Surface Albedo Research Articles

The Influence of the Installation Condition and Performance of Bifacial Solar Modules on Energy Yield

Compared with typical mono-facial photovoltaic (PV) solar modules, bifacial solar modules can make full use of reflected or scattered light from the ground and the surroundings to yield more electrical energy. The electrical energy on the rear side depends on multiple factors, such as the IV parameters of modules, packaging materials, and installation circumstances. In this work, the power generation output is simulated and researched using the PV-SYST software program, based on the different electrical parameters of bifacial solar modules and the installation conditions of the given PV systems. The influencing factors that affect the electrical energy are further analyzed using power-loss diagrams. The results show that improving the surface albedo can raise additional energy by 8.3%, thus behaving significantly better than the mono-facial module. Furthermore, improving the siting height and incidence angle modifier (IAM) of the modules can increase the additional energy by 3.1%. In contrast, adjusting the output current or voltage of the modules adds some energy, while the modules are of the same nominal power value. It was observed that the energy level of a photovoltaic system mainly depends on the installation circumstances, but the electrical parameters of the modules themselves contribute little.

A Dissection of the Inter‐Model Spread of the Aerosol Direct Radiative Effect in CMIP6 Models

AbstractThe all‐sky Aerosol Direct Radiative Effect (ADRE) varies considerably among global climate models (GCMs), which results from differences in aerosol and atmospheric states and ADRE sensitivity to aerosol‐related radiative processes. This study uses a regression method to analyze the inter‐model spread of ADRE among the GCMs of the Sixth Coupled Model Intercomparison Projects (CMIP6). The key state variables examined include scattering and absorbing aerosol optical depth, surface albedo, and shortwave cloud radiative effect. We find that differences in state variables and radiative sensitivity explain 67% and 17%, respectively, of the global ADRE anomaly. The ADRE anomaly in different models is dominated by different factors, which sometimes leads to compensating effects. For the global mean ADRE anomaly, aerosol optical depth differences dominate in CNRM‐ESM2‐1 and GFDL‐ESM4 models, while ADRE sensitivity variations to aerosol‐only scattering effect dominate in HadGEM3‐GC31‐LL, MPI‐ESM‐1‐2‐HAM, and MRI‐ESM2‐0 models.

Regime-dependent 1-min irradiance separation model with climatology clustering

Since directly measuring beam and diffuse irradiance is not feasible on many occasions, one often has to resort to estimating the beam and diffuse irradiance components from the global irradiance, which is known as separation modeling. Separation modeling is essentially a nonlinear regression problem, with the clearness index being the main input and the diffuse fraction being the output. Hundreds of separation models with various complexities have been proposed, among which the Yang4 model was recently validated using worldwide data as the quasi-universal choice for 1-min data. In this work, Yang4 is further improved by regime-dependent fitting, i.e., fitting a separate set of model coefficients for each climatological regime. Different regimes are determined through clustering of cloud cover frequency, aerosol optical depth, and surface albedo climatology maps. The new Yang5 model is able to outperform its predecessor at the 126 stations tested, covering a wide range of climate types. Overall, the normalized root mean square errors for beam normal irradiance (BNI) and diffuse horizontal irradiance (DHI) of Yang5 are 17.55% and 32.92% on average, as compared to 19.13% and 34.94% for the next best model, namely, Yang4. Furthermore, through conducting pairwise Diebold–Mariano tests, Yang5 is shown superior to Yang4 at 110/126 sites for BNI prediction and 93/126 for DHI.

Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Forest composition change and biophysical climate feedbacks across boreal North America

Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study.

Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences.

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya.

Rangelands primarily provide forage for grazing and browsing animals, yet their ecosystems are degraded due to natural causes and anthropogenic activities such as pastoralism, tourism, and ranching. Increased rangeland detrimental effects led the present research to model the severity of rangeland degradation in the Upper Ewaso Ngiro River Basin (UENRB) in Kenya between 1986 and 2021 and predict the future scenario for 2031. The severity of rangeland degradation was analysed using the multi-criteria analytic hierarchical process and principal component analysis, while the cellular automata Markov chain-analysis model was used for prediction. The models utilized datasets including land-use land cover, surface albedo, bareness index, vegetation health index, soil moisture index, topographic wetness index, reconnaissance drought index, k-factor, slope, and population density. The findings indicated that rangeland degradation varied sporadically, with the reconnaissance drought index being the significant influencing parameter, contributing to about 19.2% of the total degradation. In average, between the years under study, non-rangeland zones covered 10.4%, while low, moderate, high, and very high degradability severity covered 15.3%, 49.1%, 25.2%, and 0%, respectively. Prediction results for the year 2031 revealed that non-rangeland zones will cover 5.3%, whereas low, moderate, high and very high will cover 18.1%, 39.2%, 37.4%, and 0%, respectively. The hybrid model proved to be effective in modeling rangeland degradation. The study recommends the county and national governments to propose and adopt by-laws on legislation to regulate the exploitation of natural resources in the study area in order to restore the rangelands.

Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies

In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then used to investigate spatial and temporal trends in the summer albedo over the Antarctic from 1982 to 2018, along with their association with Antarctic sea ice changes. The SAL product matches well surface albedo observations from eight stations, suggesting its robust performance in Antarctica. Summer surface albedo averaged over the entire ice sheet shows a downward trend since 1982, albeit not statistically significant. In contrast, a significant upward trend is observed in the sea ice region. Spatially, for ice sheet surface albedo, positive trends occur in the eastern Antarctica Peninsula and the margins of East Antarctica, whereas other regions exhibit negative trends, most prominently in the Ross and Ronne ice shelves. For sea ice albedo, positive trends are observed in the Ross Sea and the Weddell Sea, but negative trends are observed in the Bellingshausen and the Amundsen Seas. Between 2016 and 2018, an unusual decrease in the sea ice extent significantly affected both sea ice and Antarctic ice sheet (AIS) surface albedo changes. However, for the 1982–2015 period, while the effect of sea ice on its own albedo is significant, its impact on ice sheet albedo is less apparent. Air temperature and snow depth also contribute much to sea ice albedo changes. However, on ice sheet surface albedo, the influence of temperature and snow accumulation appears limited.

Investigation of the Earth’s Albedo Using Meteorological Parameters over Maiduguri, Nigeria

The present study estimate and investigate the variation of albedo for Maiduguri situated in the Sahelian region of Nigeria, using meteorological data of global solar radiation obtained from the National Aeronautics and Space Administration (NASA) extending for a period between 1984 to 2021. Investigation was carried out on the variation of albedo with surface temperature, maximum wavelength, clearness index, global solar radiation, relative humidity and mean temperature. The study found that the estimated surface albedo exhibited a direct opposite relationship with the clearness index, an inverse relationship with the emitting Earth’s surface temperature and a direct relationship with the wavelength for the location. The highest value of 0.5125 and lowest value of 0.3344 were found in August and November respectively. The emitting Earth surface temperature ranged between 232.8674 K in August and 251.7177 K in November. This is in agreement with the standard emitting Earth surface temperature (255.0000 K). The values of the maximum emitting wavelength were found to be > 4 indicating longwave radiation which is within the infrared region of the electromagnetic spectrum. The results from this study will be useful for the design of solar energy collectors and researches on atmospheric radiative transfer.

Increase in aerodynamic impedance and energy uptake in response to grazing prohibition in semi-arid grasslands during the beginning of the growing season

In Inner Mongolia, China, semi-arid grasslands have strong winds and radiation in spring, but it is unclear how grazing management influences the connections between aerodynamic and energy balance during this time. During spring, we conducted measurements of radiation, wind speed, and air temperature, for both grazed and grazing-prohibited grasslands. From these results it was found that the temperature gradient differential (ΔT = T0.4m-T2m) between the ground level (T0.4m) and the high height (T2m) is normally negative at night and positive during the day. Grazed grassland was found to have very few negative ΔT values. Albedo and ΔT were higher on the grazed grasslands, while net radiation and aerodynamic roughness (Z0) were lower on the grazed grasslands. According to structural equation modeling (SEM), the grazing prohibited and grazed had no effect on the relationship between surface albedo, air temperature, and humidity. Z0 in grazed grassland exhibited a substantial positive correlation with net radiation (Rn), but was not correlated with albedo. The Z0 of the grazing-prohibited grassland exhibited a substantial negative correlation with albedo, but not with Rn, and Z0 had a significant negative correlation with air temperature. These findings explain how grazing measures affect the vertical variations of surface heat in air.

A Surface Radiation Balance Dataset from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

Abstract A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-μm atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations. significance statement Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

A dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020

The Tibetan Plateau has experienced a rapid climate rise in recent years, which is a hot issue in the study of global change. However, the meteorological stations are sparsely and unevenly distributed on the Tibetan Plateau, which negatively influences the spatial representativeness of meteorological data on the climate change of the whole plateau. Satellite remote sensing provides a new approach to the large-scale research on the climate change. First, we extracted the monthly daytime land surface temperature, monthly nighttime land surface temperature, monthly clear sky days, monthly surface albedo, monthly NDVI, monthly NDSI, altitude, astronomical radiation radiance and CTI from MODIS remote sensing data, DEM data and CTI datasets. Then, we employed the Cubist algorithm to develop models for estimating monthly average, maximum and minimum air temperature through cross-validation and parameter tuning. Finally, we obtained dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020. The dataset is helpful for further understanding the climate change on the Tibetan Plateau, and provides important database for the climate change research on the Tibetan Plateau.

Understanding Surface Air Temperature Cold Bias Over China in CMIP6 Models

AbstractThe systematic simulated cold biases in models for surface air temperature (SAT) over China have been under discussion for a long time, but the related attribution is still unclear. In this study, we investigate the main contributors and relevant physical processes of SAT biases over China based on 31 models participating in the sixth phase of the Coupled Model Intercomparison Project from a surface energy budget perspective. On annual and seasonal scales, less downward clear‐sky longwave radiation in the models, which is due to the underestimated tropospheric temperature and water vapor amount, is the main cause of cold biases over the Tibetan Plateau, Tarim Basin, Sichuan Basin, and most of eastern China. Meanwhile, the simulation biases in surface albedo and sensible heat flux induce cold biases depending on regions and seasons. In winter and spring, the overestimated surface albedo in association with more snow cover leads to prevalent cold biases over the western Tibetan Plateau and Northeast China. Excessive sensible heat release caused by the overestimated ground–air temperature difference facilitates SAT underestimation in the Sichuan Basin during summer and autumn. In addition, the cold biases in winter and spring are larger than those in summer and autumn, due to the stronger snow cover overestimation and tropospheric temperature underestimation.

Regionally sourced bioaerosols drive high-temperature ice nucleating particles in the Arctic

Primary biological aerosol particles (PBAP) play an important role in the climate system, facilitating the formation of ice within clouds, consequently PBAP may be important in understanding the rapidly changing Arctic. Within this work, we use single-particle fluorescence spectroscopy to identify and quantify PBAP at an Arctic mountain site, with transmission electronic microscopy analysis supporting the presence of PBAP. We find that PBAP concentrations range between 10−3–10−1 L−1 and peak in summer. Evidences suggest that the terrestrial Arctic biosphere is an important regional source of PBAP, given the high correlation to air temperature, surface albedo, surface vegetation and PBAP tracers. PBAP clearly correlate with high-temperature ice nucleating particles (INP) (>-15 °C), of which a high a fraction (>90%) are proteinaceous in summer, implying biological origin. These findings will contribute to an improved understanding of sources and characteristics of Arctic PBAP and their links to INP.

Constraining Thermal Emission of Pluto’s Haze from Infrared Rotational Lightcurves

The rotational lightcurves of the Pluto-Charon system were previously believed to be solely attributed to their surfaces. However, a proposed scenario of haze cooling suggests that the atmospheric haze of Pluto could significantly contribute to mid-infrared emission, which calls for a revisit of previous analyses. In this study, we employ a Bayesian retrieval approach to constrain the haze emission from the rotational lightcurves of the Pluto-Charon system. The lightcurves were observed by the Spitzer and Herschel telescopes at 24 and 70 μm, and were combined with the latest surface albedo maps of Pluto and Charon from the New Horizons spacecraft. Our results show that including the haze emission is consistent with all current observations, with the best-fit haze flux around 1.63 mJy. This is in agreement with the composition of Titan-like tholins. However, the “surface only” scenario, which excludes the haze contribution, can still explain the observations. We conclude that the current data at 24 μm cannot constrain Pluto’s haze emission due to the degeneracy with Charon’s surface emission. Regardless, some surface properties of Pluto are well constrained by the shape of the lightcurves, with a thermal inertia of approximately 8–10 MKS and a relatively low CH4 emissivity of 0.3–0.5. We suggest that observations by the JWST telescope at 18 μm, which can resolve Pluto from Charon, could directly probe the haze emission of Pluto due to the low surface emission at that wavelength.

An evaluation of the NOAA global daily gap-filled VIIRS surface albedo

The land surface albedo product of visible infrared imaging radiometer suite (VIIRS) in National Oceanic and Atmospheric Administration (NOAA)’s operational system provides real-time, global daily mean surface albedo, which is a required parameter in the estimation of the daily shortwave net radiation budget. The global gridded VIIRS albedo product is derived from the level-2 granule surface albedo product, which is generated using a Direct Estimation Method. Special gridding and compositing algorithms were developed for aggregating the granular albedo data into the gridded albedo product. This paper describes the design and evaluation of the NOAA VIIRS gridded daily surface albedo product. The cloudy condition, retrieval path, retrieval method, and observing geometry are the criteria in deciding the priority order in the composition processing. The proposed albedo product possesses a complete spatial coverage over global land and ice surface and provides a timely response to surface dynamics. The validation of satellite retrieved daily mean albedo against ground counterparts over a series of well-maintained networks demonstrates the reliability of the composed albedo considering the interference of the seasonal surface heterogeneity conditions around each site. The inter-comparison between the S-NPP and NOAA-20 VIIRS albedo shows good agreement, except for a minor bias related to solar/view angle differences. The cross-comparison between VIIRS albedo and MODIS albedo shows good consistency with some deviations related to the controversy between their upstream snow mask.

Neural network models for predicting urban albedo of urban surfaces with different reflection directional properties

The urban albedo is regarded as an important indicator for mitigating the urban heat island (UHI) phenomenon. The goal of this paper is to create neural network models that forecast the urban albedo of various reflection directional characteristic urban surfaces by simply inputting the sun’s position (altitude and azimuth) and solar radiation. In this study, two urban models with diffuse highly reflective (DHR) and retro-reflective (RR) urban coatings are created, and their urban albedo for two sunny days is calculated using standard (ASTM E1918A). Using the measured urban albedo for one sunny day, two predictive neural network models, Gaussian process (NNGP) and hyperbolic tangent function (NNTanH) are developed (August 1, 2021). The two developed neural network models are used to forecast the urban albedo for another sunny day (July 19, 2021). In the case of DHR urban coatings, the NNTanH model is more accurate with higher R-squared (R2) and lower root mean squared error (RMSE) than the NNGP model, and there is no significant difference between the two neural network models. However, it is demonstrated that the NNGP model is more accurate than the NNTanH model in predicting urban albedo in the case of RR urban coatings, with higher R2 and lower RMSE.

Climate cooling benefits of cellulosic bioenergy crops from elevated albedo

AbstractChanges in land surface albedo can alter ecosystem energy balance and potentially influence climate. We examined the albedo of six bioenergy cropping systems in southwest Michigan USA: monocultures of energy sorghum (Sorghum bicolor), switchgrass (Panicum virgatum L.), and giant miscanthus (Miscanthus × giganteus), and polycultures of native grasses, early successional vegetation, and restored prairie. Direct field measurements of surface albedo (αs) from May 2018 through December 2020 at half‐hourly intervals in each system quantified the magnitudes and seasonal differences in albedo (∆α) and albedo‐induced radiative forcing (RF∆α). We used a nearby forest as a historical native cover type to estimate reference albedo and RF∆α change upon original land use conversion, and a continuous no‐till maize (Zea mays L.) system as a contemporary reference to estimate change upon conversion from annual row crops. Annually, αs differed significantly (p < 0.05) among crops in the order: early successional (0.288 ± 0.012SE) >> miscanthus (0.271 ± 0.009) ≈ energy sorghum (0.270 ± 0.010) ≥ switchgrass (0.265 ± 0.009) ≈ restored prairie (0.264 ± 0.012) > native grasses (0.259 ± 0.010) > maize (0.247 ± 0.010). Reference forest had the lowest annual αs (0.134 ± 0.003). Albedo differences among crops during the growing season were also statistically significant, with growing season αs in perennial crops and energy sorghum on average ~20% higher (0.206 ± 0.003) than in no‐till maize (0.184 ± 0.002). Average non‐growing season (NGS) αs (0.370 ± 0.020) was much higher than growing season αs (0.203 ± 0.003) but these NGS differences were not significant. Overall, the original conversion of reference forest and maize landscapes to perennials provided a cooling effect on the local climate (RFαMAIZE: −3.83 ± 1.00 W m−2; RFαFOREST: −16.75 ± 3.01 W m−2). Significant differences among cropping systems suggest an additional management intervention for maximizing the positive climate benefit of bioenergy crops, with cellulosic crops on average ~9.1% more reflective than no‐till maize, which itself was about twice as reflective as the reference forest.

Impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

Amassing the available solar energy over the Sahara desert, through the installation of a large-scale solar farm, would satisfy the world’s current electricity needs. However, such land use changes may affect the global carbon cycle, possibly offsetting mitigation efforts. Here a fully coupled Earth System model EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of photovoltaic solar panels over the Sahara desert. The resulting changes to the carbon cycle were an enhancement of the carbon sink across Northern Africa, particularly around the Sahel but a simultaneous weakening of the carbon sink in the Amazon basin. This is observed through spatial pattern changes to the values of net biome production (NBP), more evident during Northern Hemisphere summer season. NBP changes are contributed by competing responses in the net primary production and heterotrophic respiration rates. These changes to carbon exchange correspond to a wetter and warmer climate occurring in Northern Africa and a drier and warmer climate in the Amazon, with stronger driving effects of precipitation. Due to these coupled responses and complex teleconnections, thorough investigation of remote impacts of solar farms are needed to avoid unintended consequences on the terrestrial carbon cycle.