@article{Sillmann-2017-Understanding,,
title = "Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities",
author = "Sillmann, Jana and
Thorarinsdottir, Thordis L. and
Keenlyside, Noel and
Schaller, Nathalie and
Alexander, Lisa V. and
Hegerl, Gabriele C. and
Seneviratne, Sonia I. and
Vautard, Robert and
Zhang, Xuebin and
Zwiers, Francis W.",
journal = "Weather and Climate Extremes, Volume 18",
volume = "18",
year = "2017",
publisher = "Elsevier BV",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G17-34001",
doi = "10.1016/j.wace.2017.10.003",
pages = "65--74",
abstract = "Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="Sillmann-2017-Understanding">
<titleInfo>
<title>Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jana</namePart>
<namePart type="family">Sillmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thordis</namePart>
<namePart type="given">L</namePart>
<namePart type="family">Thorarinsdottir</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Noel</namePart>
<namePart type="family">Keenlyside</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathalie</namePart>
<namePart type="family">Schaller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lisa</namePart>
<namePart type="given">V</namePart>
<namePart type="family">Alexander</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gabriele</namePart>
<namePart type="given">C</namePart>
<namePart type="family">Hegerl</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sonia</namePart>
<namePart type="given">I</namePart>
<namePart type="family">Seneviratne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Robert</namePart>
<namePart type="family">Vautard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuebin</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Francis</namePart>
<namePart type="given">W</namePart>
<namePart type="family">Zwiers</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<genre authority="bibutilsgt">journal article</genre>
<relatedItem type="host">
<titleInfo>
<title>Weather and Climate Extremes, Volume 18</title>
</titleInfo>
<originInfo>
<issuance>continuing</issuance>
<publisher>Elsevier BV</publisher>
</originInfo>
<genre authority="marcgt">periodical</genre>
<genre authority="bibutilsgt">academic journal</genre>
</relatedItem>
<abstract>Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.</abstract>
<identifier type="citekey">Sillmann-2017-Understanding</identifier>
<identifier type="doi">10.1016/j.wace.2017.10.003</identifier>
<location>
<url>https://gwf-uwaterloo.github.io/gwf-publications/G17-34001</url>
</location>
<part>
<date>2017</date>
<detail type="volume"><number>18</number></detail>
<extent unit="page">
<start>65</start>
<end>74</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities
%A Sillmann, Jana
%A Thorarinsdottir, Thordis L.
%A Keenlyside, Noel
%A Schaller, Nathalie
%A Alexander, Lisa V.
%A Hegerl, Gabriele C.
%A Seneviratne, Sonia I.
%A Vautard, Robert
%A Zhang, Xuebin
%A Zwiers, Francis W.
%J Weather and Climate Extremes, Volume 18
%D 2017
%V 18
%I Elsevier BV
%F Sillmann-2017-Understanding
%X Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.
%R 10.1016/j.wace.2017.10.003
%U https://gwf-uwaterloo.github.io/gwf-publications/G17-34001
%U https://doi.org/10.1016/j.wace.2017.10.003
%P 65-74
Markdown (Informal)
[Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities](https://gwf-uwaterloo.github.io/gwf-publications/G17-34001) (Sillmann et al., GWF 2017)
ACL
- Jana Sillmann, Thordis L. Thorarinsdottir, Noel Keenlyside, Nathalie Schaller, Lisa V. Alexander, Gabriele C. Hegerl, Sonia I. Seneviratne, Robert Vautard, Xuebin Zhang, and Francis W. Zwiers. 2017. Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities. Weather and Climate Extremes, Volume 18, 18:65–74.
