Impacts of Global Warming on a Super Madden Julian Oscillation Event in the WRF Simulation

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楊斯惟(Szu-Wei Yang) 查詢紙本館藏

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大氣科學學系

論文名稱

(Impacts of Global Warming on a Super Madden Julian Oscillation Event in the WRF Simulation)

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摘要(中)

前人研究結果顯示，在全球暖化的情境下，Madden Julian Oscillation (MJO)會增強且其時間週期會傾向縮短。因此為了更加瞭解 MJO在全球暖化下變化的情形，本篇研究使用 pseudo global warming method 以及雲解析模式針對單一強MJO個案進行模擬分析。此實驗中採用WRF3.9.1版本進行模擬，其中有兩組實驗分別為控制組以及全球暖化情境的實驗組。在控制組實驗中，使用兩層巢狀網格，其分別為 27公里以及 9公里，並以 ERA-interim再分析資料作為模擬的初始及邊界條件。實驗組則是利用CMIP5中26組系集模式的RCP8.5暖化情境資料，將氣候變異值加在控制組的初始及邊界條件上，並將做完動力降尺度的結果，視為全球暖化的情境。
模擬結果顯示，在全球暖化下，該 MJO事件在降雨強度上有顯著的增強，此外對流結構也更傾向深對流發展。相速方面，全球暖化影響MJO的相速增快。上述現象與 Gross Moist Stability (GMS)增加有密切的關係，透過 Chou et al. (2013)的方法，並針對動力部分做進一步的分析，我們發現其增加來自於，全球暖化下乾靜能梯度變化效應、水氣梯度變化效應、對流結構改變以及雲頂效應所相互造成。其中乾靜能梯度變化由溫度梯度變化所主導，溫度垂直梯度變小會使 GMS加大，進而讓大氣更加穩定；至於水氣的垂直梯度，豐沛的底層水氣而有所增加，
增強的水氣垂直梯度使環境趨向不穩定。在水氣與溫度變化效應互相抗衡之下，動力效應顯得格外重要，對流結構的加深以及對流層頂的升高使環境輸出濕靜能的效率增強GMS加大。除此之外，本研究並使用NGMS plane來探討局部地區 MJO水文循環在全球暖化情境下的變化，獲得相似的結果。

摘要(英)

The Madden Julian Oscillation (MJO) is expected to become stronger while its period tends to be shorter with a higher temperature. To know how an MJO event might change under global warming, we use the pseudo global warming approach with a cloud-resolving model to simulate a super MJO event in this study. The Weather Research and Forecasting (WRF) model with two layers of nested domains, with a horizontal resolution of 27 km and 9 km, respectively, is utilized. Initial and boundary conditions are taken from the ERA-Interim reanalysis data. Changes in atmospheric variables under global warming are calculated from the multi-model ensemble means of twenty-four CMIP5 models under the RCP8.5 scenario, and the global warming conditions are produced by adding these changes to the control simulation’s initial and boundary conditions.
The simulation results show that MJO becomes much more intense under global warming, featured by enhanced surface precipitation and stronger deep (top-heavy) convection, along with a faster MJO phase speed. The above features are associated with a greater gross moist stability (GMS) of the atmosphere. By decomposing the change in GMS into the change of gross dry stability (GDS), the change of gross moisture condensation (GMC), the change of the convection structure and the cloud top effects, we find that changes in GDS, convection structure and convective depth are responsible for a greater GMS. Furthermore, the GMS plane analysis is employed to elucidate the physical implications for a faster hydrological cycle of MJO under global warming.

關鍵字(中)

★ 馬登－朱利安振盪

關鍵字(英)

★ Madden Julian Oscillation

論文目次

摘要 ii
Abstract iii
Acknowledgements iv
Table of Contents v
List of Figures vi
List of Tables ix
Chapter 1 Introduction 1
Chapter 2 Data and methodology 5
2.1 Experiment design 5
2.2 Data 6
2.3 Selection of a Super MJO Event 7
2.4 Gross Moist Stability Analysis 7
2.5 NGMS plane analysis 8
Chapter 3 Evaluation of WRF Simulation 12
Chapter 4 Results 20
4.1 The ability of simulating super MJO by WRF model 20
4.2 How super MJO changes under climate change? 22
Chapter 5 GMS analysis 34
5.1 The change of gross moist stability in the linear theory 34
5.2 NGMS plane analysis 38
Chapter 6 Conclusion and discussion 49
Appendix A: Plots of individual super MJOs 53
References 59

參考文獻

Adames, Á. F., D. Kim, A. H. Sobel, A. Del Genio, and J. Wu, 2017a: Changes in the structure and propagation of the MJO with increasing CO2. J. Adv. Model. Earth Syst., 9, 1251–1268.
Arnold, N. P., Z. Kuang, and E. Tziperman, 2013: Enhanced MJO like variability at high SST. J. Clim., 26, 988–1001.
Arnold, N. P., M. Branson, Z. Kuang, D. A. Randall, and E. Tziperman, 2015: MJO intensification with warming in the superparameterized CESM. J. Clim., 28, 2706–2724.
Benedict, J. J., E. D. Maloney, A. H. Sobel, and D. M. W. Frierson, 2014: Gross moist stability and MJO simulation skill in three full-physics GCMs. J. Atmos. Sci., 71, 3327–3349.
Bony, S., and K. A. Emanuel, 2005: On the role of moist processes in tropical Intraseasonal variability: cloud–radiation and moisture–convection Feedbacks. J. Atmos. Sci., 62, 2770–2789.
Bui, H. X., and E. D. Maloney, 2018: Changes in Madden–Julian oscillation precipitation and wind variance under global warming. Geophys. Res. Lett., 45, 7148–7155.
Bui, H. X. and E. D. Maloney, 2019: Mechanisms for global warming impacts on Madden Julian oscillation precipitation amplitude. J. Clim., 32, 6961– 6975.
Chang, C.-W. J., W.-L. Tseng, H.-H. Hsu, N. Keenlyside, and B. J. Tsuang, 2015: The Madden–Julian Oscillation in a warmer world. Geophys. Res. Lett., 42, 6034–6042.
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.
Chen, G., and B. Wang, 2018b: Does the MJO have a westward group velocity? J. Clim., 31, 2435–2443.
Chou, C., and J. D. Neelin, 2004: Mechanisms of global warming impacts on regional tropical precipitation. J. Clim., 17:2688–2701.
Chou, C., J. D. Neelin, C. A. Chen, J. Y. Tu, 2009: Evaluating the ‘‘rich-get richer’’ mechanism in tropical precipitation change under global warming. J. Clim., 22:1982–2005.
Chou, C., and C‐A. Chen, 2010: Depth of convection and the weakening of tropical circulation in global warming, J. Clim., 23, 3019–3030.
Chou, C., T.-C. Wu, and P.-H. Tan, 2013: Changes in gross moist stability in the tropics under global warming. Climate Dyn., 41, 2481–2496.
Chou, M. D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, 98 pp.
Ciesielski, P. E., and Coauthors, 2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. J. Atmos. Oceanic Technol., 31, 741–764.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.
Feng, J., P. Liu, W. Chen, and X. Wang, 2015: Contrasting Madden–Julian oscillation activity during various stages of EP and CP El Niños. Atmos. Sci. Lett., 16:32–37.
Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 5233–5250.
Hagos, S., L. R. Leung, and J. Duddhia, 2011: Thermodynamics of the Madden–Julian oscillation in a regional model with constrained moisture. J. Atmos. Sci., 68, 1974–1989.
Ham, S., Hong, S. and S. Park, 2014: A study on air–sea interaction on the simulated seasonal climate in an ocean–atmosphere coupled model. Climate Dyn., 42, 1175–1187.
Han, J., and H.‐L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system, Weather Forecasting, 26, 520– 533.
Hannah, W. M., and E. D. Maloney, 2011: The role of moisture convection feedbacks in simulating the Madden–Julian oscillation. J. Clim., 24, 2754–2770.
Hannah, W. M., and E. D. Maloney, 2014: The moist static energy budget in NCAR CAM5 hindcasts during DYNAMO. J. Adv. Model. Earth Syst., 6, 420–440.
Hara, M., T. Yoshikane, H. Kawase, F. Kimura, 2008: Estimation of the impact of global warming on snow depth in Japan by the pseudo-global warming method. Hydrol. Res. Lett., 2:61–64.
Holzer, M., and G. J. Boer, 2001: Simulated changes in atmospheric transport climate. J. Clim., 14:4398–4420.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.
Hung, M.-P., J.-L. Lin, W. Wang, D. Kim, T. Shinoda, and S. J. Weaver, 2013: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J. Clim., 26, 6185–6214.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103.
Inoue, K., and L. E. Back, 2015b: Gross moist stability assessment during TOGA COARE: Various interpretations of gross moist stability. J. Atmos. Sci., 72, 4148–4166.
Inoue, K., and L. E. Back, 2017: Gross moist stability analysis: Assessment of satellite-based products in the GMS plane. J. Atmos. Sci., 74, 1819–1837.
Janjic, Z., I., 2000: Comment on “Development and Evaluation of a Convection Scheme for Use in Climate Models”, J. Atmos. Sci., 57. 3686.
Jiang, X., and Coauthors, 2015: Vertical structure and physical processes of the Madden–Julian oscillation: Exploring key model physics in climate simulations. J. Geophys. Res. Atmos., 120, 4718–4748.
Johnson, R. H., and P. E. Ciesielski, 2013: Structure and properties of Madden–Julian oscillations deduced from DYNAMO sounding arrays. J. Atmos. Sci., 70, 3157–3179.
Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181.
Kiladis, G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann, and M. J. Ventrice, 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea. Rev., 142, 1697–1715.
Kim, D., A. H. Sobel, E. D. Maloney, D. M. W. Frierson, and I.-S. Kang, 2011: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations. J. Clim., 24, 5506–5520,
Klotzbach, P. J., and E. C. J. Oliver, 2015: Modulation of Atlantic basin tropical cyclone activity by the Madden–Julian oscillation (MJO) from 1905 to 2011. J. Clim., 28, 204–217.
Lackmann, G. M., 2013: The south-central U.S. flood of May 2010: Present and future. J. Clim., 26, 4688–4709.
Lau, W. K. M., and D. E. Waliser, Eds., 2012: Intraseasonal Variability of the Atmosphere–Ocean Climate System. 2nd ed. Springer, 613 pp.
Lin, X., and R. H. Johnson, 1996: Kinematic and thermodynamic characteristics of the flow over the western Pacific warm pool during TOGA COARE. J. Atmos. Sci., 53, 695–715.
Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612.
Lorenz D.J., and E. T. DeWeaver, 2007: Tropopause height and zonal wind response to global warming in the IPCC scenario integrations. J. Geophys. Res., 112: D10119.
Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708.
Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci., 29, 1109–1123.
Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814–837.
Maloney, E. D., and D. L. Hartmann, 2000b: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Clim., 13, 1451– 1460.
Maloney, E. D., and S. P. Xie, 2013: Sensitivity of MJO activity to the pattern of climate warming. J. Adv. Model. Earth Syst., 5, 32–47.
Maloney, E.D., Á.F. Adames, and H.X. Bui, 2019: Madden–Julian oscillation changes under anthropogenic warming. Nature Climate Change, 9, 26– 33.
Marshall, A.G., O. Alves, and H. H. Hendon, 2009: A coupled GCM analysis of MJO activity at the onset of El Nino. J. Atmos. Sci., 66, 996-983.
Marshall, A. G., and H. H. Hendon, 2015: Subseasonal prediction of Australian summer monsoon anomalies. Geophys. Res. Lett., 42, 10 913–10 919.
McPhaden, M. J., 1999: Genesis and evolution of the 1997–98 El Niño. Science, 283, 950–954.
Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 3–12.
Neelin, J. D., and J.-Y. Yu, 1994: Modes of tropical variability under convective adjustment and the Madden–Julian oscillation. Part I: Analytical theory. J. Atmos. Sci., 51, 1876–1894.
Raymond, D. J., 2001: A new model of the Madden–Julian oscillation. J. Atmos. Sci., 58, 2807–2819.
Raymond, D. J., and Ž. Fuchs, 2007: Convectively coupled gravity and moisture modes in a simple atmospheric model. Tellus, 59A, 627–640.
Rushley, S. S., D. Kim, and Á. F. Adames, 2019: Changes in the MJO under Greenhouse Gas–Induced Warming in CMIP5 Models. J. Clim., 32, 803-821.
Sato, T., F. Kimura and A. Kitoh, 2007: Projection of global warming onto regional precipitation over Mongolia using a regional climate model. Journal of Hydrology, 333(1):144-154.
Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 125 pp.
Sobel, A. H., S. Wang, and D. Kim, 2014: Moist static energy budget of the MJO during DYNAMO. J. Atmos. Sci., 71, 4276–4291.
Subramanian, A., M. Jochum, A. J. Miller, R. Neale, H. Seo, D. Waliser, and R. Murtugudde, 2014: The MJO and global warming: a study in CCSM4. Climate Dyn., 42, 2019–2031.
Trenberth, K. E., and Coauthors, 2007: Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 235–336.
Wang, B., 1988: Dynamics of tropical low-frequency waves: An analysis of the moist Kelvin wave. J. Atmos. Sci., 45, 2051–2065.
Wang, B., and H. Rui, 1990: Dynamics of the coupled moist Kelvin–Rossby wave on an equatorial β-plane. J. Atmos. Sci., 47, 397–413.
Wang, S., A. H. Sobel, F. Zhang, Y. Q. Sun, Y. Yue, and L. Zhou, 2015: Regional simulation of the October and November MJO events observed during the CINDY/DYNAMO field campaign at gray zone resolution. J. Clim., 28, 2097–2119.
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917–1932.
Wolding, B. O., and E. D. Maloney, 2015: Objective diagnostics and the Madden–Julian oscillation. Part II: Application to moist static energy and moisture budgets. J. Clim., 28, 7786– 7808.
Yu, J.-Y., and J. D. Neelin, 1994: Modes of tropical variability under convective adjustment and the Madden-Julian Oscillation. Part II: Numerical results. J. Atmos. Sci., 51, 1895-1914.
Yu, J.-Y., and J. D. Neelin, 1997: Analytic approximations for moist convectively adjusted regions. J. Atmos. Sci., 54, 1054-1063.
Yu, J.-Y., C. Chou, and J. D. Neelin, 1998: Estimating the gross most stability of the tropical atmosphere. J. Atmos. Sci., 55, 1354-1372.
Zhang, C., 2005: Madden-Julian oscillation. Rev. Geophys., 43, RG2003.
Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the Southeast Pacific in ARW‐WRF using a modified Tiedtke cumulus parameterization scheme, Mon. Weather Rev., 139, 3489– 3513.

指導教授

余嘉裕(Jia-Yuh Yu)

審核日期

2020-7-28

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