the test rejects the null hypothesis too often, with empirical false-positive rates reaching about 20 percent when a nominal 5 percent level was intended.

The remedy is pragmatic rather than elegant. By adopting a more stringent nominal significance level, typically between 0.5 and 2 percent, users can bring the actual false-positive rate back down to the intended 5 percent. The authors verified this calibration through an enormous simulation exercise: they fitted VARX models to observations and to 27 distinct CMIP6 climate models, then generated synthetic data roughly 3.5 million simulated years in total, enough to map out the true distribution of the deviance statistic for each hypothesis. Remarkably, the whole computation ran in a few hours on a standard laptop, a reminder that some of the most consequential questions in climate statistics can be answered with modest computational resources when the mathematics is done well.

With the method validated, the authors turned it loose on real data. They used monthly mean two-meter air temperature from the ERA5 reanalysis as the observational benchmark and compared it against historical simulations from the Coupled Model Intercomparison Project Phase 6, or CMIP6, including 108 simulations in the final analysis. To keep the comparison clean, they restricted everything to the overlapping period 1950 to 2014, avoiding ambiguities from non-overlapping records, and aggregated global temperature fields into five broad regions combining tropical, Northern Hemisphere, and Southern Hemisphere land and ocean domains. The forcing inputs included well-mixed greenhouse gases, anthropogenic aerosols, and natural forcings drawn from the IPCC Sixth Assessment Report, along with six annual harmonics and an intercept to capture the seasonal cycle and climatological mean.

The results are striking. When testing whether the transfer coefficients for radiative forcing match those inferred from observations, roughly 90 percent of the CMIP6 models showed statistically significant differences at the nominal 5 percent level, and even at the more conservative 0.5 percent threshold, 72 percent still differed. For the autoregressive coefficients, which govern the memory and internal variability of the simulated climate, the picture was even more pronounced: 94 percent of models exceeded the 5 percent threshold and 87 percent exceeded the 0.5 percent threshold. When both sets of coefficients were tested together, every single CMIP6 model examined differed significantly from ERA5. The authors are careful to note that each test is interpreted model by model rather than as a global hypothesis across all models, but the pattern is difficult to dismiss as statistical noise, since the detected differences far exceed the 5 percent false-positive rate expected if the models were consistent with observations.

Why do these particular parameters matter so much? The autoregressive coefficients determine how temperature anomalies persist and decay, effectively encoding the climate system’s memory and its predictability on monthly to interannual timescales. The transfer coefficients determine how strongly the system responds to greenhouse gases, aerosols, and natural forcings, which is central to projections of future warming. Discrepancies in either quantity suggest systematic differences between the simulated and observed climate, and the study found evidence of differences in both internal variability and forced response. The authors also checked whether omitted forcings might be contaminating the results by examining nearly 29,000 pairwise correlations among model residuals, finding them small enough to account for less than one percent of the variance, which supports the interpretation that the detected differences are real rather than artifacts of missing drivers.

The framework has limits that the authors acknowledge candidly. It assumes Gaussian processes of modest dimension, so variables with strong nonlinearities, regime behavior, or long-memory dynamics may require extensions, and the dominant external forcings must be known and explicitly included. Yet the approach generalizes naturally to leading principal components of spatial fields and to multivariate state vectors, placing it alongside established tools such as Linear Inverse Models. For a field increasingly scrutinized over how well its models capture both natural fluctuations and human-driven change, the new test offers something rare: a statistically defensible, assumption-light way to ask not just whether models differ from reality, but exactly which aspects of climate behavior they get wrong. The answer, for most CMIP6 models, appears to be more aspects than previously could be proven.

Subject of Research: A likelihood ratio test for equality of autoregressive and transfer coefficients in vector autoregressive climate models with unequal noise covariances, applied to compare CMIP6 simulations with ERA5 observations

Article Title: Comparing climate time series – Part 6: Testing equality of autoregressive parameters without assuming equality of noise variances

Article References: DelSole, T., & Tippett, M. K. (2026). Comparing climate time series – Part 6: Testing equality of autoregressive parameters without assuming equality of noise variances. Advances in Statistical Climatology, Meteorology and Oceanography, 12(1), 73-86. https://doi.org/10.5194/ascmo-12-73-2026

Image Credits: AI Generated

DOI: 10.5194/ascmo-12-73-2026

Keywords: climate models, time series analysis, vector autoregressive models, CMIP6, ERA5, likelihood ratio test, noise covariance, radiative forcing, internal variability, hypothesis testing, statistical climatology, temperature variability

The team’s central finding is that the rate at which an aerosol plume spreads sideways through the marine atmosphere, while genuinely variable, barely matters for the total cooling that the Twomey effect can deliver. That result surprised the researchers, because plume spreading rates turned out to differ dramatically between the two meteorological regimes they examined. In their simulations of the northeast Pacific, the second day of a two-day air-mass trajectory, characterized by deeper boundary layers, stronger turbulence, and cleaner background air, produced plume spreading rates that exceeded the first day’s rates by more than 2 kilometers per hour at certain times. Yet when the researchers swapped the turbulence fields between the two regimes while holding everything else fixed, the resulting change in global mean Twomey forcing was smaller than 1 percent.

To reach that conclusion, the team built a modeling framework with two interlocking components. The first was a library of 54 two-day large-eddy simulations, or LES, of northeast Pacific stratocumulus clouds, run at 100-meter horizontal grid spacing over domains 51.2 kilometers on a side. These simulations were initialized along realistic Lagrangian trajectories derived from ERA5 reanalysis data, spanning the meteorological and aerosol conditions of summers from 2018 to 2021. Because LES explicitly resolves much of the energy-containing turbulence in the boundary layer, quantities like horizontal velocity variance and turbulence dissipation rate, which are usually parameterized and uncertain in coarser models, could be read directly from the simulated flow field. From the full library, the researchers selected 17 cases with median cloud fractions above 50 percent, which showed the best agreement with satellite observations from CERES and represent the most plausible candidate environments for cloud brightening.

The second component was a Langevin particle model, a statistical technique that describes the motion of individual aerosol parcels as a combination of a deterministic drift toward the mean wind and a random walk whose intensity is set by the resolved turbulence. Instead of solving the full fluid equations on a grid, which would have been computationally prohibitive across hundreds of injection scenarios, the particle model stochastically simulates thousands of superparticles released from moving ship-like sources on a 200-by-200-kilometer domain. Each superparticle represents a vast multiplicity of real aerosol particles, injected at a rate of 10 to the 16th particles per second, and the model tracks their positions, ages, and eventual removal through exponential decay timescales ranging from a quarter of a day to two days. The approach had already been validated against explicit LES of individual ship tracks in earlier work by the same group.

The clever step was merging the two components. At 20-minute intervals, the two-dimensional aerosol perturbation fields from the particle model were overlaid onto the two-dimensional reflectance fields from the LES, which preserve the detailed cloud morphology of each simulated scene. A modified version of Twomey’s albedo susceptibility equation then converts the local ratio of perturbed to background aerosol concentration into a change in reflectance, which is multiplied by the incoming solar radiation to yield the instantaneous radiative forcing in each grid column. Averaging over the domain and time, and scaling by an assumed sprayed area of about 3 percent of the global ocean surface, produces a global mean Twomey forcing. The resulting magnitudes, roughly 1.8 watts per square meter for a mid-range deployment injecting about 8 teragrams of salt per year, sit in a plausible range compared with earlier heuristic estimates, though somewhat larger because the sprayers in this study are concentrated in the cloudiest regions.

When the researchers probed why the forcing was so insensitive to spreading rate, a compensating web of effects emerged. In a sensitivity analysis, deeper boundary layers and lower cloud fractions on the second day of the trajectories each acted to reduce Twomey forcing by 10 to 20 percent, while mean winds had a negligible influence. But the cleaner background aerosol environment on day two boosted the forcing by nearly 50 percent, offsetting the losses. The physics behind this compensation is the nonlinearity at the heart of the Twomey effect: cloud albedo is most susceptible to added droplets when the background air is clean, so the same injection produces more brightening in pristine conditions. Faster spreading also reshapes the distribution of aerosol concentrations, reducing the frequency of the smallest concentrations and increasing the frequency of extreme ones, but these distributional shifts largely cancel out in the domain average.

The study also delivered practical guidance for anyone designing a cloud brightening deployment. Perhaps most strikingly, the forcing proved almost entirely insensitive to sprayer velocity across the range from stationary to 10 meters per second, as long as the ambient mean winds were realistic. Only in artificial no-wind experiments did slow-moving sprayers lose efficiency dramatically, because the plumes then pooled into saturated concentrations where additional aerosol yields diminishing returns. This suggests that stationary or slow-moving platforms could slash the energy and mechanical demands of a deployment without sacrificing radiative efficacy. The team also found that extending daily injections from 6 to 12 hours increases forcing by roughly 50 percent, and that starting injections a few hours before sunrise, rather than after, enhances forcing by 10 to 25 percent, consistent with earlier modeling by Jenkins and colleagues.

Where spreading rate does matter is in the assumptions embedded in global climate models. Most GCM studies of marine cloud brightening use uniform subgrid aerosol concentrations, an implicit assumption that plumes spread infinitely fast within a model grid box. The researchers tested the consequences using idealized simulations with a single fixed source and unity cloud fraction, designed to maximize the importance of spreading. Comparing the fastest and slowest spreading cases in their library, which differed by daily-average rates of 3.18 versus 0.2 kilometers per hour, they found that ignoring the natural range of spreading could bias forcing by 5 to 30 percent at low sprayer densities. But the infinite-spreading assumption produces far larger errors: at ship densities below 2.5 times 10 to the minus 4 ships per square kilometer, global mean Twomey forcing could be overestimated by 10 to 200 percent, depending on the assumed aerosol lifetime, and even high-end deployments could see errors above 10 percent when aerosol lifetimes fall below one day.

Indeed, aerosol lifetime emerged as the leading-order control on brightening potential, overshadowing every meteorological variable the team tested. Quadrupling the number of sprayers only doubled the forcing at a two-day lifetime, a signature of saturation, whereas changing the lifetime reshaped the forcing far more substantially. Particles older than one day contributed between 25 and 65 percent of the total forcing depending on the decay assumption, and the contribution of three-day-old particles swung from a trivial 2 percent at a one-day lifetime to 41 percent at a four-day lifetime. Because lifetime is governed by poorly constrained processes such as collision-coalescence scavenging in precipitating clouds, which can remove aerosols within hours, versus the two to three days suggested by non-precipitating LES, narrowing this uncertainty is arguably more urgent than refining plume dynamics.

The authors are careful to note the limits of their framework. By coupling aerosol fields offline to the LES reflectance fields, they assumed that all injected aerosol activates into droplets, an upper-bound choice, and that cloud adjustments, changes in liquid water path and cloud fraction triggered by the aerosol itself, are negligible compared with the Twomey effect. Observational studies increasingly support the latter assumption, but recent work suggests that in clean, precipitating regimes, cloud adjustments could enhance brightening, meaning the true picture may be more favorable than these estimates. The team also derived a promising statistical relationship, showing that the product of boundary-layer turbulent kinetic energy and the Lagrangian relaxation timescale explains roughly 70 percent of the variance in spreading rate once the first ten hours after injection are excluded, offering a ready-made parameterization for global models that carry these quantities. For now, the message is twofold: nature’s variability in plume spreading is unlikely to derail cloud brightening, but the convenient fiction of instantaneous spreading in climate models may be quietly inflating its promised cooling, and fixing that could be as simple as assuming a finite spread of about 1.5 kilometers per hour.

Subject of Research: Sensitivity of the Twomey cloud-albedo effect to aerosol plume spreading rates in marine stratocumulus and implications for Marine Cloud Brightening

Article Title: Estimating Twomey forcing sensitivity to aerosol plume spreading rates

Article References: McMichael, L. A., Erfani, E., Wood, R., & von Salzen, K. (2026). Estimating Twomey forcing sensitivity to aerosol plume spreading rates. Atmospheric Chemistry and Physics, 26(19), 14111-14132. https://doi.org/10.5194/acp-26-14111-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-14111-2026

Keywords: Twomey effect, Marine Cloud Brightening, aerosol plume spreading, marine stratocumulus, large-eddy simulation, Langevin particle model, cloud albedo, aerosol lifetime, boundary layer turbulence, climate models, radiative forcing, northeast Pacific

Surface albedo, the fraction of incoming solar radiation that a surface reflects back toward the sky, is a recognized driver of climate change in the physical science literature. Snow-covered boreal forests, dark conifer canopies, bright croplands, and grey urban rooftops each interact with sunlight in profoundly different ways. When land cover changes, so does the amount of solar energy absorbed at the surface, producing a radiative forcing that can either warm or cool the planet independently of any carbon flux. The Intergovernmental Panel on Climate Change acknowledges these biogeophysical effects, and studies stretching back to Robert Betts’ landmark 2000 paper in Nature have shown that afforestation in snowy high latitudes can offset, or even reverse, the carbon benefits of new trees because forests are darker than snow. Yet despite this well-established physics, albedo impacts are rarely quantified alongside greenhouse gas emissions in the assessment frameworks that actually inform decisions.

The barrier, the authors argue, has been data. To calculate the albedo change caused by converting one land cover type to another, an analyst needs albedo values for both the original and the new land cover at the same location, under the same atmospheric conditions, and at the same time of year. Existing simplified methods typically rely on single, static albedo values per land cover type, values that do not capture how albedo fluctuates through the day as the sun’s angle changes, nor how it varies with real atmospheric conditions such as cloud cover and aerosol loading. Albedo is not a fixed property of a surface: it depends on solar zenith angle, on whether the sky is clear or cloudy, on snow cover, and on the structure of vegetation canopies. Averaging all of that complexity into one number per land cover type introduces systematic error, and until now there has been no dataset designed specifically to support the direct calculation of albedo change between land cover types.

Loog and her colleagues set out to close that gap with a methodology that estimates daily-mean blue-sky albedo values for multiple land cover types at the same location. The term blue-sky is significant: it refers to albedo under realistic, mixed atmospheric conditions rather than the idealized clear-sky or entirely overcast scenarios that many remote sensing products assume. The approach draws on MODIS satellite data, accessed through Google Earth Engine, including the MODIS BRDF and albedo model parameters and land cover products, combined with ERA5 reanalysis data from the Copernicus Climate Data Store to characterize atmospheric conditions. By anchoring the estimates to real, spatially specific observations and weighting them toward realistic skies, the methodology produces albedo values that reflect what the atmosphere actually encounters, not a laboratory idealization.

The result is a global climatological dataset of surface albedo values for 22 distinct land cover types, each resolved in space and time. A crucial validation step was statistical: the researchers tested whether the albedo values assigned to each land cover type were genuinely distinguishable from one another, and found that all 22 types were statistically distinct. That matters because the entire premise of land-cover-based albedo accounting rests on the assumption that different land covers have meaningfully different reflectivities. If the distributions overlapped heavily, converting between two types would produce noise rather than signal. The statistical separation confirms that the dataset carries real, physically meaningful information about how land cover shapes the surface energy balance.

The comparison with existing simplified methods is where the study delivers its most striking numbers. When the new dataset was set against values derived from conventional approaches that ignore diurnal fluctuations and realistic atmospheric conditions, the simplified values underestimated surface albedo by 2.7 to 5.8 percent on average. That may sound modest, but the distribution of errors is wide: in some cases the discrepancies reached more than plus or minus 50 percent. For an analyst calculating the climate impact of converting cropland to forest, or of an urban expansion project, an error of that magnitude in the underlying albedo values could flip the sign of the estimated radiative forcing, turning a projected warming into a projected cooling or vice versa. In other words, the simplified numbers are not just imprecise; in edge cases they can be qualitatively wrong.

Why does the diurnal cycle matter so much? Albedo generally increases as the sun sits low in the sky, because light strikes surfaces at grazing angles and interacts with canopy structure and roughness in ways that enhance reflection. A daily-mean albedo that properly weights this variation, hour by hour, differs systematically from a snapshot taken when the satellite happens to pass overhead, typically in mid-morning when the sun is high and albedo is near its daily minimum. Atmospheric conditions compound the effect, since clouds and aerosols alter both the intensity and the angular distribution of incoming radiation. By integrating these effects, the new methodology produces values that better represent the true daily exchange of energy between the surface and the atmosphere, which is precisely the quantity that radiative forcing calculations need.

The practical implications reach into several influential arenas. In life cycle assessment, the standard tool for evaluating the environmental footprint of products and services, land use impacts have long been dominated by biogeochemical considerations such as carbon stock changes, while biogeophysical effects like albedo have remained optional or absent. Researchers including R. M. Bright and collaborators have proposed metrics for converting albedo-induced radiative forcing into carbon dioxide equivalence, and case studies on biofuels, forestry, pavements, and greenhouse agriculture have demonstrated that albedo can rival carbon in importance for certain land-intensive systems. A reliable, globally consistent albedo dataset removes one of the main technical excuses for leaving these effects out. The same logic applies to national and corporate carbon accounting under frameworks such as the Greenhouse Gas Protocol, whose land sector guidance has been grappling with how to treat biogeophysical impacts, and to the evaluation of nature-based climate solutions, where recent studies have warned that tree planting at northern high latitudes may be far less climate-positive than carbon accounting alone suggests.

The dataset and the tooling around it are openly available. The albedo dataset itself is deposited in a Zenodo repository, all input datasets are public, and the authors have released their scripts on GitHub, including a Python-based tool that lets users retrieve albedo values at a 500-meter spatial scale, matching the resolution of the MODIS land cover products. Mann-Kendall trend test results and proxy comparison outputs are available in a separate repository. This openness lowers the barrier for practitioners in life cycle assessment, carbon accounting, and policy analysis to begin experimenting with albedo-inclusive assessments, and it invites scrutiny and improvement of the methodology by the wider research community.

There remain challenges on the road from dataset to decision. Translating albedo change into a CO2-equivalent metric involves methodological choices about time horizons and the decay of radiative forcing that are still actively debated in the literature. The dataset is climatological, describing typical conditions rather than year-to-year variability, and land cover classifications at 500-meter resolution inevitably simplify the mosaic of real landscapes. Nonetheless, the study provides what has arguably been the missing ingredient: a defensible, spatially and temporally specific, physically realistic set of albedo values that can be dropped directly into impact assessment calculations. As governments and corporations increasingly lean on land-based climate strategies, from reforestation to regenerative agriculture to urban greening, the energy reflected back to space by those landscapes will no longer be an afterthought. With this dataset, the mirror-like half of the land-climate equation finally has numbers that accountants, assessors, and policymakers can work with.

Subject of Research: Surface albedo effects of land cover change for climate impact assessment

Article Title: Enhancing climate impact assessment with surface albedo effects of land cover changes: methodology and dataset

Article References: Loog, K., Bjørn, A., & Margni, M. (2026). Enhancing climate impact assessment with surface albedo effects of land cover changes: methodology and dataset. Journal of Industrial Ecology, 30(4), 1869-1884. https://doi.org/10.1007/s44498-026-00127-8

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00127-8

Keywords: surface albedo, land cover change, climate impact assessment, life cycle assessment, radiative forcing, MODIS, remote sensing, carbon accounting, land use, climate policy, biogeophysical effects, environmental impact assessment

Black carbon, the fine particulate matter produced by incomplete combustion of fossil fuels, biomass and other carbonaceous fuels, is one of the most important short-lived climate forcers in the atmosphere. Unlike carbon dioxide, which persists for centuries, black carbon warms the atmosphere intensely but briefly, absorbing sunlight and heating the air around it. Yet global climate assessments have long treated black carbon as a single, uniform compound. The new study argues that this simplification conceals a critical truth: black carbon exists as a continuum of particles with sharply different physical, chemical and optical properties, and those differences matter enormously for climate.

To resolve this heterogeneity, an international team led by researchers at the Southern University of Science and Technology in Shenzhen, together with colleagues at the Max Planck Institute for Chemistry and other institutions, assembled approximately 2,500 thermal–optical measurements of carbon fractions across a wide range of emission sources. Thermal–optical analysis separates elemental carbon into operationally defined fractions, allowing the researchers to distinguish between two fundamentally different subtypes of black carbon: char and soot. Char is the less light-absorbing, lower-maturity form, typically produced at lower combustion temperatures, while soot is the highly graphitized, strongly light-absorbing form generated by hot, efficient combustion processes.

Using this measurement database, the team constructed a global inventory that separately tracks char and soot emissions from 1750 to 2019, spanning the full arc of industrialization. The results reveal a striking transformation. In 1750, soot accounted for only about 16 percent of total anthropogenic black carbon emissions. By 2019, that fraction had climbed to roughly 35 percent. The driver of this shift is unmistakable: the relentless growth of fossil fuel consumption. Coal combustion, diesel engines and other high-temperature fossil fuel sources produce proportionally far more soot than the smoldering biomass burning and traditional domestic fires that dominated the pre-industrial world.

The optical consequences of this compositional change are profound. Soot absorbs light far more efficiently per unit mass than char, a property quantified by the mass absorption cross section. As the soot fraction of emissions has grown, the average mass absorption cross section of anthropogenic black carbon has risen accordingly, meaning that contemporary emissions trap more solar energy per kilogram than their historical counterparts. The researchers used radiative transfer calculations, including Lorenz–Mie theory and multiple-sphere T-matrix methods, to quantify how the changing char–soot mixture alters the absorption properties of the atmospheric black carbon burden, and coupled these estimates with simulations using the Community Earth System Model version 2.1.

Crucially, the inventory’s story is not built on models alone. The shift toward a higher soot fraction aligns with independent evidence preserved in sedimentary records. Lake sediments, such as the 150-year records from Lake Phayao in northern Thailand and from lakes on the Tibetan Plateau, archive the deposition of soot and char separately, and these archives corroborate the temporal trends reconstructed from the emission inventory. The agreement between bottom-up accounting and geological archives lends considerable weight to the central conclusion: the global warming potential of atmospheric black carbon, per unit of mass emitted, has increased since the mid-eighteenth century.

The implications ripple through climate science. Because climate models and assessments such as those of the Intergovernmental Panel on Climate Change generally treat black carbon as a single compound, they may misestimate its historical and present-day radiative forcing. If today’s black carbon is more absorbing than the black carbon of 1750, then the radiative forcing attributed to a given mass of emissions has grown over time, and the climate system’s response to soot reductions will differ from what single-compound models predict. Resolving black carbon subtypes in climate models, the authors argue, is essential for accurate estimation of black carbon’s contribution to observed warming and for projecting its future role.

The findings also carry a pointed message for mitigation policy. Not all black carbon sources are equal. Soot-rich sources, dominated by fossil fuel combustion in transport, industry and power generation, deliver far more warming per unit of emissions than char-dominated sources such as residential biomass burning. Prioritizing soot-rich sources for emission controls would therefore yield disproportionate climate benefits in the near term, a particularly attractive proposition given black carbon’s short atmospheric lifetime. Cutting soot-heavy emissions today produces almost immediate cooling, unlike reductions in long-lived greenhouse gases whose benefits unfold over decades to centuries.

The study also intersects with a broader and sometimes counterintuitive debate in aerosol–climate science. As societies clean up particulate pollution for public health reasons, some of the masking effect of reflective aerosols is lost, potentially accelerating near-term warming. Because black carbon is absorbing rather than reflecting, distinguishing its subtypes becomes even more important when weighing the climate consequences of air quality policies. A more absorbing black carbon burden means that the warming side of the aerosol ledger is heavier than previously assumed, sharpening the urgency of targeted controls on the most light-absorbing emissions.

By fusing thousands of laboratory and field measurements, a 270-year emission reconstruction, optical theory and Earth system modeling, the research delivers one of the most detailed portraits yet of how humanity has changed not just the quantity but the quality of the soot it injects into the atmosphere. The black carbon of the Anthropocene is darker, more absorbing and more warming than the black carbon of the age of sail and charcoal, and recognizing that evolution is a necessary step toward both sharper climate projections and smarter, faster-acting mitigation strategies.

Subject of Research: Historical increase in the global warming potential of atmospheric black carbon driven by a rising soot fraction from fossil fuel combustion since 1750

Article Title: Increase in global warming potential of atmospheric black carbon since 1750

Article References: Shen, H., Shen, H., Cheng, Y., Andreae, M. O., Jiao, X., Liang, X., Mai, Z., Guo, P., Zheng, L., Peng, X., Du, W., Wang, R., Zhang, G., Zhai, J., Zeng, Y., Sun, H. Z., Chen, Y., Wang, C., Huang, T., … Zhang, G. (2026). Increase in global warming potential of atmospheric black carbon since 1750. Nature Geoscience. https://doi.org/10.1038/s41561-026-02085-z

Image Credits: AI Generated

DOI: 10.1038/s41561-026-02085-z

Keywords: black carbon, soot, char, short-lived climate forcer, fossil fuel combustion, radiative forcing, mass absorption cross section, emission inventory, thermal-optical analysis, climate modeling, sediment records, Nature Geoscience