Understanding climate phenomena with data-driven models
OPEN ACCESS
Loading...
Author / Producer
Date
2020-12
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
84
Pages / Article No.
46 - 56
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Understanding; Climate models; Machine learning; Data-driven models; Representation; Grasping
Organisational unit
03777 - Knutti, Reto / Knutti, Reto
09576 - Bresch, David Niklaus / Bresch, David Niklaus
Notes
Funding
167215 - Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts (SNF)