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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

This study describes a generalized computational mathematical framework, Scalable Feature Extraction and Tracking (SCAFET), that extracts and tracks features from large climate data sets. SCAFET utilizes novel shape-based metrics that can identify and compare features from different mean states, data sets, and between distinct regions. Features of interest such as atmospheric rivers, tropical and extratropical cyclones, and jet streams are extracted by segmenting the data based on a scale-independent bounded variable called the shape index (SI). The SI gives a quantitative measurement of the local geometric shape of the field with respect to its surroundings. Compared to other widely used frameworks in feature detection, SCAFET does not use a posteriori assumptions about the climate model or mean state to extract features of interest and levelize the comparison between different models and scenarios. To demonstrate the capabilities of the method, we illustrate the detection of atmospheric rivers, tropical and extratropical cyclones, sea surface temperature fronts, and jet streams. Cyclones and atmospheric rivers are extracted to show how the algorithm identifies and tracks both the nodes and areas from climate data sets. The extraction of sea surface temperature fronts exemplifies how SCAFET effectively handles curvilinear grids. Last, jet streams are extracted to demonstrate how the algorithm can also detect three-dimensional features. As a generalized framework, SCAFET can be implemented to extract and track many weather and climate features across scales, grids, and dimensions.

Abstract Aims Timely access to surgery is an essential part of healthcare. People living with mental health (MH) conditions may have higher rates of chronic illness requiring surgical care but also face barriers to care. There is limited evidence about whether unequal surgical access contributes to health inequalities in this group. Methods We examined 1.22 million surgical procedures in public and private hospitals in New South Wales (NSW), Australia, in 2019. In a cross-sectional study of 76,320 MH service users aged 18 and over, surgical procedure rates per 1,000 population were compared to rates for 6.23 million other NSW residents after direct standardisation for age, sex and socio-economic disadvantage. Rates were calculated for planned and emergency surgery, for major specialty groups, for the top 10 procedure blocks in each specialty group and for 13 access-sensitive procedures. Subgroup analyses were conducted for hospital and insurance type and for people with severe or persistent MH conditions. Results MH service users had higher rates of surgical procedures (adjusted incidence rate ratio [aIRR]: 1.53, 95% CI: 1.51–1.56), due to slightly higher planned procedure rates (aIRR: 1.22, 95% CI: 1.19–1.24) and substantially higher emergency procedure rates (aIRR: 3.60, 95% CI: 3.51–3.70). Emergency procedure rates were increased in all block groups with sufficient numbers for standardisation. MH service users had very high rates (aIRR > 4.5) of emergency cardiovascular, skin and plastics and respiratory procedures, higher rates of planned coronary artery bypass grafting, coronary angiography and cholecystectomy but lower rates of planned ophthalmic surgery, cataract repair, shoulder reconstruction, knee replacement and some plastic surgery procedures. Conclusions Higher rates of surgery in MH service users may reflect a higher prevalence of conditions requiring surgical care, including cardiac, metabolic, alcohol-related or smoking-related conditions. The striking increase in emergency surgery rates suggests that this need may not be being met, particularly for chronic and disabling conditions which are often treated by planned surgery in private hospital settings in the Australian health system. A higher proportion of emergency surgery may have serious personal and health system consequences.

In marine ecosystems, most physiological, ecological, or physical processes are size dependent. These include metabolic rates, the uptake of carbon and other nutrients, swimming and sinking velocities, and trophic interactions, which eventually determine the stocks of commercial species, as well as biogeochemical cycles and carbon sequestration. As such, broad-scale observations of plankton size distribution are important indicators of the general functioning and state of pelagic ecosystems under anthropogenic pressures. Here, we present the first global datasets of the Pelagic Size Structure database (PSSdb), generated from plankton imaging devices. This release includes the bulk particle normalized biovolume size spectrum (NBSS) and the bulk particle size distribution (PSD), along with their related parameters (slope, intercept, and R2) measured within the epipelagic layer (0–200 m) by three imaging sensors: the Imaging FlowCytobot (IFCB), the Underwater Vision Profiler (UVP), and benchtop scanners. Collectively, these instruments effectively image organisms and detrital material in the 7–10 000 µm size range. A total of 92 472 IFCB samples, 3068 UVP profiles, and 2411 scans passed our quality control and were standardized to produce consistent instrument-specific size spectra averaged to 1° × 1° latitude and longitude and by year and month. Our instrument-specific datasets span most major ocean basins, except for the IFCB datasets we have ingested, which were exclusively collected in northern latitudes, and cover decadal time periods (2013–2022 for IFCB, 2008–2021 for UVP, and 1996–2022 for scanners), allowing for a further assessment of the pelagic size spectrum in space and time. The datasets that constitute PSSdb's first release are available at https://doi.org/10.5281/zenodo.11050013 (Dugenne et al., 2024b). In addition, future updates to these data products can be accessed at https://doi.org/10.5281/zenodo.7998799.