English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Deep Learning for Bias-Correcting CMIP6-Class Earth System Models

Hess, P., Lange, S., Schötz, C., Boers, N. (2023): Deep Learning for Bias-Correcting CMIP6-Class Earth System Models. - Earth's Future, 11, 10, e2023EF004002.
https://doi.org/10.1029/2023EF004002

Item is

Files

show Files
hide Files
:
Hess_2023_2301.01253.pdf (Preprint), 10MB
Name:
Hess_2023_2301.01253.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
:
28779oa.pdf (Publisher version), 8MB
Name:
28779oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Hess, Philipp1, Author              
Lange, Stefan1, Author              
Schötz, Christof1, Author              
Boers, Niklas1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

Details

show
hide
Language(s): eng - English
 Dates: 2023-09-102023-10-132023-10-13
 Publication Status: Finally published
 Pages: 17
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: yes - 3482
DOI: 10.1029/2023EF004002
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
PIKDOMAIN: RD3 - Transformation Pathways
Organisational keyword: RD3 - Transformation Pathways
Regional keyword: Global
Model / method: Machine Learning
OATYPE: Gold Open Access
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Earth's Future
Source Genre: Journal, SCI, Scopus, p3, oa
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 11 (10) Sequence Number: e2023EF004002 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/170925
Publisher: Wiley
Publisher: American Geophysical Union (AGU)