Comparative Study on the Seasonal Predictability Dependency of Boreal Winter 2m Temperature and Sea Surface Temperature on CGCM Initial Conditions
被引:8
作者:
论文数: 引用数:
h-index:
机构:
Ahn, Joong-Bae
[1
]
论文数: 引用数:
h-index:
机构:
Lee, Joonlee
[1
]
机构:
[1] Pusan Natl Univ, Div Earth Environm Syst, Busandaehak-Ro 63beon-gil, Busan 609735, South Korea
来源:
ATMOSPHERE-KOREA
|
2015年
/
25卷
/
02期
关键词:
AMIP;
data assimilation;
CGCM;
initial condition;
D O I:
10.14191/Atmos.2015.25.2.353
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.
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页码:353 / 366
页数:14
相关论文
共 65 条
[61]
Wang GM, 2002, MON WEATHER REV, V130, P975, DOI 10.1175/1520-0493(2002)130<0975:TBCGCM>2.0.CO
机构:
Japan Meteorol Agcy, Numer Predict Div, Tokyo, JapanUniv Maryland, Dept Atmospher & Ocean Sci, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA
机构:
Japan Meteorol Agcy, Numer Predict Div, Tokyo, JapanUniv Maryland, Dept Atmospher & Ocean Sci, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA