Special issue of Atmospheric Science Letters on using novel data science methods
Marlene Kretschmer
Dear Colleagues,
We are excited to announce the special issue of Atmospheric Science Letters on using novel data science methods (e.g., machine learning) to evaluate extremes. We encourage you to submit your research here, if relevant, and we especially welcome submissions from early career scientists. Please find more details on the special issue, and how to submit, below.
Novel data science approaches to evaluate weather and climate extremes Regional extreme weather and climate events, such as droughts, excessive heat waves, or strong storms, can cause severe societal impacts and economic damage. These extreme events and their subsequent risks are projected to amplify with ongoing anthropogenic climate change. In recent years, several new approaches and methods have been introduced in climate science, which aim to better characterise, attribute, predict, and communicate extreme events and their associated impacts. Such research initiatives include, for instance, the use of data-driven machine learning algorithms to detect and quantify local and large-scale causal drivers of extreme events, statistical frameworks to describe and understand the co-occurrence of extremes both in space and time, new tools to map extreme events to their ecological and socio-economic impacts, and methods to partition and constrain the uncertainties related to projections of extreme events. Topics for this call for papers include but not restricted to:
Approaches to better understand high-impact weather and climate extremes Behaviour in a changing climate Contributions from early career scientists to highlight innovative and diverse perspectives on the emerging risks of extreme events in a warming world
Keywords: weather and climate extremes, compound extremes, extreme event attribution, data science, machine learning, causal inference, storylines, emergent constraints
Submission deadline: Wednesday, 31 July 2024 (papers will be published on a rolling basis).
For more information and to submit your paper: https://rmets.onlinelibrary.wiley.com/hub/journal/1530261x/call-for-papers/si-2021-000485
Please do not hesitate to reach out with any questions.
Best,
Marlene
On behalf of the guest editing team: Marlene Kretschmer (lead guest editor; Leipzig University), Aglaé Jézéquel (co-guest editor; Laboratoire de Météorologie Dynamique (LMD)), Zachary Labe (co-guest editor; Princeton University and NOAA Geophysical Fluid Dynamics Laboratory), and Danielle Touma (co-guest editor; University of Texas at Austin).
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Marlene
Kretschmer, Tenure Track Professor of Climate Causality
Leipzig Institute for Meteorology (LIM), Leipzig University