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Surface solar radiation (SSR) is an essential factor in the flow of surface energy, enabling accurate capturing of long-term climate change and understanding of the energy balance of Earth's atmosphere system. However, the long-term trend estimation of SSR is subject to significant uncertainties due to the temporal inhomogeneity and the uneven spatial distribution of in situ observations. This paper develops an observational integrated and homogenized global terrestrial (except for Antarctica) station SSR dataset (SSRIH station) by integrating all available SSR observations, including the existing homogenized SSR results. The series is then interpolated in order to obtain a 5 ∘ × 5 ∘ resolution gridded dataset (SSRIH grid). On this basis, we further reconstruct a long-term (1955–2018) global land (except for Antarctica) SSR anomaly dataset with a 5 ∘ × 2.5 ∘ resolution (SSRIH 20CR) by training improved partial convolutional neural network deep-learning methods based on 20th Century Reanalysis version 3 (20CRv3). Based on this, we analysed the global land- (except for Antarctica) and regional-scale SSR trends and spatiotemporal variations. The reconstruction results reflect the distribution of SSR anomalies and have high reliability in filling and reconstructing the missing values. At the global land (except for Antarctica) scale, the decreasing trend of the SSRIH 20CR (- 1.276 ± 0.205 W m -2 per decade) is smaller than the trend of the SSRIH grid (- 1.776 ± 0.230 W m -2 per decade) from 1955 to 1991. The trend of the SSRIH 20CR (0.697 ± 0.359 W m -2 per decade) from 1991 to 2018 is also marginally lower than that of the SSRIH grid (0.851 ± 0.410 W m -2 per decade). At the regional scale, the difference between the SSRIH 20CR and SSRIH grid is more significant in years and areas with insufficient coverage. Asia, Africa, Europe and North America cause the global dimming of the SSRIH 20CR , while Europe and North America drive the global brightening of the SSRIH 20CR. Spatial sampling inadequacies have largely contributed to a bias in the long-term variation of global and regional SSR. This paper's homogenized gridded dataset and the Artificial Intelligence reconstruction gridded dataset (Jiao and Li, 2023) are both available at 10.6084/m9.figshare.21625079.v1. [ABSTRACT FROM AUTHOR]

The NASA Atmospheric Tomography Mission (ATom) completed four seasonal deployments (August 2016, February 2017, October 2017, May 2018), each with regular 0.2–12 km profiling by transecting the remote Pacific Ocean and Atlantic Ocean basins. Additional data were also acquired for the Southern Ocean, the Arctic basin, and two flights over Antarctica. ATom in situ measurements provide a near-complete chemical characterization of the ∼ 140 000 10 s (80 m by 2 km) air parcels measured along the flight path. This paper presents the Modeling Data Stream (MDS), a continuous gap-filled record of the 10 s parcels containing the chemical species needed to initialize a gas-phase chemistry model for the budgets of tropospheric ozone and methane. Global 3D models have been used to calculate the Reactivity Data Stream (RDS), which is comprised of the chemical reactivities (production and loss) for methane, ozone, and carbon monoxide, through 24 h integration of the 10 s parcels. These parcels accurately sample tropospheric heterogeneity and allow us to partially deconstruct the spatial scales and variability that define tropospheric chemistry from composition to reactions. This paper provides a first look at and analysis of the up-to-date MDS and RDS data including all four deployments (Prather et al., 2023, 10.7280/D1B12H). ATom's regular profiling of the ocean basins allows for weighted averages to build probability densities for the key species and reactivities presented here. These statistics provide climatological metrics for global chemistry models, e.g., the large-scale pattern of ozone and methane loss in the lower troposphere and the more sporadic hotspots of ozone production in the upper troposphere. The profiling curtains of reactivity also identify meteorologically variable and hence deployment-specific hotspots of photochemical activity. Added calculations of the sensitivities of the production and loss terms relative to each species emphasize the few dominant species that control the ozone and methane budgets and whose statistical patterns should be key model–measurement metrics. From the sensitivities, we also derive linearized lifetimes of ozone and methane on a parcel-by-parcel basis and average over the basins, providing an observational basis for these previously model-only diagnostics. We had found that most model differences in the ozone and methane budgets are caused by the models calculating different climatologies for the key species such as O 3 , CO, H 2 O, NO x , CH 4 , and T , and thus these ATom measurements make a substantial contribution to the understanding of model differences and even identifying model errors in global tropospheric chemistry. [ABSTRACT FROM AUTHOR]

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77 ∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at 10.11888/Atmos.tpdc.272721 (Ding et al., 2022b). [ABSTRACT FROM AUTHOR]

Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic, and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were not openly available, restricting further usage of the data for different glaciological and geophysical applications. Starting in 2020, scientists and data managers at BAS have worked on standardizing and releasing large swaths of aerogeophysical data acquired during the period 1994–2020, including a total of 64 datasets from 24 different surveys, amounting to ∼ 450 000 line-km (or 5.3 million km 2) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004–2005) and Thwaites (ITGC 2018–2019 & 2019–2020) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005–2006) and Gamburtsev Subglacial Mountains (AGAP 2007–2009). Considerable effort has been made to standardize these datasets to comply with the FAIR (findable, accessible, interoperable and re-usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/ , last access: 18 July 2022), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardize them, and introduces the new data portal and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the aerogeophysical datasets by extracting information on the continuity of englacial layering from the fully published airborne radar data. We believe these newly released data will be a valuable asset to future glaciological and geophysical studies over Antarctica and will significantly extend the life cycle of the data. All datasets included in this data release are now fully accessible at https://data.bas.ac.uk (British Antarctic Survey, 2022). [ABSTRACT FROM AUTHOR]

We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; 10.5281/zenodo.4287517;). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century. [ABSTRACT FROM AUTHOR]

HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25 ∘ , i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at 10.5281/zenodo.3871707. [ABSTRACT FROM AUTHOR]