Addressing Climate Change with Artificial Intelligence Methods
A special issue of Climate (ISSN 2225-1154).
Deadline for manuscript submissions: 30 September 2024 | Viewed by 4723
Special Issue Editor
Special Issue Information
Dear Colleagues,
Climate change is a hot topic in contemporary scientific research. Together with the study of historical climatology, dynamical modelling (via global climate/Earth system models) is the standard way to address the complexity of the climate and obtain knowledge about its past behaviour and possible future evolution.
In recent years, however, this complexity has also been addressed via the use of data-driven methods—artificial intelligence (AI) techniques in particular—as alternatives or complementary to dynamical models. The former applications include attribution or prediction studies (about global warming, but also individual phenomena), as well as research into large datsets using deep learning; the latter uses involve downscaling or finds application to specific impact studies, such as hydrological or extreme-event investigations. AI applications have shown also their usefulness in terms of extracting knowledge from large datasets (e.g., sets of satellite data) or addressing the social and economic impacts of climate change, as in cases of human migration.
In this framework, this Special Issue has the ambitious objective of publishing high-quality papers and presenting the latest research and studies dedicated to the application of AI methods to climate change topics. In particular, this Special Issue aims to publish innovative work within a large spectrum of applications.
Both research articles (for general applications and/or case studies) and reviews can be submitted.
Relevant topics of the call include (non-exhaustive list):
- Detection and attribution of climate change by AI methods;
- AI downscaling of dynamical models for obtaining better reconstruction of the past and/or prediction of high-resolution future scenarios;
- Prediction through pure AI methods;
- AI in the study of extreme events;
- AI in impact studies (general or case studies in all possible applications).
Dr. Antonello Pasini
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence methods
- climate change
- AI climate modelling
- AI climate detection/attribution
- downscaling via AI methods
- AI climate prediction
- AI in extreme-event studies
- climate impacts addressed by AI methods
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Applying Machine Learning in Numerical Weather and Climate Modeling Systems
Authors: Vladimir Krasnopolsky
Affiliation: EMC/NCEP/NWS/NOAA
Abstract: In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.
