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研究生:楊凱絜
研究生(外文):Kai-Chieh Yang
論文名稱:台灣東側海域海面高度值變動之研究
論文名稱(外文):A Study of Sea Surface Height Variability East of Taiwan
指導教授:王冑詹森詹森引用關係
指導教授(外文):Joe WangSen Jan
口試日期:2017-07-25
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:海洋研究所
學門:自然科學學門
學類:海洋科學學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:103
中文關鍵詞:海表面高度值中尺度運動黑潮雙流軸結構複經驗正交函數能量收支
外文關鍵詞:sea surface heightmesoscale motionsKuroshio dual velocity maximacomplex empirical orthogonal functionenergetics analysis
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藉由法國AVISO資料中心(Archiving, Validation and Interpretation of Satellite Oceanographic Data)所彙整的長期(由1993至2014年,共22年)衛星高度計海表面高度值(sea surface height, SSH)資料,得以探討台灣東側之西北太平洋海域在不同時間尺度(例如全年、半年以及100天)SSH的時、空變動特性。SSH之長期氣候平均顯示,在沿西北太平洋海盆西側之西方邊界流東方,SSH係呈東北-西南走向的脊狀分布,而位於此脊狀帶中之花東海盆內則有一個局部的相對高值區,顯示花東海盆內的長期平均海流呈反氣旋式環流結構;夏季時此環流中心位置較偏南,冬季則偏北,該環流中心大致上是沿花東海盆東側加瓜海脊呈季節性的南北擺動。此外,此反氣旋環流與黑潮流軸的季節性互動又會進一步影響台灣東側黑潮之行為與其(南北向流速垂直剖面)雙流軸結構的出現位置。
由三維(二維水平波數與頻率)頻譜分析結果知,西北太平洋海盆內SSH變化以往西傳遞之能量為主,能量主要分布在週期為全年、半年及100多天之頻帶內。對於波長大於600公里以及週期在130天以上之變動,這些較低頻部份的能量在波數-頻率譜上之分布大致仍符合第一斜壓模羅士培波(first-mode baroclinic Rossby wave)的頻散關係,然而較小尺度之中頻以及高頻部份的能量分布卻呈現非頻散(nondispersive)之特性,整體所對應之相速度約為8.65 km day-1(往西傳遞),此結果似乎暗示羅士培波及中尺度非線性波動(或渦旋)均並存於本海域。再進一步觀察全年、半年及100多天週期之能量在西北太平洋海盆內之空間分布則可發現,各週期之能量均呈往西遞增之趨勢,且於抵達黑潮邊緣處均快速消散;其中,半年週期及100多天週期之運動其能量在沿22.5°N緯度圈上更呈現東西走向之帶狀分布。
衛星高度計之長期觀測資料如經由複經驗正交函數法(complex empirical orthogonal function, CEOF)拆解,則可分解成為由互相獨立的不同模組特徵向量(eigenvector)以及主分量(principal component)之組合,並進一步得出各模組之空間及時間的振幅及相位,有助於以行進波模型來探討、分析SSH異常值變動之統計特徵。以能量佔比最大的前三個模組為例,CEOF mode-1在海盆內呈現為年週期、約以19°N為節線(nodal line)的南北震盪;CEOF mode-2為一向西南傳遞的波動,相速度約0.11 m s-1,其振幅在空間上於130°–133°E, 21°–23°N區域內會呈現波束狀分布;至於CEOF mode-3則其能量呈分別往西南及往西北傳遞的雙波束態勢,相速度約0.09 m s-1,所對應之平均週期約為149天,而其振幅於台灣東岸外海亦具有波束狀集中分布的特性。
經由減重力數值模式(reduced-gravity model)探討風應力旋度對SSH變動之影響,發現風應力旋度對於半年週期(100天週期)SSH變化僅有42%(33%)之貢獻,並不足以作為解釋造成西北太平洋海域SSH時空變化之主因。進一步的動力分析則使用美國海軍實驗室發展出的East Asian Seas Nowcast/Forecast System (EASNFS)數值模式長期(15年)模擬結果,經由計算能量收支後發現:低頻擾動場與背景海流之交互作用是影響西北太平洋海域內SSH變化的重要因素。低頻擾動場在約124°– 136°E區域會經由切變不穩定(shear instability)以及斜壓不穩定(baroclinic instability)等效應從平均場中獲取能量,而同樣的切變不穩定效應也會促使擾動場在接近黑潮東側邊緣(124°E以西)時能量消散(部份又轉回平均流場),由此可解釋SSH在西北太平洋海域內往西傳播時能量遞增,但在接近黑潮邊緣時能量又劇減之現象。
Satellite altimetric sea surface height (SSH) measurements from 1993 to 2014 obtained from Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) are used to characterize SSH spatial patterns and to explore its variability east of Taiwan with different timescales – annual, semiannual and O(100) days. A high SSH “ridge” extends along the western boundary of the north Pacific in the climatological field, in which a local SSH maximum exists east of Taiwan forming a local anticyclonic recirculation. This recirculation normally sits east of the meridional Gagua Ridge and shifts northward in winter and southward in summer. It plays a role in the formation position of the dual velocity maxima structure of the Kuroshio east of Taiwan.
Spatial and temporal scales of SSH variability are revealed by the power spectrum of sea surface height anomalies (SSHA). Westward propagating energy is dominant in the study region. The distribution of energy follows the dispersion curve of the first baroclinic Rossby waves for length scale larger than 600 km and timescale longer than 130 days and appears substantially nondispersive, with a constant phase speed of approximately 8.65 km day-1 westward, for shorter length and timescales. Energy peaks at annual, semiannual and O(100) days. For each dominant timescale, energy piles up in the western boundary and decays in the offshore flank of the Kuroshio. A zonal-patchy energy distribution appears approximately along 22.5°N for motions with semiannual and O(100 d) periods.
In order to explore the propagating behavior of SSH variability, the complex empirical orthogonal function (CEOF) analysis is then applied to identify the spatial-temporal patterns. The first CEOF mode represents a north-south standing oscillation (the nodal line is around 19°N) with annual period. Semiannual signals in CEOF-2 show southwestward propagating motions; the maximum spatial amplitude occurs near 130°–133°E, 21°–23°N. Two-band structures are found in CEOF-3, in which signals are propagating southwestward and northwestward, and have corresponding period of O(100 d); its spatial amplitude is also intensified east of Taiwan.
Local wind stress curl only contributes to 42%/33% of semiannual/O(100 d) SSHA variations. Other possible mechanisms caused SSHA variability east of Taiwan are evaluated using numerical simulations extracted from East Asian Seas Nowcast/Forecast System (EASNFS) developed by Naval Research Laboratory (NRL) based on a theoretical framework for the energetics analysis. The results reveal significant energy transfer from the mean flow to the fluctuation field via shear and baroclinic instabilities, and the latter has more contribution. Energy converts back to the mean field near the Kuroshio offshore flank. The spatial distribution of energy conversion consists with the inhomogeneous spatial patterns of SSHA from spectral and CEOF analysis.
口試委員會審定書 i
Acknowledgements ii
摘要 iii
Abstract v
List of Figures ix
List of Abbreviations and Symbols xvii
1. Introduction 1
2. Data 8
2.1 Satellite Altimetry 8
2.2 Seagliders 9
2.3 Surface drifters 10
2.4 Composite Sb-ADCP 10
2.5 Surface Winds 10
2.6 Numerical simulations 11
3. The characteristics of SSH east of Taiwan – climatology 14
3.1 Climatological SSH and mean flow pattern 14
3.2 Joint effect of the Kuroshio and the recirculation – the dual velocity maxima structure east of Taiwan 18
3.2.1 Geostrophic currents at Seaglider section N1 18
3.2.2 Mechanisms for the dual velocity maxima 20
3.3 Summary 26
4. The characteristics of SSH east of Taiwan – variability 31
4.1 SSH trend and anomalies 31
4.2 Energy of SSHA variation 32
4.3 Spatial-temporal patterns of SSH variability 38
4.3.1 Annual variability: CEOF-1 42
4.3.2 Semiannual variability: CEOF-2 43
4.3.3 O(100 d) variability: CEOF-3 43
4.3.4 Comparison with climate indexes 44
4.4 Summary 45
5. Mechanisms associated with SSHA variability 49
5.1 Wind stress curl 49
5.2 Energetics 52
5.2.1 Governing equations 52
5.2.2 Mean and fluctuation kinetic energy 55
5.2.3 Available potential energy (APE) equation 59
5.2.4 Energy exchange 63
5.3 EASNFS model simulations 64
5.3.1 Energy reservoirs 69
5.3.2 Energy conversion 73
5.3.3 Energy redistribution 76
5.3.4 Energy budget 80
5.4 Summary 82
6. Discussion and concluding remarks 84
References 92
Appendix A. Governing equations for the effective beta effect 100
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