A high-resolution projection dataset for solar energy across China (2015–2060)

In this study, we developed a new version of the future ocean regional projection dataset in the North Pacific (FORP-NP10) by performing an ensemble of historical and multi-scenario future projection simulations from 1960 to 2100 using a high-resolution ocean downscaling model system driven by surface forcings based on the atmospheric data of the Coupled Model Intercomparison Project (Phase 5). This version is characterized by the inclusion of simple biogeochemical processes within the simulations of the multi-case (four forcing cases) and multi-future projection scenarios (representative concentration pathway 2.6 and 8.5) using a mesoscale eddy-resolving (approximately 10 km) horizontal resolution. The dataset reasonably represented the main biogeochemical properties in the North Pacific and around Japan, such as surface CO2 flux, pH, subsurface oxygen, surface nitrate, and chlorophyll, as well as the ocean physics of the regions, including the mesoscale/frontal structures of the Kuroshio, Kuroshio Extension, and mixed layer depth. We assessed the bias tendencies and structures of these properties by comparing them with observational reference data and the low-resolution model results. Our ensemble dataset consistently projected future upper-ocean warming, acidification, deoxygenation, and changes in nutrient and primary production in the twenty-first century (reported as global analyses in recent reports of the Intergovernmental Panel on Climate Change) in the regions surrounding Japan, revealing that the future changes exhibited spatial contrasts or varying tendencies in the regions.

This study evaluates, through simulation, the technical feasibility and highlights the environmental benefits of a proposed off-grid system for electrolytic green hydrogen production using industrial effluents and solar photovoltaic and wind energy (Off-Grid GH2PS). The simulations compare the operational dynamics of the current scenario (grid electricity and natural gas) of an industry located in northeastern Brazil with those of the proposed system (renewable electricity, PEM electrolyzer, effluent conditioning system, battery storage, and hydrogen-natural gas blending). The results show strong solar-wind complementarity, with irradiance ranging from 4.77-6.92 kWh/m2/day and wind speeds from 5.04-8.49 m/s, resulting in a total generation of 1.91 GWh/year, of which 62% came from solar energy. Of this amount, the electrolyzer consumed 1.36 GWh/year (84% of the demand). Hydrogen production reached 17,976 kg/year, and annual consumption totaled 17,262 kg. The 10% hydrogen blending into natural gas reduced natural gas use by 3%, but it required a 6.5% increase in volumetric flow. Effluent conditioning showed low seasonal variability and delivered 486.8 m3/year, although only 27% reached the electrolyzer due to purification losses. It was observed that 27.54 kgH2O/kgH2 and an energy consumption of 75.90 kWh/kgH2 were required. Environmental performance showed an emission reduction of 10.99% (465.5 tCO2eq/year). Overall, the Off-Grid GH2PS demonstrates a strong potential for industrial decarbonization in regions with high availability of renewable resources and effluents. Finally, future studies should incorporate high-resolution temporal data sets, integrate degradation models for system components, and enable dynamic coupling between water treatment and the energy-hydrogen subsystems. A detailed life-cycle assessment is also recommended to strengthen the overall sustainability analysis, as well as an economic feasibility evaluation of the proposed system.

The simultaneous occurrence of both extreme droughts and heatwaves has become more frequent with global warming, resulting in increases in the frequency and potential impact of compound drought and heatwave (CDHW) globally. It is critical to evaluate the impacts of CDHW and assess global socio-economic risks to formulate appropriate risk mitigation strategies. Most studies have focused on projecting the likely variation in the multidimensional hazard of CDHW. However, the discrepancies among global population projection datasets based on shared socioeconomic pathways (SSPs) and their potential impacts on disaster risk assessments remain underexplored. In this study, multiple global high-resolution population projection datasets are used in combination with projected CDHW hazards via the multimodel ensemble from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate how different sources of population data could affect the assessment of CDHW-exposed populations under SSPs. The results show that at the global scale, the spatial pattern and temporal evolution of the CDHW-exposed population under climate change can be depicted consistently on the basis of different population data. However, at the subcontinental scale, substantial spatial heterogeneity exists in the projected exposure. For regions such as the Mediterranean, South Asia, and western Central Asia, the projections from different datasets are consistent with low uncertainty. In contrast, for regions including the northern hemisphere above 40°N, Oceania, eastern Central Asia, East Asia, the South American monsoon region, western Africa, Central Africa, etc., the uncertainty in the estimated exposed population is higher and is expected to increase from the 2020s to the end of the 21st century. Additional locational socioeconomic data should be collected in these areas to reduce uncertainty in future socioeconomic projections. The findings highlight the critical need to consider different elements-at-risk and choose fit-for-purpose datasets, providing essential guidance for disaster risk assessments that support climate adaptation strategies and sustainable development goals.

Rapid increase of the nighttime electricity demand in Beijing due to compound heatwaves

<p>The High Arctic archipelago of Svalbard is one of the fastest-warming locations on the planet. In addition to melting snow and ice which threatens a vulnerable ecosystem, thawing permafrost destabilises slopes, coastlines and man-made structures and exposes especially cultural heritage objects. Preserving the numerous cultural heritage objects is challenging because on Svalbard they receive protected status automatically if they are older than 1946. In the PCCH-Arctic project, we study the influence of climate change on cultural heritage sites, including among others the wooden houses in Ny-Ålesund and the coal cableway in Longyearbyen. To gain meaningful climate information on specific sites we apply a high-resolution regional climate model.<br>Traditionally, these high-resolution convection-permitting datasets have been produced in separate time windows for historical and future periods. However, some cryospheric phenomena such as permafrost and glaciers require longer transient simulations.<br>We here present a high-resolution climate projection dataset covering Svalbard for the years 1991-2060 at 2.5 km horizontal resolution. The simulation is produced by the regional climate model HARMONIE Climate (HCLIM) cycle 43 featuring convection-permitting HARMONIE-AROME atmospheric physics and SURFEX land-surface model with ISBA Explicit Snow snow scheme and Simple Ice (SICE) prognostic sea ice thickness. Boundary data comes from the Norwegian Earth System model (NorESM2-MM) following the SSP5-8.5 scenario. We present projected future changes in distributions, focusing on precipitation and snow, and evaluate against ERA5 and CARRA reanalyses. We also discuss spatial variability and representation of small-scale features in a challenging landscape with steep topography and large contrasts between land, sea-ice and open ocean.</p>

A new bias-corrected, statistically downscaled product, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, has been developed and released to help in understanding climate change at local to regional scales. Here, we evaluate the performance of the NEX-GDDP data in simulating daily maximum temperature (TX) and daily minimum temperature (TN) in the historical period 1961–2005 over China at national and regional scales. Projected future changes in TX and TN are assessed under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emissions scenarios. Results show that the NEX-GDDP data can capture the basic spatial patterns of TX and TN, but these results underestimate the warming trends of TX and TN from 1961 to 2005 over China. The largest biases are found in western China due to its complex terrain conditions; these biases are 2.33 and 2.21 times larger than those found in eastern China for TX and TN, respectively. The climate projections show that the difference in uncertainties is small between the east and the west, and higher warming changes correspond to greater uncertainties. The increasing trends under the RCP8.5 are 2.22 and 2.31 times the size found under the RCP4.5 by the end of the twenty-first century for TX and TN, respectively. The Tibetan plateau has the fastest warming trend under the two scenarios.

Abstract. This study explores the enhancement of UNet-based semantic segmentation for photovoltaic (PV) panels in remote sensing images by integrating attention mechanisms. Given the critical role of solar energy in achieving global sustainability, accurate PV panel detection is essential for effective energy management. Using the high-resolution PV01 dataset, which includes UAV-captured rooftop PV samples, we evaluate the impact of four distinct attention modules: Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation Networks (SE-Net), Efficient Channel Attention (ECA-Net), and Coordinate Attention (CA) on segmentation performance. Comparative analysis demonstrates that UNet models with SE and CA modules substantially outperform the baseline, achieving the highest scores in Average Accuracy (AA), Average Precision (AP), Average Recall (AR), mean Intersection over Union (mIoU), and Average F1-score (AF). In particular, UNet + SE achieved an AA of 0.9809, AP of 0.9756, AR of 0.9629, AF of 0.9692, and mIoU of 0.9403, highlighting the efficacy of attention mechanisms in refining feature representations and advancing PV panel segmentation, thereby contributing to large-scale solar energy monitoring and deployment.

Aerosols affect surface solar radiation (SSR) by modifying cloud properties. For solar energy applications, either for photovoltaics (PV) or concentrating solar power (CSP) systems, understanding of spatio-temporal variability of aerosol is essential for short-term forecasts of solar radiation.  This study aims to quantify interday variability of the commonly used proxy for optical properties of aerosols - aerosol optical depth (AOD) at the wavelength of 500 nm and to assess the deviation in direct normal irradiance (DNI) and in global horizontal irradiance (GHI) caused by AOD variation using radiative transfer modeling. The following AOD data are used: AOD forecasts for the next day from the Copernicus Atmosphere Monitoring Service (CAMS); ground-based AOD measurements from the Aerosol Robotic Network (AERONET); ModIs/Terra high spatial resolution data set (MIDAS) which is based on satellite retrievals. We select locations with different aerosol types around the globe based on AERONET stations, where at each site more than 1500 daily values for consecutive days from 2010 to 2020 are available. We adopt the persistence approach using AOD data from AERONET and MIDAS and compare them with CAMS forecasts. The AOD data from AERONET serve as ground truth for validation. Under cloudless conditions, AOD variability informs the deviation of DNI and GHI forecasts from the observation. Furthermore, we attempt a worldwide clustering of aerosol types based on chemical modeling. Interday AOD variability is high in megacities with high aerosol load and in the proximity of highly variable aerosol sources such as desert dust and biomass burning (fires). This work should deepen our knowledge of spatio-temporal variability of aerosol in the context of AOD and SSR prediction, thus contributing to the quality and reliability of solar resource assessment.

While ongoing climate change is projected to expand environmentally suitable cropland toward high-latitude regions, the practical utilization of this potential is increasingly shaped by socio-economic constraints. Previous studies have suggested that agricultural workforce availability, as a proxy for socio-economic transitions and interactions, acts as a bottleneck for cropland supply potential. In this study, we assess spatially explicit practical cropland supply potential by incorporating agricultural workforce constraints using gridded population datasets from WorldPop. A weighting map of agricultural workforce distribution was constructed based on national-level minimum distance thresholds between population pixels and cropland pixels, and was used to allocate agricultural labor spatially. Future cropland potential was then derived by applying land-to-labor ratios that represent technological advancement. Within this workflow, urbanization levels were reviewed by comparing WorldPop Global 1 and Global 2 datasets and population projection datasets, all classified based on DEGURBA definitions (EUROSTAT), with national urbanization statistics from the World Bank. In addition, agricultural workforce shares between rural and urban pixels were evaluated through comparison with ILO statistics. Our results indicate that agricultural workforce availability constrains the northward expansion of cultivable land. A southward retreat of workforce-available cropland potential is also projected in some regions, such as Central Asia, despite increasing environmental suitability. Beyond regional projections, this study further demonstrates an application channel through which high-resolution population datasets can be used to constrain and quantify human influences on the Earth system.Acknowledgment: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2021-NR055516, RS-2025-02312954).

A high-resolution, long-term solar dataset is essential for capturing the variability of solar energy resources and informing strategies to ensure grid reliability and resilience in systems with high levels of solar energy integration. This study focuses on generating unbiased, high-resolution projections of solar irradiance through a statistical downscaling framework, using Earth system model (ESM) simulations obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). The National Solar Radiation Database (NSRDB) is used to calibrate statistical downscaling models. The newly developed dataset provides solar irradiance, surface air temperature, and surface wind speed at 4-km and hourly resolutions across the contiguous United States (CONUS), based on two future scenarios (RCP4.5 and RCP8.5). This study outlines key steps in developing the high-resolution future solar dataset, including (1) regridding ESM data to a common 20-km resolution grid, (2) correcting ESM biases using the NSRDB, and (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar projections. Preliminary results indicate that downscaled projections (4-km) captured reasonable spatial patterns when compared to observations across CONUS for four variables. On average across all pixels, 4-km daily-total GHI and DNI projections showed normalized bias (nBias) less than 1% and 6% for GHI and DNI against NSRDB, respectively (nBias less than 1% and 5% for daily-average surface air temperature and surface wind speed). In terms of long-term trend for GHI and DNI, there was no strong increasing or decreasing trend (when compared to surface air temperature), but it showed a very weak decreasing trend.

Automatic gating window selection for gated three-dimensional coronary X-ray angiography