Poster session
Near-surface wind speed is a key variable in the climate system, linking atmospheric circulation, land–atmosphere interactions, and renewable energy production. Long-term changes in wind speed have been reported from station observations, reanalyses, and climate model simulations, with evidence for both large-scale and regional phenomenon across different temporal and spatial scales. These changes can influence climate extremes, alter sectoral risks, and directly affect wind power production, making their understanding critical in the context of a warming world.
Despite significant progress, key challenges remain. These include: (1) identifying and characterizing phenomena and variability in near-surface wind speed across timescales, including extremes and multi-decadal changes; (2) improving the use of station data, reanalyses, and climate model ensembles to quantify historical and projected wind speed changes; (3) attributing observed changes to internal variability, external forcings, and their interactions; (4) assessing uncertainties in model representation of wind speed climatology, variability, and extremes; (5) understanding implications of wind speed changes for wind energy assessments, risks of wind energy droughts, and future renewable energy planning; and (6) advancing methodological approaches, including emergent constraints, detection–attribution frameworks, and statistical or machine learning methods, to improve robustness of results.
Another focus of this session is the global environmental observations using optical imagers, with an emphasis on satellite - based monitoring. Optical imagers play a crucial role in detecting and analyzing large-scale environmental changes across various domains, including the atmosphere, land, ocean, and cryosphere. We aim to bring together contributions directed towards the usage of optical imagers in monitoring environmental variability, evaluating the impacts of climate change, and developing methodological approaches for integrated analysis. The session highlights the potential of new satellite missions including hyperspectral/ polarimetric/ multi-angle spaceborne observations and the importance of international collaboration in advancing global observation capabilities. The list of satellite instruments and their capabilities to be discussed is very broad. It includes EUMETSAT METOP-SG1 mission, JAXA GCOM-C and NASA PACE missions, to name a few.
Japan Aerospace Exploration Agency (JAXA) is conducting the Global Change Observation Mission (GCOM). The GCOM mission consists of two satellite missions: GCOM-W (called “SHIZUKU”) for observing water cycle and GCOM-C (called “SHIKISAI”) for observing carbon cycle and radiation budget. The GCOM-C satellite was launched from JAXA Tanegashima Space Center on December 23, 2017 (JST). It carries the Second generation Global Imager (SGLI). SGLI is a versatile, general purpose optical and infrared radiometer system covering the wavelength region from near ultraviolet to thermal infrared using two radiometers: Visible and Near Infrared Radiometer (SGLI-VNR) and the Infrared Scanning Radiometer (SGLI-IRS). SGLI is a successor of the Global Imager (GLI) on board the Advanced Earth Observing Satellite-II (ADEOS-II), which was launched in 2002.
GCOM-C provides 28 types of Standard products, including aerosols, clouds, vegetation, ocean chlorophyll, sea surface temperature (SST), snow and ice distribution, and snow grain size. In addition to these standard products, Research products are also released, supporting studies on global climate change and carbon cycle monitoring such as evapotranspiration index, fire detection index and snow surface albedo. Based on these products, the GCOM-C aims to provide comprehensive information of the Essential Climate Variables (ECVs) defined by Global Climate Observing System (GCOS). It observes 18 categories of geophysical parameters defined as ECSs related to the atmosphere, ocean, land, and cryosphere, contributing to climate change research. Since its launch, GCOM-C has been continuously observing for over eight years, generating a variety of geophysical data products. Furthermore, by integrating data from similar optical sensors such as MODIS, the mission has enabled the creation of continuous datasets spanning more than 25 years. These data are available through platforms such as G-Portal and JASMES, and for some products, API-based access is also becoming available.
How to cite: Shimada, R., Murakami, H., and Tanada, K.: Overview and latest updates of the GCOM-C/SGLI products and expanding data accessibility, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10439, https://doi.org/10.5194/egusphere-egu26-10439, 2026.
The Chlorophyll (Chl) content estimated from satellite observations often employs inversion of canopy radiative transfer models using other satellite estimates [leaf area index (LAI) and vegetation classification (VC)] and the leaf-carotenoid-content assumption, because its direct retrieval from the light absorption is difficult due to the rapid decrease in the Chl-absorption band reflectance reaching its lower limit at low Chl concentration as Chl increases in forests and grasslands. Such estimation largely depends on the accuracy of the input data, such as LAI, VC, and carotenoids, and thus cannot account for unexpected changes in VC and carotenoids. This study aims to develop a Chl-content estimation method directly from Global Change Observation Mission-Climate (GCOM-C) satellite observations, independent of other satellite estimates and assumptions. This study demonstrated that the SGLI green band (Band 6, 565 nm center wavelength) enables robust estimation due to its small but broad Chl sensitivity and lack of carotenoid sensitivity. In contrast, another green band (Band 5, 530 nm), which has the sensitivity to carotenoids, was not suitable for this purpose because carotenoid content varies across species and seasons and thus requires its assumption or estimation. In short, the green wavelength and its width were critically important. Using a green band (Band 6) and an NIR band (Band 11), one general estimation model of ground-area-based total Chl content (R2=0.87 and RMSE=0.55 g m−2) and three specific models for broad leaves, needle leaves, and grasses were created (R2=0.93, 0.91, and 0.94; RMSE=0.39 g m−2, 0.44 g m−2, and 0.36 g m−2, respectively). The ground-area-based total Chl content estimation was independent of PFTs and LAI, whereas the leaf-area-based estimation required them. The general model, which only requires SGLI’s two-band reflectance, offers computational efficiency and near-real-time detection even in areas of unexpected change driven by natural and anthropogenic disturbances, because it is independent of the carotenoid assumption and other satellite-estimated results, such as VC (e.g., land cover classification) and LAI. Besides, it has less dependence on soil moisture, which affects vegetation background reflectance, and water area fraction in a vegetated pixel. Accordingly, SGLI ground-area-based Chl estimates are independent of those factors. Such independent ground-based estimates of Chl content can provide new insights into vegetation studies.
How to cite: Akitsu, T., Kume, A., Lai, R., Kobayashi, H., Nakaji, T., Hanba, Y., and Murakami, H.: Development of GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16127, https://doi.org/10.5194/egusphere-egu26-16127, 2026.
Long-term remote sensing observations enable systematic assessment of terrestrial ecosystem dynamics and land-surface change across continents and climate zones. This contribution presents recent developments in global vegetation and energy, products generated within the Copernicus Land Monitoring Service (CLMS) using harmonized satellite time series. The portfolio delivers global land products, including canopy biophysical variables, land surface phenology, ecosystem productivity metrics, burned area, soil moisture and land surface temperature, all explicitly derived from long-term satellite observations acquired by successive spaceborne sensors.
NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), and FCover (Fractional Vegetation Cover) are provided at 300 m spatial resolution with methodological continuity—with some adaptations—with earlier 1 km products derived from SPOT-VEGETATION and PROBA-V observations. Applying a largely consistent retrieval framework supports the joint use of the 1 km and 300 m records for the analysis of vegetation dynamics since 1999, including both long-term trends and short-term anomalies across diverse ecosystems, with appropriate caution when interpreting sensor and resolution transitions. Together, LAI, FAPAR, and FCover describe canopy structure and radiative properties, providing a physically consistent basis for ecosystem functioning and surface–atmosphere interactions. Seasonal vegetation dynamics are further captured through Land Surface Phenology metrics, reflecting shifts in the timing and duration of growth cycles associated with climate variability and extreme events. Productivity indicators, like Dry Matter Productivity and Net Primary Production, quantify biomass accumulation and carbon uptake at global scale, offering insight into terrestrial carbon cycling and ecosystem functioning. In parallel, CLMS delivers daily updated global maps of fire-affected areas. With its low latency (<24h) and its quality almost equal to non-time-critical products quality, available many months after satellite acquisition, this Burned Area product supports timely assessment of wildfire extent and post-fire recovery.
Other key products delivered by CLMS are surface soil moisture (SSM 1km) and soil water index (SWI 1km and SWI 0.1°). Recently significant improvements have been made to these products integrating algorithmic advances from previous evolution activities but also ingesting improved input data. The SSM 1km product has been substantially upgraded through the implementation of a new radiative transfer model retrieval algorithm, which improves vegetation dynamics and reduces seasonal bias, as well as an enhanced preprocessing and filtering workflow mitigating subsurface scattering anomalies. These improvements directly benefit the SWI 1km, which also inherits these advancements. Furthermore, both SSM 1km and SWI 1km now feature improved masking of frozen soil conditions where retrievals are ill-posed. Finally, the 0.1° Soil Water Index (SWI 0.1°) has been enhanced by integrating the latest ASCAT surface soil moisture product, which offers superior vegetation modelling, long-term trend correction, and subsurface scattering mitigation.
In addition to the vegetation and soil moisture products, a new version of the global Land Surface Temperature (LST) product is currently under development, incorporating several updates to enhance its quality and stability. These updates include improvements to multiple components, such as (i) dynamic vegetation‑based surface emissivity, (ii) high‑frequency cloud information from SAF‑Nowcasting, (iii) a comprehensive eight‑year reprocessing effort (2018–2025) to ensure temporal consistency with near‑real‑time updates, and (iv) increased spatial resolution to 3km. The LST processing chain integrates data from the GOES‑16/19 and Himawari satellites, which are merged with the products generated by the Satellite Application Facility on Land Surface Analysis (LSA SAF) for the Meteosat Second Generation (MSG) prime and Indian Ocean (IODC) missions.
The integration of these complementary products demonstrates the value of sustained, harmonized optical satellite records for large-scale analysis of ecosystem dynamics, climate-related impacts, and operational land monitoring applications.
How to cite: Bekaert, D., Lacaze, R., Camacho, F., De Munck, D., Dutra, E., Eklundh, L., Gebruers, S., Jin, H., Lopes, F., Padilla, M., Raml, B., Sanchez-Zapero, J., Swinnen, E., Toté, C., Verger, A., and Wagner, W.: Consistent Multi-Mission Time Series for Global Assessment of Ecosystems Dynamics and Disturbance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16277, https://doi.org/10.5194/egusphere-egu26-16277, 2026.
The spectral reflectance of the snow-covered or bare sea ice in the Arctic is a critical parameter for determining the surface energy budget and for developing satellite passive remote sensing of clouds and aerosol particles. For static land surfaces, the bidirectional reflectance distribution function (BRDF) is acquired by sampling reflectance over multiple overpasses. The aggregated reflectance data are then fitted by a kernel-based approach. While the kernels were originally developed for vegetated surfaces, they have been extended to other surface types and the algorithm has been operationally implemented for imagery by Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, applying these kernels to the snow surface results in poor fitting due to its highly anisotropic reflectance. Even recently developed snow kernels, whose performance is yet to be validated against field observations, do not consider processes unique to sea ice such as surface roughness or floe drift. They are also confronted with sparse temporal and viewing angle sampling that stems from the sun-synchronous satellite orbits of MODIS and VIIRS. As a first step toward development of a kernel-based sea-ice BRDF retrieval, this study focuses on the packed, homogeneous sea ice surface before the melt onset and evaluates the performance of snow kernels. Specifically, we examine the impact of surface roughness on the directional and hemispherical reflectance using novel aircraft datasets.
We used data from the NASA Arctic Radiation-Cloud-Aerosol-Surface Interaction Experiment (ARCSIX), an aircraft campaign that took place near the northern coast of Greenland from May to August 2024. Particularly, we used airborne nadir-looking all-sky camera imagery, spectral irradiance by the Solar Spectral Flux Radiometer (SSFR) and laser altimetry data by the Land, Vegetation and Ice Sensor (LVIS). The camera imagery was radiometrically and geometrically calibrated to derive the directional reflectance. The imagery was then collocated against surface roughness derived by camera imagery and by LVIS when available, as well as against hemispherical albedo obtained from SSFR. We found that the snow kernels adequately capture the anisotropy of the camera-derived reflectance within the observed range of roughness. The kernel fit coefficients and predicted albedo showed high sensitivity to roughness, which modulated albedo by up to 10% in the shortwave infrared wavelength range. However, when limiting the viewing angle to the subset of angles that are accessible to satellite imagers, there is not enough information for the kernels to accurately predict the anisotropic reflectance and albedo perturbed by roughness. Additional constraints would likely be needed as a next step toward the retrieval of the sea ice BRDF influenced by surface roughness, leveraging other data such as multi-angle imagery (e.g., PACE, 3MI) and laser altimetry (e.g., ICESat-2, CryoSat).
How to cite: Hirata, K., Schmidt, K. S., Nataraja, V., and Hofton, M.: Impact of Arctic Sea Ice Heterogeneity on Surface Reflectance Evaluated with Airborne Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15031, https://doi.org/10.5194/egusphere-egu26-15031, 2026.
Arctic sea ice plays a key role in the polar shortwave (SW) surface energy/radiative budget. At the start of the polar day in March, the central Arctic ice pack is relatively homogeneous and slow-moving. However, by the August melt peak, the ice transitions into a highly dynamic regime characterized by rapid drift and the development of extensive melt ponds, leads/breakups, and deformations, which drastically change the albedo within a few days. This complex nature of sea ice is a key reason why climate models struggle to accurately characterize surface albedo in the Arctic. Observationally, polar-orbiting satellites encounter several unique challenges in the Arctic. First, the observations need to be clear of clouds and atmospherically corrected; however, low-level clouds are ubiquitous in the Arctic and are difficult to detect using existing cloud detection algorithms. Second, it is assumed that the surface does not move while these observations are acquired over several days, an assumption that is invalid for Arctic sea ice due to the drift. Third, traditional land Bidirectional Reflectance Distribution Function (BRDF) models are insufficient to capture the anisotropic property of snow and sea ice to accurately estimate its albedo. Finally, the sparsity of in-situ and field observations in the Arctic has limited any development of a satellite data product for sea ice albedo.
Despite these challenges, the frequent overpasses of polar-orbiting satellites over the polar regions provide valuable opportunities for Arctic surface remote sensing through the abundance of observations. We present a Lagrangian framework for tracking sea ice using a multi-overpass, multi-angular approach. Instead of observing sea ice at geographically fixed locations, we use a moving reference frame that “goes with the floe”. Using a suite of existing satellite (MODIS) data products in a scalable, modular pipeline, we employ machine learning to identify sea ice floes in a given scene. Once a floe is identified, we utilize a composite, kernel-driven snow BRDF model to populate the angular space, integrating these samples to derive daily spectral and SW broadband albedo. We then track each identified floe in a scene across multiple days (when possible), enabling us to build a spatio-temporal evolution of the albedo. Crucially, we use data from the NASA ARCSIX aircraft campaign, which took place during late spring and summer of 2024, to validate the satellite-derived albedo. Measurements from two instruments—All-Sky Camera (nadir-looking; 400–650 nm) and Solar Spectral Flux Radiometer (SSFR; 400–2000 nm)—are used to evaluate the accuracy and quantify the uncertainty in the satellite albedo product. By leveraging the multi-angular sampling from multiple MODIS instruments within this Lagrangian framework, we capture the change in albedo associated with the onset of melt and the subsequent increase in surface anisotropy and heterogeneity. Our work is a step towards developing an operational sea ice BRDF/albedo product for passive imagers like MODIS.
How to cite: Nataraja, V., Hirata, K., Chen, H., Chen, Y.-W., Meyer, K., Peterson, C., and Schmidt, S.: Go with the floe: Spatio-temporal evolution of Arctic sea ice albedo using satellite imagery in a Lagrangian framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13633, https://doi.org/10.5194/egusphere-egu26-13633, 2026.
The increasing amount of geo-spatial data goes in-line with the increasing number of Earth observation satellites in space. The integration of this spaceborne data in large storage data cubes allows the analysis of continental to global phenomena with machine-learning techniques but also requires consistent and accurate geolocation of the various input data. The unprocessed imagery contains distortions of various magnitudes depending on the off-nadir viewing angle but also the local topography imaged by the sensor. The radial distortion of nadir looking MR and HR sensors is correlated with the increasing distance of the image pixel from the scene center and must be corrected before integration in data cubes. The same applies to agile VHR sensors with their oblique viewing capability as well as side-looking SAR sensors. The final geolocation accuracy therefore depends on a detailed knowledge of the sensors’ viewing angle/direction but also the digital elevation data used on the ground segment processing chain (i.e. for the orthorectification process step).
The 30m and 90m instances of Copernicus DEM (GLO-30 & GLO-90) can be accessed via a free-and-open data policy from ESA and both represent a consistent and accurate Digital Surface Model (DSM) with input data acquired between December 2010 and January 2015. However, Earth surface is changing due to anthropogenous and environmental processes and relevant height data for the orthorectification process must be kept up-to-date but also upgraded in terms of resolution to keep pace with the improvement in spatial resolution of recent and future satellites.
This presented study will compare various orthorectification results of VHR Pléiades and Pléiades Neo satellite imagery in plain but also mountainous topographic terrain. In addition, a differentiation between urban and rural environments is applied when presenting the results of an absolute geolocation study and corresponding visual analysis. The DEM data used for orthorectification is the Copernicus DEM in its 30m version (CopDEM GLO-30; acquired 2010- to 2015) as well as more current WorldDEM Neo data (5 meters; input data acquired since 2017 until at least 2025) stem from the TanDEM-X mission. A major improvement is the rapid availability of an error-free DEM version after raw data acquisition which allows alignment to the acquisition date of the satellite-based VHR imagery. A tailoring of WorldDEM Neo DSM for orthorectification purposes is also presented.
How to cite: Fahrland, E., Schrader, H., Noel, J., and Bosch, S.: Improving the geolocation accuracy of VHR satellite imagery: orthorectification based on Copernicus DEM and its change to an up-to-date 5m DEM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8844, https://doi.org/10.5194/egusphere-egu26-8844, 2026.
The global capacity for wind power has grown rapidly in recent years, yet uncertainties in wind power density (WPD) assessments still hinder effective climate change mitigation efforts. One major challenge is the significant underestimation of WPD when using coarser temporal resolutions (∆t) of wind speed data. Here, we show that using daily ∆t results in an average underestimation of 35.6% in global onshore WPD compared to hourly ∆t. This discrepancy arises from the exponential decay of WPD with increasing ∆t, reflecting the intrinsic properties of wind speed distributions, particularly in regions with weaker winds. To address this, we propose a calibration method that introduces a correction coefficient to reduce biases and harmonize WPD estimates across temporal resolutions. Applying this method to future wind energy projections under the Shared Socioeconomic Pathway 585 scenario increases global onshore WPD estimates by 25% by 2100, compared to uncorrected daily data. These findings highlight the effectiveness of calibration in reducing uncertainties, enhancing WPD assessments, and facilitating robust policy action toward carbon neutrality.
How to cite: Hou, C.: Detecting and calibrating large biases in global onshore wind power assessment across temporal scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1416, https://doi.org/10.5194/egusphere-egu26-1416, 2026.
Low-level wind shear is a frequent hazardous phenomenon at airports in the Xinjiang region of China, mainly due to complex terrain and highly variable weather conditions. It poses a significant risk to aircraft operations, particularly during take-off and landing. In this study, Doppler wind lidar observations are used to detect and identify low-level wind shear in the vicinity of airports, with a focus on improving the performance of existing identification algorithms under complex terrain conditions. Several commonly used wind shear detection algorithms are implemented, evaluated, and further refined. Based on their complementary strengths, a joint warning algorithm is developed to provide more reliable wind shear alerts. In addition, machine learning methods are explored to directly extract wind shear signals from raw lidar data, aiming to achieve faster detection without relying on full wind field retrieval. The results show that the joint warning algorithm clearly improves warning performance compared to individual algorithms, with fewer false alarms and missed events. The machine learning approach also demonstrates promising capability for rapid wind shear identification. These results suggest that combining multi-algorithm warning strategies with data-driven methods can effectively enhance future airport wind shear warning systems in the Xinjiang region. Further improvements are expected by training machine learning models with expanded libraries of representative wind shear cases.
How to cite: He, B.: Observation and Warning Algorithms for Low-Level Wind Shear at Airports in Xinjiang, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15353, https://doi.org/10.5194/egusphere-egu26-15353, 2026.
In offshore wind energy assessment, the vertical profile of near-surface wind speed is frequently modulated by regional atmospheric phenomena, including low-level jets (LLJs), variations in atmospheric stability, and land–sea thermal contrasts, thereby inducing pronounced variability in the synoptic wind field. Under certain conditions, wind speed may even decrease with height, exhibiting negative wind shear. Such non-monotonic wind profiles are particularly common over monsoon-dominated marine regions such as the Taiwan Strait. Previous studies have indicated that conventional vertical extrapolation approaches and Monin–Obukhov similarity theory (MOST) often fail to adequately represent the actual wind field under these conditions, consequently reducing the reliability of wind resource assessment and power generation forecasting.
This study uses near-surface meteorological data from the TReAD, a dynamical downscaling model(2km spatial resolution) developed by the National Science and Technology Center for Disaster Reduction (NCDR), along with MOST to estimate wind speed at hub height. The estimates are compared against observations from the nearest meteorological mast (approximately 725 m away) to develop a data-driven bias-correction approach. A bias prediction model is trained on 2019 data and then applied to independent datasets from 2022 and 2023 to evaluate its generalization across interannual variability and varying boundary-layer conditions.
The results show that, even when trained on data from a single year, the bias-corrected MOST wind speeds consistently reduce overall errors and improve the error across all evaluated years, suggesting that the proposed method can effectively correct MOST's systematic biases under negative wind shear and cross-year nonstationary conditions. Overall, this study presents a MOST bias-correction algorithm integrated with a dynamical downscaling model to produce near-surface wind speed estimates that more closely align with meteorological mast observations, thereby providing a practical approach for offshore wind energy assessment in the Taiwan Strait.
How to cite: Ho, C.-Y., Lin, C.-C., and Tsai, I.-W.: Bias-Corrected MOST Hub-Height Wind Estimates from Dynamical Downscaling for Offshore Wind Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15848, https://doi.org/10.5194/egusphere-egu26-15848, 2026.
Accurate extended-range forecasting of wind power is important in modern energy systems. As the global share of wind energy in the power grid continues to rise, uncertainties in wind power generation pose significant challenges to grid stability, power dispatch, and energy trading. Extended-range forecasts (1–4 weeks ahead) enable grid operators to optimize power generation schedules, reduce reserve requirements, and minimize integration costs.
The subseasonal-to-seasonal (S2S) predictability of wind power density (WPD) over tropical land regions at 1–4-week lead times was investigated using observational data (Global Wind Atlas), reanalysis products (ERA5), and S2S model outputs (ECMWF). Diagnostic analysis reveals that ERA reanalysis systematically underestimates daily mean wind speeds across global land areas (global mean bias is −1.06 m/s), with larger discrepancies at higher altitude regions.
After ERA-based wind speed bias correction, the prediction skill of WPD was assessed based on correlation coefficient (Cor). ECMWF S2S models exhibit good initial skills with Cor exceeding 0.6 over most regions at 1-week lead, and the skills gradually decayed with lead time. By week 4, mid-to-high latitude predictability diminished substantially (Cor<0.2), while certain tropical regions maintained moderate skills (~0.5).
In-depth analysis of tropical regions revealed that the prediction skills were primarily modulated by the annual cycle and high-frequency components (3–10 days). The annual cycle component exhibited strongly positive correlation with predictability (the correlation coefficient was 0.84), whereas high-frequency activity exhibited a robust negative correlation (the correlation coefficient was −0.73), both exceeding the 99.9% significance level. This demonstrated that the enhanced S2S predictability skill in regions dominated by the annual cycle and reduced skill where high-frequency variability prevailed. Singular Value Decomposition (SVD) analysis indicated that the annual cycle components of tropical WPD were primarily linked to the annual cycle of solar radiation, while high-frequency activities were closely associated with tropical wave dynamics.
How to cite: Hu, F.: Extended-range Forecast Skill and Source Attribution of Daily Wind Power Density over Tropical Lands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15449, https://doi.org/10.5194/egusphere-egu26-15449, 2026.
Near-surface summer winds have significantly weakened in South Asia, most prominently over the wind energy potential regions of India, parts of Pakistan, Bangladesh, and the adjacent maritime continent over the last four decades (1980-2020). The most populated country in the region, India alone, has suffered a severe weakening of its wind energy resources by ~25%, resulting in a substantial depletion of wind power. Large ensemble climate simulations suggest this weakening is driven, in large part, by anthropogenic reasons. It further unravels that the weakened winds are primarily due to a cooling of the Indian land and a warming of the Arabian Peninsula owing to aerosol loading, with secondary contributions from changes in land-use and land-cover effects. These thermal changes have led to a mean sea level pressure increase over India and a decrease over the Arabian Peninsula, thus weakening the mean pressure gradient and, in turn, the winds. However, in a huge relief, these trends will likely reverse in the coming decades as anthropogenic aerosol loadings decrease in southeast Asia, causing a partial recovery of winds and wind energy potential.
How to cite: Upadhyaya, P. and Mishra, S. K.: Recent decline in wind energy potential in South Asia and its projected recovery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21636, https://doi.org/10.5194/egusphere-egu26-21636, 2026.
