We investigated the millennial variability (1000 A.D.-2000 A.D.) of global biogenic volatile organic compound (BVOC) emissions by using two independent numerical models: The Model of Emissions of Gases and Aerosols from Nature (MEGAN), for isoprene, monoterpene, and sesquiterpene, and Lund-Potsdam-Jena-General Ecosystem Simulator (LPJ-GUESS), for isoprene and monoterpenes. We found the millennial trends of global isoprene emissions to be mostly affected by land cover and atmospheric carbon dioxide changes, whereas monoterpene and sesquiterpene emission trends were dominated by temperature change. Isoprene emissions declined substantially in regions with large and rapid land cover change. In addition, isoprene emission sensitivity to drought proved to have significant short-term global effects. By the end of the past millennium MEGAN isoprene emissions were 634 TgC yr-1 (13% and 19% less than during 1750-1850 and 1000-1200, respectively), and LPJ-GUESS emissions were 323 TgC yr-1(15% and 20% less than during 1750-1850 and 1000-1200, respectively). Monoterpene emissions were 89 TgC yr-1(10% and 6% higher than during 1750-1850 and 1000-1200, respectively) in MEGAN, and 24 TgC yr-1 (2% higher and 5% less than during 1750-1850 and 1000-1200, respectively) in LPJ-GUESS. MEGAN sesquiterpene emissions were 36 TgC yr-1(10% and 4% higher than during 1750-1850 and 1000-1200, respectively). Although both models capture similar emission trends, the magnitude of the emissions are different. This highlights the importance of building better constraints on VOC emissions from terrestrial vegetation.

The Arctic region is warming considerably faster than the rest of the globe(1), with important consequences for the ecosystems(2) and human exploration of the region(3). However, the reasons behind this Arctic amplification are not entirely clear(4). As a result of measures to enhance air quality, anthropogenic emissions of particulate matter and its precursors have drastically decreased in parts of the Northern Hemisphere over the past three decades(5). Here we present simulations with an Earth system model with comprehensive aerosol physics and chemistry that show that the sulfate aerosol reductions in Europe since 1980 can potentially explain a significant fraction of Arctic warming over that period. Specifically, the Arctic region receives an additional 0.3Wm(-2) of energy, and warms by 0.5 degrees C on annual average in simulations with declining European sulfur emissions in line with historical observations, compared with a model simulation with fixed European emissions at 1980 levels. Arctic warming is amplified mainly in fall and winter, but the warming is initiated in summer by an increase in incoming solar radiation as well as an enhanced poleward oceanic and atmospheric heat transport. The simulated summertime energy surplus reduces sea-ice cover, which leads to a transfer of heat from the Arctic Ocean to the atmosphere. We conclude that air quality regulations in the Northern Hemisphere, the ocean and atmospheric circulation, and Arctic climate are inherently linked.

As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the micro biome. The bee micro biome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee micro biome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health.

Single-column models (SCMs) have been used as tools to help develop numerical weather prediction and global climate models for several decades. SCMs decouple small-scale processes from large-scale forcing, which allows the testing of physical parameterisations in a controlled environment with reduced computational cost. Typically, either the ocean, sea ice or atmosphere is fully modelled and assumptions have to be made regarding the boundary conditions from other subsystems, adding a potential source of error. Here, we present a fully coupled atmosphere-ocean SCM (AOSCM), which is based on the global climate model EC-Earth3. The initial configuration of the AOSCM consists of the Nucleus for European Modelling of the Ocean (NEMO3.6) (ocean), the Louvain-la-Neuve Sea Ice Model (LIM3) (sea ice), the Open Integrated Forecasting System (OpenIFS) cycle 40r1 (atmosphere), and OASIS3-MCT (coupler). Results from the AOSCM are presented at three locations: the tropical Atlantic, the midlatitude Pacific and the Arctic. At all three locations, in situ observations are available for comparison. We find that the coupled AOSCM can capture the observed atmospheric and oceanic evolution based on comparisons with buoy data, soundings and ship-based observations. The model evolution is sensitive to the initial conditions and forcing data imposed on the column. Comparing coupled and uncoupled configurations of the model can help disentangle model feedbacks. We demonstrate that the AOSCM in the current set-up is a valuable tool to advance our understanding in marine and polar boundary layer processes and the interactions between the individual components of the system (atmosphere, sea ice and ocean).

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed. No. DE-ACO2-05CH11231 and authors at Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

We have studied a wide range of transition metals to find potential carbon nanotube (CNT) catalysts for chemical vapor deposition (CVD) production. The adhesion strengths between a CNT and a metal cluster were calculated using first principle density functional theory (DFT) for all 1st, 2nd and 3rd row transition metals. We have developed the criterion that the metal-carbon adhesion strength per bond must fulfill a Goldilocks principle for catalyzing CNT growth and used it to identify, besides the well known catalysts Fe, Co and Ni, a number of other potential catalysts, namely Y, Zr, Rh, Pd, La, Ce and Pt. Our results are consistent with previous experiments performed either in a carbon arc discharge environment or by a CVD-process with regard to CNT catalyst activity.

EDGE is a complex application for computational fluid dynamics used e.g. for aerodynamic simulations in avionics industry. In this work we present the portable, high-level parallelization of EDGE for execution on multicore CPU and GPU based systems by using the multi-backend skeleton programming library SkePU. We first expose the challenges of applying portable high-level parallelization to a complex scientific application for a heterogeneous (GPU-based) system using (SkePU) skeletons and discuss the encountered flexibility problems that usually do not show up in skeleton toy programs. We then identify and implement necessary improvements in SkePU to become applicable for applications containing computations on complex data structures and with irregular data access. In particular, we improve the MapArray skeleton and provide a new MultiVector container for operand data that can be used with unstructured grid data structures. Although there is no SkePU skeleton specifically dedicated to handling computations on unstructured grids and its data structures, we still obtain portable speedup of EDGE with both multicore CPU and GPU execution by using the improved MapArray skeleton of SkePU.

The mid-Pliocene warm period (3.264-3.025 Ma) is the most recent geological period in which the atmospheric CO2 concentration was approximately equal to the concentration we measure today (ca. 400 ppm). Sea surface temperature (SST) proxies indicate above-average warming over the North Atlantic in the mid-Pliocene with respect to the pre-industrial period, which may be linked to an intensified Atlantic Meridional Overturning Circulation (AMOC). Earlier results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) show that the ensemble simulates a stronger AMOC in the mid-Pliocene than in the pre-industrial. However, no consistent relationship between the stronger mid-Pliocene AMOC and either the Atlantic northward ocean heat transport (OHT) or average North Atlantic SSTs has been found. In this study, we look further into the drivers and consequences of a stronger AMOC in mid-Pliocene compared to pre-industrial simulations in PlioMIP2. We find that all model simulations with a closed Bering Strait and Canadian Archipelago show reduced freshwater transport from the Arctic Ocean into the North Atlantic. This contributes to an increase in salinity in the subpolar North Atlantic and Labrador Sea that can be linked to the stronger AMOC in the mid-Pliocene. To investigate the dynamics behind the ensembles variable response of the total Atlantic OHT to the stronger AMOC, we separate the Atlantic OHT into two components associated with either the overturning circulation or the wind-driven gyre circulation. While the ensemble mean of the overturning component is increased significantly in magnitude in the mid-Pliocene, it is partly compensated by a reduction in the gyre component in the northern subtropical gyre region. This indicates that the lack of relationship between the total OHT and AMOC is due to changes in OHT by the subtropical gyre. The overturning and gyre components should therefore be considered separately to gain a more complete understanding of the OHT response to a stronger mid-Pliocene AMOC. In addition, we show that the AMOC exerts a stronger influence on North Atlantic SSTs in the mid-Pliocene than in the pre-industrial, providing a possible explanation for the improved agreement of the PlioMIP2 ensemble mean SSTs with reconstructions in the North Atlantic.