Precipitation from mixed-phase clouds at high-latitudes is difficult to represent correctly in numerical weather prediction models. Paired water vapour and precipitation isotope measurements provide a constraint on the integrated effect of evaporation and condensation processes, but have rarely been collected in a way that allows to use these for model validation and improvement. Here we present a collection of spatially distributed measurements of water isotopes in the different phases at high time resolution during the ISLAS2021 field campaign over the period 15 to 30 March 2021. The main observational site of this campaign was Andenes, Norway (69.3144° N, 16.1194° E). Isotopic measurements were conducted simultaneously at sea level and a mountain observatory, as well as additional coastal sites at distances of 120 km (Tromsø, Norway) and 1100 km (Bergen, Norway), enabling the assessment of spatial representativeness of vapour isotope measurements. Precipitation samples for water isotope analysis were collected on site at sub-event time resolution, and along a transect across the Vesterålen archipelago. These measurements were complemented by a suite of aerosol measurements, including ice-nucleating particles, and additional in situ and remote sensing observations of meteorological variables. During the two weeks of the ISLAS2021 field campaign, frequent alternations between mid-latitude and arctic weather systems were encountered, providing a range of different cases for more detailed process studies. The dataset is available at https://doi.org/10.1594/PANGAEA.984616(Sodemann et al., 2025), and can serve as a test bed for assessing the spatial representativeness and sampling strategies for water isotope measurements on meteorological time scales. Furthermore, we anticipate our data to be useful in various aspects related to cloud microphysics, for example the quantification of riming processes in convective clouds, the role of ice nucleating particles in marine cold-air outbreaks, and on the condensation efficiency of mid-latitude storms.
We examine differences in global and national greenhouse gas (GHG) emissions estimates, focusing on the role of varying system boundaries and conceptual approaches in driving these variations. Despite consensus among assessments and datasets that GHG emissions continue to increase and that trends are far from aligned with the Paris Agreement goals, estimates can differ significantly. Our review finds three main reasons for these differences. First, datasets vary in their coverage of gases, sectors and countries; second, there are different approaches to defining “anthropogenic” emissions and removals in the land use, land-use change and forestry (LULUCF) sector; and third, the Paris Agreement doesn't cover all relevant sources of emissions, including the cement carbonation sink and ozone depleting substances. As different assessments have different objectives, they may deal with these issues differently. We highlight three assessment conventions that report or use emissions data: those focused on interpreting national progress, policies and pledges under the Paris Agreement; those consistent with integrated assessment modelling (IAM) benchmarks of emissions under different warming scenarios; and those consistent with climate forcing assessments. Considering annual average emissions over the period 2014 to 2023, we show global totals of 44.4 GtCO2e yr−1± 4.9], 54.5 GtCO2e yr−1± 5.6], and 56.4 GtCO2e yr−1± 5.7] for these three conventions, respectively. We suggest that users of GHGhttps://doi.org/10.5281/zenodo.15126539
We present the first detailed soil property maps at multiple depths for the northwestern autonomous Kurdistan region of Iraq (Dohuk). A total of 532 soil samples from 122 sites were collected at five depth increments (0–10, 10–30, 30–50, 50–70, and 70–100 cm), and their mid-infrared (MIR) spectra were measured. A subset of 108 samples, selected via Kennard–Stone sampling, was analysed in a laboratory on ten soil properties. A Cubist model was trained and used from these measured values to predict all samples' soil properties from their MIR spectra. Digital soil mapping was conducted using a machine learning regression techniques based on a quantile random forest model, trained on the predicted soil properties and using a total of 85 covariates at 30 m pixel resolution, resulting in 50 prediction maps in total. Results were compared with the SoilGrids 2.0 product and a regional texture model. Soil depth was also mapped using a similar model with 26 covariates. Our model outperformed global SoilGrids 2.0 predictions in resolution and accuracy, with texture RMSEs (sand:
Sea ice altimetry currently remains the primary method for estimating sea ice thickness from space, however, time series of such satellite-derived estimates are of limited use without having been quality-controlled against reference measurements. Such reference measurements (a term encapsulating in situ observations and remotely sensed measurements from ground, air, and below the ice) for validation of altimetry measurements over sea ice in the polar regions are sparse and rarely presented in a manner where the time-space averaging matches that of the satellite-derived products. Here, an approach to a published comprehensive collection of sea ice reference measurements repurposed for satellite altimetry observations over sea ice is presented, which includes estimates of freeboard, thickness, draft and snow depth from sea ice-covered regions in the Northern Hemisphere (NH) and the Southern Hemisphere (SH), all of which are relevant for comparison with altimetry estimates. The measurements have been collected using airborne sensors, autonomous drifting buoys, moored and submarine-mounted upward-looking sonars, and visual observations. The data package has been prepared to match the spatial (25 km for NH and 50 km for SH) and temporal (monthly) resolutions of conventional satellite altimetry-derived sea ice thickness data products for a direct evaluation of these, and the code is publicly available and distributed for users to modify depending on their aim. This data package, also known as the Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP), was produced within the ESA CCI Sea Ice project. The current version of the CCI SIT RRDP covers the polar satellite altimetry era (1993–2024) and has ongoing efforts aimed at continuously updating the datasets. The CCI SIT RRDP has been collocated with satellite-derived sea ice thickness products from CryoSat-2, Envisat, and ERS-1/2 produced within thehttps://doi.org/10.11583/DTU.24787341(Olsen and Skourup, 2026a).
Accurate monitoring of crop yield is critical for ensuring food security. While various yield datasets covering Northeast China exist, they were produced at a coarse spatial resolution and remain inadequate for capturing small-scale spatial heterogeneity. Current yield estimation methods, such as machine learning models and the assimilation of remotely sensed biophysical variables into crop growth models, are heavily reliant on ground observations and are computationally expensive. To address these limitations, we propose a hybrid framework that couples the World Food Studies Simulation Model (WOFOST) and a Gated Recurrent Unit (GRU) model to generate a high-resolution (20 m) soybean yield dataset in Northeast China from 2019 to 2023 (NortheastChinaSoybeanYield20m). First, to generate a comprehensive training dataset, WOFOST was employed to simulate diverse soybean growth scenarios by accounting for variations in climate, crop varieties, soil types and agro-management practices. The GRU model was then trained to establish the relationships between model-simulated leaf area index (LAI) and soybean yield. The trained model was applied to estimate soybean yield in Northeast China using two stage-averaged LAI variables derived from Sentinel-2, which were validated as a feasible alternative to time-series LAI. The accuracy of estimates was evaluated using in situ measurements and government statistical data. The overall root mean squared error (RMSE) was 287.44 and 272.36 kg ha−1https://doi.org/10.5281/zenodo.14263103
Australia's unique biodiversity faces significant threats from anthropogenic activities that drive habitat destruction and degradation. This study presents the first comprehensive national-scale cumulative pressure map for terrestrial Australia since the 1980s, providing key insights into human disturbance of the landscape. We developed a Human Industrial Footprint (HIF) index map at a 100 m spatial resolution, incorporating 16 nationally relevant pressure layers, which offers a contemporary assessment of cumulative pressures on Australia's landscapes. The HIF was used to derive an Ecological Intactness Index (EII), which accounts for habitat loss, quality, and fragmentation, to provide an estimate of an area's structural intactness. A technical validation comparing visually scored pressures in 1397 stratified random samples using high-resolution satellite images revealed a strong agreement with the mapped pressure values (the HIF). We also conducted an uncertainty (sensitivity) analysis by adjusting individual pressure scores by up to ±50 % across 100 000 simulations, which showed a moderate impact on cumulative pressure scores, confirming the robustness of our approach. We believe both the HIF and EII datasets can be valuable tools for guiding conservation efforts, such as informing protected area expansion, ecosystem restoration priorities, and biodiversity offset strategies. By offering a detailed assessment of cumulative pressures and ecological integrity, this study addresses a critical knowledge gap, and can support evidence-based decision-making for Australia's biodiversity conservation and sustainable development objectives. The HIF, EII, and scaled pressure layers are available at https://doi.org/10.5281/zenodo.17606284
Stable carbon isotope composition of marine dissolved inorganic carbon (DIC), δ13CDIC, is a valuable tracer for oceanic carbon cycling. However, its observational coverage remains much sparser than that of DIC and other physical or biogeochemical variables, limiting its full potential. Here, we reconstruct δ13CDIC−0.007 ± 0.082 ‰ and an overall uncertainty of 0.11 ‰, arising from measurement (0.07 ‰), mapping (0.08 ‰), and input-variable (0.009 ‰) errors. To address validation limitations related to sparse observations, we further supplemented this work with numerical model-based validation (Claret et al., 2021), confirming the GPR model's robustness in δ13CDICδ13CDIC× 1° global interior ocean mapped climatology (Lauvset et al., 2016), producing a climatological gridded 3D δ13CDICδ13CDICδ13CDICδ13CDIChttps://doi.org/10.5281/zenodo.18481145
Contrails – thin ice clouds formed by aircraft – are a major contributor to aviation-induced climate forcing, yet their observational characterization remains limited. We present a manually labeled contrail dataset derived from observations of the Meteosat Second Generation (MSG) SEVIRI instrument over Europe and the North Atlantic, comprising 140 scenes of 256 × 256 pixels at 3 km nominal resolution. The dataset covers the time period in which Meteosat-10 was the operational satellite (from January 2013 through February 2018 and from March 2023 through March 2024) and scenes are distributed randomly over the whole SEVIRI disk. Each scene was independently annotated by three labelers, with ground truth established via majority consensus. To provide additional context, the dataset includes outputs from two satellite retrievals: CiPS (Cirrus Properties from SEVIRI) and ProPS (Probabilistic Cloud Top Phase retrieval), offering information on cloud cover and cloud top phase, cirrus probability, ice optical thickness, and ice cloud top height. These complementary variables enable detailed investigations, such as factors influencing contrail visibility. The dataset supports analyses of contrail detection, contrail characteristics, cloud-contrail interactions, and environmental conditions affecting detection. By providing high-quality labeled data with auxiliary cloud information, this resource facilitates the development and evaluation of contrail studies, contributes to improved understanding of aviation-related cloud effects, and informs strategies for climate impact mitigation. The full dataset is available under: https://doi.org/10.5281/zenodo.17669443(Santos Gabriel et al., 2025)