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Abstract Multiparametric assays for risk stratification are widely used in the management of both node negative and node positive hormone receptor positive invasive breast cancer. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. The TEAM pathology study consists of 3284 postmenopausal ER+ve breast cancers treated with endocrine therapy Using genes comprising the following multi-parametric tests OncotypeDx®, Prosigna™ and MammaPrint® signatures were trained to recapitulate true assay results. Patients were then classified into risk groups and survival assessed. Whilst likelihood χ 2 ratios suggested limited value for combining tests, Kaplan–Meier and LogRank tests within risk groups suggested combinations of tests provided statistically significant stratification of potential clinical value. Paradoxically whilst Prosigna-trained results stratified Oncotype-trained subgroups across low and intermediate risk categories, only intermediate risk Prosigna-trained cases were further stratified by Oncotype-trained results. Both Oncotype-trained and Prosigna-trained results further stratified MammaPrint-trained low risk cases, and MammaPrint-trained results also stratified Oncotype-trained low and intermediate risk groups but not Prosigna-trained results. Comparisons between existing multiparametric tests are challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. Detailed analysis of the TEAM pathology study suggests a complex inter-relationship between test results in the same patient cohorts which requires careful evaluation regarding test utility. Further prognostic improvement appears both desirable and achievable.

Abstract The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Abstract Global gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin

Whale mortality and strandings have increased in recent years, with deceased whales often brought to landfill. However, the disposal of whale remains offshore holds significant ecological importance and can be a culturally and ethically sensitive approach. Moreover, offshore disposal mitigates potential risks associated with onshore whale remains disposal, such as the spread of diseases and the logistical challenges of managing large carcasses. A challenge with offshore disposal is defining the best release location to avoid the remains drifting ashore or into shipping channels. Here we compared the drift model outputs using a drift forecast model (SARMAP) for a 14 m whale carcass that was moved offshore in southeast Queensland, Australia, and fitted with a satellite tracker over an observation period of 150 h until positioning signal ceased. The modelling was conducted using different ocean products (BLUElink, HYCOM, and Copernicus), which showed a good agreement with the tracked whale carcass, albeit with changing wind conditions and contrasting currents flowing northward along the coast and, further offshore, flowing south. This case study illustrated that wind was the foremost driver of carcass drift due to the surface area above the water surface. The drift forecast simulations allowed for a reliable prediction of the floating whale drift that can assist authorities with decision making. Offshore disposal of whale carcasses is a sustainable practice but requires good planning and scientific assessment.

Abstract Climate change is a global phenomenon, but natural selection occurs within landscapes. Many global analyses predict how climate change will shape behavior and physiology, but few incorporate information from the landscape scales at which animals actually respond to selective pressure. We compared cold‐water fish (redband trout Oncorhynchus mykiss newberrii) from neighboring habitats in a naturally warm, recently fragmented basin to understand how different responses to warming may arise from landscape constraints. Trout in warm, hydrologically connected Upper Klamath Lake fled summer temperatures and sought refuge in cool tributaries, while trout in an equally warm but fragmented reach of the Klamath River endured summer conditions. Trout in the river were more physiologically tolerant of high temperatures than trout in the lake across multiple metrics, including capacity for aerobic activity, recovery from exertion, and loss of equilibrium. Two independent metrics of energetic condition indicated that the behavioral strategy of trout in the lake came at a substantial energetic cost, while the physiological strategy of trout in the river was able to mitigate most energetic consequences of high temperatures. No clear genetic basis for increased tolerance was found in trout from the river, which may suggest tolerance was derived from plasticity, although our analysis could not rule out genetic adaptation. Our results show that landscape processes such as fragmentation can cause different climate survival strategies to emerge in neighboring populations. Connecting the mechanisms that favor similar survival strategies among related organisms at broad scales with mechanisms that drive landscape‐scale variability within taxa should be a major goal for future predictions of biological responses to climate change.

Extreme stratospheric polar vortex (SPV) events can influence winter tropospheric circulation for up to 60 d. Their impacts on air temperature have been extensively studied recently. However, there is a research gap in their effects on wind speeds and its extremes. This study aims to evaluate, for the first time, the impacts of such extreme SPV events on observed and modelled near-surface wind gusts across Europe. We have analysed wind gust data coming from: station-based observations (for the Iberian Peninsula and Scandinavia), the ERA5 reanalysis and the SEAS5 and GloSea6 seasonal forecasting systems. We assess their similarities in reproducing 4 parameters of their corresponding distributions: median, standard deviation, skewness and kurtosis. For all these datasets, the results indicate that extreme positive SPV events are followed by negative wind gust anomalies in Southern Europe and positive in Northern Europe. Whereas, negative SPV events (such as Sudden Stratospheric Warmings) have positive gust anomalies in Southern Europe and negative in the north. A central region shows negligible anomalies in both cases. This highlights the ability of SPV as a predictor for short-medium-term forecasting of extreme wind events, which would have direct applications to many socioeconomic and environmental issues such as the estimation of wind-power generation.

Abstract The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.

Monitoring changes in climate extremes is vitally important in order to provide context for both our current and possible future climates. Datasets based on climate extremes indices from in situ observations and climate reanalyses are often used for this purpose. We assess the spatial and temporal consistency between these two classes of dataset on a global basis to understand where they agree or are complementary. As expected, the temperature time series expressed as anomalies, or self-normalizing indices, agree well. While there is sometimes a large spread in absolute values between products, both long-term trends and inter-annual variability are also in agreement. Spatially the temperature indices show high correlations, but comparisons between the cumulative distributions at each grid box show differences in regions at high altitude or where interpolation has been performed across climatic zones. The agreement is lower between the time series from observation-based and reanalysis datasets for precipitation indices. Trends in these indices show larger spatial heterogeneity, and inter-annual variation in the global averages is often larger than the magnitude of the long-term trend. These indices show larger spatial heterogeneity in the trends, which results in comparatively small long-term trends in the global averages, which are also small compared to the inter-annual variation. Spatially these indices show on average smaller correlations than for the temperature indices, but large regions show strong positive correlations for some precipitation indices. A subset of the reanalyses has higher correlations with the latest in situ-based dataset, HadEX3, and also have smaller differences in the per-grid box cumulative distributions, indicating close agreement to the observation-based dataset. Also, we outline how the comparisons herein suggest that the gridding method used when creating HadEX3 may need to be updated for future versions of this dataset, in order to retain detail arising from topographic features, for example.

Atmospheric humidity plays an important role in climate analyses. Here we describe the production and key characteristics of a new quasi-global marine humidity product intended for climate monitoring, HadISDH.marine. It is an in situ multivariable marine humidity product, gridded monthly at a 5∘×5∘ spatial resolution from January 1973 to December 2018 with annual updates planned. Currently, only reanalyses provide up-to-date estimates of marine surface humidity, but there are concerns over their long-term stability. As a result, this new product makes a valuable addition to the climate record and will help address some of the uncertainties around recent changes (e.g. contrasting land and sea trends, relative-humidity drying). Efforts have been made to quality-control the data, ensure spatial and temporal homogeneity as far as possible, adjust for known biases in non-aspirated instruments and ship heights, and also estimate uncertainty in the data. Uncertainty estimates for whole-number reporting and for other measurement errors have not been quantified before for marine humidity. This is a companion product to HadISDH.land, which, when combined, will provide methodologically consistent land and marine estimates of surface humidity. The spatial coverage of HadISDH.marine is good over the Northern Hemisphere outside of the high latitudes but poor over the Southern Hemisphere, especially south of 20∘ S. The trends and variability shown are in line with overall signals of increasing moisture and warmth over oceans from theoretical expectations and other products. Uncertainty in the global average is larger over periods where digital ship metadata are fewer or unavailable but not large enough to cast doubt over trends in specific humidity or air temperature. Hence, we conclude that HadISDH.marine is a useful contribution to our understanding of climate change. However, we note that our ability to monitor surface humidity with any degree of confidence depends on the continued availability of ship data and provision of digitized metadata. HadISDH.marine data, derived diagnostics, and plots are available at http://www.metoffice.gov.uk/hadobs/hadisdh (last access: June 2019) and https://doi.org/10.5285/463b2fcd6a264a39b1e3249dab16c177 (Willett et al., 2020).

Wind energy, an important component of clean energy, is highly dictated by the disposable wind speed within the working regime of wind turbines (typically between 3 and 25 m s ^−1 at the hub height). Following a continuous reduction (‘stilling’) of global annual mean surface wind speed (SWS) since the 1960s, recently, researchers have reported a ‘reversal’ since 2011. However, little attention has been paid to the evolution of the effective wind speed for wind turbines. Since wind speed at hub height increases with SWS through power law, we focus on the wind speed frequency variations at various ranges of SWS through hourly in-situ observations and quantify their contributions to the average SWS changes over 1981–2021. We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s ^−1 , the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s ^−1 < SWS < 2.9 m s ^−1 ) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s ^−1 ) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr ^−1 , p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr ^−1 , p < 0.05) during 2011–2021.

With the ongoing warming of the globe, it is important to quantify changes in the recent behaviour of extreme events given their impacts on human health, infrastructure and the natural environment. We use the sub-daily, multivariate, station-based HadISD dataset to study the changes in the statistical distributions of temperature, dew point temperature and wind speeds. Firstly, we use zonally averaged quantities to show that the lowest temperatures during both day and night are changing more rapidly than the highest, with the effect more pronounced in the northern high latitudes. Along with increases in the zonally averaged mean temperature, the standard deviation has decreased and the skew increased (increasing positive tail, decreasing negative tail) over the last 45 years, again with a stronger, more robust signal at higher latitudes. Changes in the distribution of dew point temperature are similar to those of temperature. However, changes in the distribution of wind speeds indicate a more rapid change at higher speeds than at lower. Secondly, to assess in more detail the spatial distribution of changes as well as changes across seasons and hours of the day we study each station individually. For stations which show clear indications of change in the statistical moments, the higher the statistical moment, generally the more spatially heterogenous the patterns of change. The standard deviations of temperatures are increasing in a band stretching from Europe through China but are decreasing across North America and in the high northern latitudes, indicating broadening and narrowing of the distributions, respectively. Large seasonal differences are found in the change of standard deviations of temperatures over North America and eastern China. Temperatures in eastern Asia also have increasing skew in the winter in contrast to the remainder of the year. The dew point temperatures show smaller variation in all of the moments but similar patterns to the temperatures. For wind speeds, apart from the USA, standard deviations are decreasing across the world, indicating a decrease in variability. Finally, we use quantile regression to show changes in the percentiles of distributions over time. These show an increase in high quantiles of temperature in eastern Europe during the summer and also in northern Europe for low quantiles in the winter, also indicating broadening and narrowing of the distributions, respectively. In North America, the largest changes are at the lower quantiles in northern latitudes for autumn and winter. Quantiles of dew point temperature are changing most in the autumn and winter, especially in the northern parts of Europe.

ABSTRACT When conducting aerial surveys of wildlife populations, a number of animals go undetected by observers. The use of infrared imagery may offer a solution to improve detection rates and reduce visibility bias. Our objective was to compare the use of an infrared camera system with traditional methods using direct visual observations for estimating density of ringed seals (Pusa hispida) on ice. We conducted aerial surveys of ringed seals in the areas of Pond Inlet and western Hudson Bay in the Canadian Arctic during spring 2016 and 2017. Infrared videos were recorded within a 250‐m‐wide strip beneath the aircraft and potential seals were verified using corresponding visual images from a digital single‐lens reflex camera. Results from strip‐transect analysis of infrared observations were compared with results from strip‐transect analysis of direct visual observations and results from line‐transect analysis of a combined data set of infrared and direct visual observations. The different methods produced different density estimates with the infrared strip‐transect and line‐transect methods producing similar results that were, on average, approximately 2–3 times greater than strip‐transect analyses of observer data. Use of infrared imagery provides several advantages over traditional methods because it allows for more efficient and reliable detection of seals, eliminates the need for large teams of trained observers, and simplifies the process of data analysis and density estimation. © 2019 The Wildlife Society.

A number of studies have noted that the use of distinct reference periods when comparing indices measuring the frequency of days exceeding a particular temperature percentile threshold leads to apparently different behaviour. We show that these differences arise because of the interplay between the increasing temperatures and the choice of reference period. The time series of the indicators calculated using the different reference periods are offset, as expected, but also diverge. Linear trends calculated over the same period from the same underlying data but where different reference periods have been used are substantially different if a change in climatological conditions has occurred between the two reference periods. We show this not only occurs in our simple empirical approach, but also for the averages of gridded observational and reanalysis datasets and also at a station level. This has implications for data set comparisons using trends in temperature percentile indices that are based on different reference periods. It also has implications for updates to standard reference periods used to monitor the climate.

Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

We compare the latest observational land surface humidity dataset, HadISDH, with the latest generation of climate models extracted from the CMIP5 archive and the ERA-Interim reanalysis over the period 1973 to present. The globally averaged behaviour of HadISDH and ERA-Interim are very similar in both humidity measures and air temperature, on decadal and interannual timescales. The global average relative humidity shows a gradual increase from 1973 to 2000, followed by a steep decline in recent years. The observed specific humidity shows a steady increase in the global average during the early period but in the later period it remains approximately constant. None of the CMIP5 models or experiments capture the observed behaviour of the relative or specific humidity over the entire study period. When using an atmosphere-only model, driven by observed sea surface temperatures and radiative forcing changes, the behaviour of regional average temperature and specific humidity are better captured, but there is little improvement in the relative humidity. Comparing the observed climatologies with those from historical model runs shows that the models are generally cooler everywhere, are drier and less saturated in the tropics and extra-tropics, and have comparable moisture levels but are more saturated in the high latitudes. The spatial pattern of linear trends is relatively similar between the models and HadISDH for temperature and specific humidity, but there are large differences for relative humidity, with less moistening shown in the models over the tropics and very little at high latitudes. The observed drying in mid-latitudes is present at a much lower magnitude in the CMIP5 models. Relationships between temperature and humidity anomalies (T–q and T–rh) show good agreement for specific humidity between models and observations, and between the models themselves, but much poorer for relative humidity. The T–q correlation from the models is more steeply positive than the observations in all regions, and this over-correlation may be due to missing processes in the models. The observed temporal behaviour appears to be a robust climate feature rather than observational error. It has been previously documented and is theoretically consistent with faster warming rates over land compared to oceans. Thus, the poor replication in the models, especially in the atmosphere-only model, leads to questions over future projections of impacts related to changes in surface relative humidity. It also precludes any formal detection and attribution assessment.

HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as NetCDF files at http://www.metoffice.gov.uk/hadobs/hadisd and updated annually.