Please wait a minute...
img img
地球科学进展  2017, Vol. 32 Issue (4): 373-381    DOI: 10.11867/j.issn.1001-8166.2017.04.0373
郭准1, 2, 周天军1, 3
1.中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029;
2.中国科学院气候变化研究中心,北京 100029;
3.中国科学院大学,北京 100049
The Simulation of Stratocumulus and Its Impacts on SST:Based on the IAP Near-Term Climate Prediction System
Guo Zhun1, 2, Zhou Tianjun1, 3
1.LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
2.Climate Change Research center, Chinese Academy of Sciences, Beijing 100029, China;
3.University of Chinese Academy of Sciences, Beijing 100029, China
 全文: PDF(1012 KB)   RICH HTML

气候预测系统的海洋初始化积分试验考虑了海气相互作用,可以视为一种弱耦合同化试验。海温(SST)和层积云的关系是检验海气相互作用过程模拟效果的重要参考。分析了基于耦合气候系统模式FGOALS-s2的中国科学院大气物理研究所近期气候预测系统IAP DecPreS的海洋初始化模拟实验(简称EnOI-IAU试验)所模拟的海温—云关系。结果表明,EnOI-IAU试验较好地模拟出了SST和低云的气候态空间分布,但在主要层积云区低估了低云云量和云水,SST模拟偏高,特别在副热带东大洋沿岸和南大洋。部分原因是这些地区实际影响海表温度模拟的是模式的内部过程,而低云模拟不足导致了海表入射更多的短波辐射(强度约偏强20 W/m2),迫使局地SST模拟过高。分析显示,低云模拟不足主要是由于EnOI-IAU试验不能再现合理的边界层逆温结构,表现为大气垂直速度、温度和湿度过于集中在近地层,使得边界层垂直热输送较弱、边界层无法充分混合,进而无法有效模拟出层积云。这些结果表明,未来引入大气观测数据同化,特别是改善边界层结构的模拟,对形成完整的耦合同化系统具有必要性。

关键词: 耦合气候系统模式大气边界层层积云初始化试验海表温度    

The near-term climate prediction system (DecPreS) is built on the initialization of the ocean state, which can be regarded as a full-coupled system with “adjusted” air-sea interactions. The relationship between stratocumulus and Sea Surface Temperature (SST ) is an essential part of air-sea interactions. In this study, we investigated such a relationship in DecPreS of the Institute of Atmospheric Physics, in which the merge of the Ensemble Optimal Interpolation (EnOI) and Incremental Analysis Update (IAU) scheme was employed. EnOI-IAU generally reproduces the spatial pattern of SST and low-clouds. However, the simulated cloud fraction/liquid water path are underestimated while the SST is overestimated in stratocumulus regimes, especially in the subtropical East Pacific and South Ocean. It is partly because the unrealistic air-sea interaction dominates these regions that the underestimated stratocumulus allows more input of incoming shortwave flux (20 W/m2). The deficient stratocumulus is highly related to the unrealistic vertical structure of Atmospheric Boundary Layer (ABL), in which the moisture, temperature and vertical heat transports concentrate at the surface layer. Our results imply that stratocumulus and ABL be important in DecPreS. Clarifying the importance of ABL and stratocumulus will provide a possible way to improve the DecPreS.

Key words: Coupled GCM    Stratocumulus    Atmospheric boundary layer.    SST    near-term prediction
收稿日期: 2016-11-07 出版日期: 2017-04-20
ZTFLH:  P426.5  

公益性行业(气象)科研专项项目“基于FGOALS-s、CMA和CESM气候系统模式的年代际集合预测系统的建立与研究”(编号:GYHY201506012); 国家自然科学基金青年科学基金项目“云辐射反馈过程对东亚—西北太平洋地区海气相互作用的影响及其气候模式模拟的不确定性”(编号:41405103)资助

作者简介: 郭准(1983-),男,河南宜阳人,副研究员,主要从事云物理过程参数化研究
E-mail Alert


郭准, 周天军. IAP近期际气候预测系统海洋初始化试验中海表温度和层积云的关系[J]. 地球科学进展, 2017, 32(4): 373-381.

Guo Zhun, Zhou Tianjun. The Simulation of Stratocumulus and Its Impacts on SST:Based on the IAP Near-Term Climate Prediction System. Advances in Earth Science, 2017, 32(4): 373-381.


[1] Wood R. Stratocumulus clouds[J]. Monthly Weather Review , 2012, 140(8): 2 373-2 423.
[2] Klein S A,Hartmann D L. The seasonal cycle of low stratiform clouds[J]. Journal of Climate ,1993,6:1 587-1 606.
[3] Rozendaal M A, Leovy C B, Klein S A. An observational study of diurnal variations of marine stratiform cloud[J]. Journal of Climate , 1995, 8:1 795-1 809.
[4] Norris J,Loevy C B. Interannual variability in stratiform cloudiness and sea surface temperature[J]. Journal of Climate , 1994 , 7:1 915-1 925.
[5] Norris J, Iacobellis S F. North Pacific cloud feedbacks inferred from synoptic-scale dynamic and thermodynamic relationships[J]. Journal of Climate , 2005,18:4 862-4 878.
[6] Clement A C,Burgman Norris R,Nowis J R. Observational and model evidence for positive low-level cloud feedback[J]. Science , 2009,325(5 939):460-464.
[7] Hanson H P. Marine stratocumulus climatologies[J]. International Journal of Climatology ,1991,11:147-164.
[8] Weare B C. Interrelationships between cloud properties and sea surface temperatures on seasonal and interannual time scales[J]. Journal of Climate , 1994, 7:248-260.
[9] Stephens G L. Cloud feedbacks in the climate system: A critical review[J]. Journal of Climate ,2005, 18(2):237-273.
[10] Bony S, Dufresne J L. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models[J]. Geophysical Research Letter ,2005,32,doi:10.1029/2005GL023851.
[11] Williams K, Webb M. A quantitative performance assessment of cloud regimes in climate models[J]. Climate Dynamics , 2009 , doi:10.1007/s00382-008-0443-1.
[12] Wu B, Chen X, Song F, et al . Initialized decadal predictions by LASG/IAP climate system model FGOALS-s2: Evaluations of strengths and weaknesses[J]. Advances in Meteorology ,2015,(6):1-12.
[13] Wu Bo, Zhou Tianjun, Sun Qian. Impacts of initialization schemes of oceanic states on the predictive skills of the IAP near-term climate prediction system[J]. Advances in Earth Science ,2017, 32(4): 342-352.
.地球科学进展,2017, 32(4):342-352.]
[14] Bao Q, Coauthors. The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALS-s2[J]. Advance in Atmospheric Science , 2013, 30:561-576.
[15] Tiedtke M. A comprehensive mass flux scheme for cumulus parameterization in large-scale models[J]. Monthly Weather Review ,1989,117: 1 779-1 800.
[16] Slingo J M. A cloud parameterization scheme derived from GATE data for use with a numerical model[J]. Quarterly Journal of the Royal Meteorological Society ,1980,106:747-770.
[17] Slingo J M. A GCM parameterization for the shortwave radiative properties of water clouds[J]. Journal of the Atmospheric Science ,1989,46: 1 419-1 427.
[18] Holtslag A A M,Boville B A. Local versus nonlocal boundary-layer diffusion in a global climate model[J]. Journal of Climate ,1993,6:1 825-1 842.
[19] Edwards J M,Slingo A. A studies with a flexible new radiation code. I: Choosing a configuration for a large-sclae model[J]. Quarterly Journal of the Royal Meteorological Society ,1996, 122: 689-720.
[20] Liu H, Lin P, Yu Y, et al . The baseline evaluation of LASG/IAP Climate system Ocean Model (LICOM) version 2.0[J]. Journal of Meteorological Research ,2012, 26(3): 318-329.
[21] Oleson K W,Dai Y,Bonan G, et al . Technical description of the Community Land Model (CLM), NCAR Technical Note, NCAR/TN-461+STR[C]∥National Center for Atmospheric Research. Boulder, 2004:173.
[22] Briegleb B P, Bitz C M, Hunke E C. Scientific description of the sea ice component in the community climate system model, version three[C]∥NCAR Technical Note, NCAR/TN-461+STR,National Center for Atmospheric Research.Boulder,CO.,2004.
[23] Good S A,Martin M J,Rayner N A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates[J]. Journal of Geophysical Research : Oceans , 2013,118:6 704-6 716, doi:10.1002/2013JC009067.
[24] Stephens G L,Vane D G,Boain R J, et al . The CloudSat mission and the atrain—A new dimension of spacebased observations of clouds and precipitation[J]. Bulletin of the American Meteorological Society , 2002, 83:1 771-1 790.
[25] Guo Z, Zhou T. Seasonal variation and physical properties of Cloud System over southeastern China derived from CloudSat Products[J]. Advance in Atmospheric Science ,2015,32(5), doi:10.1007/s00376-014-4070-y.
[26] Randel D L, Greenwald T J, Vonder Haar T H. A new global water vapor dataset[J]. Bulletin of the American Meteorological Society , 1996, 77(6):1 233-1 246.
[27] Hong C C,Li T, Chen Y. Asymmetry of the Indian Ocean Basinwide SST Anomalies: Roles of ENSO and IOD[J]. Journal of Climate , 2010, 23:3 563-3 576.
[28] Medeiros B,Stevens B.Revealing differences in GCM representations of low clouds[J]. Climate Dynamics , 2011,36(12): 385-399.
[29] Guo Z,Wang Minghui,Qian Yun, et al . Parametric behaviors of CLUBB in simulations of low Clouds in the Community Atmosphere Model (CAM)[J]. Journal of Advances in Modeling Earth Systems ,2015,7(3):1 005-1 025,doi:10.1002/2014MS000405.

[1] 王蓉, 张强, 岳平, 黄倩. 大气边界层数值模拟研究与未来展望[J]. 地球科学进展, 2020, 35(4): 331-349.
[2] 韩振宇, 吴波, 辛晓歌. BCC_CSM1.1气候模式对全球海表温度年代际变化的回报能力评估[J]. 地球科学进展, 2017, 32(4): 396-408.
[3] 陈晓龙, 吴波, 周天军. FGOALS-s2海洋同化系统中东亚夏季风和前冬厄尔尼诺—南方涛动关系的年代际变化[J]. 地球科学进展, 2017, 32(4): 362-372.
[4] 吴波, 周天军, 孙倩. 海洋模式初始化同化方案对IAP近期气候预测系统回报试验技巧的影响[J]. 地球科学进展, 2017, 32(4): 342-352.
[5] 叶晓燕, 陈崇成, 罗明. 东亚夏季降水与全球海温异常的年代际变化关系[J]. 地球科学进展, 2016, 31(9): 984-994.
[6] 刘鹏, 江志红, 于华英, 秦怡. 全球海表温度在不同时间尺度的主模态对比分析[J]. 地球科学进展, 2014, 29(7): 844-853.
[7] 刘彦华,张述文,毛璐,薛宏宇. 评估两类模式对陆面状态的模拟和估算[J]. 地球科学进展, 2013, 28(8): 913-922.
[8] 张强,王胜,张杰,王润元,刘宏宜,李岩瑛. 干旱区陆面过程和大气边界层研究进展[J]. 地球科学进展, 2009, 24(11): 1185-1194.
[9] 刘罡;蒋维楣;罗云峰. 非均匀下垫面边界层研究现状与展望[J]. 地球科学进展, 2005, 20(2): 223-230.
[10] 张强, 胡隐樵. 大气边界层物理学的研究进展和面临的科学问题[J]. 地球科学进展, 2001, 16(4): 526-532.
[11] 李 昕. 混沌理论与大气边界层湍流研究[J]. 地球科学进展, 2000, 15(2): 178-183.
[12] 胡隐樵,张强. 大气边界层相似性理论及其应用[J]. 地球科学进展, 1996, 11(6): 550-554.