New techniques for improving climate models, predictions and projections

Abstract

Complex climate models are the main tools used to make climate predictions and projections. Despite decades of development, models are still imperfect and generations of models have shown persistent mean-state biases such as the “double intertropical convergence zone (ITCZ).” Model imperfections lead to drift and errors in near-term initialized climate prediction systems and uncertainties in long-term future projections. Techniques such as bias correction and drift removal have been developed to reduce the impact of model imperfection 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 was to explore new techniques for improving climate models, climate predictions, and climate projections. The 11 articles that are appearing in this special issue of Frontiers in Climate Predictions and Projections have together shown new avenues in improving the forecasts and projections, and introduce us to new science and new forecast products.