Types Of Extreme Events Research Articles

The ocean losing its breath under the heatwaves

The world’s oceans are under threat from the prevalence of heatwaves caused by climate change. Despite this, there is a lack of understanding regarding their impact on seawater oxygen levels - a crucial element in sustaining biological survival. Here, we find that heatwaves can trigger low-oxygen extreme events, thereby amplifying the signal of deoxygenation. By utilizing in situ observations and state-of-the-art climate model simulations, we provide a global assessment of the relationship between the two types of extreme events in the surface ocean (0–10 m). Our results show compelling evidence of a remarkable surge in the co-occurrence of marine heatwaves and low-oxygen extreme events. Hotspots of these concurrent stressors are identified in the study, indicating that this intensification is more pronounced in high-biomass regions than in those with relatively low biomass. The rise in the compound events is primarily attributable to long-term warming primarily induced by anthropogenic forcing, in tandem with natural internal variability modulating their spatial distribution. Our findings suggest the ocean is losing its breath under the influence of heatwaves, potentially experiencing more severe damage than previously anticipated.

Enhancing Regional Climate Downscaling through Advances in Machine Learning

Abstract Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections. Significance Statement This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.

Experience with extreme weather events increases willingness-to-pay for climate mitigation policy

We explore how extreme event experience relates to climate policy support in the U.S. We add three important yet uncommon elements to this field: we verify self-reports of extreme event experience with actual weather data; we use a willingness-to-pay measure to assess behavioral intention; and we analyze which types of extreme events have stronger impacts on WTP. People who self-report extreme weather events are willing to pay approximately $112/year more for climate mitigation policy than those who do not; people for whom those self-reports match recorded data are willing to pay $106 or $71 more (controlling for climate beliefs and political ideology and depending on how unverified reporters are treated). Wildfires have the strongest influence on WTP. Though our results show that political ideology correlates more strongly with policy support than does extreme event experience, extreme event experience exhibits a robust correlation with policy support, and could result in a minimum of billions of dollars of support annually for clean-energy policy alone.

Trends In Extreme Weather Events With Socio-Economic Damage Over The Period 1991-2019 In Russia And Its Regions

Increased number of extreme weather events is one of the most serious hazards of climate change over the territory of Russia. However, there is a lack of comprehensive analysis of the number of extreme weather events that caused social and economic damage in the country and its regions. This paper analyzes changes in the total number of events with damage (meteorological for the period 1991-2019 and agrometeorological – for 2004-2019), disaggregated by their types and by regions. The Mann-Kendall test is applied to detect statistical significance (0.05 level of significance, normal distribution). The results show an increase in the number of meteorological extreme events with damage for 1990-2019 in Russia from 130 to 257 events per year on average for the 1990s and 2010s, respectively, while the proportion of events with damage in relation to the total number of extreme events decreased over this period. We found statistically significant trends only for a few types of extreme events: hot and cold temperature, strong wind, heavy rain and droughts (increase by 0.9, 9.4, 11.4, 25.9 and 13.3 events/10 years, respectively). Number of heavy rain precipitation events is the only unidirectional stable growth trend. Unusual increasing trend in cold extreme events with damage in Russia can be attributed to the greater damage to the economy and population from cold extremes than hot ones. The regional distribution of trends across the territory of the Russian Federation is heterogeneous. However, significant changes in the number of extreme events of strong winds, heavy rains and soil drought by regions are statistically positive and observed mostly in some southern and central regions of European part and the Western Siberia. The development of adaptation plans to the negative effects of climate change is a first priority for these regions. A system for monitoring economic and non-economic damage from extreme events must be developed in Russia.

Amazon climate extremes: Increasing droughts and floods in Brazil’s state of Acre

The intensification of extreme climate events is already a reality throughout the world. In the Brazilian Amazon, the most frequent extreme events are linked to droughts and floods. This study expanded the documentation on extreme events of floods, water crisis, fires and forest fires in the state of Acre, in the southwestern Brazilian Amazon. We analyzed extreme weather events in state and municipal state-of-emergency and public-calamity decrees, reports of people who faced the impacts of these events, scientific articles, and press reports. The results show that the state of Acre recorded 202 extreme events between 1987 and 2023, with an increasing trend in the number and occurrence of various types of extreme events in the same year since 2010. Twenty-one state-of-emergency and public-calamity decrees were issued, with flood events being the most frequent. The cities of Rio Branco and Cruzeiro do Sul recorded 14 and 21 extreme events, respectively, or approximately one event every two years. These data show the urgency of implementing actions to adapt to climate extremes. Starting in 2005, the annual results indicate an increase in municipalities (counties) experiencing more than one type of extreme event, pointing to the need for effective public policies for adaptation and mitigation in the state of Acre.

Record-Breaking heavy rainfall around Henan Province in 2021 and future projection of extreme conditions under climate change

A record-breaking extreme precipitation event that occurred in July 2021 over Henan Province is investigated through observations and reanalysis data. Meanwhile, future extreme precipitation conditions are projected through an ensemble projection model using climate model outputs as input. The long duration, the wide range of influence, the extensive amount of accumulated precipitation, and the high intensity of precipitation are the four major characteristics of this event. Further, the physical causes of this event are explored. First of all, the abnormal northward movement of the western North Pacific Subtropical High is quite significant. Secondly, two slow-moving typhoons are found, which intensify inland moisture transport. Thirdly, after encountering the windward slope, the water vapor is violently lifted and continuously accumulated. Future projections of multiple extreme indices show that more extreme precipitation conditions are expected across the entire Henan Province. In addition, the contribution of various sources to the uncertainty of the ensemble projection are quantified. The global climate model is the primary contributing factor. Meanwhile, the effects explained by the interactive effects between global climate models and shared social-economic pathways cannot be neglected. Our study has constructed an analysis framework called the “Four-in-One” (spatial–temporal feature analysis, causal analysis, future projection, and uncertainty quantification). It provides further insight into changes in local extreme precipitation conditions. The overall framework of this study is equally applicable to investigating other types of extreme events.

ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)

Abstract. We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events.

Economic incentives for coastal homeowner adaptation to climate change

ABSTRACT Communities are already grappling with climate change’s acute effects, evidenced by the growing frequency and intensity of extreme events worldwide. Strategies to encourage adaptation to climate change are urgently needed, particularly to preempt common ineffective and maladaptive responses. The United States provides a notable case study for testing the potential for economic incentives to drive voluntary adaptation in vulnerable coastal communities where mandates through building codes have proven insufficient to limit economic losses. This paper analyzes a novel survey of 662 coastal households in the hurricane-exposed state of North Carolina. Our findings suggest that homeowners who believe adaptation measures increase the market value of their homes are more likely to have homes with these upgrades. Furthermore, they are more likely to have taken actions to upgrade their homes after purchase and to express stronger intentions to invest in future upgrades. While perceived affordability plays a role in their upgrading actions, it seems unrelated to future intentions. Uptake, or intended uptake, of climate adaptation measures by coastal homeowners is not driven by the perceived efficacy in preventing future losses, challenging a tactic commonly used in policy messaging. Instead, reducing climate-related hurricane losses requires a greater valuation of climate adaptation measures in real estate markets. The need to elevate market value over efficacy in governmental and non-governmental efforts to promote adaptation may be relevant to adaptation for other types of extreme events or in other locations globally where real estate is a prized investment with the potential for significant returns.

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

AbstractExtreme weather events have significant consequences, dominating the impact of climate on society. While high‐resolution weather models can forecast many types of extreme events on synoptic timescales, long‐term climatological risk assessment is an altogether different problem. A once‐in‐a‐century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low‐resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium‐range Weather Forecasting archived in the subseasonal‐to‐seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi‐centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis‐derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.

Response of summer maize growth to drought-flood abrupt alternation

Extreme events normally have negative effects on crop growth. Many studies have reported findings on drought and flood events, while only sparse studies have focused on new types of extreme events, such as drought-flood abrupt alternation (DFAA). We attempted to gain an insight on the effects of DFAA over two-year field experiment on biomass, grain yield and quality, then simulated the yield loss to DFAA in history and future in summer maize planting area in the Northern Anhui Plain. Results show that DFAA significantly reduced root biomass and shoot biomass by 77.1% and 60.1% compared with that in the control systems. The negative effect lasted until mature stage. The grain yield loss was 14.1%–38.4% in different DFAA treatments. The numerical simulation reveals that the average annual yield loss due to DFAA has been increasing in the Northern Anhui Plain, with 21.19%–30.98% during 1964–2017, 14.10%–33.40% during 2020–2050. The spatial distribution of yield loss changed as well. This study increases our knowledge of the effects of DFAA on crop production and highlights the need to consider the targeted countermeasures.

A modified extreme event-based synchronicity measure for climate time series.

Extreme event-based synchronicity is a specific measure of similarity of extreme event-like time series. It is capable to capture the nonlinear interactions between climatic extreme events. In this study, we proposed a modified extreme event-based synchronicity measure that incorporates two types of extreme events (positive and negative) simultaneously in climate anomalies to characterize the synchronization and time delays. Statistical significance of the modified extreme event synchronization measure is tested by Monte-Carlo simulations. The applications of the modified extreme event-based synchronicity measure on synthetic time series verified that it was superior to the traditional event synchronicity measure. Both synchronous and antisynchronous features between climate time series could be captured by the modified measure. It is potentially applied in investigating the interrelationship between climate extremes and climate index or constructing complex networks of climate variables. In addition, this modified extreme event-based synchronicity measure could be easily applied to other types of time series just by identifying the extreme events properly.

CZEXWED: The unified Czech extreme weather database

A crucial step in developing a strategy against natural hazards is the analysis of weather extremes in the past. Due to the multiplication of their impacts when occurring in a larger area, we strongly recommend not evaluating the extremes only at individual sites but assessing regional extreme weather events. The presented Czech Extreme Weather Database (CZEXWED) comprises six types of extreme events, namely, heat waves, cold waves, air temperature drops, windstorms, heavy precipitation events, and heavy snowfalls. To date, it covers the period 1961–2020. To minimize methodological differences in the process of evaluating various types of extreme weather events including compound events, we employed the weather extremity index (WEI), a universal indicator based on the evaluation of return periods of relevant variables. Each event is characterized not only by the WEI value but also by its spatial extent and duration.Heat and cold waves in Czechia generally reach higher WEI values than other types of extreme weather because they usually affect larger areas. The number and extremity of heat waves are increasing significantly, while the opposite may be true for cold waves and windstorms. Air temperature drops defined by declines in daily maximum air temperature are frequent in the warm half-year, but three of four top events occurred in January. Windstorms and heavy precipitation events prevailed in the cold and warm half-years, respectively, but weaker events of these types also occurred during the opposite season. A comparison of CZEXWED with event lists from the wider Central European region shows that Czech and Central European extreme events correspond well with each other.

Impact of climate change on extremes events in Morocco

Today, the increase in the evolution of certain types of extreme events in Morocco has the potential to produce impacts and damages on socio-economic sectors. An increase in heat and cold extremes manifested by strong heat waves and cold waves are clearly increased in terms of their intensity, frequency and duration. This required an in-depth study to understand the link between the two. So, this study under the following theme: “Impact of climate change on extremes events in Morocco” is devoted to studying the relationship between climate change and extreme events and the impact of one to the other, their occurrence and intensity; then it’s structured around one main phase, that aims to evaluate observed changes in climate extremes from 1984 to 2018 based on robust methods to estimate trends with Sen’s Slop test and their significance with Mann-Kendall test. The basic data obtained from 29 meteorological stations covering all climatic zones have passed quality control tests to ensure reliable detection of any changes. The results show that Morocco has experienced a significant increase in hot extremes and also in cold extremes almost generalized over our Country Morocco. As for rainfall extremes, the trends generally indicate no significant change.

Extreme event impacts on terrestrial and freshwater biota in the arctic: A synthesis of knowledge and opportunities

Extreme weather events are increasing in frequency and intensity across the Arctic, one of the planet’s most rapidly warming regions. Studies from southern latitudes have revealed that the ecological impacts of extreme events on living organisms can be severe and long-lasting, yet data and evidence from within the terrestrial Arctic biome appear underrepresented. By synthesizing a total of 48 research articles, published over the past 25 years, we highlight the occurrence of a wide variety of extreme events throughout the Arctic, with multiple and divergent impacts on local biota. Extreme event impacts were quantified using a myriad of approaches ranging from circumpolar modelling to fine-scale experimental studies. We also identified a research bias towards the quantification of impacts related to a few extreme event types in the same geographic location (e.g. rain-on-snow events in Svalbard). Moreover, research investigating extreme event impacts on the ecology of arthropods and especially freshwater biota were scant, highlighting important knowledge gaps. While current data allow for hypotheses development, many uncertainties about the long-term consequences of extreme events to Arctic ecosystems remain. To advance extreme event research in the terrestrial Arctic biome, we suggest that future studies i) objectively define what is extreme in terms of events and ecological impacts using long-term monitoring data, ii) move beyond single-impact studies and single spatial scales of observation by taking advantage of pan-Arctic science-based monitoring networks and iii) consider predictive and mechanistic modelling to estimate ecosystem-level impacts and recovery.

Measuring and improving community resilience: A fuzzy logic approach

Due to the increasing frequency of natural and man-made disasters, the scientific community has paid considerable attention to the concept of resilience engineering. On the other hand, authorities and decision-makers have been focusing their efforts on developing strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and the unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of a community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of complex data, accounting for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, three cases with different levels of data availability are performed to obtain a resilience curve and resilience index of two out of seven dimensions of the PEOPLES framework. When numerical data does not exist, descriptive data based on expert knowledge is used as input. Results show that the proposed methodology can cope with both numerical and descriptive input data with different uncertainty levels providing good estimates of resilience. The methodology can be used as a decision-support tool to assist decision-makers and stakeholders in assessing and improving their communities' resilience for future events, focusing on specific indicators that suffer from resilience deficiencies and need improvements.

Shock trace prediction by reduced models for a viscous stochastic Burgers equation.

Viscous shocks are a particular type of extreme event in nonlinear multiscale systems, and their representation requires small scales. Model reduction can thus play an essential role in reducing the computational cost for the prediction of shocks. Yet, reduced models typically aim to approximate large-scale dominating dynamics, which do not resolve the small scales by design. To resolve this representation barrier, we introduce a new qualitative characterization of the space-time locations of shocks, named the "shock trace," via a space-time indicator function based on an empirical resolution-adaptive threshold. Unlike exact shocks, the shock traces can be captured within the representation capacity of the large scales, thus facilitating the forecast of the timing and locations of the shocks utilizing reduced models. Within the context of a viscous stochastic Burgers equation, we show that a data-driven reduced model, in the form of nonlinear autoregression (NAR) time series models, can accurately predict the random shock traces, with relatively low rates of false predictions. Furthermore, the NAR model, which includes nonlinear closure terms to approximate the feedback from the small scales, significantly outperforms the corresponding Galerkin truncated model in the scenario of either noiseless or noisy observations. The results illustrate the importance of the data-driven closure terms in the NAR model, which account for the effects of the unresolved dynamics brought by nonlinear interactions.

Exploring Potential Links Between Co-occurring Coastal Terrestrial and Marine Heatwaves in Australia

Despite numerous studies examining terrestrial or marine heatwaves independently, little work has investigated potential associations between these two types of extreme events. Examination of a limited number of past events suggests that certain co-occurring terrestrial and marine heatwaves have common drivers. Co-occurring events may also interact via local land-sea interactions, thereby altering the likelihood of these events. This study explores possible links between adjacent coastal marine and terrestrial heatwaves around Australia using observation and reanalysis data. We find a significant increase in the number of terrestrial heatwave days in the presence of an adjacent co-occurring marine heatwave along the coastal belt of Australia. In most regions, this increase persists at least 150 km inland. This suggests that processes operating beyond the narrow coastal belt are important in most regions. We also show that synoptic conditions driving a terrestrial heatwave in three locations around Australia are conducive for warming the ocean, which would increase the likelihood of a marine heatwave occurring. However, ocean state must also be conducive to reach MHW conditions. Our findings suggest that co-occurring terrestrial and marine heatwaves co-occur more frequently than chance would dictate, and that large scale synoptics may be conducive to both coastal terrestrial and marine heatwaves.

The July 2019 European Heat Wave in a Warmer Climate: Storyline Scenarios with a Coupled Model Using Spectral Nudging

Abstract Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea surface temperature, and by the background climate. This separation of factors is exploited by the storyline approach in which an atmospheric model is nudged toward the observed dynamics using different climate boundary conditions to explore their influence. The storyline approach disregards uncertain climatic changes in the frequency and intensity of dynamical conditions, focusing instead on the thermodynamic influence of climate on extreme events. Here we demonstrate an advanced storyline approach that employs a coupled climate model (AWI-CM-1-1-MR) in which the large-scale free-troposphere dynamics are nudged toward ERA5 data. Five-member ensembles are run for present-day (2017–19), preindustrial, +2-K, and +4-K climates branching off from CMIP6 historical and scenario simulations of the same model. In contrast to previous studies, which employed atmosphere-only models, feedbacks between extreme events and the ocean and sea ice state, and the dependence of such feedbacks on the climate, are consistently simulated. Our setup is capable of reproducing observed anomalies of relevant unconstrained parameters, including near-surface temperature, cloud cover, soil moisture, sea surface temperature, and sea ice concentration. Focusing on the July 2019 European heat wave, we find that the strongest warming amplification expands from southern to central Europe over the course of the twenty-first century. The warming reaches up to 10 K in the 4-K-warmer climate, suggesting that an analogous event would entail peak temperatures around 50°C in central Europe. Significance Statement This work explores a new storyline method to determine the impact of climate change on specific recent extreme events. The observed evolution of the large-scale atmospheric circulation is imposed in a coupled climate model. Variations in climate parameters, including ocean temperatures and sea ice, are well reproduced. By varying the background climate, including CO2 concentrations, it is demonstrated how the July 2019 European heat wave could have evolved in preindustrial times and in warmer climates. For example, up to 10°C warmer peak temperatures could occur in central Europe in a 4°C warmer climate. The method should be explored for other types of extreme events and has the potential to make climate change more tangible and to inform adaptation measures.

Tracking heatwave extremes from an event perspective

Public perceptions of the risk posed by natural hazards and climate change are typically associated with extreme events. While there are studies focused on the detailed synergy for processes that lead to the event occurrence. Past literature working on long-term variations of global weather and climate extremes often studies the derived indices and applies further statistical models to data sampled from a fixed grid point or area. Our study, using extreme heatwave events as an example, proposes an alternative framework to track and analyze extremes from an event perspective to facilitate better climate risk communication.With a selected threshold of daily heatwave intensity and an objective spatial and temporal connectivity algorithm, we introduce an event-tracking method to automatically track all global heatwaves based on long-term reanalysis data. Two nearby but separate European heatwave events (Scandinavia and France) in 2003 are used to demonstrate the validity of our event-tracking method. We further highlight the genesis frequency, occurrence density, and tracks of all historical heatwave events over global land areas from 1979 to 2018. The contributions from heatwave intensity, the affected area, and temporal evolution to each extreme event are documented. With the retaining spatial and temporal evolutions of tracked heatwave events during their lifespans, the precursory and concurrent environmental conditions associated with the event can be further studied. The integrated size and scale measures of extreme heatwave events can be compared historically in a region and/or across different climate regimes. They also provide better information for the risk assessment on event-associated loss and damages, and can easily be extended to study compound hazards with intersections of different types of extreme events.

Identification of Extreme Wind Events Using a Weather Type Classification

The identification of extreme wind events and their driving forces are crucial to better integrating wind generation into the power system. Recent work related the occurrence of extreme wind events with some weather circulation patterns, enabling the identification of (i) wind power ramps and (ii) low-generation events as well as their intrinsic features, such as the intensity and time duration. Using Portugal as a case study, this work focuses on the application of a weather classification-type methodology to link the weather conditions with wind power generation, namely, the different types of extreme events. A long-term period is used to assess and characterize the changes in the occurrence of extreme weather events and corresponding intensity on wind power production. High variability is expected under cyclonic regimes, whereas low-generation events are most common in anticyclonic regimes. The results of the work provide significant insights regarding wind power production in Portugal, enabling an increase in its predictability.