Climate variability describes how climate diverges from the average for a certain time period. In order to understand what drives climate variability and to disentangle man-made from natural variability, scientists mainly rely on climate model simulations. This is because observational data are sparse and not evenly distributed over time and space. To obtain a continuous three-dimensional reconstruction of climate variability, reanalyses are created. The reanalysis makes the best use of observational data to constrain model dynamics using a mathematical method called data assimilation. The project CoRea has produced a coupled reanalysis of high importance to the climate research community. The new reanalysis provides an estimate of the climate from 1850 to the present with uncertainty. The project CoRea has been led by three early-career researchers, fostering the development of a new generation of climate researchers. CoRea has also helped to continue interdisciplinary cooperation between NERSC, NORCE, and UiB within the Bjerknes Centre for Climate Research. It has also stimulated the research group that operates at the forefront of international climate research. In CoRea, we have only used the ocean observations, and as a consequence the reanalysis produced by CoRea is of great use in understanding the role of the ocean among the rest of the climate system (e.g., the atmosphere and sea ice). The produced reanalysis has been the first one to use the advanced data assimilation method, which combines dynamical propagation of background uncertainty with forecasting and hindcasting of observational data.
Dr. Wang (project manager) has gained his first experience in leading a project, having the opportunity to establish new and long-lasting national and international collaborations (e.g., UiB, NORCE, ECMWF, and ENPC), and to acquire new expertise in DA and climate reanalysis. CoRea will encourage Dr Wang to submit new applications on climate reconstruction and keep working on this topic in the coming years. CoRea has provided the first experience of Dr Svendsen and Dr Raanes (early career researchers) in leading a WP in a research project. This experience in leadership has been beneficial for them and has led them to pursue their research ideas and submit their research applications.
The reanalysis produced by CoRea (CoRea1860+) will be a powerful tool for new insights for climate research, e.g., to understand climate change and initialise long-term climate predictions and hindcasts. The assimilation techniques developed by CoRea will be useful for modelling communities across many scientific fields.
Climate reanalysis products are highly in demand by the climate research community, for the following purposes: the study and evaluation of historical model simulations, the understanding of climate change, teleconnections and variability, the investigation of climate change impacts, and the initialisation of climate predictions and hindcasts. However, most reanalyses are either atmospheric reanalyses or oceanic reanalyses produced with uncoupled systems, and/or do not cover the entire 20th century. CoRea will produce the first-ever ensemble-based (probabilistic) coupled climate reanalysis from 1850 to present with the assimilation of ocean data only.
CoRea will develop a novel multi-timescale ensemble smoother data assimilation method, in order to accurately and efficiently propagate information from the contemporary observation networks backwards in time for several decades without introducing dynamical inconsistency. To the best of our knowledge, any ensemble smoother data assimilation methods have not been used so far to produce reanalyses. CoRea will implement both the traditional and multi-timescale ensemble smoother data assimilation methods into the Norwegian Climate Prediction Model (NorCPM). CoRea will compare advanced localisation techniques that have recently been developed in theory to maximise the use of observations while limiting the introduction of spurious signals. Localisation is particularly important for ocean reanalyses, for which observations are taken at the surface (e.g. SST and SSH data) or from the upper ocean (e.g. Argo and XBT data). The absence of observation from the deep ocean causes it to accumulate spurious variability. Moreover, CoRea will study the role of the slow ocean influencing climate variability and change in the 20th century, which may address the scientific question on the relative importance of the ocean and the atmosphere in influencing climate variability.
