DAPS - Paleoclimate Reanalyses, Data Assimilation and Proxy System modeling

Summary

DAPS is designed to stimulate the development and the application of methods for the joint use of observations and models in paleoscience. This requires that models simulate the variables measured for a direct comparison and to apply techniques that optimally combine both sources of information, taking into account the uncertainties.
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Goals

- Review and evaluate existing methodologies.
- Develop synergies and joined activities where gaps are identified.
- Provide practical applications and training to potential users.
- Stimulate the expansion of proxy system modeling and data assimilation in new areas.

Leaders

Hugues Goosse (Université catholique de Louvain, Belgium) (mailing list administrator)
Mike Evans (University of Maryland, USA)
Samar Khatiwala (Oxford University, UK)

Timeline

Launch
 
 
Synthesis
 
 
 
 
July 2016
  2017
  2018
  2019

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.

daps graphic web

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.


Workshops

The first DAPS workshop will be held 29-31 May 2017.
http://pastglobalchanges.org/calendar/upcoming/127-pages/1657-daps-1st-wshop-2017


Learn more and participate

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