Machine Learning for Climate Modeling: Current State and Future Developments

Dear Colleagues,

This Issue focuses on the current state of machine learning (ML) applications in climate modeling. It aims to provide a comprehensive overview of how ML techniques are currently being employed to enhance climate simulations, forecast climate change impacts, and develop more accurate climate models.

The scope of this Special Issue extends to various ML methodologies, including deep learning, reinforcement learning, and supervised/unsupervised learning techniques, as they have been applied to different aspects of climate modeling. This includes, but is not limited to, data analysis, pattern recognition in climate variables, predictive modeling, and uncertainty quantification in climate projections.

The purpose of this Special Issue is to present a clear picture of where the field stands today and to identify promising areas for future research and development. The Issue aims to foster discussion and collaboration among climate scientists and machine learning experts, highlighting challenges, successes, and potential pathways forward.

Relationship to the Existing Literature:

This Special Issue will supplement the existing literature by providing a curated collection of state-of-the-art research and insights into how machine learning is currently shaping climate modeling. It will offer a unique blend of perspectives, bridging the gap between traditional climate science approaches and modern computational techniques. By highlighting recent advancements, challenges, and potential future directions, this issue will serve as a valuable resource for researchers and practitioners in both fields, encouraging further exploration and innovation at the intersection of machine learning and climate science.

Additionally, this Issue will help to contextualize the progress made so far in this interdisciplinary area, providing a benchmark for future research and a comprehensive source for academics, policymakers, and industry professionals interested in the evolving role of machine learning in climate modeling.

Dr. Manmeet Singh
Dr. Sukhpal Singh Gill
Guest Editors

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. AI is an international peer-reviewed open access quarterly 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 1600 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.

• machine learning in climate science
• climate model enhancement
• predictive climate modeling
• deep learning for climate analysis
• climate data processing
• AI in meteorology
• climate change projections
• computational approaches in climatology
• data-driven climate research
• uncertainty quantification in climate predictions
• climate pattern recognition
• reinforcement learning in environmental studies
• supervised and unsupervised learning in climate studies
• climate simulation and forecasting
• big data analysis in climatology