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
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
Received: 14 Nov 2016 – Discussion started: 13 Jan 2017 – Revised: 21 Apr 2017 – Accepted: 21 Sep 2017 – Published: 17 Nov 2017
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.
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...