Schwannomatosis comprises a group of hereditary tumor predisposition syndromes characterized by, usually benign, multiple nerve sheath tumors, which frequently cause severe pain that does not ...typically respond to drug treatments. The most common schwannomatosis‐associated gene is NF2, but SMARCB1 and LZTR1 are also associated. There are still many cases in which no pathogenic variants (PVs) have been identified, suggesting the existence of as yet unidentified genetic risk factors. In this study, we performed extended genetic screening of 75 unrelated schwannomatosis patients without identified germline PVs in NF2, LZTR1, or SMARCB1. Screening of the coding region of DGCR8, COQ6, CDKN2A, and CDKN2B was carried out, based on previous reports that point to these genes as potential candidate genes for schwannomatosis. Deletions or duplications in CDKN2A, CDKN2B, and adjacent chromosome 9 region were assessed by multiplex ligation‐dependent probe amplification analysis. Sequencing analysis of a patient with multiple schwannomas and melanomas identified a novel duplication in the coding region of CDKN2A, disrupting both p14ARF and p16INK4a. Our results suggest that none of these genes are major contributors to schwannomatosis risk but the possibility remains that they may have a role in more complex mechanisms for tumor predisposition.
Geoengineering by stratospheric aerosol injection has been proposed as a policy response to warming from human emissions of greenhouse gases, but it may produce unequal regional impacts. We present a ...simple, intuitive risk-based framework for classifying these impacts according to whether geoengineering increases or decreases the risk of substantial climate change, with further classification by the level of existing risk from climate change from increasing carbon dioxide concentrations. This framework is applied to two climate model simulations of geoengineering counterbalancing the surface warming produced by a quadrupling of carbon dioxide concentrations, with one using a layer of sulphate aerosol in the lower stratosphere, and the other a reduction in total solar irradiance. The solar dimming model simulation shows less regional inequality of impacts compared with the aerosol geoengineering simulation. In the solar dimming simulation, 10% of the Earth's surface area, containing 10% of its population and 11% of its gross domestic product, experiences greater risk of substantial precipitation changes under geoengineering than under enhanced carbon dioxide concentrations. In the aerosol geoengineering simulation the increased risk of substantial precipitation change is experienced by 42% of Earth's surface area, containing 36% of its population and 60% of its gross domestic product.
Abstact
On February 23, 2017, a significant low‐pressure system named Storm Doris crossed the Republic of Ireland and the UK causing widespread disruption. As an early example of a storm named ...through the Met Office and Met Eireann “Name Our Storms” project, this provided an excellent opportunity to study how information about extreme weather in the UK spread through the media. In traditional media, the forecast of Storm Doris was widely reported upon on February 21–22. On the February 23, newspaper coverage of the event rapidly switched to reporting the impact of the storm. Around three times the number of words and twice the number of articles were published on the impacts of Storm Doris in comparison with its forecast. Storm Doris rapidly became a broader cultural topic with an imprint on political news because of two by‐elections that occurred by coincidence on February 23. In the social media, the rapid growth in the number of tweets about Storm Doris closely mirrored the growth of newspaper articles about the impacts of the storm. The network structure of the tweets associated with Storm Doris revealed the importance of both the Met Office official Twitter account and newspaper and rail company accounts in disseminating information about the storm. Storm names, in addition to their benefit for forecast communication, also provide researchers with a useful and easily collected target to study the development and evolution of public understanding of extreme weather events.
The present study analyses one of the first storms to be given a name by the Met Office and Met Eireann, Storm Doris, in order to understand how this storm was communicated in the traditional and social media. An example of the growth of large, weakly connected social networks discussing the storm is shown (derived from Twitter data). The quantity of information shared about the storm shows the usefulness of storm names as both a communication and a research tool.
We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the ...stratopause and relatively fine stratospheric vertical resolution (high‐top), and those that have a model top below the stratopause (low‐top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low‐top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low‐top models with less than half the frequency of events observed in the reanalysis data and high‐top models. The lack of stratospheric variability in the low‐top models affects their stratosphere‐troposphere coupling, resulting in short‐lived anomalies in the Northern Annular Mode, which do not produce long‐lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low‐top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high‐top models compared to the low‐top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions.
Keypoints
We assess and compare the performance of CMIP5 models in the stratosphere.Low‐top models lack stratospheric variability.Stratosphere‐troposphere coupling is hence weaker in low‐top models.
Many recent studies have confirmed that variability in the stratosphere is a significant source of surface sub‐seasonal prediction skill during Northern Hemisphere winter. It may be beneficial, ...therefore, to think about times in which there might be windows‐of‐opportunity for skillfull sub‐seasonal predictions based on the initial or predicted state of the stratosphere. In this study, we propose a simple, minimal model that can be used to understand the impact of the stratosphere on tropospheric predictability. Our model purposefully excludes state dependent predictability in either the stratosphere or troposphere or in the coupling between the two. Model parameters are set up to broadly represent current sub‐seasonal prediction systems by comparison with four dynamical models from the Sub‐Seasonal to Seasonal Prediction Project database. The model can reproduce the increases in correlation skill in sub‐sets of forecasts for weak and strong lower stratospheric polar vortex states over neutral states despite the lack of dependence of coupling or predictability on the stratospheric state. We demonstrate why different forecast skill diagnostics can give a very different impression of the relative skill in the three sub‐sets. Forecasts with large stratospheric signals and low amounts of noise are demonstrated to also be windows‐of‐opportunity for skillfull tropospheric forecasts, but we show that these windows can be obscured by the presence of unrelated tropospheric signals.
Plain Language Summary
For successful forecasts of surface winter conditions between two weeks and one season ahead, the stratosphere has been shown to be a key source of information. Despite many studies examining how well the stratosphere can be predicted in computer‐based forecasting systems, there remains a lack of understanding of which surface forecasts the stratosphere is most important for. This study is an attempt to step back from examining the role of the stratosphere in any particular forecasting system and instead to determine a simple framework that can be used to understand when and how the stratosphere is important. Using our framework we can construct a series of simple experiments that help to understand how important the stratosphere is in the longer range forecasting problem. Our experiments show that forecasts made during periods in which the Arctic stratospheric winds are unusually strong or weak have greater skill, but this does not depend on how unusually weak or strong the stratospheric winds are. The results are particularly important for thinking about the times in which longer range forecasts might be more skillfull than on average, so called windows‐of‐opportunity, and how these depend on the stratosphere.
Key Points
We propose a model that demonstrates how forecast skill present in the lowermost stratosphere contributes to tropospheric forecast skill
The model can explain the greater correlation skill in the troposphere for forecasts during weak or strong vortex events
The model shows how tropospheric skill arising from the stratosphere can sometimes be confounded by uncorrelated tropospheric signals
Abstract There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, ...there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.
In many countries, wintertime cold weather is linked to ill-health and intense pressure on public health services. This study examines how both long-term climate change and sub-seasonal variability ...contribute to the temperature extremes that increase pressures on the UK's National Health Service. The impact of temperature on fractional mortality and hospital admissions due to chronic obstructive pulmonary disease are used as metrics of wintertime pressure on the health system. The focus of the study is on days during the year in which the fractional mortality and hospital admissions attributable to cold weather exceed the five-year return period. These days are henceforth called winter pressure days since they likely to lead to significant pressure on the health service to meet demand. On interdecadal and longer timescales, winter pressure days show a robust decline over recent decades with a reduction from a probability of 0.29 in the pre-industrial period to 0.11 for the period 2000–2016. Comparing the risk of winter pressure days in two different climate model simulations of the historical period and a counterfactual ensemble of only natural climate forcings shows that this decline can be clearly attributed to anthropogenic activity. The average Fraction of Attributable risk due to anthropogenic activity for these two climate models for winter pressure days is −0.94. On sub-seasonal timescales, weather drivers of winter pressure days are assessed through analysis of diagnostics of weather regime lifecycles. This analysis shows winter pressure days occur almost exclusively in the Greenland Blocking regime. Although the risk of winter pressure days is likely to continue to decline with current climate trends, there remains a substantial weather driven risk to the UK health system. Preparing for weather events that cause stress on the system should focus on the analysis and prediction of the Greenland Blocking regime on weekly timescales.
Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of ...other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr
of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.
Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric ...circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.
PDF
