Implementation of a one-dimensional enthalpy sea-ice model in a simple pycnocline prediction model for sea-ice data assimilation studies

To further explore enthalpy-based sea-ice assimilation, a one-dimensional (1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled wit...

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Bibliographic Details
Published in:Advances in Atmospheric Sciences
Main Authors: Wu, Xinrong, Zhang, Shaoqing, Liu, Zhengyu
Other Authors: Wu, XR (reprint author), State Ocean Adm, Natl Marine Data & Informat Serv, Key Lab Marine Environm Informat Technol, Tianjin 300171, Peoples R China., State Ocean Adm, Natl Marine Data & Informat Serv, Key Lab Marine Environm Informat Technol, Tianjin 300171, Peoples R China., Princeton Univ, Natl Ocean & Atmospher Adm, Geophys Fluid Dynam Lab, Princeton, NJ 08542 USA., Univ Wisconsin, Clin Res Ctr, Madison, WI 53706 USA., Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI 53706 USA., Peking Univ, Lab Ocean Atmosphere Studies, Beijing 100871, Peoples R China.
Format: Journal/Newspaper
Language:English
Published: ADVANCES IN ATMOSPHERIC SCIENCES 2016
Subjects:
sea ice
enthalpy
coupled model
data assimilation
ensemble Kalman filter
CLIMATE
IMPACT
ADJUSTMENT
SIMULATION
FILTER
Sea ice
Online Access:https://hdl.handle.net/20.500.11897/438751
https://doi.org/10.1007/s00376-015-5099-2
Description
Summary:To further explore enthalpy-based sea-ice assimilation, a one-dimensional (1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model. National Natural Science Foundation [41206178, 41306006, 41376015, 41376013, 41176003]; National Basic Research Program [2013CB430304]; National High-Tech RD Program [2013AA09A505]; Global Change and Air-Sea Interaction Program of China [GASI-01-01-12] SCI(E) 中国科技核心期刊(ISTIC) ARTICLE xinrong_wu@yahoo.com 2 193-207 33

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