Land surface models are the bridge between the atmosphere and the ground in climate simulations. They calculate how sunlight, heat, and water move between the soil, the vegetation, and the air, and they feed those exchanges upward into the atmospheric component of a climate model. Historically, the models built for global circulation ran on grids tens to hundreds of kilometers wide, where a city was too small to matter. But computing power has changed the game. Global models are now pushing toward kilometer-scale grids, and regional models are running climate-length simulations over huge domains. At those resolutions, cities are no longer invisible specks; they are resolved features whose energy and water budgets can distort temperature, rainfall, and runoff if they are represented badly.

ORCHIDEE is a heavyweight in this world. It simulates coupled water, energy, and carbon cycles and serves as the land component of the IPSL climate model. Its architecture divides each grid cell into a mosaic of plant functional types, with separate soil columns for bare ground, high vegetation, and low vegetation. Until now, however, anything urban was simply assigned to the bare-soil category. That meant no reflective rooftops, no tall buildings stirring the wind, no concrete storing the afternoon sun, and no sealed asphalt blocking rain from soaking into the earth. The model could simulate a forest or a wheat field with sophistication, but a metropolis looked like a patch of exposed dirt.

The new scheme is deliberately parsimonious. Rather than adding an elaborate three-dimensional canyon geometry with separate energy budgets for roofs, walls, and roads, the team introduced a single urban tile, or PFT, into the existing mosaic. This tile carries urban-specific values for albedo, building height, thermal conductivity, and heat capacity, all of which can be prescribed from global or local datasets. The approach follows a lesson from the Urban-PLUMBER intercomparison project, which evaluated thirty urban land surface models and found that simpler slab schemes often outperform more complex ones, provided that vegetation, water availability, and imperviousness are represented realistically. Geometry, in other words, is not everything; what the surface is made of matters just as much.

The genuine novelty lies in how the scheme handles imperviousness. Most urban models treat sealed ground as a surface phenomenon: rain hits the pavement and runs off. Lalonde and colleagues went deeper, literally. Instead of splitting the urban tile into pervious and impervious sub-tiles, they modify the saturated hydraulic conductivity of the entire soil column beneath the city. Two options were tested. The first, called Urban1, slashes conductivity to ten percent of its reference value throughout the column, representing a heavily sealed city. The second, Urban2, scales the reduction linearly with the impervious fraction, so a city that is fifty-five percent sealed sees its conductivity cut proportionally. This single parameter change ripples through the whole hydrology: it suppresses infiltration, boosts surface runoff, reduces soil water storage, and limits the water available for evaporation, all through the model’s existing Richards-equation soil physics rather than any bolted-on machinery.

The thermal side of the scheme is equally consequential. Urban materials conduct and store heat very differently from soil. In grid cells where urban cover exceeds fifty percent, the model now prescribes a thermal conductivity of 3.24 watts per meter per kelvin and a heat capacity of 1890 kilojoules per cubic meter per kelvin, values borrowed from the Noah-MP land surface model. Compare that with the median thermal conductivity of natural soils in the study region, around 1.20 watts per meter per kelvin, and the physical implication is clear: cities soak up far more energy during the day and release it slowly through the night. That stored heat is the engine of the urban heat island, and the model now captures it.

To test the scheme, the team ran standalone simulations at twenty urban flux tower sites drawn from the harmonized Urban-PLUMBER dataset, spanning thirteen countries and climates from tropical Singapore to dry Phoenix and continental Europe. Each simulation was spun up for forty years until soil moisture and temperature reached equilibrium, then run over twelve to sixteen years of observations, with fluxes computed every thirty minutes. The benchmark was the old bare-soil treatment of cities. The verdict, measured by mean absolute error against tower observations, was unambiguous: the new Urban1 configuration reduced the average error in latent heat flux from 30.3 to 27.3 watts per square meter and in sensible heat flux from 41.9 to 36.9 watts per square meter across all sites. At sites with more than fifty percent impervious cover, the gains were larger still, with sensible heat error falling from 46.3 to 38.2 watts per square meter.

The detailed physics behind those numbers is telling. At the reference site of AU-Preston in Melbourne, the urban configurations reproduced the observed summer sensible heat flux far better than the old scheme, both during the day and at night. The improved daytime heat storage, driven by the higher thermal conductivity, left less energy to be dumped into the atmosphere as sensible heat, cutting a summer overestimation that reached a median bias of 77 watts per square meter in the bare-soil runs down to roughly 14 to 18 watts per square meter in the urban runs. Nocturnal sensible heat, which cities keep elevated by releasing stored warmth, also improved in both seasons. Not everything was fixed: daytime sensible heat remained underestimated in winter, and latent heat stayed generally underestimated in summer, likely because the scheme lacks active urban vegetation, irrigation, and anthropogenic heat from traffic and buildings, which can reach nearly 93 watts per square meter at highly urbanized sites like Seoul’s Jungnang district.

The hydrological results reveal both the promise and the limits of the new imperviousness parameterization. Under the strong Urban1 hypothesis, annual surface runoff roughly doubled at most stations, and the balance flipped so that surface runoff exceeded subsurface runoff at fifteen of the twenty sites, exactly the signature urbanization leaves on real catchments. But the moderate Urban2 option produced smaller and sometimes non-monotonic responses, and the simulated reduction in subsurface runoff sits awkwardly with observational studies showing that urban groundwater recharge can actually increase, thanks to leaking pipes and irrigation. The authors are candid that without runoff or soil moisture observations at the tower sites, the water-budget analysis remains a sensitivity assessment rather than a validation.

What comes next is where the story gets ambitious. The team plans convection-permitting simulations over Paris at resolutions below three kilometers, where cities are explicitly resolved and an inaccurate urban energy balance would feed wrong signals straight into the atmosphere. They also intend to add anthropogenic heat fluxes, refine the thermal parameters against measured data, embed active vegetation with seasonal phenology inside the urban tile, and exploit datasets like WUDAPT and the Copernicus Urban Atlas to make albedo, building height, and imperviousness vary across real cityscapes. Notably, most Earth system models contributing to CMIP still ignore cities altogether, a gap that becomes untenable as climate risk assessments increasingly focus on the built environment. A computationally cheap, physically grounded urban tile that works from the neighborhood scale to the globe is exactly the kind of tool that can close it, and ORCHIDEE now has one.

Subject of Research: Development and benchmarking of a new urban land surface scheme with imperviousness-dependent soil hydraulics in the ORCHIDEE 2.2 land surface model

Article Title: Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model

Article References: Lalonde, M., Bastin, S., Oudin, L., Arboleda-Obando, P. F., & Ducharne, A. (2026). Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model. Geoscientific Model Development, 19(19), 9463-9488. https://doi.org/10.5194/gmd-19-9463-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9463-2026

Keywords: ORCHIDEE, land surface model, urban climate, imperviousness, saturated hydraulic conductivity, surface energy balance, Urban-PLUMBER, sensible heat flux, latent heat flux, urban hydrology, convection-permitting simulations, Geoscientific Model Development

The starting point of the analysis is a striking statistic. In 2004, built-up areas accounted for just 4.3 percent of the land surface in and around Hyderabad. By 2024, that figure had climbed to 11.7 percent, nearly tripling in two decades as the city and its periphery absorbed waves of new residential, commercial, and industrial development. This is not merely a change in what the ground looks like from above. Every hectare converted from vegetated or bare soil to impervious surface alters the exchange of energy and water between the land and the atmosphere, changing how sunlight is absorbed, how heat is stored, and how much moisture evaporates back into the air.

To isolate the meteorological fingerprint of this transformation, the team ran a full year of simulations, from December 2023 to November 2024, under two contrasting land use and land cover scenarios. One scenario represented the less urbanized landscape of 2004 to 2005, while the other represented the highly urbanized present day. By holding everything else constant, including the large-scale weather patterns of the simulation year, the researchers could attribute any differences in temperature, humidity, wind, and atmospheric structure directly to the change in land cover. This experimental design is a powerful way of separating the signal of urbanization from the noise of natural climate variability.

The most visible consequence is the urban heat island, the well-known phenomenon in which cities run warmer than their rural surroundings. The simulations confirm that Hyderabad exhibits a persistent heat island across all seasons, with urban areas remaining approximately 2 to 3 degrees Celsius warmer than nearby rural regions. What makes the finding particularly important for a tropical city is its timing. The nocturnal heat island is especially pronounced during winter and the post-monsoon season, when clear skies and weak winds allow the heat absorbed by urban materials during the day to be released slowly back into the air through the night. For residents, this means that the relief of nightfall is measurably diminished precisely during the seasons when people are least prepared for heat stress.

The physics behind this nighttime warming lies in the thermal properties of urban materials. Concrete, brick, and asphalt store far more of the incoming solar energy than soil or vegetation, and they release that stored heat with a delay that stretches well into the night. In the older, less urbanized scenario, more of the daytime energy budget was consumed by evaporation and transpiration, processes that cool the surface but are largely absent once vegetation is replaced by pavement. The new simulations quantify this shift directly: urbanization increases the sensible heat flux, the portion of energy that goes directly into warming the air, while reducing the latent heat flux, the portion consumed by evaporating water.

That redistribution of energy has a second, less obvious consequence that the authors highlight as an urban dry island. Because less water is evaporated and transpired over the built-up surface, the near-surface air over the city and its expanding periphery holds significantly less water vapor than it would under the older landscape. The atmosphere above Hyderabad is not only warmer but drier than it would otherwise be. This drying effect compounds the discomfort of heat, since humid heat is harder for the human body to dissipate, and it also has implications for cloud formation, fog occurrence, and the local water balance. Similar urban dry island effects have been documented in cities from Turin to southern China, but documenting the effect in a semi-arid tropical inland city adds an important data point to the global picture.

Perhaps the most technically significant result concerns the planetary boundary layer, the lowest part of the atmosphere where the air responds directly to the surface within hours. The boundary layer is where pollutants disperse, where clouds initiate, and where the day’s heat is mixed downward from the surface. The simulations show that two decades of urbanization have deepened this layer, with daytime boundary layer heights increasing by roughly 100 meters, particularly during winter and the post-monsoon season. The mechanism is straightforward: stronger surface heating drives stronger convective mixing, pushing the top of the mixed layer higher. A deeper boundary layer can dilute pollutants over a larger volume, but it also changes the dynamics of cloud formation and can alter the dispersion pathways of everything from vehicle exhaust to industrial emissions.

Critically, the largest changes were not found over the historic urban core, which had already been densely built decades ago, but over the rapidly urbanizing peripheral regions. These are the neighborhoods and satellite towns at the city’s edge where farmland and scrubland have been converted to development most recently and most rapidly. The finding carries a clear planning message: the meteorological cost of urbanization is paid most heavily at the frontier of growth, often in areas whose infrastructure and populations are least equipped to cope with intensifying heat. It also suggests that land use decisions made today at the periphery will determine the climate burden of tomorrow.

The study sits within a growing body of evidence that land cover change is a first-order driver of regional climate in India, not a secondary effect. Previous work has linked urbanization signatures to heavy rainfall climatology across Indian cities, shown that irrigation and agriculture exert a dominant control on heat island intensity, and documented strong nighttime surface heat islands across the country’s major population centers. What distinguishes the Hyderabad analysis is its explicit pairing of a two-decade land cover contrast with a full annual cycle of simulations, allowing the seasonal structure of the urban effect to emerge. The finding that winter and post-monsoon nights bear the strongest warming is directly relevant to heat stress management, since these are the seasons in which nighttime cooling is most relied upon for recovery.

For a city that has positioned itself as a global technology destination, the results are a reminder that the atmosphere keeps its own ledger. Every square kilometer of new built-up area adds heat, removes moisture, and deepens the boundary layer, and these changes accumulate in ways that standard weather records, which blend urban and rural influences, tend to obscure. The authors’ scenario-based approach demonstrates that the tools now exist to forecast the climatic consequences of alternative development pathways before the concrete is poured. As Indian cities continue to expand at some of the fastest rates in the world, studies of this kind offer a quantitative basis for choices about green cover, surface materials, and growth corridors that could determine whether the next two decades of urbanization repeat the thermal trajectory of the last.

Subject of Research: The influence of land use and land cover change on surface meteorology and planetary boundary layer characteristics over Hyderabad, India

Article Title: Assessing the influence of land use/land cover on regional meteorology over the tropical inland urban city of Hyderabad, India

Article References: Reddy, T. R., & Rao, T. N. (2026). Assessing the influence of land use/land cover on regional meteorology over the tropical inland urban city of Hyderabad, India. Theoretical and Applied Climatology, 157(10), Article 649. https://doi.org/10.1007/s00704-026-06539-7

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06539-7

Keywords: urban heat island, Hyderabad, land use land cover, urbanization, planetary boundary layer, WRF model, sensible heat flux, latent heat flux, urban dry island, tropical climate, India, Theoretical and Applied Climatology

The observational record is striking. Drawing on measurements from 150 meteorological stations operated by the National Meteorological Information Center of the China Meteorological Administration, the team found that annual mean near-surface wind speed over the Tibetan Plateau declined at a rate of 0.159 meters per second per decade between 1979 and 2014. The decline was not spread evenly through the year. Spring showed the steepest weakening, with wind speeds falling by 0.235 meters per second per decade, a seasonal signal that matters because spring winds play a central role in the plateau’s exchange of heat and moisture with the surrounding atmosphere and in shaping the Asian monsoon system downstream.

To test whether climate models could reproduce this stilling, the researchers turned to the Coupled Model Intercomparison Project Phase 6, specifically its High-Resolution Model Intercomparison Project, known as HighResMIP. This experiment was designed in part to answer a long-standing question in climate science: would simply cranking up the horizontal resolution of global models improve their simulations? The team assembled 22 historical simulations from 11 model pairs, each pair consisting of a higher-resolution and a lower-resolution version of the same model, and compared them against the station observations over the 1979 to 2014 period.

The verdict on resolution was sobering. Both the higher-resolution and lower-resolution ensemble means captured the broad spatial pattern of wind speed across the plateau, but both badly underestimated the magnitude of the decline. The higher-resolution ensemble produced an annual trend of only 0.024 meters per second per decade, while the lower-resolution ensemble managed just 0.020 meters per second per decade. In other words, the models reproduced only about one-seventh of the observed stilling. Perhaps more telling, the difference between the two resolution tiers was small, suggesting that increasing horizontal resolution alone offers limited improvement in capturing the historical wind decline over this complex, high-altitude terrain.

This widespread stilling bias is not merely an academic curiosity. Near-surface wind speed governs a host of processes with real-world consequences: the evaporation of water from land surfaces, the dispersal of air pollutants, the mixing of the atmospheric boundary layer, and the output of wind turbines. The Tibetan Plateau is also the source region of major Asian rivers and a critical driver of monsoon circulation, so systematic errors in simulating its winds can propagate into broader failures in regional climate prediction. Previous studies have linked wind declines across China to factors ranging from urbanization and increased surface roughness to weakening pressure gradients and changes in large-scale circulation, but representing these processes faithfully in global models has remained elusive.

To dig into why the models fall short, the team applied a random forest machine learning analysis, a technique that can rank the relative importance of many candidate variables in explaining an outcome. The analysis pointed to biases in key dynamical and thermodynamical processes, with snow variability and surface sensible heat flux emerging as particularly influential factors. This makes physical sense. Snow cover on the plateau alters surface albedo and the partitioning of energy between sensible and latent heat, which in turn modulates the temperature contrasts that drive near-surface winds. If a model misrepresents how snow accumulates and melts across the plateau’s rugged topography, the resulting errors in surface heating can distort the very circulation features that generate wind.

Having identified which simulations performed best against observations using multiple statistical metrics, the researchers selected seven optimal models and constructed an optimal ensemble mean for future projections. Under the high-emission Shared Socioeconomic Pathway 5-8.5, the projections indicate that the stilling of the Tibetan Plateau will continue through the coming decades. Between 2015 and 2049, the ensemble projects an annual wind speed decline of approximately 0.02 meters per second per decade, unfolding alongside continued regional warming. While the projected rate is gentler than the historical observed trend, the direction of change is consistent, lending robustness to the conclusion that winds over the plateau will keep weakening in a warming world.

The juxtaposition of a robust future decline with a poorly simulated past decline creates something of a paradox for climate scientists. If models underestimate how much wind has already slowed, should we trust their projections of future slowing? The authors argue that the consistency of the projected decline across the selected optimal models, combined with the physical plausibility of continued warming-driven circulation changes, supports confidence in the sign of the projection even if the magnitude remains uncertain. At the same time, the study makes clear that closing the gap between simulated and observed trends requires more than computational horsepower.

That message may be the study’s most consequential takeaway for the modeling community. HighResMIP was conceived on the premise that finer grids would better resolve steep orography, coastlines, and mesoscale processes, and for some variables that promise has held. But for near-surface wind over the Tibetan Plateau, the near-identical performance of high- and low-resolution model pairs indicates that the bottleneck lies elsewhere, in the representation of physical processes such as snow-atmosphere interactions, surface energy budgets, and boundary-layer dynamics. Improving these process representations, the study suggests, may matter more than simply increasing spatial resolution for reducing persistent model biases in wind simulation.

For the people and ecosystems of the plateau, the implications extend well beyond model evaluation. Weakening winds influence glacier mass balance through altered turbulent heat exchange, affect the timing and intensity of the monsoon onset that billions of people depend on, and reshape the viability of wind energy development in one of the world’s most promising but fragile high-altitude environments. As the Third Pole continues to warm faster than the global average, the slow, steady quieting of its winds serves as another reminder that climate change reshapes even the most fundamental features of the atmosphere, and that our models, for all their sophistication, are still learning to listen.

Subject of Research: Evaluation and projection of near-surface wind speed trends over the Tibetan Plateau using CMIP6 HighResMIP simulations and station observations

Article Title: Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection

Article References: You, Q., Wu, F., Wu, T., Cai, Z., Sun, G., Jin, Z., Jiang, Z., Ullah, S., & Li, M. (2026). Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection. Climate Dynamics, 64(10), Article 411. https://doi.org/10.1007/s00382-026-08342-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08342-6

Keywords: Tibetan Plateau, near-surface wind speed, global stilling, CMIP6, HighResMIP, climate modeling, Climate Dynamics, SSP5-8.5, random forest, snow cover, sensible heat flux, monsoon