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Background: Children and adolescents are considered to be particularly vulnerable to the psychological effects of climate change, such as extreme weather events. What are the protective factors and stressors for the mental health of the young population after extreme weather events in Germany? Methods: Nine semi-structured interviews with representatives of occupational groups providing care to children, adolescents, and political stakeholders were conducted in Simbach am Inn, a German town affected by flooding in 2016. The interviews were analyzed using qualitative content analysis according to Mayring. Results: The interviews show that the parents’ dealing with what they had experienced and the concern for their relatives were the most influential stressors for children and adolescents. As protective factors, they felt that conversations with familiar people and restoring a certain “normality” were particularly important. The interviewees described both, the time of the flooding, and the time after the initial state of shock had subsided, as particularly stressful. Consequently, the experts reported on children and adolescents acutely complaining of fear, helplessness, and extreme tension. Nevertheless, the demand for psychological care increased only slightly after the flooding in Simbach am Inn. Conclusion: The social environment of children and adolescents is essential for their psychological well-being after an extreme weather event. Research, especially on children and adolescents who have already been affected, must increase in order to be able to describe influencing factors even more precisely, to protect individuals from adverse mental health effects, and to identify healthcare requirements.

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposed Extreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patterns More advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

Extreme space weather events, while rare, can have a substantial impact on our technologically-dependent society. And, although such events have only occasionally been observed, through careful analysis of a wealth of space-based and ground-based observations, historical records, and extrapolations from more moderate events, we have developed a basic picture of the components required to produce them. Several key issues, however, remain unresolved. For example, what limits are imposed on the maximum size of such events? What are the likely societal consequences of a so-called “100-year” solar storm? In this review, we summarize our current scientific understanding about extreme space weather events as we follow several examples from the Sun, through the solar corona and inner heliosphere, across the magnetospheric boundary, into the ionosphere and atmosphere, into the Earth’s lithosphere, and, finally, its impact on man-made structures and activities, such as spacecraft, GPS signals, radio communication, and the electric power grid. We describe preliminary attempts to provide probabilistic forecasts of extreme space weather phenomena, and we conclude by identifying several key areas that must be addressed if we are better able to understand, and, ultimately, predict extreme space weather events.

Transportation is affected by weather and extreme weather events, and there is evidence that heatwaves, heavy precipitation, storms, wildfires, and floods increasingly affect transport infrastructures, operations, and travel behavior. Climate change is expected to reinforce this trend, as mean weather parameters change, and the frequency and intensity of extreme events increases. This paper summarizes interrelationships of weather and transport for different transport modes from both supply and demand side perspectives on the basis of a literature review. To further explore the complexity of these interrelationships, it also evaluates news items (n = 839) in a sample of global media news outlets covering the world and population-dense world regions. Results confirm that extreme events have become disruptive of transport systems at the micro and macro scale, also affecting transport behavior. There are implications for environment, economy, technology, health, and society. Interrelationships are illustrated and discussed: Climatic impact drivers can be expected to increase transport vulnerabilities and risks, and have relevance for transport planning and adaptation.

Climate change is a major source of concern in the Middle East and North Africa (MENA) region, and migration is often understood as one of several strategies used by households to respond to changes in climate and environmental conditions, including extreme weather events. This study focuses on the link between climate change and migration. Most micro-level studies measure climate change either by the incidences of extreme weather events or by variation in temperature or rainfall. A few studies have found that formal and informal institutions as well as policies also affect migration. Institutions that make government more responsive to households (for example through public spending) discourage both international and domestic migration in the aftermath of extreme weather events. Migration is often an option of last resort after vulnerable rural populations attempting to cope with new and challenging circumstances have exhausted other options such as eating less, selling assets, or removing children from school. This study is based in large part on new data collected in 2011 in Algeria, Egypt, Morocco, Syria, and the Republic of Yemen. The surveys were administered by in-country partners to a randomly selected set of 800 households per country. It is also important to emphasize that neither the household survey results nor the findings from the qualitative focus groups are meant to be representative of the five countries in which the work was carried, since only a few areas were surveyed in each country. This report is organized as follows: section one gives synthesis. Section two discusses household perceptions about climate change and extreme weather events. Section three focuses on migration as a coping mechanisms and income diversification strategy. Section four examines other coping and adaptation strategies. Section five discusses perceptions about government and community programs.

Described are the methods and results of the verification of deterministic forecasts of precipitation, air temperature, and wind speed based on the COSMO-Ru and NMMB models issued during the test events (from January 1 to March 15, 2013) in the region of the Sochi-2014 Olympic Games. Considered are the specific features of the Pierce Skill Score (PSS) and Extremal Dependence Index (EDI) used for verifying the forecasts of events whose base rate decreases as critical thresholds increase or decrease. Presented are the applicability limits of these measures for verifying the forecasts of various meteorological parameters as well as several analytic and graphic estimates of their interconnection are represented.

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from “noisy” weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in 1° × 1° geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVTmodel with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y hightemperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors-including mean climate as well as climate extremes-explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

A deeper understanding of extreme hot weather are needed in cities sensitive to heat effects, an investigation was done in the tropical town of Kaohsiung in Taiwan. Its 11 districts were divided into three climatic classes varying from high urban heat, low levels of green space and lack of proximity to water bodies to low urban heat, adequate green space and proximity to water bodies. Daily data on natural mortality, meteorological variables, and pollutants from May-October 1999-2008 were analysed using generalised additive models for the time-series data. Subgroup analyses were conducted, stratifying decedents according to the level of planning activity required in order to mitigate adverse heat effects in their residential areas, classifying districts as \"level 1\" for those requiring a high level of mitigation action; \"level 2\" for those requiring some action; and \"level 3\" for those that need only preserve existing conditions. Stratified analyses showed that mortality increases per 1 °C rise on average, either on the same day or in the previous 4 days (lags 0-4), were associated with 2.8%, 2.3% and -1.3% for level 1, 2 and 3 districts, respectively. The slope describing the association between temperature and mortality was higher above 29.0 °C resulting in corresponding increases of 4.2%, 5.0% and 0.3% per per 1 °C rise in temperature, respectively. Other meteorological variables were not significantly associated with mortality. It is concluded that hot season mortality in Kaohsiung is only sensitive to heat effects in districts classified as having unfavourably climatic conditions and requiring mitigation efforts in city planning. Urban planning measures designed to improve climatic conditions could reduce excess mortality resulting from extreme hot weather.