Per- and Polyfluorinated Alkyl Substances (PFAS) constitute a group of often toxic, persistent, and bioaccumulative substances. We constructed a global Emissions model and inventory based on multiple datasets for 23 widely used PFAS. The model computes temporally and spatially resolved model ready emissions distinguishing between emissions to air and emissions to water covering the time span from 1950 up until 2020 on an annual basis to be used for chemistry transport modelling.

We introduce GLBD-FED (1981–2024), a global daily temperature dataset spanning ~17 000 stations. To harmonize varying national reporting times, we reconstruct sub-daily observations into consistent 24 h physical windows, preferentially aligned to 00:00–24:00 UTC. This effectively corrects Time of Observation Biases. GLBD-FED provides a globally uniform, first-hand observational baseline highly valuable for synoptic weather evaluation and validating numerical weather prediction models.

Extreme sea levels during storms can lead to coastal flooding and sediment displacement. In the paper, we present an expanded global dataset of sea levels, produced using a global ocean model driven by ERA5 reanalysis – a state-of-the-art dataset of historical weather and climate. The strengths and limitations of the dataset are discussed by comparing it to observed sea levels. The dataset covers a period of 1950–2024, improving our understanding of the global frequency of extreme water levels.

This study presents a corrected 2019–2023 dataset for the FengYun-4A Lightning Mapping Imager, with geolocation accuracy better than 15 km, referenced to the World Wide Lightning Location Network. This open‑access resource enhances lightning monitoring and atmospheric research.

Here we present the updated International Soil Radiocarbon Database version 2, which compiles radiocarbon data from over 500 studies at 1700 locations worldwide. The database provides various soil radiocarbon measurements to improve our understanding of factors that influence the age distributions of carbon in soils. While we have made progress expanding the database and improving its structure, important gaps remain in tropical regions, deep soils, and certain measurement types.

We present a dataset of signal strength measurements from 103 wireless links in a community internet network in New York City, paired with data from 37 personal weather stations. By tracking how rain and snow weaken these signals at one-minute intervals across Manhattan and Brooklyn, we show that community networks can serve as low-cost weather sensing systems. The dataset and analysis demonstrate its use for real-time precipitation monitoring in cities.

Clouds containing supercooled water droplets are important for improving weather prediction, climate models, and safe aviation operations because they can lead to aircraft icing. To better understand these clouds, research flights were conducted over Europe using instruments that measure cloud particles and their water content. This study presents the resulting dataset of cloud properties together with the methods used to process and evaluate the measurements collected during the flights.

Temperature and salinity (T&S) are fundamental in physical oceanography. Objective analysis can generate highly reliable gridded T&S datasets. However, most existing representative products have relatively coarse resolution. This study develops a global 1/4° high-resolution product by integrating spatiotemporal correlations and physical constraints. Results show that the dataset achieves excellent overall accuracy and unbiased performance, while effectively capturing abundant mesoscale features.

Meltwater from seasonal snow sustains over a billion people globally, making the timing of snowmelt runoff onset a critical hydrological parameter. We used satellite radar images, which can detect liquid water in snowpack regardless of cloud cover, to create a global dataset of snowmelt runoff onset timing from 2015 to 2024 at 80 m resolution. The dataset shows close agreement with a network of ground-based snow sensors, and can support water resource management in snow-dominated watersheds.

Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as regime leave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.

Understanding how forest fires reshape landscapes requires information not only on where fires occur, but also on the size, shape, and spatial organization of fire patches. We created a global 30 m dataset of forest fire patches spanning 1984–2022 using the Landsat satellite archive. Including nearly 12 million fire patches, this dataset facilitates ecological research at regional to global scales by characterizing fire patch structure and spatial organization over time.

The ozone sounding data from the electrochemical concentration cell (ECC) at the Haute Provence Observatory (OHP) for the period 1991–2020 were homogenized in 2022 using the recommendations of the ECC data quality assessment committee. Comparisons with lidar and satellite observations show that the ECC pump temperature measurements for the period 2002–2007 need to be corrected in addition to the 2022 homogenization. This correction improves the consistency of the ECC ozone dataset at the OHP.

Floating solar is growing rapidly in China, but its long-term development remains poorly documented. We created a decade-long map of floating solar installations in the Yangtze River Delta using satellite observations and visual verification. The mapped area increased from 17.4 square kilometers in 2015 to 145.4 square kilometers in 2024. This dataset supports renewable energy planning and future studies of environmental changes related to floating solar expansion.

We develop a generalized bottom-up framework for producing spatially explicit estimates of slum populations in data-sparse environments. The resulting dataset provides the first comprehensive inventory at an approximate spatial resolution of 6.72 km across 129 Global South countries. It addresses the underestimation in prior studies and supports national- and regional-scale assessments of urban sustainability and vulnerable populations, with potential applications for improving human well-being.

In this data description paper, we present the ERA5-FWI-SN dataset, which consists in a gridded dataset of the daily Forest Fire Weather System (FWI) components across Canada, derived from the hourly ERA5 reanalysis products. For the first time, FWI calculations use surface weather readings taken at the local solar noon. This new method enables a more physically realistic spatial distribution of FWI components values across the east–west extent of a single time zone.

Grounded icebergs anchor Antarctic sea ice and support marine ecosystems, yet their continent-wide distribution was previously unknown. Using satellite radar imagery and an automated artificial intelligence tool, we mapped nearly 40 000 stationary icebergs. We discovered that tiny, frequently overlooked icebergs actually dominate both the total number and area. This public dataset offers a crucial new baseline for modelling coastal ice stability and understanding broader environmental changes.

Sea level is a key indicator of ocean change, but it is hard to measure around Antarctica, where sea ice hides the ocean from satellites. Yet satellites flying over the ice still receive clear echoes from openings in the ice, where water is exposed. We reprocessed these echoes from four satellite missions and combined them with open-water data, building the longest sea level record for the whole Southern Ocean (2003 to 2021). It offers a new way to track how this rapidly changing region evolves.

We produced a 10 m maize yield dataset for Northeast China covering 2019–2024 by combining process-based crop simulations with deep learning. The framework avoids the need for field yield labels during training while maintaining good accuracy against independent observations. The dataset captures both regional yield patterns and fine-scale field variability, supporting agricultural monitoring and management.

We present a 225-year (1799–2024) homogenized daily water level record for the Vistula River in Warsaw, comprising 82 453 observations. Adjustments were made for gauge relocations, zero shifts, unit differences, and Julian–Gregorian discrepancies. Missing data were reconstructed using the Long Short-Term Memory (LSTM) method, applied to stage–stage relationships with downstream stations. The dataset provides a robust basis for long-term hydrological, climatic, and socio-environmental research.

We present an emission uncertainty estimate to support atmospheric monitoring of greenhouse gas and air pollutant emissions using observations. We find significant uncertainties in country-total emissions and in the spatial distribution of emissions, largely dependent on the pollutant considered. A dataset is provided that lists emission uncertainties for a state-of-the-art European emission inventory.

Satellite land surface temperature (LST) data are vital for earth system studies, but current LST products face limitations: the impact of angular effect, lacking coverage under clouds, and the coarse resolution. This study innovatively generated hourly, angular-normalized, all-weather, high-resolution (0.01°) LST data from 2018 to 2023 using FY-4A official data, which support better LST-related studies of earth system science.

We have developed an innovative methodology that uses machine learning to correct errors in chemical models by using satellite data as a guide. In this latest update, we detail improvements to our process for creating a gap-free data of two major ozone-depleting substances: CFC-11 and CFC-12. By combining the strengths of both chemical models and satellites, we have produced a reliable, global dataset that allows researchers to track long-term trends and better evaluate the chemical models.

Bioaerosols exert significant influences on climate, the environment, ecosystems, and public health. This study establishes an integrated bioaerosol dataset across East Asia using unified sampling and analytical methods. It was found that bioaerosol concentrations are negatively correlated with local Normalized Difference Vegetation Index (NDVI), and their relationship with atmospheric temperature is nonlinear. The α-diversity indices were negatively correlated with NDVI.