Comparison Studies of the Effect of Tibetan Plateau and Rocky Mountains on ENSO Variability
-
摘要: 本文利用耦合气候模式研究了“有/无”青藏高原和落基山脉对厄尔尼诺—南方涛动（ENSO）的影响，并从温度变率方程的角度详细分析了ENSO变化的成因，结果表明：移除青藏高原或落基山脉均会造成ENSO变率增强；ENSO变率在无青藏高原试验中增强的幅度比在无落基山脉试验中更大。ENSO变率在地形敏感性试验中的变化与热带太平洋平均气候态的改变密切相关。移除青藏高原后热带太平洋信风减弱，大气对流中心东移，混合层变浅，温跃层变平，呈现出El Niño型海温分布，这些平均态的变化使海表风应力敏感性，Ekman抽吸敏感性以及温跃层敏感性幅度增强，最终导致ENSO振幅增大60%。然而，在移除落基山脉的情景下，热带太平洋信风变化更加复杂，大气对流中心稍有东移，混合层加深，温跃层变平，呈现出类La Niña型海温分布。这些变化增强了风应力敏感性和温跃层敏感性，最终导致ENSO振幅仅增大15%左右。本文研究表明，在地质时间尺度上青藏高原和落基山脉的抬升均抑制了ENSO变率。Abstract: In this study, the influence of the Tibetan Plateau and the Rocky Mountains on ENSO variability is investigated using a fully coupled climate model. Compared to that in the real world, the amplitude of ENSO variability becomes larger after removing the Tibetan Plateau or the Rocky Mountains. The ENSO amplitude is considerably stronger in a world without the Tibetan Plateau than that without the Rocky Mountains. The ENSO variability is closely linked with the mean climate in the tropical Pacific. The removal of the Tibetan Plateau results in weakened trade winds, an eastward shift of the atmospheric convection center, a shallower mixed layer depth, and an El Niño-like sea surface temperature (SST) distribution. These mean climate changes result in enhanced wind-stress sensitivity, Ekman upwelling sensitivity, and thermocline sensitivity, finally leading to a 60% increase in the ENSO amplitude. However, without the Rocky Mountains, the tropical Pacific shows a more complicated trade wind change, with a slight eastward shift of the atmospheric convection center, a deeper mixed layer depth, a flattened thermocline, and a La Niña-like SST distribution. These mean climate responses strengthen the wind stress and thermocline sensitivities, leading to a 10% increase in the ENSO amplitude. This study suggests that the uplift of the Tibetan Plateau and the Rocky Mountains during the geological period has played an important role in suppressing the ENSO variability.
-
Key words:
- Tibetan Plateau /
- Rocky Mountain /
- ENSO variability
-
图 1 试验地形设置（单位：m）：（a）控制试验；（b）无青藏高原试验；（c）无落基山脉试验
Figure 1. Topographical configuration in model experiments (units: m): (a) Control simulation with realistic topography (CTRL); (b) experiment without the Tibetan Plateau (NoTibet); (c) experiment without the Rocky Mountains (NoRocky)
图 2 Niño-3区域（5°S～5°N, 150°～90°W）平均的（a）SST异常（SSTA，单位：°C）及（b）SST标准差（σ，单位：°C）和（c）功率谱的时间序列。为了能清楚地呈现ENSO的信号，SSTA已进行5～85月的带通滤波;（b）中，σ的计算选取了41年的滑动窗口，分别计算每一个时间窗口的SST方差，进而得到图中的时间序列;。图中绿实线、黑实线、红实线和蓝实线分别代表ERSST在1901～2000年间的观测结果、控制试验、无青藏高原试验和无落基山脉试验。图（c）中虚线代表对应试验95%的置信水平
Figure 2. Time series of (a) SST anomalies (SSTA, units: °C), (b) standard deviation of SSTA σ (SST, units: °C), and (c) power spectrum of SSTA averaged over the Niño-3 region (150°–90°W, 5°S–5°N). The SSTA field is smoothed with a 5–85-month band-pass filter. In (b), the σ (SST) field is smoothed with a sliding window of 41 years. In (a–c), the green, black, red, and blue curves represent data from ERSST observation (1901–2000), CTRL, NoTibet, and NoRocky experiments, respectively. In (c), the dashed curves represent 95% confidence levels
图 3 准平衡态SSTA标准差（单位：°C）的空间分布：（a）控制试验；（b）无青藏高原试验；（c）无落基山脉。绿框代表Niño-3区域
Figure 3. Spatial patterns of the standard deviations of SSTA (units: °C) in the tropical Pacific: (a) CTRL experiment; (b) NoTibet experiment; (c) NoRocky experiment during quasi-equilibrium stage. The green box outlines the Niño-3 region
图 4 准平衡态月数据求得的Niño-3区域SSTA的分布：（a）观测结果；（b）控制试验；（c）无青藏高原试验；（d）无落基山脉试验。SSTA进行了5～85个月的带通滤波。按照李艳等（2019）的做法，本文将每个月距平大于1定义为El Niño事件，小与1定义为La Niña事件
Figure 4. Distributions of SSTA in the Niño-3 region in (a) observation, (b) CTRL, (c) NoTibet, and (d) NoRocky experiments based on the monthly data during the quasi-equilibrium stage. The SSTA field is smoothed with a 5–85-month band-pass filter. The ENSO event represents the magnitude of SSTA larger than 1°C, following Li et al. (2019)
图 5 准平衡态热带太平洋气候态的变化：（a，e）海表风应力（箭头；单位：10−5 N cm−2）和海表温度（填色；单位：°C）的变化；（b，f）降水减去蒸发（单位：10−5 kg m−2 s−1）的变化；（c，g）混合层深度（单位：m）的变化；（d，h）温跃层深度（单位：m）的变化。（a–d）无青藏高原试验的结果；（e–h）无落基山脉试验的结果
Figure 5. Quasi-equilibrium changes in the mean tropical climate: (a, e) SST (units: °C) and surface wind stress (units: 10−5 N cm−2); (b, f) precipitation minus evaporation (PmE, units: 10–5 kg m−2 s−1); (c, g) mixed layer depth (units: m); (d, h) thermocline depth (units: m). (a–d) NoTibet experiment; (e–h) NoRocky experiment
图 6 准平衡态下无落基山脉试验中各物理场的变化：（a）850 hPa位势高度（填色,单位：m）和风矢量（箭头；单位：m s−1）；（b）垂直积分的水汽输送（箭头；单位：kg m−1 s−1）和辐合辐散（填色;
$-{\rho }_{a}\nabla {\boldsymbol{vq}}$ ; units: 10−5 kg m−2 s−1; 正值代表辐合，负值代表辐散）Figure 6. Quasi-equilibrium changes in the NoRocky experiment: (a) Geopotential height (shading; units: m) and wind (vector; m/s) at 850 hPa; (b) vertical integrated moisture transport (vector; units: kg m−1 s−1) and its convergence (shading;
$-{\rho }_{a}\nabla {\boldsymbol{vq}}$ ; 10–5 kg m−2 s−1; positive for convergence while negative for divergence)
图 7 Niño-3区域平均的温度变率方程各项的时间序列（单位：10−6 °C2 s−1）。（a）控制试验;（b）无青藏高原试验;（c）无落基山脉试验。每个变量采用了21年的滑动窗口平均
Figure 7. Time evolution of the terms in the temperature variance equation (units: 10−6 °C2 s−1): (a) CTRL experiment; (b) NoTibet experiment; (c) NoRocky experiment. A 21-year sliding window has been applied to each curve
图 8 Niño-3区域平均的垂直温度平流项分解后各个项的时间序列。左边是控制试验结果，中间是无青藏高原试验结果，右边是无落基山脉试验结果。（a, d, g）代表控制试验中的垂直平流项，其中黑线、蓝线、红线和灰线分别代表温度变率方程中的垂直平流（
$-{wT}'{T}_{z}$ ）、扰动的垂直上升流和平均温度梯度（$-{{w}'T}'\bar{{T}_{z}}$ ）、平均垂直平流和扰动温度梯度（$-\bar{w}T'{{T}'}_{z}$ ）以及非线性项（$-{{w}'T}'{{T}'}_{z}$ ）。（b, e, h）代表局地项的贡献，其中浅蓝线、蓝线和虚灰线代表垂直热通量（$-{{w}'T}'$ ）、扰动的垂直上升流和平均温度梯度（$-w'{T}'\bar{{T}_{z}}$ ）以及平均垂直温度梯度（$\bar{{T}_{z}}$ ），绿线和粉线代表Ekman抽吸异常（$w'$ ）和温度异常（${T}'$ ）。（c, f, i）代表非局地项的贡献，其中褐线、虚灰线和红线代表$-{T}'{T'}_{z}$ ，平均上升流（$\bar{w}$ ）以及平均上升流和扰动温度梯度（$-\bar{w}T'{T'}_{z}$ ），绿线和粉线代表扰动温度的垂直梯度（${T'}_{z}$ ）和温度异常（${T}'$ ）。图中每一项都经过了21年的滑动窗口滤波。（a，d，g）各项的单位是10−6 °C2 s−1，（b，e，h，c，f，i）各项乘以换算系数以便能放在一张图上。$ w $ 和$ {T}_{z} $ 的单位分别是是cm s−1和°C cm−1。$\bar{w}\text{、}\bar{{T}_{z}}$ 、${w'T}'\text{、}{T}'{T'}_{z}\text{、}{T}'\text{、}{T'}_{z}$ 和$w'$ 则分别乘以了2×10−3、10−2、10−3、10−3、10、10−3和10−3Figure 8. Decomposition of the vertical temperature advection term averaged in the Niño-3 region over the thermocline for CTRL (left), NoTibet (middle), and NoRocky (right) experiments. (a, d, g) the black, blue, red, and grey lines represent the total vertical temperature advection (
$-w{T}'{T}_{z}$ ), perturbation upwelling of mean temperature gradient ($-w'{T}'\bar{{T}_{z}}$ ), mean upwelling of perturbation temperature gradient ($-\bar{w}T'{T'}_{z}$ ), and pure nonlinear term ($-w'{T}'{T'}_{z}$ ), respectively. (b, e, h) the light blue, blue, and dashed grey curves represent the vertical heat flux ($-w'{T}'$ ), perturbation upwelling of mean temperature gradient ($-w'{T}'\bar{{T}_{z}}$ ), and mean vertical temperature gradient (${\bar{T}}_{z}$ ), respectively. The green and pink curves represent the Ekman pumping ($w'$ ) and temperature (${T}'$ ) anomalies, respectively. (c, f, i) the dark red, dashed grey, and red curves represent$-{T}'{T'}_{z}$ , mean upwelling ($\bar{w}$ ), and the mean upwelling of perturbation temperature gradient ($-\bar{w}T'{T'}_{z}$ ), respectively. The green and light red curves represent the vertical gradient of temperature anomaly (${T'}_{z}$ ) and temperature anomaly (${T}'$ ), respectively. A 21-year sliding window has been applied to each term. (a, d) the units are 10–6 oC2 s−1. (b, c, e, f) The terms are scaled by a factor of a certain constant such that they can be plotted in the same figure. The units of$ w $ and Tz are cm s−1 and °C cm−1, respectively. The real values of$\bar{w}$ ,${\bar{T}}_{z}$ ,$w'T'$ ,${T}'T'_{z}$ ,${T}'$ ,${T'}_{z}$ , and${w}'$ are plotted after being multiplied by 2×10–3, 10–2, 10–3, 10–3, 10–3, and 10–3, respectively
图 9 Niño-3区域平均的垂直平流项的局地项贡献和非局地项贡献的柱状图分布：（a）局地项及其三个子项；（b）非局地项及其三个子项。各个项的单位和换算系数和图8一致
Figure 9. Bar chart for the mean values of (a) the local term and its components and (b) the remote term and its components averaged in the Niño-3 region over the thermocline. The units and scale factors of these terms are the same as those in Fig. 8
图 10 （a）控制试验中Niño-3区域平均的温度变率与局地项（蓝线）、垂直热通量项（浅蓝线）、非局地项（红线）以及
${-T}'{T'}_{z}$ （深红线）的超前滞后相关（横坐标代表超前/滞后时间，正值代表温度变率滞后于其他变量）。（b）控制试验中Niño-3区域平均的温度异常（$ T' $ ）、海水垂直上升流（$ {w}' $ ）和 Ekman抽吸（$ {{w}'}_{e} $ ）之间的超前滞后相关（黑线、灰线和虚黑线分别代表$ {w}' $ 和$ T' $ 之间、$ {{w}'}_{e} $ 和$ T' $ 之间的相关系数，以及$ {w}'$ 和$ {{w}'}_{e} $ 之间的相关系数。正值代表$ T'$ 滞后于$ {w}' $ ，$ {T}' $ 滞后于$ {{w}'}_{e} $ ，以及$ {{w}'}_{e} $ 滞后于$ {w}' $ ）。（c，d）无青藏高原试验的结果；（e，f）无落基山脉试验的结果Figure 10. (a) Lagged correlations between the temperature variability and local term (blue), between the temperature variability and vertical heat flux (light sky blue), and between the vertical remote term (red) and
$ {-T}'{T'}_{z} $ (dark red) in the Niño-3 region in Real. A positive month means temperature variability lags the other terms. (b) Lagged correlations among the anomalous temperature ($ T' $ ), upwelling ($ {w}' $ ), and Ekman pumping ($ {{w}'}_{e} $ ) over the thermocline in the Niño-3 region in Real. The black, grey, and dashed black curves represent the correlations between$ {w}' $ and$ T' $ , between$ {{w}'}_{e} $ and$ T' $ , and beween$ {w}' $ and$ {{w}'}_{e} $ , respectively. A positive month means the time that$ {T}' $ lags$ {w}' $ ,$ {T}' $ lags$ {{w}'}_{e} $ , and$ {{w}'}_{e} $ lags$ {w}' $ . (c) and (d) are the same as (a) and (b), except for the NoTibet experiment. (e) and (f) are the same as (a) and (b), except for the NoRocky experiment
图 11 （a）控制试验中海表风应力对Niño-3区海温异常的敏感性（
${\;\mu }_{a}$ ，单位：10−3 N m−2 °C−1）；（b）无青藏高原试验与控制试验$ {\;\mu }_{a} $ 的差异；（c）无落基山脉试验与控制试验$ {\;\mu }_{a} $ 的差异；（d）控制试验中温跃层深度上海水垂直速度异常对Niño-3区海表风应力异常的敏感性（$ {\;\beta }_{w} $ ，单位：10−5 m s−1 N−1 m−2）；（e）无青藏高原与控制试验$ {\;\beta }_{w} $ 的差异；（f）无落基山脉与控制试验$ {\;\beta }_{w} $ 的差异Figure 11. Spatial patterns of regression coefficients between the surface wind stress anomalies and the Niño-3 SSTA (
$ {\;\mu }_{a} $ , units: 10–3 N m−2 °C−1) in (a) CTRL, (b)$ {\;\mu }_{a} $ difference between the NoTibet and CTRL experiments, and (c)$ {\;\mu }_{a} $ difference between the NoRocky and CTRL experiments. (d–f) the same as (a–c), except for the regression coefficient between the anomalous upwelling velocity averaged at the thermocline depth and the surface wind stress anomalies averaged over the Niño-3 region ($ {\;\beta }_{w}， $ units: 10–5 m s−1 N−1 m−2) -
[1] Abe M, Yasunari T, Kitoh A. 2004. Effects of large-scale orography on the coupled atmosphere–ocean system in the tropical Indian and Pacific Oceans in boreal summer [J]. J. Meteor. Soc. Japan. Ser. II, 82(2): 745−759. doi: 10.2151/jmsj.2004.745 [2] Ashok K, Behera S K, Rao S A, et al. 2007. El Niño Modoki and its possible teleconnection [J]. J. Geophys. Res.: Oceans, 112(C11): C11007. doi: 10.1029/2006JC003798 [3] Barnett T P, Pierce D W, Latif M, et al. 1999. Interdecadal interactions between the tropics and midlatitudes in the Pacific basin [J]. Geophys. Res. Lett., 26(5): 615−618. doi: 10.1029/1999GL900042 [4] Burgers G, van Oldenborgh G J. 2003. On the impact of local feedbacks in the central Pacific on the ENSO cycle [J]. J. Climate, 16(14): 2396−2407. doi: 10.1175/2766.1 [5] Cai W J, Wang G J, Dewitte B, et al. 2018. Increased variability of eastern Pacific El Niño under greenhouse warming [J]. Nature, 564(7735): 201−206. doi: 10.1038/s41586-018-0776-9 [6] Capotondi A, Wittenberg A, Masina S. 2006. Spatial and temporal structure of tropical Pacific interannual variability in 20th century coupled simulations [J]. Ocean Modelling, 15(3–4): 274–298. doi:10.1016/j.ocemod.2006.02.004 [7] Carton J A. 1991. Effect of seasonal surface freshwater flux on sea surface temperature in the tropical Atlantic Ocean [J]. J. Geophys. Res. :Oceans, 96(C7): 12593−12598. doi: 10.1029/91JC01256 [8] Chen L, Yu YQ, Sun DZ. 2013. Cloud and water vapor feedbacks to the El Niño warming: Are they still biased in CMIP5 models [J]. J. Climate, 26(14): 4947−4961. doi: 10.1175/JCLI-D-12-00575.1 [9] Clarke A J, Van Gorder S, Colantuono G. 2007. Wind stress curl and ENSO discharge/recharge in the equatorial Pacific [J]. J. Phys. Oceanogr., 37(4): 1077−1091. doi: 10.1175/JPO3035.1 [10] Collins M, An S I, Cai W J, et al. 2010. The impact of global warming on the tropical Pacific Ocean and El Niño [J]. Nat. Geosci., 3(6): 391−397. doi: 10.1038/ngeo868 [11] Deser C, Phillips A S, Tomas R A, et al. 2012. ENSO and Pacific decadal variability in the community climate system model version 4 [J]. J. Climate, 25(8): 2622−2651. doi: 10.1175/JCLI-D-11-00301.1 [12] Fallah B, Cubasch U, Prömmel K, et al. 2016. A numerical model study on the behaviour of Asian summer monsoon and AMOC due to orographic forcing of Tibetan Plateau [J]. Climate Dyn., 47(5): 1485−1495. doi: 10.1007/s00382-015-2914-5 [13] Fedorov A V, Philander S G. 2000. Is El Niño changing? [J]. Science, 288 5473): 1997–2002. doi: 10.1126/science.288.5473.1997 [14] Fedorov A V, Philander S G. 2001. A stability analysis of tropical ocean–atmosphere interactions: Bridging measurements and theory for El Niño [J]. J. Climate, 14(14): 3086−3101. doi: 10.1175/1520-0442(2001)014<3086:ASAOTO>2.0.CO;2 [15] Feng R, Poulsen C J. 2014. Andean elevation control on tropical Pacific climate and ENSO [J]. Paleoceanography and Paleoclimatology, 29(8): 795−809. doi: 10.1002/2014PA002640 [16] Gent P R, Danabasoglu G, Donner L J, et al. 2011. The community climate system model version 4 [J]. J. Climate, 24(19): 4973−4991. doi: 10.1175/2011JCLI4083.1 [17] Guan C, McPhaden M J. 2016. Ocean processes affecting the twenty-first-century shift in ENSO SST variability [J]. J. Climate, 29(19): 6861−6879. doi: 10.1175/JCLI-D-15-0870.1 [18] Guilyardi E. 2006. El Niño-mean state-seasonal cycle interactions in a multi-model ensemble [J]. Climate Dyn., 26(4): 329−348. doi: 10.1007/s00382-005-0084-6 [19] Guilyardi E, Braconnot P, Jin F F, et al. 2009. Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme [J]. J. Climate, 22(21): 5698−5718. doi: 10.1175/2009JCLI2815.1 [20] Huang B Y, Banzon V F, Freeman E, et al. 2015. Extended reconstructed sea surface temperature version 4 (ERSST. v4). Part I: Upgrades and intercomparisons [J]. J. Climate, 28(3): 911−930. doi: 10.1175/JCLI-D-14-00006.1 [21] Huber M, Caballero R. 2003. Eocene El Niño: Evidence for robust tropical dynamics in the “Hothouse” [J]. Science, 299(5608): 877−881. doi: 10.1126/science.1078766 [22] Hunke E C, Lipscomb W H. 2010. CICE: The Los Alamos Sea Ice Model, documentation and software user’ s manual, version 4.1. Version 4.0. LA-CC-06-012, Los Alamos National Laboratory Im S H, An S I, Kim S T, et al. 2015. Feedback processes responsible for El Niño–La Niña amplitude asymmetry [J]. Geophys. Res. Lett., 42(13): 5556−5563. doi: 10.1002/2015GL064853 [23] Jiang R, Yang H J. 2021. Roles of the Rocky Mountains in the Atlantic and Pacific meridional overturning circulations [J]. J. Climate, 34(16): 6691−6703. doi: 10.1175/JCLI-D-20-0819.1 [24] Jin F F, Kim S T, Bejarano L. 2006. A coupled-stability index for ENSO [J]. Geophys. Res. Lett., 33(23): L23708. doi: 10.1029/2006GL027221 [25] Kang X B, Huang R H, Wang Z G, et al. 2014. Sensitivity of ENSO variability to Pacific freshwater flux adjustment in the Community Earth System Model [J]. Adv. Atmos. Sci., 31(5): 1009−1021. doi: 10.1007/s00376-014-3232-2 [26] Kim S T, Jin F F. 2011. An ENSO stability analysis. Part II: Results from the twentieth and twenty-first century simulations of the CMIP3 models [J]. Climate Dyn., 36(7): 1609−1627. doi: 10.1007/s00382-010-0872-5 [27] Kim S T, Cai W J, Jin F F, et al. 2014. Response of El Niño sea surface temperature variability to greenhouse warming [J]. Nat. Climate Change, 4(9): 786−790. doi: 10.1038/nclimate2326 [28] Kirtman B P, Schopf P S. 1998. Decadal variability in ENSO predictability and prediction [J]. J. Climate, 11(11): 2804−2822. doi: 10.1175/1520-0442(1998)011<2804:DVIEPA>2.0.CO;2 [29] Kitoh A. 1997. Mountain uplift and surface temperature changes [J]. Geophys. Res. Lett., 24(2): 185−188. doi: 10.1029/96GL03953 [30] Kittel T G F, Thornton P E, Royle J A, et al. 2002. Climates of the Rocky mountains: Historical and future patterns [M]//Baron J S. Rocky Mountain Futures: An Ecological Perspective. Washington: Island Press, 59–92. doi: 10.13140/RG.2.1.1683.0487 [31] Knutson T R, Manabe S, Gu D F. 1997. Simulated ENSO in a global coupled ocean–atmosphere model: Multidecadal amplitude modulation and CO2 sensitivity [J]. J. Climate, 10(1): 138−161. doi: 10.1175/1520-0442(1997)010<0138:SEIAGC>2.0.CO;2 [32] Kürschner W M, Kvaček Z, Dilcher D L. 2008. The impact of Miocene atmospheric carbon dioxide fluctuations on climate and the evolution of terrestrial ecosystems [J]. Proceedings of the National Academy of Sciences of the United States of America, 105(2): 449−453. doi: 10.1073/pnas.0708588105 [33] Latif M, Sterl A, Maier-Reimer E, et al. 1993. Structure and predictability of the El Niño/Southern Oscillation phenomenon in a coupled ocean-atmosphere general circulation model [J]. J. Climate, 6(4): 700−708. doi: 10.1175/1520-0442(1993)006<0700:SAPOTE>2.0.CO;2 [34] Lawrence D M, Oleson K W, Flanner M G, et al. 2012. The CCSM4 land simulation, 1850-2005: Assessment of surface climate and new capabilities [J]. J. Climate, 25(7): 2240−2260. doi: 10.1175/JCLI-D-11-00103.1 [35] Levine A, Jin F F, McPhaden M J. 2016. Extreme noise-extreme El Niño: How state-dependent noise forcing creates El Niño–La Niña asymmetry [J]. J. Climate, 29(15): 5483−5499. doi: 10.1175/JCLI-D-16-0091.1 [36] Li T M, Hogan T F. 1999. The role of the annual-mean climate on seasonal and interannual variability of the tropical Pacific in a coupled GCM [J]. J. Climate, 12(3): 780−792. doi: 10.1175/1520-0442(1999)012<0780:TROTAM>2.0.CO;2 [37] 李艳, 马百胜, 杨宣. 2019. 两类ENSO事件对中国东部地区极端降水的影响 [J]. 长江流域资源与环境, 28(2): 469−482. doi: 10.11870/cjlyzyyhj201902023Li Yan, Ma Baisheng, Yang Xuan. 2019. Impact of two types of ENSO events on the extreme precipitation in eastern China [J]. Resources and Environment in the Yangtze Basin (in Chinese), 28(2): 469−482. doi: 10.11870/cjlyzyyhj201902023 [38] Liu Z Y, Lu Z Y, Wen X Y, et al. 2014. Evolution and forcing mechanisms of El Niño over the past 21, 000 years [J]. Nature, 515(7528): 550−553. doi: 10.1038/nature13963 [39] Lübbecke J F, McPhaden M J. 2014. Assessing the twenty-first-century shift in ENSO variability in terms of the Bjerknes stability index [J]. J. Climate, 27(7): 2577−2587. doi: 10.1175/JCLI-D-13-00438.1 [40] Maffre P, Ladant J B, Donnadieu Y, et al. 2017. The influence of orography on modern ocean circulation [J]. Climate Dyn., 50(3): 1277−1289. doi: 10.1007/s00382-017-3683-0 [41] Meehl G A, Arblaster J M. 1998. The Asian–Australian monsoon and El Nino-Southern Oscillation in the NCAR climate system model [J]. J. Climate, 11(6): 1356−1385. doi: 10.1175/1520-0442(1998)011<1356:TAAMAE>2.0.CO;2 [42] Meehl G A, Gent P R, Arblaster J M, et al. 2001. Factors that affect the amplitude of El Nino in global coupled climate models [J]. Climate Dyn., 17(7): 515−526. doi: 10.1007/PL00007929 [43] Naiman Z, Goodman P J, Krasting J P, et al. 2017. Impact of mountains on tropical circulation in two earth system models [J]. J. Climate, 30(11): 4149−4163. doi: 10.1175/JCLI-D-16-0512.1 [44] Park S, Bretherton C S, Rasch P J. 2014. Integrating cloud processes in the community atmosphere model, version 5 [J]. J. Climate, 27(18): 6821−6856. doi: 10.1175/JCLI-D-14-00087.1 [45] Philip S, van Oldenborgh G J. 2006. Shifts in ENSO coupling processes under global warming [J]. Geophys. Res. Lett., 33(11): L11704. doi: 10.1029/2006GL026196 [46] 钱维宏, 丑纪范. 1996. 地气角动量交换与ENSO循环 [J]. 中国科学(D辑), 26(1): 80–86. Qian Weihong, Chou Jifan. 1996. Atmosphere-earth angular momentum exchange and ENSO cycle [J]. Science in China (Series D), (2): 215–224. [47] Santoso A, Cai W, England M H, et al. 2011. The role of the Indonesian throughflow on ENSO dynamics in a coupled climate model [J]. J. Climate, 24(3): 585−601. doi: 10.1175/2010JCLI3745.1 [48] Schmittner A, Silva T A M, Fraedrich K, et al. 2011. Effects of mountains and ice sheets on global ocean circulation [J]. J. Climate, 24(11): 2814−2829. doi: 10.1175/2010JCLI3982.1 [49] Sinha B, Blaker A T, Hirschi J J M, et al. 2012. Mountain ranges favour vigorous Atlantic meridional overturning [J]. Geophys. Res. Lett., 39(2): L02705. doi: 10.1029/2011GL050485 [50] Smith R S, Gregory J M. 2009. A study of the sensitivity of ocean overturning circulation and climate to freshwater input in different regions of the North Atlantic [J]. Geophys. Res. Lett., 36(15): L15701. doi: 10.1029/2009GL038607 [51] Stefanick M. 1982. Interannual atmospheric angular momentum variability 1963-1973 and the Southern Oscillation [J]. J. Geophys. Res. :Oceans, 87(C1): 428−432. doi: 10.1029/JC087iC01p00428 [52] Su B H, Jiang D B, Zhang R, et al. 2018. Intensified Atlantic vs. weakened Pacific meridional overturning circulations in response to Tibetan Plateau uplift [J]. Climate Past Discuss.: 1−27. doi: 10.5194/cp-2017-110 [53] Sun Y, Wang F, Sun D Z. 2016. Weak ENSO asymmetry due to weak nonlinear air–sea interaction in CMIP5 climate models [J]. Adv. Atmos. Sci., 33(3): 352−364. doi: 10.1007/s00376-015-5018-6 [54] Tett S. 1995. Simulation of El Nino–Southern oscillation-like variability in a global AOGCM and its response to CO2 increase [J]. J. Climate, 8(6): 1473−1502. doi: 10.1175/1520-0442(1995)008<1473:SOENSO>2.0.CO;2 [55] Timmermann A, Oberhuber J, Bacher A, et al. 1999. Increased El Niño frequency in a climate model forced by future greenhouse warming [J]. Nature, 398(6729): 694−697. doi: 10.1038/19505 [56] Valdes P J, Hoskins B J. 1991. Nonlinear orographically forced planetary waves [J]. J. Atmos. Sci., 48(18): 2089−2106. doi: 10.1175/1520-0469(1991)048<2089:NOFPW>2.0.CO;2 [57] Vecchi G A, Soden B J. 2007. Global warming and the weakening of the tropical circulation [J]. J. Climate, 20(17): 4316−4340. doi: 10.1175/JCLI4258.1 [58] Vega-Westhoff B, Sriver R L. 2017. Analysis of ENSO’s response to unforced variability and anthropogenic forcing using CESM [J]. Sci. Rep., 7(1): 18047. doi: 10.1038/s41598-017-18459-8 [59] 王绍武, 龚道溢. 1999. 近百年来的ENSO事件及其强度 [J]. 气象, 25(1): 9−14. doi: 10.3969/j.issn.1000-0526.1999.01.002Wang Shaowu, Gong Daoyi. 1999. ENSO events and their intensity during the past century [J]. Meteorological Monthly (in Chinese), 25(1): 9−14. doi: 10.3969/j.issn.1000-0526.1999.01.002 [60] Wen Q, Döös K, Lu Z Y, et al. 2020. Investigating the role of the Tibetan Plateau in ENSO variability [J]. J. Climate, 33(11): 4835−4852. doi: 10.1175/JCLI-D-19-0422.1 [61] Wolf W L, Smith R B. 1987. Length-of-day changes and mountain torque during El Niño [J]. J. Atmos. Sci., 44(24): 3656−3660. doi: 10.1175/1520-0469(1987)044<3656:LODCAM>2.0.CO;2 [62] Yang H J, Zhang Q. 2008. 3 [J]. J. Climate, 21(24): 6539−6555. doi: 10.1175/2008JCLI2324.1 [63] Yang H J, Li Q, Wang K, et al. 2015. Decomposing the meridional heat transport in the climate system [J]. Climate Dyn., 44(9): 2751−2768. doi: 10.1007/s00382-014-2380-5 [64] Yang H J, Shen X C, Yao J, et al. 2020. Portraying the impact of the Tibetan Plateau on global climate [J]. J. Climate, 33(9): 3565−3583. doi: 10.1175/JCLI-D-18-0734.1 [65] Yang H J, Wen Q. 2020. Investigating the role of the Tibetan Plateau in the formation of Atlantic meridional overturning circulation [J]. J. Climate, 33(9): 3585−3601. doi: 10.1175/JCLI-D-19-0205.1 [66] Zebiak S E, Cane M A. 1987. A model El Niño–southern oscillation [J]. Mon. Wea. Rev., 115(10): 2262−2278. doi: 10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2 [67] Zhang T, Sun D Z. 2014. ENSO asymmetry in CMIP5 models [J]. J. Climate, 27(11): 4070−4093. doi: 10.1175/JCLI-D-13-00454.1 -
下载:
点击查看大图
计量
- 文章访问数: 472
- HTML全文浏览量: 86
- PDF下载量: 109
- 被引次数: 0
