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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃彥婷(Yen-Ting Hwang) | |
| dc.contributor.author | Yuan-Jen Lin | en |
| dc.contributor.author | 林沅箴 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:06:30Z | - |
| dc.date.copyright | 2022-07-05 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-06-29 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84218 | - |
| dc.description.abstract | 此篇論文的最終目標是建立一個對於氣候敏感度之不確定性更全面的理解。具體而言，此論文闡述海洋在影響氣候敏感度所扮演的角色。透過改變海表溫度的結構，高緯度的海洋熱吸收對全球的對流層穩定度與輻射收支都相當關鍵。同時，本論文將多個完整耦合的氣候模式裡的氣候敏感度之不確定性部分追溯至北大西洋深海環流模擬之不確定性，並且提供物理過程來解釋北大西洋深海環流模擬的不確定性與其和氣候平均場的連結。 在大多數完整耦合的氣候模式中，淨氣候回饋在二氧化碳變為兩倍或四倍的模擬下，於數十年內會越趨敏感，這代表使用數十年的暖化資料來推估平衡態的氣候敏感度會造成低估。過去的研究將此低估歸因至熱帶太平洋的海表溫度之結構變化。在此研究中，我更深入地去理解海表溫度之結構的形成機制。我將簡化的淺水模式裡所建構之格林函數與完整耦合模式裡所診斷之海洋熱吸收兩者進行整合，由此將氣候回饋隨時間的改變定量地歸因至不同區域的海洋熱吸收。研究結果顯示南大洋的熱吸收隨著時間有不同的減弱幅度，此為造成模式 CESM1 裡氣候回饋發生改變的主要因素。在二氧化碳增加為四倍的數十年過後，南大洋的熱吸收改變能透過遙相關過程使熱帶東南太平洋的海表溫度有增強的暖化作用，此將使對流層的穩定度降低，導致雲的輻射回饋更加敏感。 當模式們大致上都呈現南大洋熱吸收隨時間減弱與東南太平洋的暖化隨時間增強，模式們對於北大西洋的中高緯海洋熱吸收與海表溫度的隨時間反應卻有著很大的不確定性。無論是CMIP5或CMIP6 模式，研究結果均顯示模式間之所以在氣候回饋改變之推估上有所差異，部分可以歸因至各個模式的北大西洋深海環流在暖化下有不同的隨時間反應。那些預測北大西洋深海環流在暖化後期有強度回復的模式，會伴隨北半球更顯著的升溫，此結果與北大西洋深海環流將南半球的能量傳往北半球之特性一致。更顯著的北半球升溫將透過降低低對流層的穩定度，導致穩定度所貢獻的輻射回饋與短波雲輻射回饋隨時間更加敏感。此二者貢獻至淨氣候回饋隨時間越趨敏感的變化。 鑒於北大西洋深海環流的強度改變能夠對具重要輻射意義的海表溫度產生影響，此研究就北大西洋深海環流的預測不確定性之時間尺度與物理過程進行探討。我發現若模式在氣候平均態有較強的北大西洋深海環流，將會對應層化較弱的拉不拉多海，亦即當地較強的混合作用。因此，當二氧化碳增加時，這樣的模式可以將海表的暖水更有效率地混合到拉不拉多海的次表層，尤其是在每年的冬春季。無論是在完整的耦合模式裡進行診斷或是在純海洋模式裡進行溫度強迫之模擬，都顯示拉不拉多海的次表層相對暖化會在數年後導致副熱帶的北大西洋深海環流強度減弱。此結果強調了全球暖化下海洋反應的預測很大程度被海洋的氣候平均場所控制。 綜上所述，本篇論文分別闡述了兩個看似獨立的問題：（一）氣候回饋與氣候敏感度在中長期預測的不確定性（二）海洋環流的模擬不確定性。同時，本論文也連結了上述兩項不確定性。海洋不僅可以透過影響當地的熱吸收來影響氣候回饋的發展，也能透過遙相關過程影響全球的海溫結構與對流層穩定度，進而改變氣候回饋隨時間的變化。 | zh_TW |
| dc.description.abstract | The overarching goal of this thesis is to construct a more comprehensive understanding regarding the uncertainty of climate sensitivity. Specifically, the thesis demonstrates the role of ocean in altering the climate sensitivity. Through shaping the sea surface temperature (SST) pattern, the ocean heat uptake in high latitudes is key to variations in global tropospheric stability and radiative budget. Part of the climate sensitivity uncertainty in multiple fully-coupled climate models are traced to the uncertainty of the Atlantic Meridional Overturning Circulation (AMOC) projections. Physical processes to explain the uncertain AMOC and their mean state dependence are also provided. In most fully-coupled climate models, the net radiative feedback becomes more amplifying few decades after CO2 doubling or quadrupling, indicating an underestimation of equilibrium climate sensitivity inferred from decadal warming. Previous studies have attributed the underestimation to variations in the sea surface temperature patterns over the tropical Pacific. In this study, I take a step further to understand the forming mechanisms of the surface temperature patterns. I quantify the dependence of time-evolving radiative feedbacks on regional ocean heat uptake (OHU) by convolving the Green's Function derived from a simplified, slab-ocean model with the diagnosed OHU from a fully-coupled model. The results suggest that the time-dependent weakening of OHU over the Southern Ocean is the main contribution to the net radiative feedback change in the model CESM1. The remote impact from OHU over the Southern Ocean gives rise to increasingly enhanced surface warming in the Southeastern Pacific, which leads to decreasing tropospheric stability and more sensitive cloud feedback decades after quadrupling CO2. While models generally show robust weakening of the Southern Ocean heat uptake and enhanced Southeastern Pacific warming with time, their projections of OHU and surface warming over North Atlantic are highly uncertain. In both CMIP5 and CMIP6 models, results show that the inter-model spread of changes in net radiative feedback can be partially traced to the time evolution of the AMOC in each model. Models with stronger AMOC recovery tend to project more amplified warming in the Northern Hemisphere, consistent with stronger northward heat transport by the AMOC. The relatively amplified warming in the Northern Hemisphere leads to larger increases in both lapse‐rate and shortwave cloud feedbacks through decreasing the low-level tropospheric stability, accounting for a more amplifying net radiative feedback with time. Since the changes in the AMOC strength could modulate the radiatively-important surface warming pattern, timescales and processes for the inter-model spread of the projected AMOC weakening are investigated. I report that the models with stronger AMOC strength in the mean state climate are associated with less stratified upper Labrador Sea, allowing for stronger mixing. Hence, in response to CO2 increase, the surface warming is mixed to the subsurface Labrador Sea more efficiently, especially in winter and spring. Both diagnostics from fully-coupled models and temperature-perturbing simulations within an ocean-only model suggest that the relatively enhanced subsurface warming in the Labrador Sea would further lead to stronger AMOC weakening in subtropics in several years, highlighting the mean state control of ocean circulation in projected oceanic responses under global warming. Overall, the thesis elaborates on two seemingly independent questions: (1) the uncertainty of long-term projections of climate feedback and sensitivity (2) the uncertainty of ocean circulation projections. At the same time, the thesis fills the gap by linking the above two uncertainties — the ocean modifies the time evolution of climate feedback not only through the local ocean heat uptake, but also via the teleconnections that shape the global SST pattern and tropospheric stability. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:06:30Z (GMT). No. of bitstreams: 1 U0001-2706202213365300.pdf: 20347125 bytes, checksum: dda8a1d5a373e08fd503ad2f6a756428 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Table of Contents Chapter 1. Introduction 1 1.1. Radiative feedback and climate sensitivity 1 1.2. Pattern effect and Green’s Function — sea surface temperature perspective 2 1.3. Formation of the warming pattern 4 1.4. The changing ocean under global warming 5 1.5. The approach of this study and the thesis outline 7 Chapter 2. Attributing the time dependence of radiative feedback to regional ocean heat uptake 9 2.1. Introduction 9 2.2. Linearity of the coupled climate 11 2.3. Green’s Function approach — ocean heat uptake perspective 13 2.3.1. Patch experiments 13 2.3.2. Constructing the Green’s Function matrix 15 2.3.3. Convolving the Green’s Function matrix with ocean heat take 15 2.4. Attribution of the radiative feedback evolution 16 2.5. The dominant role of Southern Ocean heat uptake 19 2.6. Conclusion 25 Chapter 3. Uncertainty of the time dependence of radiative feedback traced to the strength of the Atlantic Meridional Overturning Circulation 27 3.1. Introduction 27 3.2. Data and Methods 28 3.3. AMOC strength and the time-evolving radiative feedback 30 3.4. Interpretating the inter-model uncertainty of the radiative feedback time evolution 33 3.4.1. Surface warming pattern time evolution 33 3.4.2. Tropospheric stability time evolution 34 3.4.3. Radiative feedback time evolution 37 3.5. The unforced variability of the AMOC: impacts on the SST pattern and radiative imbalance 40 3.6. Conclusion 42 Chapter 4. The mean state control of the slowdown of the Atlantic Meridional Overturning Circulation 43 4.1. Introduction 43 4.2. AMOC strength: evaluation, climatology, and projections 44 4.2.1. CMIP6 models data 44 4.2.2. AMOC strength evaluation 45 4.2.3. Dependence of AMOC weakening on AMOC climatology 45 4.3. Mean state climate in the North Atlantic 47 4.4. Surface climate in the Labrador Sea 48 4.4.1. Sea ice 48 4.4.2. Surface energy budget 50 4.5. Subsurface responses in the Labrador Sea 52 4.5.1. Subsurface warming 52 4.5.2. Attribution of the subsurface buoyancy flux 54 4.5.3. 1pctCO2 simulations 57 4.5.4. Time series analysis 57 4.6. Idealized Experiments 59 4.6.1. Model 59 4.6.2. Experiment design 60 4.6.3. Results 61 4.7. Conclusion 64 Chapter 5. Summary and Discussion 67 References 71 Figures 115 Tables 152 Appendices 158 Index of Figures Figure 2.1. Global-mean (a) ΔRnet and (b) ΔTS in CESM1 (black). The red lines indicate the equilibrium responses in the slab-ocean model. The blue lines are the Green’s Function reconstructed responses with transient OHU (equation (2.7)). The dashed gray lines are the summation of the red and blue lines. (c) Zonal-mean OHU in CESM1 (positive down). (d) Scatterplot of global-mean ΔRnet and ΔTS in CESM1 (Gregory et al., 2004). 116 Figure 2.2. (Reproduction of Figure 1 in F. Liu et al. (2018), with two more pairs of simulations) Configuration of 108 ocean q-flux perturbation patches, with each being illustrated by the 6 Wm-2 contours. Note that the size of the patches is actually larger than the contoured area. 117 Figure 2.3. Schematic diagram of the decomposition of net radiative feedback (λ), modified from Roe (2009). 118 Figure 2.4. (a) Decomposition of the increase in net radiative feedback (δλ). (b) Same as (a), but with full data (including the first five years). 119 Figure 2.5. (a) δλCld pattern in CESM1. (b) δλCld pattern reconstructed by the Green’s Function with global OHU evolution. (c) δλCld pattern reconstructed by the Green’s Function with OHU only in 30°S-90°S. (d, e, f) Same as (a, b, c), but for evolution of surface temperature (δTS). (g, h, i) Same as (a, b, c), but for evolution of EIS (δEIS). 120 Figure 2.6. Same as Figure 2.5, but with full data (including the first five years). 121 Figure 2.7. (a) 30S-90S averaged OHU in 25 CMIP5 models. The black line denotes the multimodel mean and the blue line marks the CESM1. (b) Same as (a), but for 30N-90N averaged OHU. The used CMIP5 models are listed in Table 2.1. (c) 30S-90S averaged OHU in 30 CMIP6 models. The black line denotes the multimodel mean and the blue line marks the three models from the CESM2 group. (b) Same as (a), but for 30N-90N averaged OHU. The used CMIP6 models are listed in Table 2.2. 122 Figure 2.8. Shortwave cloud feedback changes in CESM1 due to changes in (a) scattering (b) cloud fraction, determined by the approximate partial radiative perturbation (APRP) method, which estimates shortwave cloud radiative anomalies and their components of cloud amount, scattering, and absorption (Taylor et al., 2007). (c) The evolution of liquid water path in 30S-90S and in Southeastern Pacific (gray box in (a) and (b)). 123 Figure 2.9. WP index and S index from the model output (black), from the Green’s Function reconstruction (blue), and their attribution to regional OHU. 124 Figure 2.10. (a) Residuals from the Green’s Function reconstruction of the change in surface warming pattern (i.e., the difference between Figure 2.5(d) and 2.5(e)) (b) Residuals from the Green’s Function reconstruction of the change in net cloud feedback (i.e., the difference between Figure 2.5(a) and 2.5(b)). 125 Figure 2.11. (a, b, c) Nonlinear TS response to uniform 1 Wm-2 warming/cooling over (a) 30S-90S, (b) 30S-30N, and (c) 30N-90N. (d, e, f) Similar to (a, b, c), but for net cloud radiative anomaly. Numbers in the top left corner of each figure denote global-mean values. The responses are normalized by their relative global-sum forcing amplitudes. 126 Figure 2.12. (a) Change in net cloud feedback pattern due to 30N-90N OHU. (b) Change in surface warming pattern due to 30N-90N OHU. (c) Change in EIS pattern due to 30N-90N OHU. 127 Figure 3.1. (a) The time evolution of AMOC strength in abrupt4×CO2 simulations. The strength at year 0 is the 150-year mean in corresponding parallel piControl simulations. The black line indicates the multimodel mean, while the thick red (blue) line indicates the high (low) AMOC index composite mean, and the thin red (blue/gray) lines are from individual models with high (low/medium) AMOC index. (b) δλ versus the AMOC index. Each dot is one model, labeled in the box and colored according to the AMOC index. (c-d) Same as (a-b), but for CMIP6 models. 128 Figure 3.2. (a) Multimodel-mean pattern evolution of surface air temperature (δTAS). Hatching denotes an absolute multimodel mean < 1 standard deviation across models. (b) The first EOF pattern of δTAS across models. Statistical significance is assessed by regressing δTAS onto the PC according to the first EOF. (c) The regression slopes of δTAS against the AMOC index. (d) Zonally-averaged δTAS. The meaning of colored lines is the same as in Figure 1a. The gray shading represents the multimodel mean ± 1 standard deviation (K/K) across models. Meshing in (b) and (c) denotes the significance at 95% confidence level. (e) The PC corresponding to the first EOF of δTAS versus the AMOC index. 129 Figure 3.3. Same as Figure 3.2, but for CMIP6 models. 130 Figure 3.4. (a) The shading shows the multimodel-mean GMOC evolution (Sv/K). Contours show the GMOC climatology (Sv), with clockwise direction in solid contours and counterclockwise direction in dashed contours. Dots indicate the regions where 1 standard deviation of GMOC evolution across models is larger than its multimodel-mean magnitudes. (b) The shading shows the first EOF pattern of the GMOC evolution across models, which explains 49.1% of the total variance. Contours show the GMOC climatology (Sv), with clockwise direction in solid contours and counterclockwise direction in dashed contours. 131 Figure 3.5. (a) Multimodel-mean pattern evolution of estimated inversion strength (δEIS). Hatching indicates that the absolute value of the multimodel mean is smaller than 1 standard deviation of the inter-model spread. 132 Figure 3.6. The regression slopes of (a) EIS evolution (δEIS) (d) zonal-mean potential temperature evolution (δθ), and (e) 250 hPa zonal wind evolution (δU250) against the AMOC index. Stippling and meshing denote the significance at 95% confidence level. Contours in (a) denote the anomalous δTAS relative to the warm pool (black box), with solid red (dashed green) indicating a more positive (negative) δTAS. This is done only in the tropics. (b) Zonally-averaged δEIS. The meaning of colored lines and shading is the same as in Figure 2d. (c) Global-mean δEIS versus the AMOC index. 133 Figure 3.7. (a) The regression slopes of lapse-rate feedback evolution (δLR) against the AMOC index, with meshing denoting the significance at 95% confidence level. (b) Zonally-averaged δLR. The meaning of colored lines and shading is the same as in Figure 2d. (c) The global-mean δLR versus the AMOC index. (d, e, f) Same as (a, b, c) but for shortwave cloud feedback evolution (δSWcld). 134 Figure 3.8. Same as Figure 3.7, but for CMIP6 models. 135 Figure 3.9. Shading shows the regression slopes of the zonal-mean zonal wind evolution against the AMOC index, with gray stippling denoting the significance at the 95% confidence level. Contours indicate the regression slopes of the atmospheric mass streamfunction evolution against the AMOC index, with clockwise direction in solid contours and counterclockwise direction in dashed contours. 136 Figure 3.10. Same as Figure 3.5, but for (a-c) surface albedo feedback, (d-f) relative humidity feedback, (g-i) longwave cloud feedback, and (j-l) net cloud feedback. 137 Figure 3.11. Same as Figure 3.10, but for CMIP6 models. 138 Figure 3.12. Multimodel-mean regression slopes of 5-year low-pass-filtered (a) surface air temperature, (c) EIS, (e) lapse-rate radiative anomaly, and (g) shortwave cloud radiative anomaly against 5-year low-pass-filtered AMOC strength while holding global-mean surface air temperature anomaly fixed in pre-industrial simulations. Meshing indicates that 10 out of the 15 analyzed models agree on the sign. (b, d, f, h) The black line is the zonal average of (a), (c), (e), and (g), respectively, and light gray lines are the zonal averages of individual models. 139 Figure 4.1. (a) Climatological AMOC strength in 31 CMIP6 models. The strongest 10 models are colored in red (S10) and the weakest 10 models are in blue (W10). The remaining 11 models are in gray. Solid lines denote the ensemble mean of AMOC climatology in each group. Dashed lines denote the 11-30 years mean AMOC strength in abrupt-4×CO2 simulations in each group. Dotted lines denote the 131-150 years mean AMOC strength in 1pctCO2 simulations in each group. (b) AMOC weakening in abrupt-4×CO2 simulations, sorted by the climatological AMOC strength. The black line denotes the 31-models mean. Red and blue lines denote the ensemble mean from S10 and W10 models, respectively. (c) Same as (b), but for 1pctCO2 simulations. 140 Figure 4.2. Dependence of climatological potential density relative to the surface, potential temperature, and salinity on AMOC climatology. Regression slopes of climatological, 0–200m averaged (a) potential density relative to the surface (b) potential temperature (c) salinity against the AMOC climatology. Stippling denotes the significance at 95% confidence level. (d)–(f) Same as (a)–(c), but for the cross-section from the Labrador Sea to GIN sea (track shown by the black line in the top row). Area-weighted average of (g) potential density relative to the surface (h) potential temperature (i) salinity in the Labrador Sea (region shown by the polygon in the top row). Black line denotes the 31-models mean, with red and blue lines denoting the ensemble mean from S10 and W10 models, respectively. Black lines on the right panel show the difference between red and blue lines (S10-W10). 141 Figure 4.3. (a) Regression slopes of climatological sea ice cover against AMOC climatology. Stippling denotes the significance at 95% confidence level. (b) Monthly sea ice cover climatology (repeating annual cycle; crosses lines) and in abrupt-4×CO2 simulations (dots lines). The red and blue lines denoting the ensemble mean from S10 and W10 models, respectively. Note that the three models BCC-CSM2-MR, BCC-ESM1, MCM-UA-1-0 are not included due to unavailable sea ice data. 142 Figure 4.4. Downward anomalies of (a) net surface flux (b) the sum of the two turbulent fluxes (sensible heat flux and latent heat flux) (c) net shortwave radiation, and (d) net longwave radiation averaged over the Labrador Sea in abrupt-4×CO2 simulations. Positive values indicate downward anomalies and negative values indicate upward anomalies. The left column shows the monthly-mean responses in the first 5 years, and the right column shows the annual-mean responses in the first 10 years. Note that the model MCM-UA-1-0 is not included due to unavailable data. 143 Figure 4.5. Time evolution of the Labrador Sea averaged potential temperature responses in the models with (a) strong AMOC climatology (S10 models) (b) weak AMOC climatology (W10 models), and (c) their difference (S10-W10) in abrupt-4×CO2 simulations. The left column shows the monthly-mean responses in the first 5 years, and the right column shows the annual-mean responses in the first 10 years. 144 Figure 4.6. Time evolution of the Labrador Sea averaged (a) potential density responses. Here we only show the difference between S10 and W10 ensemble mean. The potential density changes can be further decomposed into (b) the contribution from potential temperature (ΔρT), (c) the contribution from salinity (ΔρS), and (d) the nonlinear term (Δρnonlinear). The left column shows the monthly-mean responses in the first 5 years, and the right column shows the annual-mean responses in the first 10 years. 145 Figure 4.7. Time evolution of the Labrador Sea averaged potential temperature responses in the models with (a) strong AMOC climatology (S10 models) (b) weak AMOC climatology (W10 models), and (c) their difference (S10-W10) in 1pctCO2 simulations. 146 Figure 4.8. The comparison between the year when the subsurface (500-1500m) density in the Labrador Sea first has a significant decreasing trend at 99% confidence level (p<0.01; orange circles) and the AMOC strength first has a significant weakening trend at 99% confidence level (p<0.01; black crosses) in (a) abrupt-4×CO2 simulations and (b) 1pctCO2 simulations. The model on the y-axis is sorted by its AMOC climatology. Note that in 1pctCO2 simulations, the starting point for calculating the trend is the 40th year, as this is the time when the magnitudes of AMOC weakening starts to differ among models. 147 Figure 4.9. (a) Potential temperature forcing (Tforcing) in the Labrador Sea in six experiments. (b) AMOC strength at 35°N and 1,000 m depth. For each experiment, ensembles are shown as the thin lines and the ensemble mean as the thick line. 148 Figure 4.10. Ensemble-mean anomalies of mass streamfunctions at 1000 m in each experiment. 149 Figure 4.11. Ensemble-mean responses of (a) potential density relative to the surface (b) potential temperature, and (c) salinity along the cross-section of 35°N at the year 5 in each experiment. 150 Figure 4.12. The winter and spring schematic illustrating the contrasting mean state climate and projected climate under CO2 forcing in the Labrador Sea between the models with (a) strong and (b) weak mean state AMOC strength. Fturb indicates the sum of the two turbulent fluxes (sensible heat flux and latent heat flux) in each climate state. Similarly, the blue vortex sign represents the strength of the upper ocean mixing and the blue line shows the vertical density profile in the Labrador Sea in each climate state. To better describe the CO2-induced responses, “Δ” indicates the anomalous quantity under CO2 forcing relative to the mean state climate. For example, ΔSW shows the net shortwave responses and ΔT shows the potential temperature responses to CO2 forcing. 151 Index of Tables Table 2.1. List of the 25 CMIP5 models used in this doctoral thesis. 154 Table 2.2. List of the 31 CMIP6 models used in this doctoral thesis. For ocean heat uptake calculation, 30 out of the above 31 models are used (except for MCM-UA-1-0 due to the unavailable data; Figure 2.7). 156 Table 3.1. The correlation coefficients between AMOC indices that are calculated based on different cutoff years between the fast and slow responses. For example, 0.85 is the correlation coefficient between the AMOC indices based on cutoffs at Year 20 and Year 5. 157 | |
| dc.language.iso | en | |
| dc.subject | 北大西洋深海環流 | zh_TW |
| dc.subject | 氣候敏感度 | zh_TW |
| dc.subject | 氣候回饋 | zh_TW |
| dc.subject | 海洋熱吸收 | zh_TW |
| dc.subject | 海表溫度形成 | zh_TW |
| dc.subject | 大氣海洋交互作用 | zh_TW |
| dc.subject | 南大洋 | zh_TW |
| dc.subject | atmosphere–ocean interaction | en |
| dc.subject | Atlantic Meridional Overturning Circulation | en |
| dc.subject | Southern Ocean | en |
| dc.subject | climate sensitivity | en |
| dc.subject | radiative feedback | en |
| dc.subject | ocean heat uptake | en |
| dc.subject | sea surface temperature formation | en |
| dc.title | 氣候回饋與海洋：全球暖化下兩者的不確定性與交互作用 | zh_TW |
| dc.title | Climate feedback and the ocean: uncertainties and their interaction under global warming | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0002-0710-9143 | |
| dc.contributor.oralexamcommittee | 隋中興(Chung-Hsiung Sui),羅敏輝(Min-Hui Lo),吳健銘(Chien-Ming Wu),曾于恒(Yu-Heng Tseng),李時雨(Shih-Yu Lee),布萊恩羅絲(Brian E. J. Rose) | |
| dc.subject.keyword | 氣候敏感度,氣候回饋,海洋熱吸收,海表溫度形成,大氣海洋交互作用,南大洋,北大西洋深海環流, | zh_TW |
| dc.subject.keyword | climate sensitivity,radiative feedback,ocean heat uptake,sea surface temperature formation,atmosphere–ocean interaction,Southern Ocean,Atlantic Meridional Overturning Circulation, | en |
| dc.relation.page | 160 | |
| dc.identifier.doi | 10.6342/NTU202201140 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-06-30 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 大氣科學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-05 | - |
| 顯示於系所單位： | 大氣科學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2706202213365300.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 19.87 MB | Adobe PDF |
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