【作者】 周娟；

【导师】 李培良；

【作者基本信息】 中国海洋大学 ， 流体力学， 2011， 硕士

【摘要】 海平面变化影响着人类的生活,在全球变暖、海平面上升的背景下,研究海平面变化,掌握其变化规律,对人类社会和自然都有着深远的意义。本文利用PSMSL验潮站资料,AVSIO海平面异常数据及SODA温、盐资料,研究了印度洋海平面变化特征及规律。利用HYCOM海洋模式模拟了印度洋1979-2008年海表高度变化。验潮站的观测资料显示印度洋海平面是上升的,三个记录时间最长站点,即印度半岛西北部的Mumbai站、孟加拉湾西部的Vishakhapatnan站及澳大利亚沿岸的Fremantle站海平面上升率分别为0.73mm/yr,0.99mm/yr及1.45mm/yr。1979-2008年,14个验潮站的平均上升率为2.1mm/yr;1993-2008年,28个验潮站的平均上升率为1.8mm/yr。气压能引起2-3cm左右的海平面变化,印度洋海平面变化大多受季节周期以内信号的控制,季节变化能占据60%以上的海平面变化。除具有显著的半年和季节变化周期外,各站点还具有2-3年及6年左右的年际变化;根据小波分析结果Mumbai站具有15年的年代际变化,而Fremantle站具有10年周期的年代际变化。1993-2008年,卫星高度计资料所得到的海平面平均上升速率为2.35mm/yr,这比由验潮站所得结果略高,平均方差为6.36cm,低于验潮站所得方差,这是由于验潮站资料反应的是局地变化,受当地气象条件影响较大,而高度计资料反映了整个印度洋的平均变化。比容海平面的方差与上升率分别为7.9cm和0.79mm/yr,其中由于热膨胀导致的海平面方差及上升率分别为8.5cm和0.73mm/yr。印度洋海平面EOF分析前三个模态的方差总贡献为26%,其中第一和第三模态均为季节性变化模态,第一模态的最低值出现在8月,最高值出现在12月或次年1月。第二模态最低值出现在3-4月,最高值出现在10-11月。第二模态为ENSO模态。比容海平面前三模态的累计方差贡献为37.4%。比容海平面EOF第一模态为ENSO模态,其时间系数滞后两个月时,与ENSO相关系数可达到-0.66,而其时间系数滞后一个月时与海平面EOF2的时间系数相关性则高达0.85。比容海平面的第二、三模态均为季节性模态,同时又受ENSO影响;比容海平面第二模态和海平面第三模态呈正相关,当前者时间系数超前后者的时间系数1个月时,两者相关性高达0.82;比容海平面第三模态还存在更长周期的年代际震荡,1994-2001年比容海平面呈现出上升趋势,而2002年后比容海平面又开始下降。HYCOM的模拟结果的温度、盐度、海表流场及海表高度能基本反映现实中印度洋相应海洋要素的分布特征。1979-2008年,模拟得到的印度洋平均方差为18cm,平均上升率为7.71mm/yr均大于高度计和验潮站资料所得到的结果。海平面纬向平均方差和上升速率总体都体现出从南至北逐渐上升的趋势。更多还原

【Abstract】 Sea-level change affects the human life and environmental change greatly, under the background of global warming and sea-level rising, it has far-reaching significance for human society and nature to study sea level variation and to grasp the law of its change. In this paper, PSMSL tide gauge data, AVSIO sea level abnormal data and the temperature, salinity data of the SODA data were used to study the characteristics and laws of the sea level change in the Indian Ocean. The HYCOM ocean model was also used to simulate the real-time sea surface height and ocean circulation from 1979 to 2008.The sea level is rising at most of the selected tide gauges. Mumbai station located in the northwest of the indian peninsula, Vishakhapatnan station seated in the west side of the Bay of Bengal and Fremantle station have the longest records, the rise rate at the three stations is 0.73mm / yr, 0.99mm/yr and 1.45mm/yr respectively. The average rise rate of the 14 eligible stations from 1978-2008 is 2.1mm/yr, The average rise rate of the 28 eligible stations from 1993-2008 is 1.8mm/yr. 2 -3cm of the amplitude can be caused by the inverse barometric effect around the Indian Ocean. most of the sea level change signal comes from the seasonal cycle which accounts for 60% of the sea-level changes. In addition to significant semiannual and seasonal change in cycle, each tide tauge also has a 2 to 3 years and 6 yeasr period cycle. the wavelet analysis shows that the interdecadal variation period is 15 years at the Mubai station, while the Fremantle station have interdecadal variation of 10 years period.The average rise rate of sea level is 2.35mm/yr in the Indian Ocean from the satellite altimeter data between 1993-2008, it is slightly higher than the corresponding result of the tide tauge data. The average amplitude is 6.36cm obtained from the satellite altimeter data, it is lower than the amplitude from the tide gauge. It is mainly because the tide gauge data is a response to local changes, which is influenced by local weather conditions, but altimeter data reflects the average variation in the Indian Ocean. Amplitude and rise rate of the Steric Sea level is 7.9cm and 0.79mm/yr respectively, the amplitude and rise rate caused by thermal expansion is 8.5cm and 0.73mm/yr respectively. The 3 most important Empirical Orthogonal Functions(EOF) can explain 26% of the sea level change in the Indian Ocean with the first and third EOFs being the seasonal modes, the lowest sea level abnormal appears in August and the peak appears in December or the following January of the next year in the first mode. While the sea level reachs the minimum in March to April and peaks in October to November in the third mode. The second EOF being the ENSO mode. The 3 most important Empirical Orthogonal Functions(EOF) can explain 37.4% of the steric sea level change in the Indian Ocean. The first EOF being the ENSO mode, when its time factor drag the ENSO index two month, its correlation coefficient with the ENSO index can archive -0.66. while its time factor drag sea-level EOF2’s time factor one month, the correlation coefficient between them can be as big as 0.85. Both of the second and third EOFs being the seasonal modes, which are also affected by the ENSO at the same time; its second EOFs and the third EOFs of the sea level is positively correlated, the correlation coefficient can reach 0.82, if the former leads the latter one month; the third EOFs of the steric sea level change owns longer decadal oscillation cycle, the steric sea level showed a rising trend during 1994 to 2001, but it began to decline after 2002.The simulated temperature, salinity, sea surface flow field and sea surface height can roughly accurately reflect the corresponding real distribution partern of marine elements in the Indian Ocean. During 1979 to 2008, the simulated average amplitude and average rise rate of sea level change in the Indian Ocean is 18cm and 7.71mm/yr respectively, which are both greater than the altimeter data and tide gauge results obtained. Both of the zonal mean amplitude and rise rate of sea level chenge are increased gradually from south to north.更多还原