English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., Saynisch-Wagner, J. (2021): Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. - Nature Machine Intelligence, 3, 8, 667-674.
https://doi.org/10.1038/s42256-021-00374-3

Item is

Files

show Files
hide Files
:
Boers etal_AI_ESM_Perspective_UncorrectedProofs-2.pdf (Preprint), 2MB
 
File Permalink:
-
Name:
Boers etal_AI_ESM_Perspective_UncorrectedProofs-2.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-
:
25856.pdf (Publisher version), 2MB
 
File Permalink:
-
Name:
25856.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Irrgang, C.1, Author
Boers, Niklas2, Author              
Sonnewald, M.1, Author
Barnes, E. A.1, Author
Kadow, C.1, Author
Staneva, J.1, Author
Saynisch-Wagner, J.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth and predicting how it might change in the future under ongoing anthropogenic forcing. In recent years, however, artificial intelligence (AI) methods have been increasingly used to augment or even replace classical ESM tasks, raising hopes that AI could solve some of the grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI in Earth system and climate research, and propose a methodological transformation in which deep neural networks and ESMs are dismantled as individual approaches and reassembled as learning, self-validating and interpretable ESM–network hybrids. Following this path, we coin the term neural Earth system modelling. We examine the concurrent potential and pitfalls of neural Earth system modelling and discuss the open question of whether AI can infuse ESMs or even ultimately render them obsolete.

Details

show
hide
Language(s):
 Dates: 2021-07-122021-08-172021-08-17
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s42256-021-00374-3
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Organisational keyword: RD4 - Complexity Science
MDB-ID: No data to archive
Research topic keyword: Extremes
Research topic keyword: Nonlinear Dynamics
Regional keyword: Global
Model / method: Machine Learning
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Nature Machine Intelligence
Source Genre: Journal, SCI, Scopus
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 3 (8) Sequence Number: - Start / End Page: 667 - 674 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nature-machine-intelligence