DAPS - Paleoclimate Reanalyses, Data Assimilation and Proxy System modeling
Reanalyses combine observations with the knowledge of the dynamics of a system, as represented in a model, to obtain an estimate of the state of this system. They have some clear advantages compared to more traditional methods. In particular, the data assimilation techniques that allow blending observations and model results do not rely on the stationarity of a statistical relationship between the record and the reconstructed variable. Reanalysis provide physically-consistent estimates for different variables such as temperature, precipitation, atmospheric and oceanic circulations. Furthermore, they take into account explicitly the uncertainties on all the available sources of information in one single process in order to reduce the uncertainty of the reanalysis itself.
Nevertheless, many challenges still remain to be addressed to apply them more systematically in paleosciences. Specifically, the data assimilation techniques needs to be adapted to observations with large and poorly known systematic uncertainties arising from resolution, chronology as well as to the complex response in those records to climatic and non-climatic factors, and to biased observing networks. To obtain unbiased results, a model-data fusion requires the development and inclusion in the process of forward (proxy system) models that explicitly reproduce the observed quantity from model outputs allowing the measured variable to be directly assimilated into simulations.
Schematic representation of the procedure leading to a reanalysis (i.e. a reconstruction of the state of a system) using data assimilation (modified from Goosse, 2016).
Reference: Goosse H, 2016. An additional step towards comprehensive paleoclimate reanalyses. Journal of Advances in Modeling Earth Systems 8, 1501–1503, doi:10.1002/2016MS000739.
The first DAPS workshop will be held in May 2017.
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