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In this study, the global land surface albedo namely GAC43 was retrieved for the years 1979 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) data onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. We provide a comprehensive retrieval process of the GAC43 albedo, followed by a comprehensive assessment against in situ measurements and three widely used satellite-based albedo products, the third edition of the CM SAF cLoud, Albedo and surface RAdiation (CLARA-A3), the Copernicus Climate Change Service (C3S) albedo product, and MODIS BRDF/albedo product (MCD43). Our quantitative evaluations indicate that GAC43 demonstrates the best stability, with a linear trend of ±0.002 per decade at nearly all pseudo invariant calibration sites (PICS) from 1982 to 2020. In contrast, CLARA-A3 exhibits significant noise before the 2000s due to the limited availability of observations, while C3S shows substantial biases during the same period due to imperfect sensors intercalibrations. Extensive validation at globally distributed homogeneous sites shows that GAC43 has comparable accuracy to C3S, with an overall RMSE of approximately 0.03, but a smaller positive bias of 0.012. Comparatively, MCD43C3 shows the lowest RMSE (~0.023) and minimal bias, while CLARA-A3 displays the highest RMSE (~0.042) and bias (0.02). Furthermore, GAC43, CLARA-A3, and C3S exhibit overestimation in forests, with positive biases exceeding 0.023 and RMSEs of at least 0.028. In contrast, MCD43C3 shows negligible bias and a smaller RMSE of 0.015. For grasslands and shrublands, GAC43 and MCD43C3 demonstrate comparable estimation uncertainties of approximately 0.023, with close positive biases near 0.09, whereas C3S and CLARA-A3 exhibit higher RMSEs and biases exceeding 0.032 and 0.022, respectively. All four albedo products show significant RMSEs around 0.035 over croplands but achieve the highest estimation accuracy better than 0.020 over deserts. It is worth noting that significant biases are typically attributed to insufficient spatial representativeness of the measurement sites. Globally, GAC43 and C3S exhibit similar spatial distribution patterns across most land surface conditions, including an overestimation compared to MCD43C3 and an underestimation compared to CLARA-A3 in forested areas. In addition, GAC43, C3S, and CLARA-A3 estimate higher albedo values than MCD43C3 in low-vegetation regions, such as croplands, grasslands, savannas, and woody savannas. Besides the fact that the new GAC43 product shows the best stability covering the last 40 years, one has to consider the higher proportion of backup inversions before 2000. Overall, GAC43 offers a promising long-term and consistent albedo with good accuracy for future studies such as global climate change, energy balance, and land management policy.
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Throughout the industrial period, anthropogenic aerosols have likely offset approximately one-third of the warming caused by greenhouse gases. Marine cloud brightening aims to capitalize on one aspect of this phenomenon to potentially mitigate global warming by enhancing cloud reflectivity through adjustments in cloud droplet concentration. This study employs a simplified yet comprehensive modeling framework, integrating an open-source parcel model for aerosol activation, a radiation transport model based on commercial computational fluid dynamics code, and assimilated meteorological data. The reduced complexity model addresses the challenges of rapid radiation transfer calculations while managing uncertainties in aerosol–cloud-radiation (ACR) parameterizations. Despite using an uncoupled ACR mechanism and omitting feedback between clouds and aerosols, our results closely align with observations, validating the robustness of our assumptions and methodology. This demonstrates that even simplified models, supported by parcel modeling and observational constraints, can achieve accurate radiation transfer calculations comparable to advanced climate models. We analyze how variations in droplets size and concentration affect cloud albedo for geoengineering applications. Optimal droplet sizes, typically within the 20–35-µm range, significantly increase cloud albedo by approximately 28%–57% across our test cases. We find that droplets transmit about 29% more solar radiation than droplets. Effective albedo changes require injection concentrations exceeding background levels by around 30%, diminishing as concentrations approach ambient levels. Considerations must also be given to the spray pattern of droplet injections, as effective deployment can influence cloud thickness and subsequently impact cloud albedo. This research provides insights into the feasibility and effectiveness of using a reduced complexity model for marine cloud brightening with frontal cyclone and stratus cumulus clouds, and emphasizes the need to also consider background droplets size and concentration than just meteorological conditions.
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The increasing global adoption of variable renewable energy (VRE) sources has transformed the use of forecasting, scenario planning, and other techniques for managing their inherent generation uncertainty and interdependencies. What were once desirable enhancements are now fundamental requirements. This is more prominent in Brazil, given the large hydro capacity that has been installed. Given the need to understand the interdependencies within variable renewable energy systems, copula-based techniques are receiving increasing consideration. The objective is to explore and model the correlation and complementarity, based on the copula approach, evaluating the potential of this methodology considering a case test composed of hydro, wind, and solar assets. The proposed framework simulated joint scenarios for monthly natural energy (streamflows transformed into energy), wind speed and solar radiation, applied to a small case test, considering historical data from the Brazilian energy system. The results demonstrate that simulated scenarios are validated by their ability to replicate key statistical attributes of the historical record, as well as the interplay and complementarity among hydrology, wind speed, and solar radiation.
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This study proposes a new lightning scheme applicable at the global scale, predicting lightning rates from climatic variables. Using satellite lightning records spanning a period of 29 years, we apply machine learning methods to derive a functional relationship between lightning and climate reanalysis data. In particular, we design a tree‐based regression scheme, representing different lightning regimes with separate single hidden layer neural networks of low dimensionality. We apply multiple complexity constraints in the development stages, which makes our lightning scheme straightforward to implement within global climate models (GCMs). We demonstrate that, for years not used for training, our lightning scheme captures of the daily global spatio‐temporal lightning variability, which corresponds to a relative improvement compared to well‐established lightning schemes. Similarly, the scheme correlates well with lightning observations for the monthly climatology , inter‐annual variability , and latitudinal and longitudinal distributions . Most notably, the lightning scheme brings a critical improvement in representing lightning magnitude and variability in the three tropical lightning chimney regions: central Africa, the Amazon, and the Maritime Continent. We implement the lightning scheme in the Community Earth System Model to verify its stability and performance as a GCM component, and we provide detailed implementation guidelines. As an intermediate approach between high‐dimensional machine learning models and first‐order lightning parameterizations, our lightning scheme offers GCMs a straightforward and efficient tool to improve lightning simulation, which is critical for representing atmospheric chemistry and naturally ignited wildfires.
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