Estimating concurrent climate extremes: A conditional approach

Huang, Whitney K.; Monahan, Adam H.; Zwiers, Francis W.

doi:10.1016/j.wace.2021.100332

Cited by 16 publications

(7 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extreme events are either investigated using a univariate (Coles & Casson, 1998; Mishra & Singh, 2010) or a compound event framework (Ganguli et al., 2020; Konapala et al., 2020; Mukherjee & Mishra, 2021; Yaddanapudi & Mishra, 2022; Zscheischler et al., 2018). Several studies investigated the compound impact of multiple flood drivers, including storm surges and precipitation (Wahl et al., 2015; Wu et al., 2018), sea‐level rise, and fluvial flooding (Ganguli & Merz, 2019a, 2019b; Moftakhari et al., 2017), and wind‐precipitation events at a regional scale (Huang et al., 2021; Owen et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Compound Wind and Precipitation Extremes in Global Coastal Regions Under Climate Change

Yaddanapudi

Mishra

Huang

et al. 2022

Geophysical Research Letters

View full text Add to dashboard Cite

Compound wind and precipitation (CWP) extreme events often associated with tropical cyclones in coastal regions can trigger significant human and socio-economic losses. Examples of recent major coastal hazards (economic losses) along the Atlantic and Gulf coasts include Harvey ($136 billion), Maria ($98 billion), and Irma ($54 billion) (Smith, 2020). In 2018, tropical cyclone Jebi (Japan) resulted in $13 billion in insured losses (Swiss Re, 2020). Climate variability and global warming can significantly impact the evolution of extreme events (

show abstract

Section: Introductionmentioning

confidence: 99%

Compound Wind and Precipitation Extremes in Global Coastal Regions Under Climate Change

Yaddanapudi

Mishra

Huang

et al. 2022

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…Finally, the proposed model is fit to observations while neglecting spatial and temporal dependence. Modeling multidimensional extremes (as would be needed to explicitly account for spatial and/or temporal dependence) is an on-going challenge for the community (Huser and Wadsworth, 2020), where various techniques have been proposed, relying on hierarchical models (Gaetan and Grigoletto, 2007), copulas (Lee and Joe, 2018;Krupskii and Genton, 2021), and conditional modeling (Wadsworth and Tawn, 2019;Simpson and Wadsworth, 2021;Huang et al, 2021). Most of these methods ignore the bulk of the data or only focus on a single tail.…”

Section: Discussionmentioning

confidence: 99%

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

Krock¹,

Bessac²,

Stein³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails.To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

show abstract

“…For example, statistical weather generators, which are used to simulate realistic weather conditions, can -in principle -allow sampling unobserved extreme events and estimating the likelihood of very rare events, reducing issues related to the limited sample size of observations 76 . To meet the challenge of studying different aspects of compound events, new statistical methodologies of different levels of complexity are being developed 2,18,[81][82][83][84] . However, novel statistical tools need to be tested and evaluated, and the limited sample size of observational records is a constraining factor in this regard.…”

Section: Developing and Evaluating Statistical Tools For Compound Eve...mentioning

confidence: 99%

Advancing our understanding of compound weather and climate events via large ensemble model simulations

Bevacqua

Jézéquel²,

Suárez-Gutiérrez³

et al. 2022

Preprint

View full text Add to dashboard Cite

Most societally relevant weather impacts result from compound events, that is, rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, we illustrate that robust analyses of compound events – such as frequency and uncertainty analysis under present-day and future conditions, event attribution, exploration of low-probability-high-impact events – require very large sample sizes. In particular, the required sample is much larger than that needed for routinely considered univariate extremes. We demonstrate how large ensemble simulations from multiple climate models are crucial for advancing our understanding of compound events and for constructing robust projections. Importantly, this will avoid providing practitioners with potentially misleading information on climate risks.

show abstract

Estimating concurrent climate extremes: A conditional approach

Cited by 16 publications

References 67 publications

Compound Wind and Precipitation Extremes in Global Coastal Regions Under Climate Change

Compound Wind and Precipitation Extremes in Global Coastal Regions Under Climate Change

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

Advancing our understanding of compound weather and climate events via large ensemble model simulations

Contact Info

Product

Resources

About