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.