Bathymetry and boundaries of the Arctic domain in the model - source Yang et al., 2011
Each month when we consider who to feature next, we encounter more and more scientists from outside the United States and Europe taking MITgcm and using it to persue oceanographic and atmospheric modeling studies. This month we shine light on recently published work by investigators Yang Qinghua (National Marine Forecasting Center, Beijing), Liu Jiping (Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing), Zhang Zhanhai (Polar Research Institute of China, Shanghai) and others who have been using MITgcm to study Arctic sea ice.
In a paper published this May in the Chinese Journal of Atmospheric Sciences entitled “A Preliminary Study of the Arctic Sea Ice Numerical Forecasting: Coupled Sea Ice – Ocean Modelling Experiments Based on MITgcm”, Yang and his team describe their MITgcm based modeling study and present preliminary results.
Arctic sea ice concentration on October 10th, 2009 (a) The initialized results in expts 1 and 2; (b) the initialized results in expt 3; (c) the initialized results in expt 4; (d) SSM/I data - source Yang et al. 2011
Using foring based on NCEP reanalysis data from the period 1992-2009, a coupled sea-ice model, based on MITgcm, shows that the simulated variabilities of the Arctic sea ice extent/ area are in good agreement with the observations derived from the SSM/I (Special Sensor Microwave Imager). On the basis of this, the ability to make coupled ice-ocean forecasts of Arctic sea ice is investigated. The team select two cases to analyse, one during the melting period and the other during the freezing period of 2009. They then carry out four forecasting experiments using atmospheric forcing from the NCEP reanalysis data and GFS (Global Forecast System), which use partly and entirely initialized SSM/I sea ice concentrations.
Preliminary results show that the model does have Arctic sea ice forecast capability. They also find that the sea ice forecast is not very sensitive to different atmospheric forcings, whereas initialization using SSM/I sea ice concentrations can much improve the sea ice forecast.
References:
Q. Yang, Liu J.,Zhang Z. et al (2011) A Preliminary Study of Arctic sea ice numerical forecasting: Coupled sea ice – ocean modeling experiments based on MITgcm, Chinese Journal of Atmos. Sciences, 35, 3, pp. 473-482.