Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 min, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, G_CC), and how it varies in space and time. These phenological cameras (i.e., “PhenoCams”) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (cameraNDVI), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both G_CCG_CCG_CCith potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at https://doi.org/10.3334/ORNLDAAC/2364https://doi.org/10.3334/ORNLDAAC/2389

This data paper presents an overview of the cloud spectrometers deployed during the Pallas Cloud Experiment (PaCE) in autumn 2022, a coordinated measurement campaign in the Finnish subarctic that took place between 12 September and 15 December 2022. Four cloud spectrometers – the Cloud and Aerosol Spectrometer (CAS); the Forward Scattering Spectrometer Probe (FSSP-100); the Cloud Droplet Analyzer (CDA); and ICEMET – were operated as ground-based setups, providing high-resolution in-cloud measurements of droplet size distributions and key microphysical properties, such as number concentration (N_c), liquid water content (LWC), median volume diameter (MVD), and effective diameter (ED). The dataset is complemented by meteorological observations of temperature, humidity, wind speed, and visibility at a 1 min resolution. The measurements collected during PaCE 2022 offer valuable insights into aerosol–cloud interactions and cloud evolution in subarctic cloud systems. This dataset is suitable for researchers in cloud microphysics, atmospheric science, and climate modeling, as well as for instrument calibration and validation in future campaigns. The data can also be integrated with complementary concurrent in situ aerosol, remote sensing, UAV, and balloon-borne observations during PaCE 2022 to provide a more comprehensive understanding of cloud microphysics and atmospheric processes in the subarctic environment. The dataset is publicly available at https://doi.org/10.5281/zenodo.15045294

Doppler wind lidars (DWL) offer high-resolution wind profile measurements that are valuable for understanding atmospheric boundary layer (ABL) dynamics. Here six ground-based DWL, deployed in a multi-institutional effort along a 40 km transect through the centre of Paris (France), are used to retrieve horizontal wind speed and direction through the ABL at 18–25 m vertical and 1–60 min temporal resolution. Data are available for June 2022–March 2024 (three DWL) and two Intensive Observation Periods (six DWL) across 9 weeks in September 2023–December 2023. Data from all sensors are harmonised in terms of quality control, file format, as well as temporal and vertical resolutions. The quality of this DWL dataset is evaluated against in-situ measurements at the Eiffel Tower and radiosonde profiles. This unique, spatially dense, open dataset will allow urban boundary layer dynamics to be explored in process-studies, and is further valuable for the evaluation of high-resolution weather, climate, inverse and air pollution models that resolve city-scale processes. The dataset is available at https://doi.org/10.5281/zenodo.14761503

Evapotranspiration (ET) is an important component of the terrestrial water cycle, carbon cycle, and energy balance. Currently, there are four main types of ET datasets: remote sensing–based, machine learning–based, reanalysis–based, and land–surface–model–based. However, most existing ET fusion datasets rely on a single type of ET dataset, limiting their ability to effectively capture regional ET variations. This limitation hinders accurate quantification of the terrestrial water balance and understanding of climate change impacts. In this study, the accuracy and uncertainty of thirty ET datasets (across all four types) are evaluated at multiple spatial scales, and a fusion dataset BMA (Bayesian model averaging)-ET, is obtained using BMA method and dynamic weighting scheme. ET from FLUXNET2015 as reference, the study recommends remote sensing- and machine learning-based ET datasets, especially Model Tree Ensemble Evapotranspiration (MTE), Penman-Monteith-Leuning (PML) and Process-based Land Surface Evapotranspiration/Heat Fluxes (PLSH), but the optimal selection depends on season and vegetation type. At the basin scale, most of ET datasets demonstrate superior performance. Relative uncertainty based on remote sensing and machine learning is low at the grid point scale. The fusion dataset BMA-ET accurately captures trends in ET, showing a global terrestrial increasing trend of 0.65 (0.51–0.78) mm yr^−1https://doi.org/10.5281/zenodo.15470621

Sedimentary recycling of phosphorus is a key aspect of eutrophication. Here, we present data on benthic fluxes of dissolved inorganic phosphorus (DIP) from the Baltic Sea, an area with a long eutrophication history. The presented dataset contains 498 individual fluxes measured in situ with three types of benthic chamber landers at 59 stations over 20 years, and data cover most of the Baltic Sea subbasins (Hylén et al., 2025, https://doi.org/10.5281/zenodo.14812160). The dataset further contains information about bottom-water dissolved oxygen (O₂) concentrations, sedimentary organic carbon (OC) content and sediment type. The DIP fluxes differ considerably between basins depending on OC loading and the level of O₂₂₂^−1₂

Accurate flood damage data are essential for developing reliable flood risk assessments and designing effective risk management strategies. However, empirical flood damage data remain limited, particularly at the object level, hindering the calibration and validation of predictive models. Existing datasets are often highly aggregated and lack the granularity required for detailed analysis. This paper presents two comprehensive, micro-scale datasets documenting flood damage to 256 buildings, comprising both residential buildings and business premises, surveyed in the aftermath of the 2022 flood event in the Marche region of Italy. The georeferenced datasets include information on hazard characteristics, buildings' vulnerability features, physical damage description across structural and non-structural components, indirect damage, and implemented mitigation measures. In addition, original survey forms are provided to support future data collections in different contexts. Datasets and survey forms are available at the link: https://doi.org/10.5281/zenodo.15591850

Tracking greenhouse gas (GHG) emissions is essential for understanding the drivers of climate change and guiding global mitigation strategies. The Emissions Database for Global Atmospheric Research (EDGAR) and submissions by Parties to the United Nations Framework Convention on Climate Change (UNFCCC) are two key sources of GHG emissions data. While EDGAR provides comprehensive and globally consistent estimates, UNFCCC submissions are based on nationally reported inventories, which adhere to specific guidelines and reflect country-specific circumstances and practices. This study presents a detailed comparison between EDGAR and UNFCCC GHG emissions inventories, focusing on G20 countries, which account for nearly 80 % of global emissions, as well as Annex I countries, including the EU27. By examining sectoral discrepancies, methodological variations, and the impact of reporting timelines, the paper identifies key areas of alignment and divergence in emissions estimates. While CO₂₄₂O estimates exhibit substantial discrepancies due to differences in methodologies, emission factors, uncertainties, and reporting practices. Our findings emphasise the need for enhanced methodological harmonization and more frequent reporting, particularly in non-Annex I countries, where limited capacity and irregular updates reduce comparability. Addressing these inconsistencies is crucial for improving transparency, aligning national and independent datasets, and strengthening climate policy decisions under the Paris Agreement (UNFCCC Secretariat, 2021b).

South America is a global hotspot for land use and land cover (LULC) change, marked by dramatic agricultural land expansion and deforestation. While previous studies have documented land use and land cover changes in South America over recent decades, there is still a lack of spatially explicit and time-series maps of crop types that capture shifts in crop distribution. Therefore, developing high-resolution, long-term, and crop-specific datasets is crucial for advancing our understanding of human–environment interactions and for assessing the impacts of agricultural activities on carbon and biogeochemical cycles, biodiversity, and climate. In this study, we integrated multi-source data, including high-resolution remote sensing data, model-based data, and historical agricultural census data, to reconstruct the historical dynamics of four major commodity crops (i.e., soybean, maize, wheat, and rice) in South America at an annual timescale and 1 km × 1 km spatial resolution from 1950 to 2020. The results showed that soybean and maize cultivation expanded rapidly in South America by encroaching on other vegetation (i.e., forest, pasture/rangeland, and unmanaged grass/shrubland) over the past 70 years, whereas wheat and rice areas remained relatively stable. Specifically, soybean is one of the most dramatically expanded crops, increasing from essentially zero in 1950 to 48.8 Mha in 2020, resulting in a total loss of 23.92 Mha of other vegetation. In addition, the area of maize increased by a factor of 2.1 from 12.7 Mha in 1950 to 26.9 Mha in 2020. The newly developed crop type dataset provides important insights for assessing thehttps://doi.org/10.5281/zenodo.14002960

International databases of disaster impacts are crucial for advancing disaster risk research, particularly as climate change intensifies the frequency and intensity of many natural hazards – including temperature extremes. However, many widely-used disaster impact databases lack information on the physical dimension of the hazards associated with an impact, and on the exposure to such hazards. This hinders analysing drivers of severe disaster outcomes. To bridge this knowledge gap, we present SHEDIS-Temperature, a dataset that provides Subnational Hazard and Exposure information for temperature-related DISaster impact records (https://doi.org/10.7910/DVN/WNOTTC; Lindersson and Messori, 2025). This open-access dataset links temperature-related impact records from the Emergency Events Database (EM-DAT) with subnational data on their locations, associated meteorological time series, and population maps. SHEDIS-Temperature provides hazard and exposure data for 2835 subnational locations associated with 382 disaster records from 1979–2018 in 71 countries. Detailed hazard metrics, derived from 0.1° 3 hourly data, encompass absolute indicators, such as the heat stress measure apparent temperature accounting for humidity and wind speed, as well as percentile-based indicators of when and where temperatures exceeded local thresholds. Population exposure data include annual population figures for impacted subnational administrative units and person-days of exposure to threshold-exceeding temperatures. Outputs are available at grid-point level as well as zonally aggregated to administrative subdivision units, and disaster-record levels. Technical validation against a station-based dataset indicated minor systematic biases – slightly overestimated minimum and underestimated maximum temperatures – but confirmed high consistency between datasets, with correlation coefficients ≥0.9≤2 °C. By providing comprehensive attributes across the hazard-exposure spectrum, SHEDIS-Temperature supports interdisciplinary research on past temperature-related disasters, offering valuable insights for future risk mitigation and resilience strategies.

High-resolution gridded precipitation data is scarce, especially at time intervals shorter than daily. However hydrological applications for example benefit from a finer temporal resolution of rainfall information. In this context, we introduce an hourly precipitation dataset for Belgium, featuring a resolution of 1 km. An hourly high-resolution gridded precipitation product over Belgium can provide valuable insights into the dynamics of both short-term and long-term rainfall events, which can be used for wide-ranging applications such as flash flood forecasting and warning systems, studying precipitation extremes and trends, validating weather and climate models or detecting changes in precipitation patterns due to climate change. Similar products such as EURADCLIM (Europe) (Overeem et al., 2023)(Winterrath et al., 2018), both radar-based gauge-adjusted datasets, have already been created and published. Both datasets are high spatial resolution dataset (2 and 1 km, respectively).

Ice-marginal lakes form at the edge of the Greenland Ice Sheet and its surrounding peripheral glaciers and ice caps (PGIC), where outflowing glacial meltwater is trapped by a moraine, or by the ice itself, and create a reservoir that is in contact with the adjacent ice. While glacial meltwater is typically assumed to flow directly into the ocean, ice-marginal lakes temporarily store a portion of this runoff, influencing glacier dynamics and ablation, ecosystems, and downstream hydrology. Their presence, and change in abundance and size, remain under-represented in projections of sea level change and glacier mass loss. Here, we present an eight-year (2016–2023) inventory of 2918 automatically classified ice-marginal lakes (≧0.05 km²) across Greenland, tracking changes in lake abundance, surface extent, and summer surface temperature over time. Fluctuations in lake abundance were most pronounced at the north (22 %) and northeast (14 %) PGIC margins and the southwest Ice Sheet margin (8 %). Over the study period, an increase in surface lake area was evident at 283 lakes, a decreasing trend was evident at 240 lakes, and 1373 remained stable (±0.05 km²). The northeast region contained the largest lakes, with a median size of 0.40 km²km²°C°C−809±5 %. Surface temperature estimates showed strong agreement with in situ measurements (r²=0.87, RMSE =1.68 °C, error ±1.2 °C). This dataset provides a crucial foundation for quantifying meltwater storage at ice margins and refining sea level contribution projections while supporting research on glacier-lake interactions, Arctic ecology, and environmental management. The inventory series is openly accessible on the GEUS Dataverse (https://doi.org/10.22008/FK2/MBKW9N, How et al., 2025) with full metadata and documentation, and a reproducible processing workflow.