Search

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.

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.

It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the following question: given a “plausible” AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of eight researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of “plausible” AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction.Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations that do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models.New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.

A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment—without the need for a priori knowledge of the error model. Here, we apply this technique to topological color codes. We demonstrate that a recurrent neural network with long short-term memory cells can be trained to reduce the error rate ϵ _L of the encoded logical qubit to values much below the error rate ϵ _phys of the physical qubits—fitting the expected power law scaling ${\epsilon }_{{\rm{L}}}\propto {\epsilon }_{\mathrm{phys}}^{(d+1)/2}$ , with d the code distance. The neural network incorporates the information from ‘flag qubits’ to avoid reduction in the effective code distance caused by the circuit. As a test, we apply the neural network decoder to a density-matrix based simulation of a superconducting quantum computer, demonstrating that the logical qubit has a longer life-time than the constituting physical qubits with near-term experimental parameters.

Mesozooplankton are cosmopolitan within the sunlit layers of the global ocean. They are important in the pelagic food web, having a significant feedback to primary production through their consumption of phytoplankton and microzooplankton. In many regions of the global ocean, they are also the primary contributors to vertical particle flux in the oceans. Through both they affect the biogeochemical cycling of carbon and other nutrients in the oceans. Little, however, is known about their global distribution and biomass. While global maps of mesozooplankton biomass do exist in the literature, they are usually in the form of hand-drawn maps for which the original data associated with these maps are not readily available. The dataset presented in this synthesis has been in development since the late 1990s, is an integral part of the Coastal and Oceanic Plankton Ecology, Production, and Observation Database (COPEPOD), and is now also part of a wider community effort to provide a global picture of carbon biomass data for key plankton functional types, in particular to support the development of marine ecosystem models. A total of 153 163 biomass values were collected, from a variety of sources, for mesozooplankton. Of those 2% were originally recorded as dry mass, 26% as wet mass, 5% as settled volume, and 68% as displacement volume. Using a variety of non-linear biomass conversions from the literature, the data have been converted from their original units to carbon biomass. Depth-integrated values were then used to calculate an estimate of mesozooplankton global biomass. Global epipelagic mesozooplankton biomass, to a depth of 200 m, had a mean of 5.9 μg C L−1, median of 2.7 μg C L−1 and a standard deviation of 10.6 μg C L−1. The global annual average estimate of mesozooplankton in the top 200 m, based on the median value, was 0.19 Pg C. Biomass was highest in the Northern Hemisphere, and there were slight decreases from polar oceans (40–90°) to more temperate regions (15–40°) in both hemispheres. Values in the tropics (15° N–15° S) were intermediate between those at the northern and southern temperate latitudes. Datasets available at doi:10.1594/PANGAEA.785501.

To model stratocumulus clouds in the regional climate model, RegCM4.1, the University of Washington (UW) turbulence parametrization has been coupled to RegCM. We describe improvements in RegCM's coastal and near-coastal climatology, including improvements in the representation of stratiform clouds. By comparing output from a 27-yr (1982–2009) simulation of the climate of western North America to a wide variety of observational data (station data, satellite data, and aircraft in situ data), we show the following: (1) RegCM-UW is appropriate for use in general regional climate studies, and (2) the UW model distinctly improves the representation of the marine boundary layer in RegCM. These model–data comparisons also show that RegCM-UW has a slight cold bias, a (wet) precipitation bias, a systematic low bias in the vertically-integrated liquid water content near the coast, and a high bias in the fractional cloud coverage. The model represents well the diurnal, monthly, and interannual variability in low clouds. These results show RegCM-UW as a nascent mesoscale stratocumulus model that is appropriate for stratocumulus investigations at scales ranging from hourly to decadal. The source code for RegCM-UW is publicly available, under the GNU license, through the International Centre for Theoretical Physics.

The Fermi surface of a conventional two-dimensional electron gas is equivalent to a circle, up to smooth deformations that preserve the orientation of the equi-energy contour. Here we show that a Weyl semimetal confined to a thin film with an in-plane magnetization and broken spatial inversion symmetry can have a topologically distinct Fermi surface that is twisted into a figure-8—opposite orientations are coupled at a crossing which is protected up to an exponentially small gap. The twisted spectral response to a perpendicular magnetic field B is distinct from that of a deformed Fermi circle, because the two lobes of a figure-8 cyclotron orbit give opposite contributions to the Aharonov–Bohm phase. The magnetic edge channels come in two counterpropagating types, a wide channel of width $\beta {l}_{m}^{2}\propto 1/B$ and a narrow channel of width ${l}_{m}\propto 1/\sqrt{B}$ (with ${l}_{m}=\sqrt{{\hslash }/{eB}}$ the magnetic length and β the momentum separation of the Weyl points). Only one of the two is transmitted into a metallic contact, providing unique magnetotransport signatures.