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FRINATEK-Fri mat.,naturv.,tek

Coupled reanalysis of the climate back to 1850

Alternative title: Koplet reanalyse av klimavariasjoner tilbake til 1850

Awarded: NOK 7.6 mill.

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2020 - 2023


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 is working to produce a coupled reanalysis of high importance to the climate research community. The new reanalysis will provide an estimate of the climate from 1850 to the present with uncertainty. The project CoRea is led by three early-career researchers, fostering the development of a new generation of climate researchers. CoRea also helps to continue interdisciplinary cooperation between NERSC, NORCE, and UiB within the Bjerknes Centre for Climate Research. It also stimulates the research group that operates at the forefront of international climate research. In CoRea, we only use the ocean observations, and as a consequence the reanalysis produced by CoRea will be 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 will be the first one to use the advanced data assimilation method known as the Ensemble Kalman Smoother, which combines dynamical propagation of background uncertainty with forecasting and hindcasting of observational data. We have revisited this method to maximise the influence of the rich contemporary observations backwards in time for several decades. In addition, we found a way to assimilate the ocean surface observations into the reanalysis without falsifying what happens in the deep ocean. This will enhance the overall quality of the reanalysis.

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.


FRINATEK-Fri mat.,naturv.,tek