Event Attribution Research Articles

Comparing public and scientific extreme event attribution to climate change

Comparing public and scientific extreme event attribution to climate change

Anthropogenic influence on the extremely low September sea ice and hot summer of 2020 over the Arctic and its future risk of occurrence

During 2020, the Arctic is marked by extremely low sea ice coverage and hot climate. September Sea Ice Extent (SIE) was about 2.3 million km2 below the 1979–2014 mean and the 2nd lowest on the 1979–2020 record, while regional summer (June–August, JJA) mean 2 m air temperature (TAS) was about 1.3 °C above the 1979–2014 mean and was the hottest on record at the time. Locally, September Sea Ice Concentration (SIC) was approximately 70% lower and JJA TAS can be as much as 6.0 °C higher than the 1979–2014 mean. Although the proximate cause for the extreme event was the continuously favorable atmospheric circulation patterns, wind conditions and ice-albedo feedback, the main objective of this paper is probabilistic extreme event attribution studies to assess the anthropogenic influence. Based on the CMIP6 multi-model ensemble products, modeled long-term trends of Arctic sea ice and TAS are consistent with observed trends when including anthropogenic forcing or greenhouse gas (GHG) forcing, while cannot exhibit observed trends with only aerosol or natural forcing. Further analysis reveals that human influence including GHG forcing has substantially increased the probability of occurrence of the 2020-like extreme events, which are rare in aerosol-only or natural-only forcing. The frequencies of 2020-like low SIE increase by 19 times with all forcing and 16 times with GHG forcing than with natural forcing. Future climate simulations under different Shared Socioeconomic Pathway (SSP) scenarios of SSP126, SSP245 and SSP585 show that the 2020-like extreme event that is currently considered rare is projected to become the norm and almost occur 1-in-1 year beyond 2041–2060. The probabilities will be approximately in the range of 0.84–1.00 for SIE and 0.76–0.99 for TAS from low emission of SSP126 to high emission of SSP585.

Perceptions of Halal-friendly attributes: a quantitative study of tourists’ intention to travel non-Islamic destination

PurposeThailand's Halal-friendly destination attributes are the focus of this study. The purpose of this study is to investigate the impact of Halal-friendly attributes on tourists’ intentions to visit non-Muslim destinations. Also, this study analyzes the impact of Muslim tourists’ attitudes on Halal-friendly characteristics and their intentions to visit countries that are not predominantly Muslim.Design/methodology/approachTo accomplish objectives, a survey was used to gather the data by using a convenient non-probability sampling approach from Muslim-majority countries Malaysia, Indonesia and Brunei. A proposed structural equation model was used to assess the influence of the variables above on the outcomes.FindingsThis study found that the factors of Halal-friendly facilities and Halal-friendly service positively influence the visiting intentions of Indonesian, Malaysian and Brunei Muslim tourists. In contrast, the social environment, food/beverages and local and staff attributes do not influence visit intentions. The tourist attitude mediates the relationship between the intention to visit and three dimensions of Halal-friendly attributes (social environment; food/beverage; and locals and staff).Research limitations/implicationsThis study did not investigate different kinds of dimensions for Muslim tourists visiting Thailand, such as preferences regarding personnel and families, cultural and artistic characteristics and the attributes of events and festivals. These dimensions cover a wide range of Halal-friendly tourism aspects. Second, this research did not investigate the associations between the demographics of Muslim tourists (age, gender, education level and income level) and the model variables. Such an investigation could have helped business developers and marketers in Thailand develop an efficient strategy for target marketing. The results of this research provide fundamental guidance and information for tourist enterprises in non-Muslim nations on how to enhance Muslim travellers’ images and intentions. However, because of the influence of other variables, locations in various areas of Asia, Africa and Europe may exhibit somewhat different effective qualities (e.g. environment, culture, history, etc.).Practical implicationsThis study sheds light on the factors contributing to visitor satisfaction in the context of a desire to go to a Halal-friendly site. This insight from this study can be adapted and applied by tourism managers and marketing executives to build or develop the Halal-friendly destination image of a tourist destination (Phuket) in the right dimensions. It is a possibility that the image of a tourist attraction has a tight relationship with travelers’ perceived value, contentment and loyalty (Hsu et al., 2008; Khunrattanaporn, 2013; Pike, 2008). Battour et al. (2022) found that Muslims’ behavioural intentions are affected by the Halal attributes of the destination. It indicates that Halal attributes are very important for Muslim tourists when selecting a destination. To attract Muslim tourists, this study can be used for strategies and planning.Originality/valueThis study sheds fresh light on the concept of Halal tourism. It examines the perspective of Muslim tourists using Halal goods or services in anticipation of a future trip to a non-Muslim location (Phuket). This study is crucial for destination tourism operators, managers and marketers who lead Muslim tourists to non-Muslim destinations.

A New GFSv15 With FV3 Dynamical Core Based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability

AbstractNOAA Climate Prediction Center (CPC) has generated a 100‐member ensemble of atmospheric model simulations from 1979 to present using the Global Forecast System version 15 (GFSv15) with FV3 dynamical core. The intent of this study is to document a development in an infrastructure capability with a focus to demonstrate the quality of these new simulations is on par with the previous GFSv2 Atmospheric Model Intercomparison Project simulations. These simulations are part of CPC's efforts to attribute observed seasonal climate variability to sea surface temperature (SST) forcings and get updated once a month by available observed SST. The performance of these simulations in replicating observed climate variability and trends, together with an assessment of climate predictability and the attribution of some climate events is documented. A particular focus of the analysis is on the US climate trend, Northern Hemisphere winter height variability, US climate response to three strong El Niño events, the analysis of signal to noise ratio, the anomaly correlation for seasonal climate anomalies, and the South Asian flooding of 2022 summer, and thereby samples wide aspects that are important for attributing climate variability. Results indicate that the new model can realistically reproduce observed climate variability, trends, and extreme events, better capturing the US climate response to extreme El Niño events and the 2022 summer South Asian record‐breaking flooding than GFSv2. The new model also shows an improvement in the wintertime simulation fidelity of US surface climate, mainly confined in the Northern and Southeastern US for precipitation and in the east for temperature.

Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

A hyper-knowledge graph system for research on AI ethics cases

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.

Studying the Connections between Different Variants of Causal Explanations in the Attributional Style

This paper examined the features of the relationship between several causal explanations differentiated by the personalization effect: opportunity, reinforcement, influence of others, and chance circumstances. Two groups of subjects took part in the event, 244 and 270 people each, respectively. The subjects were asked to fill out a specially designed differential questionnaire and the attributional style and USK questionnaires. In the questionnaire, test takers had to evaluate a list of reasons about the measurements: constancy and globality. To study the structure of connections, the Principal component method and correlation analysis are used. It has been established that all the analyzed causes of negative events are combined into one factor, and the attribution of events into two: a group of internal causes (abilities and efforts) and a group of external consequences (others and accidents). There were also significant correlations between assessments of globality and stability, both internal and external attributions, with optimism. The results indicate the mixed role of externalities for optimism and pessimism.

Possible role of anthropogenic climate change in the record-breaking 2020 Lake Victoria levels and floods

Abstract. Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture, and infrastructure in the riparian countries of Uganda, Kenya, and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding by applying a multi-model extreme event attribution methodology. Using remote-sensing-based flood mapping tools, we find that more than 29 000 people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least 3 decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8–15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0–14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in eastern Africa and the African Great Lakes region.

ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

BackgroundInteraction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research.ResultsTo address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis.ConclusionsORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events.

Therapeutic strategies and outcomes in neuropsychiatric systemic lupus erythematosus: an international multicentre retrospective study.

The management of neuropsychiatric systemic lupus erythematosus (NPSLE) poses considerable challenges due to limited clinical trials. Therapeutic decisions are customized based on suspected pathogenic mechanisms and symptoms severity. This study aimed to investigate therapeutic strategies and disease outcome for patients with NPSLE experiencing their first neuropsychiatric (NP) manifestation. This retrospective cohort study defined NP events according to the American College of Rheumatology case definition, categorizing them into three clusters: central/diffuse, central/focal and peripheral. Clinical judgment and a validated attribution algorithm were used for NP event attribution. Data included demographic variables, SLE disease activity index, cumulative organ damage, and NP manifestation treatments. The clinical outcome of all NP events was determined by a physician seven-point Likert scale. Predictors of clinical improvement/resolution were investigated in a multivariable logistic regression analysis. The analysis included 350 events. Immunosuppressants and corticosteroids were more frequently initiated/escalated for SLE-attributed central diffuse or focal NP manifestations. At 12 months of follow-up, 64% of patients showed a clinical improvement in NP manifestations. Focal central events and SLE-attributed manifestations correlated with higher rates of clinical improvement. Patients with NP manifestations attributed to SLE according to clinical judgment and treated with immunosuppressants had a significantly higher probability of achieving clinical response (OR 2.55, 95%CI 1.06-6.41, P = 0.04). Age at diagnosis and focal central events emerged as additional response predictors. NP manifestations attributed to SLE by clinical judgment and treated with immunosuppressants demonstrated improved 12-month outcomes. This underscores the importance of accurate attribution and timely diagnosis of NPSLE.

Clinical and translational attributes of immune-related adverse events.

With immune checkpoint inhibitors (ICIs) becoming the mainstay of treatment for many cancers, managing their immune-related adverse events (irAEs) has become an important part of oncological care. This Review covers the clinical presentation of irAEs and crucial aspects of reversibility, fatality and long-term sequelae, with special attention to irAEs in specific patient populations, such as those with autoimmune diseases. In addition, the genetic basis of irAEs, along with cellular and humoral responses to ICI therapy, are discussed. Detrimental effects of empirically used high-dose steroids and second-line immunosuppression, including impaired ICI effectiveness, call for more tailored irAE-treatment strategies. We discuss open therapeutic challenges and propose potential avenues to accelerate personalized management strategies and optimize outcomes.

Arctic marine heatwaves forced by greenhouse gases and triggered by abrupt sea-ice melt

Since 2007, unprecedented marine heatwave events are occurring over the Arctic Ocean. Here we identify the fraction of the likelihood of Arctic marine heatwaves magnitude that is attributable to greenhouse gas forcing. Results reveal that Arctic marine heatwaves are primarily triggered by an abrupt sea-ice retreat, which coincides with the maximum downward radiative fluxes. Up to 82% of the sea surface temperature variability over the shallow Arctic marginal seas, where marine heatwaves are prone to occur, can be explained by net accumulation of seasonal surface heat flux in the ocean. Event attribution analysis demonstrates that the 103-day long 2020 event – the most intense (4 ∘C) recorded so far in the Arctic – would be exceptionally unlikely in the absence of greenhouse gas forcing in terms of both intensity and duration. Our further results imply that if greenhouse gas emissions continue to rise, along with the expansion of first-year ice extent, moderate marine heatwaves in the Arctic will very likely persistently reoccur.

Text2EL + : Expert Guided Event Log Enrichment using Unstructured Text

Through the application of process mining, business processes can be improved on the basis of process execution data captured in event logs. Naturally, the quality of this data determines the quality of the improvement recommendations. Improving data quality is non-trivial and there is great potential to exploit unstructured text, e.g. from notes, reviews, and comments, for this purpose and to enrich event logs. To this end, this paper introduces Text2EL + a three-phase approach to enrich event logs using unstructured text. In its first phase, events and (case and event) attributes are derived from unstructured text linked to organisational processes. In its second phase, these events and attributes undergo a semantic and contextual validation before their incorporation in the event log. In its third and final phase, recognising the importance of human domain expertise, expert guidance is used to further improve data quality by removing redundant and irrelevant events. Expert input is used to train a Named Entity Recognition (NER) model with customised tags to detect event log elements. The approach applies natural language processing techniques, sentence embeddings, training pipelines and models, as well as contextual and expression validation. Various unstructured clinical notes associated with a healthcare case study were analysed and completeness, concordance, and correctness of the derived event log elements were evaluated through experiments. The results show that the proposed method is feasible and applicable.

Trail Runners’ Consumer Profiles: The Landscape Seekers, Sport Devotees, and Eco-Group Travellers

This study aims to segment trail runners employing psychographic segmentation, specifically sport commitment, as an understudied variable in sport tourism. The purpose is to identify specific trail runner profiles. An online survey was made available via Facebook through sponsored advertisements in South Africa. The final sample consisted of n=412 trail runners. Three distinct segments were identified using cluster analysis: landscape seekers, sport devotees, and eco-group travelers. These segments differed significantly regarding the factors influencing their sport commitment, with cluster membership influenced by demographic characteristics, behavioral intentions, and sought-after event attributes. To foster the expansion of trail running events in South Africa, it is imperative to concentrate efforts on enhancing accessibility through diverse routes across varied terrains, implementing comprehensive training programs catering to runners of all levels, and deploying strategic marketing initiatives to raise awareness and participation.

Integrating Climatological‐Hydrodynamic Modeling and Paleohurricane Records to Assess Storm Surge Risk

AbstractSediment cores from blue holes have emerged as a promising tool for extending the record of long‐term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological‐hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias‐correct the climatological‐hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500‐year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias‐corrected climatological‐hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500‐year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2‐4.5 and SSP5‐8.5, respectively by the end of the 21st century.

Buffering Effect of Atmosphere–Ocean Coupling on Intensity Changes of Tropical Cyclones Under a Changing Climate

AbstractThe effect of atmosphere–ocean coupling on intensity changes of tropical cyclones (TCs) under global warming was examined using a regional high‐resolution three‐dimensional atmosphere–ocean coupled model. A storyline event attribution approach was applied to four historical intense TCs in the western North Pacific. The results indicate that atmosphere–ocean coupling buffers TC intensification as global warming progresses. This buffering effect increased as storms traveled northward. Moreover, the effect intensified as warming progressed, because reductions in sea surface temperature induced by the storm increased as the storm strengthened in future warmer climates. The magnitude of the buffering effect depended on the storm's size and translation speed; a large, slow‐moving storm had significant resilience against global warming, whereas a compact, fast‐moving storm was sensitive to global warming. A high‐resolution atmosphere–ocean coupled model is important for more reliable future projections of TC intensity under the changing climate.

Challenges in the attribution of river flood events

AbstractAdvances in the field of extreme event attribution allow to estimate how anthropogenic global warming affects the odds of individual climate disasters, such as river floods. Extreme event attribution typically uses precipitation as proxy for flooding. However, hydrological processes and antecedent conditions make the relation between precipitation and floods highly nonlinear. In addition, hydrology acknowledges that changes in floods can be strongly driven by changes in land‐cover and by other human interventions in the hydrological system, such as irrigation and construction of dams. These drivers can either amplify, dampen or outweigh the effect of climate change on local flood occurrence. Neglecting these processes and drivers can lead to incorrect flood attribution. Including flooding explicitly, that is, using data and models of hydrology and hydrodynamics that can represent the relevant hydrological processes, will lead to more robust event attribution, and will account for the role of other drivers beyond climate change. Existing attempts are incomplete. We argue that the existing probabilistic framework for extreme event attribution can be extended to explicitly include floods for near‐natural cases, where flood occurrence was unlikely to be influenced by land‐cover change and human hydrological interventions. However, for the many cases where this assumption is not valid, a multi‐driver framework for conditional event attribution needs to be established. Explicit flood attribution will have to grapple with uncertainties from lack of observations and compounding from the many processes involved. Further, it requires collaboration between climatologists and hydrologists, and promises to better address the needs of flood risk management.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Paleoclimates and Current Trends > Detection and Attribution Assessing Impacts of Climate Change > Observed Impacts of Climate Change

Ukrainian Vytynanka Art: Historical Perspective and Modernity

The purpose of the research is to highlight of the vytynanka art as an integral component of traditional Ukrainian culture. The research methodology is based on the application of a systemic approach and includes the following methods: analytical, comparative-historical, art history, cultural for comprehensive reflection of the outlined issues and the formulation of substantiated conclusions. The scientific novelty consists in an attempt to comprehensively consider the art of vytynanka in historical retrospect; the periodisation of traditional Ukrainian vytynanka art was presented; the main trends of the folk vytynanka development are defined; the types of paper ornaments as well as the technique of their manufacture, their typology and motifs of ornaments, tools and materials for the manufacture are revealed. Conclusions. Ukrainian vytynanka is a traditional type of decoration characteristic of Ukrainian culture. This art consists in creating patterns and images by cutting motifs out of paper. Vytynanka has deep historical and symbolic roots, associated with Ukrainian national identity. The main elements of Ukrainian vytynanka are geometric shapes, flowers, animals, birds, symbols and motifs from nature. The ears of corn, the sun, roosters and other symbols are popular motifs, because they have a deep cultural and symbolic meaning. It is often associated with holidays, especially spring and summer, when decorations from vytynanka are used to decorate houses and attributes of festive events. Thus, Ukrainian vytynanka is a beautiful form of decorative art, as well as the important symbol of Ukrainian culture and national identity, passing on heritage and traditions from generation to generation.
 Keywords: Ukrainian vytynanka, traditional culture, symbols, ornaments.

Learning from User-driven Events to Generate Automation Sequences

Enabling smart devices to learn automating actions as expected is a crucial yet challenging task. The traditional Trigger-Action rule approach for device automation is prone to ambiguity in complex scenarios. To address this issue, we propose a data-driven approach that leverages recorded user-driven event sequences to predict potential actions users may take and generate fine-grained device automation sequences. Our key intuition is that user-driven event sequences, like human-written articles and programs, are governed by consistent semantic contexts and contain regularities that can be modeled to generate sequences that express the user's preferences. We introduce ASGen, a deep learning framework that combines sequential information, event attributes, and external knowledge to form the event representation and output sequences of arbitrary length to facilitate automation. To evaluate our approach from both quantitative and qualitative perspectives, we conduct two studies using a realistic dataset containing over 4.4 million events. Our results show that our approach surpasses other methods by providing more accurate recommendations. And the automation sequences generated by our model are perceived as equally or even more rational and useful compared to those generated by humans.

Decadal to centennial extreme precipitation disaster gaps — Long-term variability and implications for extreme value modelling

A material consequence of climate change is the intensification of extreme precipitation in most regions across the globe. The respective trend signal is already detectable at global to regional scales, but long-term variability still dominates local observational records, which are the basis for extreme precipitation risk assessment. Whether the frequency of extreme events is purely random or subject to a low-frequency internal variability forcing is therefore highly relevant for modelling the expected number of extreme events in a typical observational record. Based on millennial climate simulations, we show that long-term variability is largely random, with no clear indication of low-frequency decadal to multidecadal variability. Nevertheless, extreme precipitation events occur highly irregularly, with potential clustering (11% probability of five or more 100-year events in 250 years) or long disaster gaps with no events (8% probability for no 100-year events in 250 years). Even for decadal precipitation records, a complete absence of any tail events is not unlikely, as, for example, in typical 70-year observational or reanalysis data, the probability is almost 50%. This generally causes return levels – a key metric for infrastructure codes or insurance pricing – to be underestimated. We also evaluate the potential of employing information across neighbouring locations, which substantially improves the estimation of return levels by increasing the robustness against potential adverse effects of long-term internal variability. The irregular occurrence of events makes it challenging to estimate return periods for planning and for extreme event attribution.