English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Early prediction of extreme stratospheric polar vortex states based on causal precursors

Kretschmer, M., Runge, J., Coumou, D. (2017): Early prediction of extreme stratospheric polar vortex states based on causal precursors. - Geophysical Research Letters, 44, 16, 8592-8600.
https://doi.org/10.1002/2017GL074696

Item is

Files

show Files
hide Files
:
7725oa.pdf (Publisher version), 937KB
Name:
7725oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Kretschmer, Marlene1, Author              
Runge, J.2, Author
Coumou, Dim1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low‐frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response‐guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r 2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1–15 (16–30) days with false‐alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long‐lead predictions.

Details

show
hide
Language(s):
 Dates: 2017
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/2017GL074696
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7725
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Model / method: Machine Learning
Organisational keyword: RD1 - Earth System Analysis
Regional keyword: Europe
Regional keyword: North America
Working Group: Earth System Modes of Operation
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Geophysical Research Letters
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 44 (16) Sequence Number: - Start / End Page: 8592 - 8600 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals182