The tool, called METEORv1.6, is the latest evolution of a climate emulation framework first introduced by the same group. At its heart lies a technique known as pattern scaling, which decomposes a climate model’s response to forcing into spatial patterns that evolve over characteristic timescales. Where traditional pattern scaling assumes that the same spatial fingerprint of warming applies regardless of what caused it, METEOR takes a more sophisticated view. It represents the climate response as a sum of impulse response patterns, each with its own emergence timescale, driven by the forcing trajectory of each agent separately. Greenhouse gas patterns are derived from an idealized experiment in which carbon dioxide concentrations are abruptly quadrupled, while aerosol responses are estimated from residual signals in concatenated historical and future simulations. The result is a model that can capture hysteresis effects and time-evolving responses, which are essential for scenarios involving temperature overshoots or shifting mixes of short-lived and long-lived forcers.

Version 1.6 adds two capabilities that the original framework lacked. The first is a monthly climate variability model, dubbed METEOR-NOISE, which generates realistic sub-annual climate sequences complete with seasonal cycles and year-to-year fluctuations. The second is a modular impact assessment framework that translates those climate projections directly into sector-relevant metrics. The initial implementation focuses on heating and cooling degree days, quantities that drive estimates of energy demand for buildings and that cannot be meaningfully computed from annual mean temperatures alone. Together, these additions turn METEOR from an annual-resolution pattern scaler into an end-to-end pipeline that runs from emission pathways to probabilistic impact estimates at the monthly resolution that sectoral applications actually require.

The technical machinery behind METEOR-NOISE is a two-stage statistical construction. First, the model characterizes how the seasonal cycle itself changes as the planet warms. At every grid point, a harmonic regression captures the annual and semi-annual cycles using sine and cosine functions, but crucially the amplitudes and phases of those harmonics are allowed to modulate linearly with smoothed global mean temperature. This temperature-dependent parameterization lets the model represent genuinely non-stationary seasonal behavior, such as the well-documented pattern in which Arctic winters warm far faster than summers as sea ice retreats, shifting both the amplitude and the timing of the seasonal cycle. The regression uses just nine features per location, a remarkably compact description that nevertheless captures spatially varying changes in seasonality across the globe.

What remains after the seasonal signal is removed are the monthly anomalies: the chaotic, weather-driven fluctuations that no deterministic model can predict individually but whose statistical structure can be learned. METEOR-NOISE applies Principal Component Analysis to these anomalies, identifying by default forty spatial patterns that together explain the bulk of the variance. For surface temperature, the seasonal model accounts for roughly 91 percent of total variance, and the forty principal components capture 75 to 80 percent of what remains, for a combined total near 98 percent. Precipitation, being inherently noisier and strongly skewed, is harder: the seasonal model explains about 32 percent of its variance, and the components add another 29 to 45 percent. The leading patterns often project onto recognizable modes of variability such as the El Niño-Southern Oscillation or the North Atlantic Oscillation, though the authors are careful to note that the components are treated as a statistical basis for reproducing covariance rather than as physically labelled processes.

The temporal evolution of those forty pattern amplitudes is then modeled with a Vector Autoregressive process with exogenous variables, or VARX. Each month’s value of every component depends on the previous two months’ values of all components, plus global temperature and a seasonal signal as external predictors, plus a random shock drawn from a multivariate Gaussian distribution whose covariance matrix is learned directly from the training data. This design captures three things at once: persistence, since anomalies tend to linger for months; teleconnections, since activity in one spatial pattern can influence others with a lag; and modulation by the warming trend itself. It is a meaningful departure from approaches that apply independent autoregressive processes at each grid point, because the cross-mode dependencies preserve the spatial coherence that makes regional climate statistics realistic.

Training is strikingly frugal. The model learns from a single ensemble member of a CMIP6 Earth system model, concatenating the historical simulation with a mid-range future scenario to build a record of roughly 250 years, supplemented by the carbon dioxide quadrupling experiment for the core pattern scaling. Once trained, generation is fast: producing one hundred ensemble members of regional or global mean temperature spanning 350 years at monthly resolution takes about ninety seconds on a standard laptop, and point-based extraction for a single city takes roughly ten seconds, meaning thousand-member ensembles can be generated in under two minutes. Training itself takes between thirty and sixty seconds. The trick is that regional means can be computed directly from the forty principal component time series without ever reconstructing full spatial fields, reducing memory demands by two to three orders of magnitude.

Validation against CMIP6 output suggests the framework earns its keep. When trained on the SSP2-4.5 scenario of the Norwegian Earth System Model, the emulator reproduces the distribution, spread, and seasonal structure of the source model’s output across global, regional, and city scales, and captures long-term trends through 2100. In city-level tests, the emulator reproduces the shrinking seasonal temperature cycle in Delhi under warming and the near-total absence of cooling demand in Oslo until the middle of the century, a threshold behavior it was never explicitly trained to represent. More demanding still, when trained only on SSP2-4.5 and then asked to project scenarios it had never seen, including a strong mitigation pathway, a high-emissions pathway, and a temperature overshoot scenario, the framework tracked the non-monotonic temperature trajectories of five different Earth system models across all spatial scales. Temperature skill ratios comparing emulated members against the actual model output cluster near unity, indicating the realizations are statistically indistinguishable from genuine ensemble members.

The framework is candid about its limits. Precipitation changes under the most extreme emissions scenario are somewhat underestimated, reflecting the stronger nonlinearity of hydrological responses, and some regional precipitation ensembles prove slightly underdispersive. The Gaussian noise assumption likely understates the probability of extremes, with diagnostics showing heavier-than-Gaussian tails, and the authors suggest future extensions using t-distributed innovations or empirical resampling. Nonlinear catastrophes such as a collapse of the Atlantic Meridional Overturning Circulation, and novel interventions like solar radiation management, remain beyond the reach of any pattern scaling approach. Daily and sub-daily generation, needed for extreme event statistics, is on the roadmap.

What makes METEORv1.6 potentially transformative is less any single statistical trick than the combination: a physically motivated, forcing-aware core paired with a statistically calibrated variability model and an extensible impact layer, all open-source and fast enough for interactive use. The authors envision it embedded within integrated assessment models, supplying spatially resolved, probabilistic climate feedbacks in real time as users explore alternative emission pathways. In a field where the distance between an emission choice and its regional consequences has traditionally been measured in months of supercomputing time, a tool that makes that relationship explorable on demand represents a genuine shift in what climate risk assessment can look like.

Subject of Research: Development and validation of the METEORv1.6 climate emulator for monthly climate variability and integrated impact assessment

Article Title: METEORv1.6: Spatial climate variability and integrated impact emulation

Article References: Sanderson, B. M., Sandstad, M., Dewey, M., Steinert, N. J., & Fjeldså, J. L. (2026). METEORv1.6: Spatial climate variability and integrated impact emulation. Geoscientific Model Development, 19(19), 9263-9288. https://doi.org/10.5194/gmd-19-9263-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9263-2026

Keywords: climate emulation, pattern scaling, METEOR, CMIP6, seasonal cycle, internal variability, PCA, VARX, degree days, energy demand, climate risk assessment, Geoscientific Model Development

The research, led by Fatemeh Salehipour Bavarsad of the Czech Technical University in Prague together with colleagues at Shahrood University of Technology and Osaka Metropolitan University, tackles this blind spot head-on. The team developed high-resolution, site-specific urban weather datasets that explicitly account for the urban heat island effect, the well-documented phenomenon in which cities run several degrees warmer than their rural surroundings because asphalt, concrete and building mass absorb and re-emit heat while vegetation and moisture are scarce. Rather than treating the city as a passive backdrop, the researchers coupled statistically downscaled future climate projections with a physics-based urban canopy model known as the Urban Weather Generator, or UWG, to produce multiple urbanized future weather files tailored to a specific urban site.

The Urban Weather Generator works by taking an upstream rural weather signal and transforming it through a physically grounded representation of the urban fabric. It accounts for how much of the site is covered by buildings, how tall those buildings are, how closely they are packed, the thermal properties of their walls and roofs, and the heat released by human activity and traffic. As solar radiation heats horizontal surfaces during the day and those surfaces release stored energy at night, the model computes the resulting air temperature differences between the rural reference and the urban site, hour by hour. This means the output is not a generic city correction but a weather file that reflects the actual three-dimensional geometry and material composition of the neighborhood surrounding a given building.

To disentangle the many forces at play, the team designed a structured three-scenario framework. The first scenario isolates regional climate change, asking how a future atmosphere alone alters building behavior. The second adds the urban heat island effect on top of that changed climate, capturing the extra thermal burden that urbanization imposes. The third goes further and incorporates the surrounding urban morphology, meaning the actual arrangement of neighboring buildings and their radiative and shading interactions with the building under study. By comparing results across these scenarios, the researchers could attribute changes in building performance to each driver separately, and, crucially, observe how the drivers interact with one another in ways that simple additive assumptions miss.

The test case is a representative mid-rise office building in Osaka, Japan, a metropolis that combines a hot, humid summer climate with intense urban density, making it an ideal laboratory for studying the collision between global warming and urban heat. The team simulated the building’s energy use and indoor thermal conditions under conventional weather files and under their new urbanized future weather files, tracking metrics that include cooling energy demand, indoor overheating degree, indoor overcooling degree, indoor discomfort degree and thermal autonomy, a measure of how much of the year a building maintains comfort without active systems. The contrast between the datasets was stark and, in places, counterintuitive.

The headline finding is that conventional weather datasets systematically underestimate indoor thermal discomfort in dense urban settings. Because the reference data comes from cooler rural or airport locations, simulations driven by such files paint an optimistic picture of future comfort, understating both the frequency and the intensity of overheating episodes. For a building designer, that optimism is dangerous. It can lead to undersized cooling systems, insufficient attention to shading and facade design, and a false sense of thermal resilience that evaporates during the first serious heat wave. As climate change pushes regional temperatures upward, the penalty for ignoring the urban heat island grows larger, because the extra urban warming compounds the baseline shift rather than merely adding to it.

Yet the study also delivers a more nuanced and arguably more surprising message: urban form is not simply an enemy of thermal comfort. When the researchers included the surrounding three-dimensional urban morphology in their weather generation, they found compensating effects. Neighboring buildings shade one another from direct solar radiation, and surfaces exchange longwave radiation in ways that can moderate conditions at the facade. A building in a tight urban canyon may suffer from the heat island’s warmer air, but it also receives less sun on its walls and windows than an isolated building in an open field. These interactions are nonlinear, meaning their combined effect cannot be predicted by summing individual contributions, and they can materially alter both energy use and indoor thermal conditions depending on the specific geometry of the site.

This nonlinearity is precisely why the researchers argue that urban microclimate must be treated as an integral part of building performance simulation rather than an optional refinement. The interplay between regional warming, urban heat island intensity and urban geometry produces outcomes that no single-factor analysis can anticipate. A design decision that looks prudent under a rural weather file, such as a generous window-to-wall ratio to capture daylight, may perform very differently when the same glazing faces a heat-soaked urban canyon. Conversely, dense urban form that raises local air temperatures may simultaneously deliver shading benefits that reduce cooling loads. Capturing these trade-offs requires weather data that carries the fingerprint of the actual urban environment, which is exactly what the new framework produces.

The practical implications extend well beyond Osaka. Building performance simulation underpins energy codes, green building certification, HVAC system sizing and climate adaptation planning worldwide, and all of these applications currently inherit the rural bias of standard weather files. The framework proposed by the Bavarsad team offers a workflow that practitioners can follow: take future climate projections, downscale them statistically, pass them through an urban canopy model parameterized with local morphology, and feed the resulting urbanized weather files into standard building simulation tools. The approach is deliberately compatible with existing simulation practice, using formats and tools already familiar to the industry, which lowers the barrier to adoption. It also supports climate-responsive urban design, because by varying morphological parameters, planners can test how different configurations of street width, building height and density shape both neighborhood climate and building performance before anything is built.

The broader stakes are considerable. Heat is already the deadliest weather-related hazard in many regions, and the buildings people occupy are the first line of defense between urban populations and dangerous temperatures. As the Intergovernmental Panel on Climate Change has emphasized, even modest levels of global warming translate into substantial increases in extreme heat exposure, and cities concentrate that exposure. Studies of temperature-related mortality under Paris Agreement scenarios have shown that the difference between warming targets is measured in lives, and indoor conditions during heat waves are a critical part of that equation. A simulation methodology that understates urban heat therefore does not just mispredict energy bills; it risks misinforming the very decisions that determine whether buildings shelter people or endanger them during the extreme events of the coming decades.

What makes this study resonate beyond its technical contribution is the clarity of its central lesson: the city itself is a climate machine, and pretending otherwise corrupts every downstream calculation. The researchers demonstrate that accurate representation of urban microclimate, particularly the combined effects of the urban heat island and urban geometry, is essential for reliable assessment of building performance and thermal resilience under future climate conditions. Their three-scenario framework gives the field a reusable template for separating and recombining the effects of global climate change, urban warming and urban form, and their Osaka case study shows concretely how much conventional methods miss. As cities race to retrofit existing stock and design new districts capable of withstanding a hotter century, tools like the Urban Weather Generator, coupled with downscaled climate futures, offer a way to stop designing for a countryside that the building will never experience, and start designing for the city as it truly is, and as it is becoming.

Subject of Research: Integrating urban heat island effects and urban morphology into future weather files for building performance simulation

Article Title: Integrating urban heat island effects into future weather data for building performance simulation

Article References: Bavarsad, F. S., Mohajerani, M., Tywoniak, J., & Yuan, J. (2026). Integrating urban heat island effects into future weather data for building performance simulation. Theoretical and Applied Climatology, 157(10), Article 630. https://doi.org/10.1007/s00704-026-06571-7

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06571-7

Keywords: urban heat island, building performance simulation, Typical Meteorological Year, Urban Weather Generator, climate downscaling, urban morphology, overheating risk, thermal comfort, Osaka, future climate scenarios, energy demand, urban microclimate

The research team, led by Augusto Cesar Laviola de Oliveira and Natalia dos Santos Renato, built their analysis around Thompson’s model, a semi-empirical formulation dating to 1972 that describes how grain dries in thick layers under low airflow and near-ambient temperatures. The model divides a silo’s grain column into thin virtual layers and, hour by hour, balances the energy and moisture exchanged between the drying air and the beans. Heat exchange is treated as adiabatic, and equilibrium moisture is calculated with the modified Halsey equation, while psychrometric properties of the air follow standards from the American Society of Agricultural Engineers. The algorithm iterates until the relative humidity of the air and grain converge within a tolerance of half a percent, using a secant-method approach that the authors found robust across the vast majority of conditions.

To ground the simulation in reality, the team adopted a standardized storage silo, about 18 meters in diameter and nearly 13 meters tall, holding 2,675 metric tons of soybeans at an initial moisture content of 20 percent wet basis. Drying was considered complete when the topmost layer reached the target of 13 percent, the level needed to prevent fungal contamination during storage. Real weather drove every simulation: dry bulb temperature, relative humidity, and atmospheric pressure recorded at INMET automatic weather stations across four harvests, from 2012/13 to 2021/22. After preprocessing and convergence checks, the database contained 55,798 valid simulation samples spanning the Brazilian territory from January through August of each crop year.

The results reveal how dramatically climate shapes the energy bill. Electricity demand, which powers the fans that push air through the grain, proved relatively stable, ranging from about 72,800 to 97,900 kilowatt-hours for a standard silo load depending on the day simulated. Thermal energy, supplied by burning liquefied petroleum gas when ambient air is too humid to finish the job, swung far more wildly, from roughly 46,000 to 156,000 kilowatt-hours. On the cheapest drying days the air was warm and dry; on the most expensive, cold and saturated. Multiple regression confirmed that every variable mattered: rising temperature cut both electrical and thermal demand, rising humidity raised them, and atmospheric pressure pushed electricity up while nudging gas consumption down, all with p-values near zero.

Geography compounds the climate effect. Using inverse-distance-weighted interpolation in QGIS, the team converted point simulations into continuous national maps and extracted statistics for all 27 states. The Midwest, home to Mato Grosso, Brazil’s largest soybean producer, accounted for 42 to 44 percent of national demand, with Mato Grosso alone requiring between 1.3 and 2.7 terawatt-hours per harvest. The South followed closely, where winter humidity pushes demand to its national peak: Rio Grande do Sul recorded the highest specific demand, up to 130 kilowatt-hours per ton of grain. In the Northeast, by contrast, hot dry air does much of the work for free, with Piauí posting the lowest specific demand at under 54 kilowatt-hours per ton.

Statistical analysis of variance added a temporal dimension. For most states, energy demand fluctuated significantly from month to month but remained stable across harvests, suggesting that a single average year can represent long-term planning. The South showed a steady climb in demand from January to August as autumn and winter arrived, while states in the Center-West and North trended downward over the same period. Only Acre, Amazonas, and Rio Grande do Norte showed significant differences between harvests, hinting at shifting climate patterns in those regions.

The study then asked whether the crop could pay its own energy bill. Soybean hulls, about 5 percent of grain mass and normally sold as animal feed, carry a calorific value that, when briquetted and burned in a Rankine cycle at 37 percent efficiency, can generate roughly 1.18 megawatt-hours of electricity per ton. Nationally, that generation could technically cover the entire electrical demand of the drying sector. Coverage varies sharply by state: Piauí and Bahia could produce more than 100 percent of their needs, while Rio Grande do Sul and Paraná would fall short at 42 to 88 percent, and Mato Grosso would land between 61 and 128 percent depending on the year.

The carbon arithmetic is equally compelling. Accounting for carbon dioxide released by grain respiration during drying, by LPG combustion, and by the electricity drawn from the grid, then crediting the emissions avoided by hull-based cogeneration, the national balance ranges from a net sequestration of about 595,000 tons of carbon dioxide to net emissions of about 778,000 tons. In favorable states and years, drying soybeans could actually remove carbon from the atmosphere; in others, it remains a modest source. The economics stay manageable either way: energy costs run from about 4.37 dollars per ton in Piauí to 14.73 dollars in Acre, or between 0.99 and 3.79 percent of the soybean’s market value per 60-kilogram bag.

Perhaps the most forward-looking contribution is the artificial intelligence layer. The team trained feed-forward neural networks on the 55,798 Thompson simulations, using temperature, humidity, and pressure as inputs to predict electrical, thermal, and total energy demand. With 90 neurons in the hidden layers for the electrical and thermal models and 30 for total energy, the networks achieved coefficients of determination above 0.997, with root mean square errors as low as 0.27 percent for electricity. In independent validation, deviations from the deterministic model stayed within roughly 2.4 percent. Because the neural surrogate runs in a fraction of the time and avoids the convergence failures that occasionally plague Thompson’s formulation, it opens the door to real-time decision support, letting operators adjust fan and burner settings as weather shifts.

The authors are careful to note the framework’s boundaries: by holding silo geometry, fan efficiency, and airflow constant, they isolated climate as the driver of demand, leaving operational optimization for future sensitivity studies. But the template travels well. Any country or crop with representative weather and production data could rerun the same pipeline, from deterministic physics to geospatial statistics to machine learning. For Brazil, whose electricity matrix remains nearly 62 percent hydroelectric and increasingly exposed to climate volatility, the message is clear: the humble act of drying grain is a national-scale energy enterprise, and the residue left behind after cleaning the crop may be the cheapest, cleanest way to power it.

Subject of Research: Energy demand modeling of low-temperature soybean drying in Brazil

Article Title: Analysis and modeling of energy demand in the low-temperature drying process of soybean in Brazil

Article References: Oliveira, A. C. L. D., Oliveira, V. H. L. D., Silva, L. C. D., Moraes, C. A., & Renato, N. D. S. (2026). Analysis and modeling of energy demand in the low-temperature drying process of soybean in Brazil. Cleaner Engineering and Technology, 34, Article 101313. https://doi.org/10.1016/j.clet.2026.101313

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101313

Keywords: soybean drying, low-temperature drying, energy demand, Thompson model, artificial neural networks, geospatial analysis, biomass energy, soybean hulls, carbon emissions, Brazil, agribusiness, energy efficiency