Research Topic

New techniques for improving climate models, predictions and projections

About this Research Topic

Complex climate models are the main tool used to make climate predictions and projections. Models are imperfect and generations of models have shown persistent mean-state biases such as the ‘double ITCZ’. Model imperfections lead to drift and errors in near-term initialised climate prediction systems and ...

Complex climate models are the main tool used to make climate predictions and projections. Models are imperfect and generations of models have shown persistent mean-state biases such as the ‘double ITCZ’. Model imperfections lead to drift and errors in near-term initialised climate prediction systems and uncertainties in long-term future projections. Techniques such as bias correction and drift removal have been developed to alleviate the impact of imperfect models in the case of predictions. Techniques such as emergent constraints and model selection have been used in projection studies. Are these techniques adequate, could they be improved upon, or should the community be investing their efforts into significantly improving the performance of climate models? Will higher resolution bring greater accuracy? Are there new techniques which can significantly improve climate predictions and projections?

The goal of this research topic is to explore new techniques for improving climate models, climate predictions and climate projections. Techniques from Data Science, Complex Networks, Artificial Intelligence and Machine Learning have been proposed for both representing physical processes and for post-processing model output. The new CMIP6 experiments present opportunities to test many different climate processes and to measure improvements in modelling over previous CMIP generations. Higher-resolution models are now available. New observations may permit much more detailed evaluation of models and processes.We encourage submissions in all aspects of improvements in climate models, predictions and projections.

Submissions are welcomed on the following themes and related areas:

• Data science, complex networks, artificial intelligence and machine learning in climate
• Improving climate models, including new processes
• New results from CMIP6
• Post-processing of climate predictions and/or projections
• Evaluation of model, predictions, projections
• Uncertainty quantification


Keywords: Uncertainty quantification, climate perdictions, climate projections, climate change, climate modelling, Improving climate models, Machine learning in climate, New results from CMIP6, Complex climate models, bias correction, drift removal, model selection, emergent constraints, CMIP6


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Recent Articles

Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

20 January 2021 Manuscript
22 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

20 January 2021 Manuscript
22 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..