Climate model pluralism beyond dynamical ensembles
Bibliographic Data
| ID | 12929485 |
|---|---|
| Authors | Fulvio Mazzocchi (0000-0002-4223-4847, Institute for Complex Systems), Antonello Pasini (0000-0001-7130-0762, Institute of Atmospheric Pollution Research, corresponding author) |
| Year | 2017 |
| Volume | 8 |
| Issue | 6 |
| Publication date | 2017-05-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Wiley Interdisciplinary Reviews Climate Change (JOURNAL) |
| Journal identifiers | ISSN: 1757-7780 • E-ISSN: 1757-7799 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/wcc.477 |
| OpenAlex | W2619826495 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
Using pluralist research strategies can be a profitable way to study complex systems. This contribution focuses on the approaches for studying the climate that make use of multiple different models, aiming to increase the reliability (in terms of robustness) of attribution results. This Opinion article argues that the traditional approach, which is based on ensemble runs of global climate models, only partially allows the application of a robustness scheme, owing to the difficulty to match or evaluate the conditions required for robustness (i.e., independence or heterogeneity among models). An alternative ‘multi‐approach’ strategy is advanced, beyond dynamical modeling but still preserving the idea of model pluralism. Such a strategy, which uses a set of ensembles of different model types by combining dynamical modeling with data‐driven methodological approaches (i.e., neural networks and Granger causality), seems to better match the condition of independence. In addition, neural networks and Granger causality lead to achievements in attribution studies that can complement those obtained by dynamical modeling. WIREs Clim Change 2017, 8:e477. doi: 10.1002/wcc.477 This article is categorized under: Climate Models and Modeling > Earth System Models
Artificial neural network · Attribution · Causal model · Climate change · Climate model · Econometrics · Robustness (evolution · Statistics · Atmospheric and Environmental Gas Dynamics · Climate variability and models · Computer Science · Ecosystem dynamics and resilience · Mathematics · Artificial Intelligence · Ecology
Characterizing the Robustness of Science
Simulation and Similarity
Re-Engineering Philosophy for Limited Beings
The use of the multi-model ensemble in probabilistic climate projections
The Potential to Narrow Uncertainty in Regional Climate Predictions
Investigating Causal Relations by Econometric Models and Cross-spectral Methods
Holism, entrenchment, and the future of climate model pluralism
Evidence of recent causal decoupling between solar radiation and global temperature
Use of models in detection and attribution of climate change
What does it mean when climate models agree? A case for assessing independence among general circulation models
A multi-approach strategy in climate attribution studies
Global climate science, uncertainty and politics
The role of ‘complex’ empiricism in the debates about satellite data and climate models
Model robustness as a confirmatory virtue
Robustness Analysis
Scientific Perspectivism
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |