跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 戴世忠
Shih-Chung Tai
論文名稱： 應用秩等級分布恆常性於氣候預測的可行性研究與層位渦收支分析初探
The feasibility of using the constancy of rank distribution for climate projections and the preliminary study of layered potential vorticity budget analysis
指導教授： 李永安
Yung-An Lee
口試委員:
學位類別： 博士
Doctor
系所名稱： 地球科學學院 - 大氣科學學系
Department of Atmospheric Sciences
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 104
中文關鍵詞： 秩等級分布 、完美模式法 、秩等級直方圖修正法 、層位渦 、位渦收支
外文關鍵詞： rank distribution, perfect model approach, rank histogram calibration method, layered potential vorticity, potential vorticity budget
相關次數： 點閱：31  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本文涉及兩個主要的研究課題：第一部分中，探討了將一個已知多模式系集(multi model ensemble, MME)秩等級分布(rank distribution)的恆常性作為emergent constraint，以針對相應的未來預測進行偏差校正的可行性。首先，吾人應用「完美模式法(perfect model approach)」來評估一個已知多模式系集的秩等級分布是否和此約束一致。結果顯示，CMIP5 RCP 8.5情境下MME的全球地表平均溫度、地表溫度以及全球多數格點上的降水場均與此約束一致。接著，採用排序修正法(the rank histogram calibration method)來修正上述量場的預測偏差。此法有效地將21世紀末全球地表平均溫度的不確定範圍減少了一半。就地表溫度而言，2081至2100期間的平均MME中位數經修正後，相較於原始的MME中位數呈現出更加平滑與均質的空間變異。更有趣的是，修正後之2081至2100平均降水的MME中位數完全地排除了長期存在的雙間熱帶輻合區偏差。這些結果顯示，此處所提出結合emergent constraint與排序修正的處理步驟有能力針對地表溫度以及降水產生更為精確且可靠的預測。更加重要的是，因為秩等級分布約束的恆常性並不依賴氣候模式未來變化以及當前狀態之間的線性統計關係，所以較先前經驗證並提出的emergent constraint而言，我們可以預期這個方法具備更廣泛的適用性。
    為了檢驗其他動力變量應用於氣候預測評估的可行性，在本文的第二部分中，吾人將先嘗試利用層位渦(layered potential vorticity, LPV)來了解它能否掌握綜觀尺度系統的特徵。由於中緯度系統的層位渦分析在前人的研究中已有所著墨，並獲致正面的結果，我們將著重於熱帶地區的探討，並選取2015年的強烈颱風蘇迪勒(typhoon Soudelor)作為代表個案，實施層位渦的收支分析。研究結果證實，相較於將大氣切分為兩層的分析結果而言，由整層對流層所計算出來的層位渦收支分項能更清楚地凸顯控制方程式中顯著影響因子的效應；另一方面，不管在颱風的增強或減弱階段，層位渦的平流項均主導著系統的發展。非絕熱效應、摩擦效應、垂直傳送與紊流渦度通量輻合等因子對於局部的層位渦趨勢並不具備決定性的影響。

    There are two parts involved in the following work. In the first one, we explored the feasibility of using the constancy of rank distribution of a given multi model ensemble (MME) as an emergent constraint to calibrate the corresponding future projections. We first applied the perfect model approach to evaluate whether the rank distribution of a given MME is consistent with the constraint. Results show that the global mean surface temperature (GMST) as well as the surface temperature and the precipitation fields at most grids on the global from the CMIP5 RCP 8.5 scenario MME are consistent with the constraint. We then applied the rank histogram calibration method to calibrate future projections of these fields. For the GMST, we successfully narrow the 5-95 uncertainty range by one half at the end of the 21st century. For the surface temperature field, the calibrated MME medians averaged over 2081-2100 exhibit more smooth and homogeneous spatial variations than those original MME medians. More interestingly, the calibrated MME medians of precipitation field averaged over 2081-2100 are completely free from the long-standing double ITCZ bias. These results suggest that the proposed emergent constraint together with the rank histogram calibration procedure is capable of yielding sharper and more reliable future projections for both the surface temperature and the precipitation fields. More importantly, because the constancy of rank distribution constraint does not depend on linear statistic relations between future changes and current states of climate models, one expects that it has wider range of applicability than previously identified and proposed emergent constraints.
    In order to assess the applicability of another dynamic variable for climate projection evaluations, the layered potential vorticity (LPV) was exploited to understand if it could grasp the characteristics of synoptic scale systems at firsthand in the second part of this dissertation. The budget patterns of Typhoon Soudelor in 2015, which stands for the representatively remarkable tropical system, was then focused on since the mid-latitudinal study had been done in the previous investigation.
    It is validated that the LPV budgets analysis deduced from the entire level of troposphere could present the significant affecting term in the governing equation more clearly, comparing to the multi-layer analyses. The LPV advection effect dominates the whole course of the system development no matter in the strengthening or in the declining period. The synthetic influence from diabatic effect, frictional force, vertical transport and eddy vorticity flux convergent doesn’t show a robust impact for the local LPV tendency.

    中文摘要 i English Abstract iii Acknowledgements v Table of Contents vi List of Tables viii List of Figures ix Symbolic Description xv 1. The Feasibility of Using the Constancy of Rank Distribution for Climate Projections 1 1-1 Introduction 1 1-2 Data and Methods 4 1-2-1 Data Resource and Preprocessing 4 1-2-2 The Perfect Model Approach 5 1-2-3 The Definition of Rank Constancy Indicator 6 1-2-4 The Rank Histogram Calibration Method 7 1-2-5 The Constrained Rank Histogram Calibration 8 1-3 Results and Discussion 9 1-3-1 The Non-constrained Results 9 1-3-2 The Constrained Results 14 1-4 Summary and Conclusions 17 1-5 Future Works 19 2. The Preliminary Study of Layered Potential Vorticity Budget Analysis 20 2-1 Introduction 20 2-1-1 Case Description 22 2-2 Data and Methods 24 2-2-1 Data Resource 24 2-2-2 Theoretical Basis 25 2-2-3 The LPV Governing Equation: Formulation 27 2-2-4 The Choice of the Layer Boundaries 30 2-2-5 Compositing of the Case 31 2-2-6 Numerical implementation 32 2-3 Results and Discussion 33 2-3-1 Comparison Between Different Aerosphere Division 33 2-3-2 The Main Factor from Entire Layer of Troposphere for TC Evolution 34 2-3-3 The Influences from Diabatic Effect and Different Layers 36 2-3-4 The LPV Budget During the Deflection of Soudelor 38 2-4 Summary and Conclusions 39 2-5 Future Works 40 Bibliography 43 Tables 47 Figures 49

    Abramowitz, G., and C. H. Bishop, 2015: Climate model dependence and the ensemble dependence transformation of CMIP projections. J. Clim., 28, 2332–2348, https://doi.org/10.1175/JCLI-D-14-00364.1.

    Argence, S., D. Lambert, E. Richard, J. P. Chaboureau, P. Arbogast, and K. Maynard, 2009: Improving the numerical prediction of a cyclone in the Mediterranean by local potential vorticity modifications. Q. J. R. Meteorol. Soc., 135, 865–879, https://doi.org/10.1002/qj.422.

    Bracegirdle, T. J., and D. B. Stephenson, 2012: Higher precision estimates of regional polar warming by ensemble regression of climate model projections. Clim. Dyn., 39, 2805–2821, https://doi.org/10.1007/s00382-012-1330-3.

    Caldwell, P. M., C. S. Bretherton, M. D. Zelinka, S. A. Klein, B. D. Santer, and B. M. Sanderson, 2014: Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett., 41, 1803–1808, https://doi.org/10.1002/2014GL059205.

    Chou, C., and J. D. Neelin, 2004: Mechanisms of Global Warming Impacts on Regional Tropical Precipitation. J. Clim., 17, 2688–2701, https://doi.org/10.1175/1520-0442 (2004)017<2688:mogwio>2.0.co;2.

    ——, ——, C. A. Chen, and J. Y. Tu, 2009: Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J. Clim., 22, 1982–2005, https://doi.org/10.1175/2008JCLI2471.1.

    Davis, C. A., 1992: Piecewise potential vorticity inversion. J. Atmos. Sci., 49, 1397–1411, https://doi.org/10.1175/1520-0469(1992)049<1397:PPVI>2.0.CO;2.

    Demirtas, M., and A. J. Thorpe, 1999: Sensitivity of Short-Range Weather Forecasts to Local Potential Vorticity Modifications. Mon. Weather Rev., 127, 922–939, https://doi.org/10.1175/1520-0493(1999)127<0922:SOSRWF>2.0.CO;2.

    Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.

    Friedman, A. R., Y. T. Hwang, J. C. H. Chiang, and D. M. W. Frierson, 2013: Inter-hemispheric temperature asymmetry over the twentieth century and in future projections. J. Clim., 26, 5419–5433, https://doi.org/10.1175/JCLI-D-12-00525.1.

    Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33, https://doi.org/ 10.1029/2005GL025127.

    ——, P. Cox, C. Huntingford, and S. Klein, 2019: Progressing emergent constraints on future climate change. Nat. Clim. Chang., 9, 269–278, https://doi.org/10.1038/s41558-019-0436-6.

    Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta–RSM Short-Range Ensemble Forecasts. Mon. Weather Rev., 125, 1312–1327, https://doi.org/10.1175/1520-0493(1997)125<1312:voersr>2.0.co;2.

    ——, and ——, 1998: Evaluation of Eta–RSM Ensemble Probabilistic Precipitation Forecasts. Mon. Weather Rev., 126, 711–724, https://doi.org/10.1175/1520-0493(1998)126<0711:eoerep>2.0.co;2.

    Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Clim., 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1.
    Holton, J. R., and G. J. Hakim, 2012: An introduction to dynamic meteorology: Fifth edition. Academic Press, 1–532pp.

    Hoskins, B. J., M. E. McIntyre, and A. W. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Q. J. R. Meteorol. Soc., 111, 877–946, https://doi.org/10.1002/qj.49711147002.

    Hsieh, Y. -H., and Y. Lee, 2006: Using potential vorticity of two layer model to explore the characteristics of winter midlatitude synoptic-scale system. National Central University, 75pp.

    Knutti, R., J. Sedláček, B. M. Sanderson, R. Lorenz, E. M. Fischer, and V. Eyring, 2017: A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/ 2016GL072012.

    Lin, J. L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean-atmosphere feedback analysis. J. Clim., 20, 4497–4525, https://doi.org/10.1175/ JCLI4272.1.

    Mechoso, C. R., and Coauthors, 1995: The Seasonal Cycle over the Tropical Pacific in Coupled Ocean–Atmosphere General Circulation Models. Mon. Weather Rev., 123, 2825–2838, https://doi.org/10.1175/1520-0493(1995)123<2825:tscott>2.0.co;2.

    Michel, Y., and F. Bouttier, 2006: Automated tracking of dry intrusions on satellite water vapour imagery and model output. Q. J. R. Meteorol. Soc., 132, 2257–2276, https://doi.org/10.1256/qj.05.179.

    Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, https://doi.org/10.1029/2011JD017187.

    Namias, J., 1940: An Introduction to the Air Mass and Isentropic Analysis. 4th ed. R.G.Stone, Ed. The American Meteorological Society, 232pp.

    Platzman, G. W., 1949: The Motion of Barotropic Disturbances in the Upper Troposphere. Tellus, 1, 53–64, https://doi.org/10.1111/j.2153-3490.1949.tb01266.x.

    Putnam, A. E., and W. S. Broecker, 2017: Human-induced changes in the distribution of rainfall. Sci. Adv., 3, e1600871, https://doi.org/10.1126/sciadv.1600871.

    Rossby, C. -G., 1937: Isentropic Analysis. Bull. Am. Meteorol. Soc., 18, 201–209, https:// doi.org/10.1175/1520-0477-18.6-7.201.

    Santurette, P., and C. Georgiev, 2005: Weather Analysis and Forecasting: Applying Satellite Water Vapor Imagery and Potential Vorticity Analysis. 1st ed. F.Cynar, Ed. Elsevier Inc., 200pp.

    Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/ BAMS-D-11-00094.1.

    Weldon, R., and S. J. Holmes, 1991: Water vapor imagery : interpretation and applications to weather analysis and forecasting. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, 213pp.

    Xie, P., and P. A. Arkin, 1997: Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs. Bull. Am. Meteorol. Soc., 78, 2539–2558, https://doi.org/10.1175/1520-0477(1997)078 <2539:gpayma>2.0.co;2.
    中央災害應變中心, 2015: 蘇迪勒颱風災害應變處置報告第9報. 5pp.

    QR CODE
    :::