Agroecosystems cover over half of Indian land surface, yet their long-term and spatial variability in crop physiology and terrestrial fluxes is not well understood. Most previous studies rely on site-scale eddy covariance observations, and the only regional assessment over Indian agroecosystems (Reddy et al., 2023) focused solely on wheat with limited calibration. Reddy et al. (2025) calibrated CLM5 using multi-site data to simulate Indian wheat and rice. In this study, we use this capability of CLM5 to provide the first comprehensive regional analysis of long-term (1970-2014) trends in crop variables and terrestrial fluxes across major croplands of India. Further, numerical experiments are conducted with CLM5 to evaluate the role of climate, CO2, nitrogen fertilisation, and irrigation in driving the trends. The results show that LAI, yield, and dry matter of the crops increased more than twofold since the 1970s, with carbon uptake doubling and respiratory losses decreasing during this period. Nitrogen fertilisation and irrigation have the largest impact on the observed trends of crop variables and terrestrial fluxes, followed by CO2. This study highlights the important role of management practices in increasing crop productivity and carbon uptake under a changing climate and demonstrates that a robust modeling framework is now available for testing management strategies across Indian agroecosystems.

Power production from a renewable energy (RE) source such as a wind farm or urban roof-top solar panel installation is highly sensitive to the obstacles around it, particularly those which are in the upstream direction. RE installations can avoid or minimize the effects of obstacles using proper planning. However, obstacles that come up after the plant is operational can lead to significant loss in power production and revenue. In this study we quantitatively explore two common examples – shading effect of neighbouring buildings on roof-top solar plants and wake effects of upstream wind turbines on offshore wind farms. The first example considers a horizontal solar panel atop an urban building in a relatively congested neighbourhood. We built a model to quantify the shading effects of neighbouring tall buildings on the solar panel. The model calculates the position of the Sun on the celestial dome at every minute with astronomical accuracy. Then the solar irradiance is calculated for a clear-sky environment. After that the shadow profile is calculated and visualized for obstacle buildings with any height and at any distance. And finally, the loss in available insolation and the power production is calculated. The results show significant power loss due to the building shading effect. For example, a roof-top solar panel surrounded by a 20m taller building at 20m distance can reduce power generation by more than 50%. The second example is where a new wind farm is constructed upstream of an existing wind farm. We used two different models to quantify the meteorological effects of the upstream wind turbines on downwind turbines. The first one involves Jensen Wake Model (JWM), a static wake recovery model to simulate the wake effects of upstream obstacle turbine on downwind turbine. The second approach makes use of the Wind Turbine Parameterization (WTP) in WRF. This method implements wake loss using a wind turbine power curve data and wake recovery through atmospheric vertical mixing. A case study has been conducted for a hypothetical offshore wind farm situated in Palk Strait between India and Sri Lanka by placing wind farms of different shapes and dimensions in the upwind direction. The results show a range of losses in annual power production between 3 – 12 MW, which roughly converts into €1.1M – €4.1M. This study demonstrates that the effects of upstream obstacles on RE sources are non-trivial and can have serious impacts on the performance on RE installations. Currently, local zoning laws in India and many countries do not protect RE installations from future constructions that can act as obstacles. Hence, effective policies are required to safeguard the return on investments in the RE industry.

Accurate representation of croplands is essential for simulating terrestrial water, energy, and carbon fluxes over India because croplands constitute more than 50 % of the Indian land mass. Wheat and rice are the two major crops grown in India, covering more than 80 % of the agricultural land. The Community Land Model version 5 (CLM5) has significant errors in simulating the crop phenology, yield, and growing season lengths due to errors in the parameterizations of the crop module, leading to errors in carbon, water, and energy fluxes over these croplands. Our study aimed to improve the representation of wheat and rice crops in CLM5. Unfortunately, the crop data necessary to calibrate and evaluate the models over the Indian region are not readily available. This study used comprehensive wheat and rice novel crop data for India created by digitizing historical observations. This dataset is the first of its kind, covering 50 years and over 20 sites of crop growth data across tropical regions, where data have traditionally been spatially and temporally sparse. We used eight wheat sites and eight rice sites from the recent decades. Many sites have multiple growing seasons, taking the total up to nearly 20 growing seasons for each crop. We used these data to calibrate and improve the representation of the sowing dates, growing season, growth parameters, and base temperature in CLM5. The modified CLM5 performed much better than the default model in simulating the crop phenology, yield, and carbon, water, and energy fluxes compared to site-scale data and remote sensing observations. For instance, Pearson's r for monthly leaf area index (LAI) improved from 0.35 to 0.92, and monthly gross primary production (GPP) improved from −0.46 to 0.79 compared to Moderate Resolution Imaging Spectroradiometer (MODIS) monthly data. The r value of the monthly sensible and latent heat fluxes improved from 0.76 and 0.52 to 0.9 and 0.88, respectively. Moreover, because of the corrected representation of the growing seasons, the seasonality of the simulated irrigation matched the observations. This study demonstrates that global land models must use region-specific parameters rather than global parameters for accurately simulating vegetation processes and corresponding land surface processes. The improved CLM5 can be used to investigate the changes in growing season lengths, water use efficiency, and climate impacting crop growth of Indian crops in future scenarios. The model can also help provide estimates of crop productivity and net carbon capture abilities of agroecosystems in future climate.

Long-term wind speed forecasting is still in its early stages, particularly in India. Due to lack of operational forecasts the Indian wind industry is forced to rely on climatological averages, that do not incorporate interannual variability. The overall goal of our study is to evaluate and enhance the capability of the Indian Institute of Tropical Meteorology Coupled Forecast System Version 2.0 (IITM CFSv2) model to forecast the summer monsoon (June-September) 10m wind speeds over India at seasonal scales as a part of the Monsoon Mission III program. The model runs were conducted in hindcast mode for the period 1981-2017. Initially, we conducted a systematic evaluation to assess the quality of the forecasts initialized in February and March for selected stations by comparing them against observations from the Global Summary of the Day (GSOD) dataset. Our findings indicate that the raw forecasts are poor quality with Symmetric Mean Absolute Percentage Error (SMAPE) in the 70% and 90% range. Next, we developed calibration algorithms using ML techniques to improve the quality of the forecasts. Linear Regression, Random Forest, XGBoost, LSTM, Conv-LSTM, GRU were employed as regression models. The outcomes from the best-performing model demonstrate that calibration significantly enhances the quality of the forecasts. After calibration, the mean absolute error (MAE) values typically fall within the range of 0.5 to 0.9 m/s for most stations, though a few stations exhibit values exceeding 1 m/s, in contrast to the raw forecasts where the error range extends from 1.2 to 2 m/s. The SMAPE is reduced to between 30% and 60% after calibration. When compared with 30-year climatology, the calibrated forecasts in 60% of the stations show a positive Root Mean Square Error Skill Score (RMSESS) ranging from 0.01 to 0.3 whereas the scores for the raw forecasts are showing highly negative skill. This study demonstrates that ML based calibration is a promising technique that can significantly improve the quality of numerical model forecasts and perform significantly better than climatology.

This study endeavors to enhance the performance of Hybrid Data Assimilation (DA) system, specifically focusing on the complex meteorological conditions of the Indian region. The optimization process involves adjusting five key parameters: (i) incorporating Background Error Statistics dependent on flow (FB); (ii) selecting different assimilation datasets; (iii) fine-tuning the optimal sample size for generating FB ensembles; (iv) establishing a detailed weighting scheme for FB against Static Background Error Statistics (SB); and (v) refining convergence criteria for the DA process. Numerical DA experiments (using WRF-4.3.3) were carried out with a 30‐km horizontal grid spacing, covering ten cases from 2018 to 2022, with a particular emphasis on significant rainfall events during the South West Monsoon season, associated with Depressions or more significant weather systems over the Bay of Bengal region. Three distinct FB formulations were formulated using ensembles: (i) B-MPCU (varying microphysics cumulus schemes) (ii) B-RAD (varying microphysics, cumulus, short and long wave radiation schemes), and (iii) B-PERT (only perturbing initial conditions). The SB was derived using the National Meteorological Center method. A comparative analysis against ground truth sources, including radio sonde, real-time metar observations, and model forecasts with GPM 0.1°X 0.1° rainfall estimator outputs, established the superiority of Hybrid DA techniques under specific conditions: (i) using B-MPCU as FB; (ii) setting FB's contribution against SB at 99

AbstractThis study demonstrates a framework to improve the skill of raw 10 m wind speed forecasts from numerical models at the subseasonal to seasonal (S2S) time scales. Monthly mean 10 m wind speeds from the ECMWF‐SEAS5 are calibrated using JRA‐55 as reference by employing three statistical methods, bias‐adjustment, quantile‐mapping, and ratio of predictable components (RPC), and four decision tree‐based ML methods, random forest (RF) and light gradient boosting machine (LGBM), RF and LGBM with past observations that is, RF_lags and LGBM_lags respectively, for all 12 months of the year at 1, 2, 3, 4, and 5 months lead time over the homogenous climate zones of India. The quality and skill of raw and calibrated forecasts are evaluated using root mean squared error (RMSE), ratio of standard deviation, and continuous ranked probability skill score (CRPSS). The raw forecasts have large RMSE values, often >1 m/s and mostly do not have any skill. The calibrated forecasts have an RMSE of ∼0.5 m/s, CRPSS ∼0.4, and RMSE ∼0.3 m/s and CRPSS ∼0.7 from statistical and ML‐based methods respectively. The ML‐based methods therefore produce better S2S wind speed forecasts than the statistical methods. This is a timely study with broad impacts especially for the wind energy industry that requires skillful S2S forecasts for financial planning and decision‐making.

Reanalyses are often regarded as the most reliable datasets that give a comprehensive picture of meteorological parameters all over the globe. They are the first choice of dataset for researchers interested in analysing meteorological variables. Recognizing their importance, several agencies worldwide produce reanalysis datasets using different methods. This study aims to reduce the uncertainty associated with choosing among these reanalyses by identifying the best reanalysis representing the near-surface wind speeds over India. For this purpose, 10 m wind speed data from six reanalyses are used: National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP1), National Centers for Environmental Prediction-Department of Energy reanalysis (NCEP2), Japanese 55-year Reanalysis (JRA55), Indian Monsoon Data Assimilation and Analysis (IMDAA), European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2). The evaluation is done by comparing the spatially-averaged reanalyses against the station observations obtained from National Centers for Environmental Information (NCEI) over seven homogenous climate zones of India for the 1980-2020 period. Results demonstrate wide divergence between the reanalyses and observations in terms of monthly mean statistics, interannual variability (IAV), and annual mean trend. Despite the differences, JRA55 best represents the trends, IAV, and shows the closest resemblance to observations. On an annual scale, the 10 m wind speed observations over India have a weak correlation with the Nino 3.4 index; however, they have a significant negative correlation with the Dipole Mode index (DMI), implying that SST anomalies over the Indian Ocean have a considerable impact on the weather and climate patterns of the Indian subcontinent. JRA55 closely reflects these relationships with large-scale ocean-atmospheric features as well. Overall, we conclude that JRA55 can be considered a proxy for 10 m wind speed observations over India.

Carbon exchange from agroecosystems contributes to the fluctuations of the carbon cycle. The present research employs the Community Land Model version 5 (CLM5) to examine the effects of climate and agricultural management methods, such as fertilization and irrigation, on carbon fluxes in the primary agroecosystems of India. In this study, CLM5 is calibrated and validated against the crop phenology dataset of spring wheat and rice. The crop phenology data is an unprecedented dataset that we have compiled by gathering information from many agricultural institutes around India. The crop dataset covers the period from 1970 to 2020. We have comprehensively tested and validated the CLM5 crop module in the Indian region. Subsequently, regional-scale simulations were conducted. The findings indicated that there are large variations in fluxes among different climatic regions of India, primarily due to disparities in growing circumstances. Throughout the study period, all fluxes exhibited statistically significant upward trends (p

AbstractHigh‐resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10‐km gridded hydrometeorological dataset over India by dynamic downscaling of the bias‐corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state‐of‐the‐art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid‐century (2041–2050) and end‐century (2091–2100) periods. The dataset includes 2‐m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies.

<p>This study explores the potential of slow-varying components of the earth system to predict the monthly mean wind speeds over the seven homogenous climate zones of India at subseasonal to seasonal time-scales. The following set of predictors are selected for that purpose: sea-surface temperature, mean sea-level pressure, 10 m wind speed, wind speed at 850 hPa, and geopotential height at 850 hPa. With the exception of sea-surface temperature which is obtained from HadISST, the rest of the variables are obtained from the JRA55. Besides, the popular indices such as the Nino 3.4 index and the Dipole mode index are also used as predictors. The forecasts are made at 1, 2, 3, 4, and 5 months of leadtime for the monsoon months of June, July, August, and September when the wind speeds are the highest throughout the country. The regions of significant correlations of the predictor fields with the spatially-averaged wind speeds of each homogenous region are determined using the past 6 month lagged composites. Once identified, the variables over these regions are spatially averaged and are mapped to the 10 m wind speeds from JRA55, since it is the closest representation of observed wind speeds over India. This predictor-based forecasting is carried out using the following approaches: multi-linear regression, decision tree based regression, and K nearest neighbours regression. The models use data from 1958-2018 for training and 2019-2021 for testing. The deterministic predictions are evaluated using mean absolute error (MAE) and the skill compared to a climatological forecast is estimated using the root mean squared error skill score (RMSESS). Results show that different sets of predictor combinations are responsible for giving the best forecasts for individual months and leadtimes. These forecasts have MAE of  around 0.2 m/s and RMSESS values ranging from 0.5-0.7. Although we are looking at deterministic predictions here, a combination of multiple models and predictors used above can lead to the production of ensemble forecasts as well, which will be of further added value to the wind energy sector.</p>

Carbon fluxes from agroecosystems contribute to the variability of the carbon cycle and atmospheric [CO2]. This study used the Integrated Science Assessment Model (ISAM) to investigate carbon fluxes and their variability in Indian spring wheat agroecosystems. First, ISAM was run in site-scale mode to validate GPP, TER, and NEP over an experimental spring wheat site in north India. When compared to flux-tower observations, the spring wheat module in ISAM outperformed the generic crop model. Following that, regional-scale runs were performed to simulate carbon fluxes across the country from 1980 to 2016. The results revealed that fluxes vary significantly across regions, owing primarily to differences in planting dates. Fluxes peak earlier in the country's eastern and central regions, where crops are planted earlier. During the study period, all fluxes show statistically significant increasing trends (p.01). GPP, NPP, Autotrophic Respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr(-2), respectively. Numerical experiments were conducted to investigate how natural forcings such as changing temperature and [CO2] levels and agricultural management practices such as nitrogen fertilization and water availability could contribute to the rising trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and irrigation water contributed to increased carbon fluxes, with nitrogen fertilization having the most significant effect.

We thank the referees for their thorough reviews and the editor for giving us a chance to respond to the referees’ comments. We did additional work involving rerunning the model and acquiring new data to properly address the referees’ comments. One major critique from both referees was the limited model evaluation against observed data. Addressing this issue was a major challenge because site-scale crop phenology, yield and other observations are not readily available in India and almost none in the public domain. That is why we decided to look for unconventional sources for crop data. We realised that there are many agricultural institutes across India where students conduct field experiments on crops grown in India and report the data in tabular form in their thesis. The thesis are rarely published and data from these field experiments are never made public. Accessing the theses was an issue until recently when an online thesis repository, KRISHIKOSH, was established where many of the old thesis were uploaded. We took this opportunity to extract data from these thesis and digitize them in machine readable format. In all, we have digitised data covering 25 growing seasons from 9 spring wheat sites [Table 1]. We used this data to evaluate our simulations for the revised manuscript. We will also make the data available in the public domain so that it can be used by other researchers. The digitization took more time than anticipated and hence we thank the editor for granting us extra time to revise this manuscript. Mr. Gudimetla Venkateshwara Varma made a significant contribution to find, extract, digitise and analyse the crop data. Hence, we would like to add him to the

Changes in temperature, precipitation, wind speed, and relative humidity due to climate change are likely to alter future fire regimes. We quantified the impact of such changes on the fire weather of Indian forests using a fire weather index and high-resolution downscaled climate projections. While conventional wisdom contends that future temperature increases will increase fire weather indices, we find this to be true only in dry forests. In humid forests, the fire weather index will decrease despite the warming due to future increases in precipitation and/or relative humidity. Days with severe fire weather danger will increase by up to 60% in dry forests but will reduce by up to 40% in humid forests. The fire season will be longer by 3–61 days across the country and the pre-monsoon fire season will become more intense over 55% of forests. This study suggests for countries like India with fragmented forests and diverse ecoclimates, standards and mitigation strategies must be developed at regional instead of national level.

<p>Forest fires strongly depend on local weather conditions. Weather conditions conducive for occurrence and growth of fires is known as “fire weather”. This work investigates how climate change can affect the future fire weather in Indian forests using the Canadian Forest Fire Danger Rating System – Fire Weather Index (CFFDRS-FWI), a well-known fire danger assessment system. To drive this model, we used a high-resolution dynamically downscaled climate projection DSCESM for a baseline (2006-2015) and an end-century (2091-2100) period to compute the metric ‘Fire Weather Index (FWI)’. We divided the forest areas of the country into 5 zones based on climate and forest types viz., Himalayan (HIM), Northeast (NE), Central India (CEN), Deccan (DEC) and Western Ghats (WG) zones. Then, we developed thresholds for five fire weather danger classes using the baseline FWI in conjunction with observed fire count from MODIS active fire data. The baseline and future FWI, fire weather danger, Seasonal Severity Ratings (SSR) and characteristics of the fire weather season were compared to estimate the effect of climate change on forest fire danger.</p> <p>Results show that there is considerable heterogeneity in the baseline as well as future fire weather danger across, and even within, the different forest zones of India. Climate change is likely to have a strong effect on fire weather. Days exceeding the Very High FWI threshold are likely to increase by about 30-40 days by the end-century despite a modest increase of about 5% in annual FWI values. SSR analysis suggests a maximum increase in fire disturbances during the pre-monsoon months of March-April-May.  About 55% of forest area over India will experience increased fire danger in this season. The least effect will be in the post monsoon season in September-October-November. The fire season is also expected to lengthen up to 59 days depending upon the forest type. The forests which are most likely to be affected by fire disturbances by end-century are moist deciduous and evergreen forests in the northern WG, mixed dry deciduous forests in central and southern CEN, Pine and Sal forests in the HIM and the scrub forests in the DEC zone. Climate change is unlikely to affect the fire weather danger in the NE.</p> <p>This study is one of the first attempts to quantify the effects of climate change on forest fire hazard in India. It has significant policy implications and can be valuable for fire management authorities for designing appropriate fire suppression and mitigating measures.</p> <p> </p>

>Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution[1]. Over the last several decades, wind energy development has increased worldwide, expanding from ～20 to ～900 GW(gigawatt)during 2001–2022 [1].

The land surface is an essential component of the Earth's System that interacts with the atmosphere via mass, momentum, and energy exchange. Croplands are one of the most common types of land use. Therefore, a comprehensive understanding of land-atmosphere interactions requires understanding the biogeochemical and biogeophysical processes and interactions in agroecosystems.Earth System Models (ESMs) can simulate the complex physical, chemical, and biological processes within and between the earth's land, atmosphere, ocean, and other spheres. Croplands have not received adequate attention in ESMs and were previously represented as grasslands. Land components in ESMs, such as the Community Land Model version 5 (CLM5) in the Community Earth System Model (CESM), have recently begun to include specific crops. The addition of crops to land models improved the simulation of energy, carbon, and water fluxes from land. CLM5 can represent a wide range of crops all over the world. However, there are significant errors in crop representation for the Indian region, including cropping areas, cropping season, irrigation, and crop characteristics. CLM5's estimated annual yield of wheat and rice has significant biases compared to UN-FAO estimates due to differences in growing seasons. Furthermore, observational data on the phenology of spring wheat and rice are scarce in the Indian region. As a result, crop growth model simulations in the Indian region suffer from poor calibration and validation.India is the world's second-largest producer of wheat and rice. Rice and wheat croplands cover more than 70 million ha combined. The current study aims to improve CLM5's representation of spring wheat and rice crops. This is accomplished by incorporating a crop planting window based on observations, wheat and rice cultivated area and irrigated cropland maps from district-level data. To further improve the crop models, we digitized historical crop phenology data and used them for model calibration and validation.Correcting the spring wheat and rice growing seasons in CLM5 over India has greatly improved crop phenology, yield, and irrigation pattern. As a result, the energy, carbon, and water fluxes are better estimated than the default CLM5 model. If the improved CLM5 is incorporated into the CESM, this can also improve the simulation of atmospheric phenomena.

The data consists of the following: Site-scale carbon flux data for an IARI experimental wheat site for the growing season 2013–2014 in New Delhi (28°40' N, 77°12' E). The simulation data in NetCDF format comprises carbon fluxes such as GPP, NPP, Ra, Rh, and NEE. Harvested wheat area of spring wheat across the Indian wheat-growing regions. Site-scale NEP (gC/m2/mon) measured at Meerut (29°05′33″N, 77°41′53″E; growing season 2009-2010) and Saharanpur (29° 52′ 19.139″ N and 077° 34′ 01.621″ E; growing season 2014-15) extracted from published work (Patel et al., 2011; Patel et al., 2021, respectively)

The study attempts to quantitatively understand the impact of dynamic vegetation on land-surface atmosphere interactions over spring wheat croplands in India. A new modeling tool capable of simulating these interactions was developed by incorporating the crop growth module of the Simple and Universal Crop growth Simulator (SUCROS) crop model into the Weather Research and Forecasting (WRF) mesoscale model. An earlier study had calibrated and evaluated the stand-alone SUCROS crop model with observed data for spring wheat collected from an experimental site in northwestern India. The crop growth module of the calibrated SUCROS model was implemented in the Noah-MP land module of WRF to build the coupled WRF_NOAHMP_SUCROS model. Numerical experiments were conducted with WRF_SUCROS that simulates the simultaneous evolution of meteorological drivers and crop Leaf Area Index (LAI) and the two-way interactions between these processes. These experiments were compared with WRF simulations driven by observed climatological mean LAI. These experiments only simulate the effects of changes in LAI on meteorology but not the other round. Results show that the coupled WRF_NOAHMP_SUCROS model is able to simulate the LAI better than the default dynamic vegetation module in WRF. It also produces realistic simulations of the near-surface meteorological parameters. The latent heat flux (LHF) varies directly with LAI, and sensible heat flux (SHF) varies inversely with LAI. As the crop grows, the energy transfer occurs more in latent heat flux than sensible heat flux due to increased evapotranspiration. Hence the growing crops result in near-surface cooling due to decreased Bowed Ratio. The mixing ratio is also increased due to increased latent heat flux. The uncoupled WRF model also shows similar patterns except in the juvenile crop stage where it overestimates the sensible heating and temperature but underestimates latent heat fluxes and mixing ratio.

Wind power is an environmentally sustainable technology that is likely to be part of the solution to the climate change, air pollution, and energy security problems. Despite many positive benefits, the rapid development of wind power has raised concerns about some potential adverse environmental impacts. While converting wind׳s kinetic energy into electricity, wind turbines (WTs) modify properties of the atmospheric boundary layer (ABL) including the vertical profiles and surface-atmosphere exchanges of energy, momentum, mass, moisture, and trace gases. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WT-atmosphere-surface interactions and assessing potential environmental impacts of WFs are of significant scientific, societal and economic importance. Here we review our progress in assessing potential impacts of onshore wind power on weather, climate and vegetation activity. A consensus is emerging based on observations and modeling studies that WFs cause a local to regional warming effect, particularly at nighttime, while the impacts on precipitation, wind patterns, crop yields and vegetation activity are uncertain. The warming effect results simply from vertical heat redistribution within the ABL due to turbine-enhanced vertical turbulent mixing in the wakes. At the global scale, with a substantial installation of WFs, mesoscale and climate models predicted large regional changes but small global impacts on temperature, while the impacts on precipitation, clouds, wind patterns and large-scale circulation have large uncertainties and are region specific and scale dependent. Despite increasing number of research efforts, our assessment of potential WF impacts is still very limited. Although the WF impacts are mostly local and limited to the near-surface ABL, this is the layer where we live and plants grow. Hence, more studies are needed to improve our understanding of WT-atmosphere-surface interactions and our capability to model and project the weather, climatic and ecological impacts of large WFs.