English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Complex systems approaches for Earth system data analysis

Boers, N., Kurths, J., Marwan, N. (2021): Complex systems approaches for Earth system data analysis. - Journal of Physics: Complexity, 2, 1, 011001.
https://doi.org/10.1088/2632-072X/abd8db

Item is

Basic

show hide
Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_24999 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_24999_6
Genre: Journal Article

Files

show Files
hide Files
:
24999oa.pdf (Publisher version), 3MB
Name:
24999oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Boers, Niklas1, Author              
Kurths, Jürgen1, Author              
Marwan, Norbert1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: Time series analysis; Machine Learning
 Abstract: Complex systems can, to a first approximation, be characterized by the fact that their dynamics emerging at the macroscopic level cannot be easily explained from the microscopic dynamics of the individual constituents of the system. This property of complex systems can be identified in virtually all natural systems surrounding us, but also in many social, economic, and technological systems. The defining characteristics of complex systems imply that their dynamics can often only be captured from the analysis of simulated or observed data. Here, we summarize recent advances in nonlinear data analysis of both simulated and real-world complex systems, with a focus on recurrence analysis for the investigation of individual or small sets of time series, and complex networks for the analysis of possibly very large, spatiotemporal datasets. We review and explain the recent success of these two key concepts of complexity science with an emphasis on applications for the analysis of geoscientific and in particular (palaeo-) climate data. In particular, we present several prominent examples where challenging problems in Earth system and climate science have been successfully addressed using recurrence analysis and complex networks. We outline several open questions for future lines of research in the direction of data-based complex system analysis, again with a focus on applications in the Earth sciences, and suggest possible combinations with suitable machine learning approaches. Beyond Earth system analysis, these methods have proven valuable also in many other scientific disciplines, such as neuroscience, physiology, epidemics, or engineering.

Details

show
hide
Language(s):
 Dates: 2020-12-172021-01-062021-04-08
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Extremes
Research topic keyword: Paleoclimate
Organisational keyword: RD4 - Complexity Science
DOI: 10.1088/2632-072X/abd8db
OATYPE: Gold Open Access
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Physics: Complexity
Source Genre: Journal, other, oa
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 2 (1) Sequence Number: 011001 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-physics-complexity
Publisher: IOP Publishing