MASNUM海浪模式对波数离散角度的敏感性数值实验

doi:10.11978/2014019

热带海洋学报 ›› 2016, Vol. 35 ›› Issue (1): 82-95.doi: 10.11978/2014019CSTR: 32234.14.2014019

MASNUM海浪模式对波数离散角度的敏感性数值实验

滕涌^{1, 2}, 韩磊^{1, 2}, 杨永增^{1, 2}, 乔方利^{1, 2}, 孙宝楠^{1, 2}, 芦静^{1, 2}

1. 国家海洋局第一海洋研究所, 山东青岛 266061;
2. 海洋环境科学与数值模拟国家海洋局重点实验室, 山东青岛 266061

收稿日期:2014-03-06 出版日期:2016-01-10 发布日期:2016-02-02
作者简介:滕涌(1973—), 男, 山东省青岛市人, 硕士研究生, 研究方向为海浪数值模拟。Email: tengyong2003@fio.org.cn
基金资助:
国家自然科学基金项目(40906017)

Numerical experiments on wavenumber directional discretization in the MASNUM Wave Model

TENG Yong^{1, 2}, HAN Lei^{1, 2}, YANG Yongzeng^{1, 2}, QIAO Fangli^{1, 2}, SUN Baonan^{1, 2}, LU Jing^{1, 2}

1. First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;
2. Key Lab of Marine Science and Numerical Modeling, State Oceanic Administration, Qingdao 266061, China;

Received:2014-03-06 Online:2016-01-10 Published:2016-02-02
Contact: YANG Yongzeng. Email: yangyz@fio.org.cn
Supported by:
The National Natural Science Foundation of China (40906017)

摘要/Abstract

摘要： 本研究基于MASNUM第三代海浪数值模式, 利用2005年太平洋区域17个浮标的有效波高数据做检验, 对海浪模式波向在12、24、36和48四种离散划分进行了数值实验。结果表明, 在太平洋区域中纬度和太平洋东部地区, 36个角度划分是较好的选择, 而在太平洋低纬度, 24个波向划分是较好的选择; 冬、夏季数值模拟与检验对比表明, 较好的角度划分数分别是24和36; 有效波高在2.0~4.0m时, 较好的波向划分数是24; 有效波高小于2.0m时, 24与36个波向划分数值模拟结果非常相近。而在高海况下, 36个角度划分是较好的选择。上述结果均比采取波向离散12的结果有明显改进。文章认为, 应使用高于12的波数离散数划分, 24或36个波数角度离散划分是较好选择。

关键词: MASNUM, 海浪, 波浪模式, 波数离散划分

Abstract: This study shows some numerical experiments based on the 3^rd generation wave model of MASNUM, in four cases of different wavenumber directional discretization numbers: 12, 24, 36, and 48, respectively in the Pacific Ocean in 2005, and compares the model results with 17 buoy observations. These results indicate that the discretization number of 36 is the best choice for the model setting in the mid-latitude area and the eastern Pacific, while the discretization number of 24 is the best choice in the low latitude region. Comparison shows that the best discretization number is 24 in winter while 36 in summer. We also find that when significant wave height is between 2.0 and 4.0 m, the best discretization number is 24. When significant wave height is lower than 2.0 m, 24 and 36 are both proper choices. We recommend choosing 36 when under poor ocean conditions. All above results show obvious improvements comparing with the case of discretization number 12. Therefore, we believe that the discretization number of the MASNUM wave model should be greater than 12, and that the number of 24 or 36 is a better choice.

Key words: MASNUM, wave, wave model, discretization

滕涌, 韩磊, 杨永增, 乔方利, 孙宝楠, 芦静. MASNUM海浪模式对波数离散角度的敏感性数值实验[J]. 热带海洋学报, 2016, 35(1): 82-95.

TENG Yong, HAN Lei, YANG Yongzeng, QIAO Fangli, SUN Baonan, LU Jing. Numerical experiments on wavenumber directional discretization in the MASNUM Wave Model[J]. Journal of Tropical Oceanography, 2016, 35(1): 82-95.

参考文献

1 黄卓, 徐海明, 杜岩, 等, 2009. 厄尔尼诺期间和后期南海海面温度的两次显著增暖过程[J]. 热带海洋学报, 28(5): 49-55. HUANG ZHUO, XU HAIMING, DU YAN, et al, 2009. Two sea-surface warming events in the South China Sea during and after El Niño[J]. Journal of Tropical Oceanography, 28(5): 49-55.
2 ALORY G, WIJFFELS S, MEYERS G, 2007. Observed temperature trends in the Indian Ocean over 1960-1999 and associated mechanisms[J]. Geophys Res Lett, 34(2): L02606. doi:10.1029/2006GL028044.
3 CHOWDARY J S, XIE S P, TOKINAGA H, et al., 2012. Interdecadal variations in ENSO teleconnection to the Indo-Western Pacific for 1870-2007[J]. J Climate, 25: 1722-1744.
4 COMPO G P, WHITAKER J S, SARDESHMUKH P D, et al, 2011. The Twentieth Century Reanalysis project[J]. Quart J Roy Meteor Soc, 137: 1-28.
5 DU Y, XIE S P, 2008. Role of atmospheric adjustments in the tropical Indian Ocean warming during the 20th century in climate models[J]. Geophys Res Lett, 35: L08712. doi:10.1029/2008GL033631.
6 DU Y, XIE S P, HUANG G, et al, 2009. Role of air-sea interaction in the long persistence of El Niño-induced north Indian Ocean warming[J]. J Climate, 22: 2023-2038.
7 DU Y, XIE S P, YANG Y L, et al, 2013. Indian Ocean variability in the CMIP5 multimodel ensemble: The Basin Mode[J]. J Climate, 26: 7240-7266.
8 GIESE B S, RAY S, 2011. El Niño variability in simple ocean data assimilation (SODA), 1871-2008[J]. J Geophys Res, 116: C02024. doi:10.1029/2010JC006695.
9 HUANG R, WU Y, 1989. The influence of ENSO on the summer climate change in China and its mechanism[J]. Advances in Atmospheric Science, 6(1): 21-32.
10 HUANG G, HU K, XIE S P, 2010. Strengthening of tropical Indian Ocean teleconnection to the northwest Pacific since the mid-1970s: An atmospheric GCM study[J]. J Climate, 23: 5294-5304.
11 ISHII M, SHOUJI A, SUGIMOTO S, et al, 2005. Objective analyses of sea surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe collection[J]. Int J Climatol, 25: 865-879.
12 KAPLAN A, CANE M A, KUSHNIR Y, et al, 1998. Analyses of global sea surface temperature 1856-1991[J]. J GeophysRes, 103: 18567-18589.
13 KARA A B, ROCHFORD P A, HURLBURT H E, 2000. Mixed layer depth variability and barrier layer formation over the north pacific ocean[J]. Journal of Geophysical Research Atmospheres, 105(C7): 16783-16801.
14 KLEIN S A, SODEN B J, LAU N C, 1999. Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge[J]. J Climate, 12: 917-932.
15 LAU N C, NATH M J, 2000. Impact of ENSO on the variability of the Asian-Australian monsoons as simulated in GCM experiments[J]. J Climate, 13: 4287-4309.
16 LIU Q, JIANG X, XIE S P, LIU W T, 2004. A gap in the Indo-Pacific warm pool over the South China Sea in boreal winter: Seasonal development and interannual variability[J]. J Geophys Res, 109: C07012. doi:10.1029/2003JC002179.
17 RAYNER N A, PARKERD E, HORTON E B, et al, 2003. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century[J]. J Geophys Res, 108: 4407. doi:10.1029/2002jd002670.
18 REYNOLDS R W, RAYNER N A, SMITH T M, et al, 2002. An improved in situ and satellite SST analysis for climate[J]. J Climate, 15: 1609-1625.
19 SCHOTT F A, XIE S P, MCCREARY J P, 2009. Indian Ocean circulation and climate variability[J]. Rev. Geophys., 47, RG1002. doi:10.1029/2007RG000245.
20 SMITH T M, REYNOLDS R W, PETERSON T C, et al, 2008. Improvements NOAAs Historical Merged Land-Ocean Temp Analysis (1880-2006)[J]. Journal of Climate, 21: 2283-2296.
21 TRENBERTH K E, CARON J M, STEPANIAK D P, et al, 2002. Evolution of El Niño-Southern Oscillation and global atmospheric surface temperatures[J]. Journal of Geophysical Research Atmospheres, 107(D8): AAC 5-1-AAC 5-17.
22 WANG B, WU R and FU X, 2000. Pacific-East Asian teleconnection: How does ENSO affect East Asian climate[J]. J Climate, 13: 1517-1536.
23 WANG C, WEISBERG R H, VIRMANI J I, 1999. Western Pacific interannual variability associated with the El Niño Southern Oscillation[J]. J Geophys. Res.: Oceans, 104(C3): 5131-5149.
24 WANG C, WANG W, WANG D, et al, 2006. Interannual variability of the South China Sea associated with El Niño[J]. J Geophys Res, 111: C03023. doi:10.1029/2005JC003333.
25 WOODRUFF S D, WORLEY S J, LUBKER S J, et al, 2011. ICOADS release 2.5: Extensions and enhancements to the surface marine meteorological archive[J]. Int J Climatol, 31: 951-967.
26 WORLEY S J, WOODRUFF S D, REYNOLDS R W, et al, 2005. ICOADS release 2.1 data and products[J]. Int J Climatol, 25: 823-842. doi:10.1002/joc.1166.
27 WU R, KIRTMAN B P, KRISHNAMURTHY V, 2008. An asymmetric mode of tropical Indian Ocean rainfall variability in boreal spring[J]. J Geophys Res, 113: D05104. doi:10.1029/2007JD009316.
28 XIE S P, XIE Q, WANG D X, et al, 2003. Summer upwelling in the South China Sea and its role in regional climate variations[J]. J Geophys Res, 108: 3261. doi:10.1029/2003JC001867.
29 XIE S P, HU K, HAFNER J, et al, 2009. Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño[J]. J Climate, 22: 730-747.
30 XIE S P, DU Y, HUANG G, et al, 2010. Decadal shift in El Niño influences on Indo-western Pacific and East Asian climate in the 1970s[J]. J Climate, 23: 3352-3368.
31 YANG J, LIU Q, XIE S P, et al, 2007. Impact of the Indian Ocean SST basin mode on the Asian summer monsoon[J]. Geophys Res Lett, 34: L02708. doi:10.1029/2006GL028571.
32 YANG Y L, XIE S P, DU Y, et al, 2015. Interdecadal difference of interannual variability characteristics of SCS SST associated with ENSO[J]. J Climate, 28: 7145-7160.

[1]	徐庆跃, 潘远超, 马浩翔, 李长征, 刘炜灏, 薛亮, 韩昌报, 杨阳. 用于收集岸基海浪能的弹簧辅助摩擦纳米发电机^*[J]. 热带海洋学报, 2023, 42(4): 176-183.
[2]	徐啸, 陶爱峰, 李雪丁, 郑祥靖, 林伊楠. 基于实测数据的台湾海峡中部波浪特征分析 ^*[J]. 热带海洋学报, 2021, 40(1): 12-20.
[3]	刘子龙, 史剑, 蒋国荣, 肖林. 海浪混合作用对热带海洋海表面温度模拟的影响*[J]. 热带海洋学报, 2017, 36(4): 77-86.
[4]	崔超然, 管玉平, 朱耀华, 王辉, 黄瑞新. 风对南海波浪的能量输入及其长期变化[J]. 热带海洋学报, 2016, 35(4): 21-30.
[5]	孙璐, 黄楚光, 蔡伟叙, 王盛安, 曾丽丽. 广海湾海浪要素的基本特征及典型台风过程的波浪分析[J]. 热带海洋学报, 2014, 33(3): 17-23.
[6]	郑崇伟, 林刚, 孙岩, 杨松. 近45年太平洋海浪特征分析[J]. 热带海洋学报, 2012, 31(6): 6-12.
[7]	江丽芳, 尹毅, 齐义泉, 张志旭. 钦州湾台风浪的多年一遇极值推算[J]. 热带海洋学报, 2012, 31(4): 8-16.
[8]	任启峰,张杰,尹训强,杨永增,. Envisat ASAR海浪谱资料的最优插值同化试验[J]. 热带海洋学报, 2010, 29(5): 17-23.

MASNUM海浪模式对波数离散角度的敏感性数值实验

Numerical experiments on wavenumber directional discretization in the MASNUM Wave Model

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

Metrics

本文评价

推荐阅读 0