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 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.