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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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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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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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Surface albedo (SAL), a critical factor in climate studies, significantly impacts the Earth's radiation budget and sea ice dynamics. The long‐term spatial and temporal variability of Antarctic SAL were derived from the third edition of the Cloud, Albedo, and Surface Radiation Dataset (CLARA‐A3). The analysis focused on spring and summer across five longitudinal sectors around Antarctica. The relationships of sea ice concentration (SIC) and SAL with climatic variables such as sea surface temperature (SST), 2 m air temperature (T2m), turbulent heat flux, and total cloud cover are explored in detail. The study examined SAL changes in two distinct timescales, pre‐2015 (1979–2015) and post‐2015 (2016–2021), to understand sea ice variations and trends in Antarctic climate change. The study revealed contrasting summer SAL trends, with a positive trend pre‐2015 and a decreasing trend post‐2016 across most of Antarctica, except the Amundsen‐Bellingshausen Sea, which showed an opposite trend. West Antarctica exhibited higher SAL compared to East Antarctica. SAL and SIC were significantly negatively correlated with SST, T2m, and turbulent heat flux across all sectors. Cross‐seasonal lead–lag analysis indicated that increased turbulent heat flux was followed by an increase in SAL after 1–5 months. Wind patterns showed that winds from higher to lower latitudes increased SIC and SAL, while winds from lower to higher latitudes reduced SIC. Post‐2015, notable wind direction reversals were observed in the Antarctic Peninsula during spring. Sectors with higher cloud cover absorbed more ocean heat, reducing turbulent heat flux and affecting SAL. Overall, post‐2015 observations highlighted major shifts in sea ice dynamics and SAL trends during both spring and summer seasons. The SIC decreased markedly across all sectors, with the Weddell Sea showing the most significant reduction. This study highlights regional and seasonal variations in SAL and its interactions with SIC and climatic factors, emphasising shifts in trends post‐2015.
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The presence of sea breeze (SB) is analyzed at nine meteorological stations in the northwest of Costa Rica (Peninsula and Gulf of Nicoya, GF); two from the Ticosonde-NAME experiment, University of Costa Rica, and seven from the National Meteorological Institute, for the period from July 1 to September 16, 2004. An objective detection algorithm for SB is applied to hourly data from the stations and sea surface temperature (SST). The algorithm uses temperature gradient and wind direction. Pinilla and Guacalillo stations show 64% of SB on the 78 days analyzed. Liberia (20 km inland) presents 44.9% of SB associated with weak synoptic winds from the east. Puntarenas presents doubtful cases due to wind errors, while the other stations do not present complete records. Some of the non-SB days are dominated, on one hand, by strong synoptic flow from the northeast associated with the low-level Caribbean jet that in turn coincides with the periods of reduced rainfall or mid-summer drought and, on the other hand, by synoptic flow from the southwest associated with the passage of weather systems in the western Caribbean. The algorithm shows a good ability to detect SB despite the poor spatial resolution of SST. Consistent with a typical SB circulation, precipitation at almost all stations is characterized by coastal convective activity and precipitation in the late afternoon and evening hours. The results are encouraging for their potential application to artisanal fishing, agriculture, tourism, and regional air quality, as there are very active ports in the Gulf of Nicoya (Puntarenas and Caldera), points of intense movement of tourist and commercial ships that negatively impact environmental conditions.
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Ocean evaporation (
) is the major source of atmospheric water vapor and precipitation. While it is widely recognized that
may increase in a warming climate, recent studies have reported a diminished increase in the global water vapor since ∼2000s, raising doubts about recent changes in
. Using satellite observations, here we show that while global
strongly increased from 1988 to 2017, the upward trend reversed in the late 2000s. Since then, two‐thirds of the ocean have experienced weakened evaporation, leading to a slight decreasing trend in global‐averaged
during 2008–2017. This suggests that even with saturated surface, a warmer climate does not always result in increased evaporation. The reversal in
trend is primarily attributed to wind stilling, which is likely tied to the Northern Oscillation Index shifting from positive to negative phases. These findings offer crucial insights into diverse responses of global hydrological cycle to climate change.
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Atmospheric optical turbulence limits the performance of free-space optical communication links between the ground and space. The bandwidth provided by optical links enables the realization of ubiquitous, high-bandwidth and secure communications anywhere on Earth. However, currently very little is known about the nature and dynamics of vertical optical turbulence in critical urban environments, close to data centers and users that require high-bandwidth connections. TURBO is a turbulence monitoring facility, based on the 24-hour Shack-Hartmann Image Motion Monitor (24hSHIMM) instrument, capable of measuring optical atmospheric turbulence 24-hours a day in stronger turbulence conditions. Here we show a demonstration of continuous turbulence monitoring in an urban environment for the first time in Barcelona, Spain. TURBO will be an autonomous monitor generating valuable data for the community.
