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Volume 24, issue 4 | Copyright
Nonlin. Processes Geophys., 24, 681-694, 2017
https://doi.org/10.5194/npg-24-681-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 17 Nov 2017

Research article | 17 Nov 2017

Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

Yuxin Zhao1, Xiong Deng1,2, Shaoqing Zhang3, Zhengyu Liu4,5, Chang Liu1,2, Gabriel Vecchi6, Guijun Han7, and Xinrong Wu7 Yuxin Zhao et al.
  • 1College of Automation, Harbin Engineering University, Harbin, 150001, China
  • 2GFDL-Wisconsin Joint Visiting Program, Princeton, NJ 08540, USA
  • 3Physical Oceanography Laboratory/CIMST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
  • 4Atmospheric Science Program, Department of Geography, Ohio State University, Columbus, OH 43210, USA
  • 5Laboratory for Climate and Ocean-Atmosphere Studies (LaCOAS), Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
  • 6Atmospheric and Oceanic Program, Princeton University, Princeton, NJ 08540, USA
  • 7National Marine Data and Information Service, Tianjin, 300171, China

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

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Here with a simple coupled model that simulates typical scale interactions in the climate system, we study the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA. Results show that an optimal OTW determined from the de-correlation timescale provides maximal observational information that best fits the characteristic variability of the coupled medium during the data blending process.
Here with a simple coupled model that simulates typical scale interactions in the climate...
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