Abstract. This paper reports on an innovative mass concentration (mascon) solution obtained with the short-arc approach, named “GCL-Mascon2024”, for estimating spatially enhanced mass variations on the Earth's surface by analyzing K- and Ka-band ranging satellite-to-satellite tracking data collected by the Gravity Recovery And Climate Experiment (GRACE) mission. Compared to contemporary GRACE mascon solutions, this contribution has three notable and distinct features: first, this solution recovery process incorporates frequency-dependent data-weighting techniques to reduce the influence of low-frequency noise in observations. Second, this solution uses variably shaped mascon geometry with physical constraints such as coastline and basin boundary geometries to more accurately capture temporal gravity signals while minimizing signal leakage. Finally, we employ a solution regularization scheme that integrates climate factors and cryospheric elevation models to alleviate the ill-posed nature of the GRACE mascon inversion problem. Our research has led to the following conclusions: (a) GCL-Mascon2024 mass anomaly estimates from GRACE data show strong agreement with the (Release) RL06 versions of mascon solutions (GSFC, CSR, JPL) in both spatial and temporal domains; (b) in Greenland and global hydrologic basins, the correlation coefficients of estimated mass changes between GCL-Mascon2024 and other RL06 mascon solutions exceed 95.0 %, with comparable amplitudes, and, especially over non-humid river basins, the GCL-Mascon2024 suppresses random noise by 27.8 % compared to contemporary mascon products; and (c) in desert regions, the analysis of residuals calculated after removing the climatological components from the mass variations indicates that the GCL-Mascon2024 solution achieves noise reductions of over 29.3 % as compared to the GSFC and CSR RL06 mascon solutions. The GCL-Mascon2024 gravity field solution (Yan and Ran, 2025) is available at https://doi.org/10.5281/zenodo.15525467.

Abstract. Climate reanalyses are essential for studying climate variability, understanding climate processes, and initializing climate predictions. We present CoRea1860+ (Wang and Counillon, 2025, https://doi.org/10.11582/2025.00009), a 30-member coupled reanalysis spanning from 1860 to the present, produced using the Norwegian Climate Prediction Model (NorCPM) and assimilating sea surface temperature (SST) observations. NorCPM combines the Norwegian Earth System Model with the ensemble Kalman filter data assimilation method. SST, available throughout the entire period, serves as the primary source of instrumental oceanic measurements prior to the 1950s. CoRea1860+ belongs to the category of sparse-input reanalyses, designed to minimize artefacts arising from changes in the observation network over time. By exclusively assimilating oceanic data, this reanalysis offers valuable insights into the ocean's role in driving climate system variability, including its influence on the atmosphere and sea ice. This study first describes the numerical model, the SST dataset, and the assimilation implementation used to produce CoRea1860+. It then provides a comprehensive evaluation of the reanalysis across four key aspects, namely reliability, ocean variability, sea ice variability, and atmospheric variability, benchmarked against more than 10 independent reanalyses and observational datasets. Overall, CoRea1860+ demonstrates strong reliability, particularly in observation-rich periods, and provides a reasonable representation of climate variability. It successfully captures key features such as multi-decadal variability and long-term trends in ocean heat content, the Atlantic meridional overturning circulation, and sea ice variability in both hemispheres. Furthermore, to some extent, CoRea1860+ agrees with the reference atmospheric datasets for surface air temperature, precipitation, sea level pressure, and 500 hPa geopotential height, especially in the tropics where air–sea interactions are most pronounced.

Abstract. Evapotranspiration (ET) is a crucial component of the terrestrial hydrological cycle. Latent heat flux (LE, equivalent to ET in W m−2) observed by the eddy covariance (EC) technique, commonly known as LEEC, has been widely recognized as a highly accurate benchmark for global ET estimation. Currently, there is an increasing need for long-time-series benchmark data to support climate change analysis, construction of new models, and validation of new products. However, existing LEEC datasets, like FLUXNET2015, face significant challenges due to limited observation periods and extensive data gaps, which hinders their application in ET modeling and global change analysis. To address these issues, we developed a gap-filling and prolongation framework for LEEC data and established a benchmark dataset for global ET estimation from 2000 to 2022 across 64 sites at various timescales. The framework mainly includes three parts: site selection and data pre-processing, generation of gap-filled half-hourly/hourly LE data, and generation of prolonged daily LE data. We selected 64 sites from FLUXNET2015 based on rigorous filtering criteria. A novel bias-corrected random forest (RF) algorithm was used for gap-filling and prolongation in the framework to produce seamless half-hourly and daily LE data. After analysis, the framework using the novel bias-corrected RF algorithm achieves excellent performance in both hourly gap-filling and daily prolongation, with mean root mean square error values of 33.86 and 16.58 W m−2, respectively. The algorithm significantly improves the gap-filling performance for long gaps and extreme values compared with the original RF and marginal distribution sampling algorithm. The results demonstrate robust prolongation performance of our framework in both prolongation directions and temporal stability. Furthermore, our gap-filled dataset demonstrates strong consistency with FLUXNET2015 in terms of data distribution. In conclusion, we have published the first benchmark dataset for global ET estimation based on FLUXNET2015 from 2000 to 2022. This dataset can effectively provide data support for ET modeling, water–carbon cycle monitoring, and climate change analysis. It is made freely available via the following repository: https://doi.org/10.5281/zenodo.13853409 (Li et al., 2024b).

Abstract. Accurate cropland mapping is essential for understanding agricultural dynamics in Africa and critical for achieving Sustainable Development Goals (SDGs) such as Zero Hunger. Large-scale cropland mapping encounters several challenges, including the varying landscape characteristics of cropland across different regions, extended cultivation periods, and limited availability of reference data. This study develops a 30 m resolution African annual cropland distribution (namely AFCD) dataset spanning the years 2000 to 2022. To extract this large-scale cropland distribution data, we employed random forest (RF) classification and continuous change-detection (CCD) algorithms on the Google Earth Engine platform. Robust training samples were generated, and a locally adaptive model was applied for cropland extraction. The final output consists of annual binary crop/non-crop maps from 2000 to 2022. Independent validation samples from numerous third-party sources confirm that the map's accuracy is 0.86 ± 0.01. A comparison of the cropland area estimates from AFCD with those of the Food and Agriculture Organization (FAO) for Africa yielded an R2 value of 0.86. According to our estimates, Africa's cropland expanded from 1.9435×108 ha in 2000 to 2.1092×108 ha in 2022, marking a net increase of 8.53 %. Prior to 2005, changes in Africa's cropland area were gradual, but after 2006, there has been a marked acceleration in cropland expansion. Despite this continued growth, Africa also experienced significant cropland abandonment. By 2018, abandoned cropland accounted for 11.52 % of the total active cropland area. AFCD also avoided the misclassification of buildings, roads, and trees surrounding cropland common in existing products. The study further highlights the unique advantage of AFCD in providing a dynamic annual cropland dataset at 30 m resolution for Africa. This dataset is a crucial resource for understanding the spatial–temporal dynamics of cropland and can support policies on food security and sustainable land management. The cropland dataset is available at https://doi.org/10.5281/zenodo.14920706 (Lou et al., 2025).

Abstract. Fires are a significant disturbance in Earth's systems. Smoke aerosols emitted from fires can cause environmental degradation and climatic perturbations, leading to exacerbated air pollution and posing hazards to public health. However, research on the climatic and health impacts of fire emissions is severely limited by the scarcity of air pollution data directly attributed to these emissions. Here, we develop a global daily fire-sourced PM2.5 concentration ([PM2.5]) dataset at a spatial resolution of 0.25° for the period 2000–2023, using the GEOS-Chem chemical transport model driven with two fire emission inventories, the Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and the Quick Fire Emission Dataset version 2.5r1 (QFED2.5). Simulated all-source [PM2.5] is bias-corrected using a machine learning algorithm, which incorporates ground observations from over 9000 monitoring sites worldwide. Then the simulated ratios between fire-sourced and all-source [PM2.5] at individual grids are applied to derive fire-sourced [PM2.5]. Globally, the average fire-sourced [PM2.5] is estimated to be 2.04 µg m−3 with GFED4.1s and 3.96 µg m−3 with QFED2.5. Both datasets show consistent spatial distributions with regional hotspots in central Africa and widespread decreasing trends over most areas. While the mean levels of fire-sourced [PM2.5] are much higher at low latitudes, fire episodes in the boreal regions can cause PM2.5 levels that are comparable to those of the tropics. This dataset, available at https://doi.org/10.5281/zenodo.15493914 (Hu et al., 2025a) and https://doi.org/10.5281/zenodo.15496596 (Hu et al., 2025b), serves as a valuable tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health, enabling accurate assessments and support for decision-making in environmental management and policy.

Abstract. The work Histoire physique de la mer, authored by Luigi Ferdinando Marsili (or Marsigli) and published in 1725, is one of the earliest texts detailing observations of the physical, biological, and bathymetric characteristics of the sea, mainly concentrating on the Gulf of Lion in southern France's Mediterranean area. Nonetheless, understanding Marsili's findings is difficult due to the application of non-standard measurement units and the imprecision of georeferencing data. The MACMAP project (A Multidisciplinary Analysis of Climate Change Indicators in the Mediterranean and Polar Regions), which is funded by the Istituto Nazionale di Geofisica e Vulcanologia, has involved a thorough recalibration of Marsili's observations. This project focused on transforming water weight measurements obtained from different locations in the Gulf of Lion from June 1806 to January 1807 into water density values. The sampling sites were digitised, bathymetric profiles were reconstructed, and tide amplitudes were examined. The main objective is to make these historical data available to compare with current measurements. The data are available under the following DOIs: http://oceano.bo.ingv.it/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=marsili (Locritani et al., 2024b), https://doi.org/10.13127/histoiremarsili/sealevel (Locritani et al., 2024a), and https://doi.org/10.13127/histoiremarsili/waterdensity (Locritani and Garvani, 2024).

Abstract. We present top-down global gridded emissions of NOx for the year 2022. This dataset is constructed from retrievals of tropospheric vertical column densities (VCDs) of NO2 by the TROPOspheric Monitoring Instrument (TROPOMI) spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of a mass-balance atmospheric inversion. The dataset has a spatial resolution of 0.0625° × 0.0625°, and delivers a detailed overview of the distribution of emissions. It allows the identification of intense area sources, such as cities, and isolated emitters, such as power plants or cement kilns, but does not correctly represent biomass burning. At global level, the emissions obtained are consistent with the EDGARv6.1 bottom-up inventory, although there are differences at regional level, particularly in emerging countries and countries with low observation densities. The emissions of the three largest emitting countries, China, the United States and India, are 6 %, 14 %, and 4% lower than EDGAR estimates, respectively. Uncertainties remain high, and a quantitative analysis of emissions over several averaging periods indicates that averaging emissions uniformly across the year may be sufficient to obtain estimates consistent with annual averages, in regions of the world with high retrieval densities. This dataset is designed to be updated with a low latency to help policymakers monitor emissions and implement energy savings and clean air quality policies. The data can be accessed at https://doi.org/10.5281/zenodo.13758447 as monthly files (Rey-Pommier et al., 2025).

Abstract. Nitrate plays a crucial role in marine ecosystems, as it influences primary productivity. Despite its ecological significance, accurately mapping its three-dimensional (3D) concentration on a large scale remains a considerable challenge due to the inherent limitations of existing methodologies. To address this issue, this study proposes a continual-learning-based multilayer perceptron (MLP) model to reconstruct the 3D ocean nitrate concentrations above 2000 m depth over the pan-European coast. The continual-learning strategy enhances the model generalization by integrating knowledge from Copernicus Marine Environmental Monitoring Service (CMEMS) nitrate data, effectively overcoming the spatial limitations of Biogeochemical Argo (BGC-Argo) observations in comprehensive nitrate characterization. The proposed approach integrates the advantages of extensive spatial remote sensing observations, the precision of BGC-Argo measurements, and the broad knowledge from simulated nitrate datasets, exploiting the capacity of neural networks to model their nonlinear relationships between multisource sea surface environmental variables and subsurface nitrates. The model achieves excellent performance in profile cross-validation (R2=0.98, RMSE = 0.592 µmol kg−1) and maintains robustness across diverse 3D validation scenarios, suggesting its effectiveness in filling observational gaps and reconstructing the 3D nitrate field. Then, the spatiotemporal distribution of the reconstructed 3D nitrate field from 2010 to 2023 reveals a spatial distribution pattern, an interannual upward trend, and the degree of consistency in vertical variation. The contributions of all 22 input features to the model's estimation were quantified using Shapley additive explanation values. This study reveals the potential of the proposed approach to overcome observational limitations and provide further insights into the 3D ocean condition. The reconstructed 3D nitrate dataset is freely available at https://doi.org/10.5281/zenodo.14010813 (Yu et al., 2024).

Abstract. Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches such as shipborne sounding and airborne bathymetric light detection and ranging (lidar) surveys are expensive, time-consuming, and constrained in politically sensitive regions. Moreover, satellite-altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open-source, and cost-effective technique for comprehensively characterizing the bathymetry of a region. Given the scarcity of in situ water depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS (Hohai University 2024 Shallow-Water Depth Model of South China Sea), was developed using a linear band model by integrating 1298 Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers over 120 islands and reefs in the SCS region at a resolution of 10 m. Validation against independent ICESat-2 depth data yielded a root mean square error for the model of 0.53–1.24 m (< 5 % of the maximum depth). Further validation using independent airborne lidar bathymetry data in the Lingyang Reef demonstrated an accuracy of 1.01 m. Comparisons with existing bathymetry models revealed the superior performance of the developed model. While traditional bathymetry models exhibit errors of up to tens of meters or larger over island regions and should therefore be used with caution, HHU24SWDSCS demonstrated good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. The HHU24SWDSCS data are freely available at https://doi.org/10.5281/zenodo.13852568 (Wu et al., 2024a).

Abstract. The surface shortwave cloud radiative effect (CRE) plays a critical role in modulating the Earth's energy balance and climate change. However, accurately quantifying the CRE remains challenging due to significant uncertainties in downwelling surface shortwave radiation (DSSR) and cloud parameter estimates, especially in the Arctic. This paper introduces a novel approach that enhances the accuracy of CRE estimation by constructing computationally efficient, long-term gridded surface cloud fraction radiative kernels (GCF-CRKs) and integrating refined DSSR estimates and a high-precision cloud fraction (CF). By leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources, we construct a CF-dependent model to refine DSSR estimates. Based on this model, we construct GCF-CRKs using the CF as the sole perturbation parameter to isolate the CF CRE. Our results indicate that this method significantly improves the accuracy of DSSR estimation under partially cloudy conditions (0 % < CF < 100 %), aligning more closely with ground-based observations. In Arctic-wide validation experiments, the root mean square error (RMSE) was decreased by approximately 2.5 W m−2, and the bias was reduced by 1.23 W m−2, which was an improvement of 8.7 % (reduction in RMSE) against the CERES EBAF (Clouds and the Earth's Radiant Energy System Energy Balanced and Filled). Even greater improvements were achieved at stations in Greenland (RMSE was reduced by 4.53 W m−2, and bias was reduced by ∼ 6.89 W m−2, with an accuracy improvement of about 11.1 %). The GCF-CRKs exhibit similar signs and patterns and enhanced stability compared to existing kernels. The sensitivity analysis results reveal that seasonal and interannual variations introduce GCF-CRK uncertainties of approximately 1 and 0.1 W m−2 %−1, respectively, while spatial variations within the same latitude range can cause CRK uncertainties of 0.2–1.2 W m−2 %−1. These uncertainties can result in CRE biases ranging from 5 to 50 W m−2, which demonstrates the limitations of existing methods that utilize short-term, small-area parameter data to produce global CRKs. Using these GCF-CRKs, we estimated the spatiotemporal properties of the surface shortwave CRE in the Arctic over a 21-year period (2000–2020), and the trend result indicates that, despite the increasing influence of the CF on the Arctic DSSR, the smaller magnitude and interannual trend of the annual average surface shortwave CRE suggest that previous studies may have overestimated the magnitude and rate of the cooling effect of clouds on the Arctic DSSR by up to 4 and 0.5 W m−2 per decade, particularly in Greenland. This study provides a more accurate and efficient assessment of the CRE, and the results underscore the need for more effective measures to mitigate the impact of Arctic amplification on the surface radiative energy balance, which is crucial for understanding and addressing regional and global climate change. The GCF-CRKs are freely available to the public at https://doi.org/10.5281/zenodo.13907217 (Liu, 2024).