Impact of Optimal Observational Time Window on Coupled Data Assimilation: Simulation with a Simple Climate Model
Yuxin Zhao1, Xiong Deng1,3, Shaoqing Zhang2, Zhengyu Liu4,5, Chang Liu1,3, Gabriel Vecchi6, Guijun Han7, and Xinrong Wu71College of Automation, Harbin Engineering University, Harbin, 150001, China 2Key Laboratory of Physical Oceanography, MOE. China, Ocean University of China, Qingdao, 266003, China 3NOAA/GFDL-University of Wisconsin-Madison Joint Visiting Program, Princeton, NJ08540, USA 4Laboratory for Climate and Ocean-Atmosphere Studies (LaCOAS), Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China 5Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA 6NOAA/GFDL, Princeton, NJ08540, USA 7National Marine Data and Information Service, Tianjin, 300171, China
Received: 14 Nov 2016 – Accepted for review: 10 Jan 2017 – Discussion started: 13 Jan 2017
Abstract. Climate signals are the results of interactions of multiple time scale 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. Given different time scales 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, we address this issue here. Results show that required by retrieval of characteristic variability of each coupled medium, an optimal OTW determined from the de-correlation time scale provides maximal observational information that best fits characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulted CDA improves the analysis of climate signals greatly. The simple model results provide a guideline 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.
Zhao, Y., Deng, X., Zhang, S., Liu, Z., Liu, C., Vecchi, G., Han, G., and Wu, X.: Impact of Optimal Observational Time Window on Coupled Data Assimilation: Simulation with a Simple Climate Model, Nonlin. Processes Geophys. Discuss., doi:10.5194/npg-2016-68, in review, 2017.