Climate reanalyses are dynamically consistent data series produced by the combination of observations and a numerical model through a mathematical method called data assimilation. They can be either uncoupled or coupled. The uncoupled reanalyses such as atmospheric and ocean reanalyses are produced with uncoupled simulation systems and can achieve a high degree of accuracy. Coupled reanalyses are produced by coupled models and so account for coupled dynamics of the climate.
Climate reanalysis products are highly in demand by the climate research community, especially for the application of:
• The study and evaluation of historical model simulations;
• The understanding of climate change, teleconnections and variability;
• The investigation of climate change impacts;
• The initialisation of climate predictions and hindcasts.
The goal of this Research Topic is to explore recent advances in model development, new techniques and/or novel observations for improving climate reanalysis. Eddy-permitting and eddy resolving models are now available. Artificial Intelligence/Machine Learning can also be included into the model to represent physical processes. New techniques such as coupled data assimilation and the combination of data assimilation and Machine Learning have recently emerged to better combine observations and the numerical model. Novel observations may be promising to improve the accuracy of the climate reanalysis.
Both research and review articles are welcomed on the following themes and related areas:
• State-of-the-art reanalysis products
• Intercomparison of reanalysis products
• Benefit of novel observations in climate reanalysis
• Applications, e.g., climate predictions
• Artificial Intelligence/Machine Learning in climate reanalysis
• New data assimilation techniques for climate reconstructions (e.g., coupled data assimilation)
• Other new techniques in reanalysis production to constraint model biases, maximise the use of existing observations or optimise observational systems.
Climate reanalyses are dynamically consistent data series produced by the combination of observations and a numerical model through a mathematical method called data assimilation. They can be either uncoupled or coupled. The uncoupled reanalyses such as atmospheric and ocean reanalyses are produced with uncoupled simulation systems and can achieve a high degree of accuracy. Coupled reanalyses are produced by coupled models and so account for coupled dynamics of the climate.
Climate reanalysis products are highly in demand by the climate research community, especially for the application of:
• The study and evaluation of historical model simulations;
• The understanding of climate change, teleconnections and variability;
• The investigation of climate change impacts;
• The initialisation of climate predictions and hindcasts.
The goal of this Research Topic is to explore recent advances in model development, new techniques and/or novel observations for improving climate reanalysis. Eddy-permitting and eddy resolving models are now available. Artificial Intelligence/Machine Learning can also be included into the model to represent physical processes. New techniques such as coupled data assimilation and the combination of data assimilation and Machine Learning have recently emerged to better combine observations and the numerical model. Novel observations may be promising to improve the accuracy of the climate reanalysis.
Both research and review articles are welcomed on the following themes and related areas:
• State-of-the-art reanalysis products
• Intercomparison of reanalysis products
• Benefit of novel observations in climate reanalysis
• Applications, e.g., climate predictions
• Artificial Intelligence/Machine Learning in climate reanalysis
• New data assimilation techniques for climate reconstructions (e.g., coupled data assimilation)
• Other new techniques in reanalysis production to constraint model biases, maximise the use of existing observations or optimise observational systems.
