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Hand in Hand Tropical Cyclones and Climate Change: Investigating the Response of Tropical Cyclones to the Warming World

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Hand in Hand Tropical Cyclones and Climate Change: Investigating the Response of Tropical Cyclones to the Warming World UNIVERSITY OF EXETER Hand in Hand Tropical Cyclones and Climate Change: Investigating the Response of Tropical Cyclones to the Warming World by Kopal Arora A thesis submitted in partial fulfillment for the degree of Master of Philosophy in Mathematics in the Faculty of Mathematics College of Engineering, Mathematics and Physical Sciences 12 February 2018 Declaration of Authorship \Hand in Hand Tropical Cyclones and Climate Change: Investigating the Re- sponse of Tropical Cyclones to the Warming World" is submitted by Kopal Arora, to the University of Exeter as a thesis for the degree of Master of Philosophy, submitted on October 29, 2018, for examination. This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identified and that no material has previously been submitted and approved for the award of a degree by this or any other University. Signed: Date: October 29, 2018 i UNIVERSITY OF EXETER Abstract Faculty of Mathematics College of Engineering, Mathematics and Physical Sciences Master of Philosophy in Mathematics by Kopal Arora iii What are the primary factors governing Tropical Cyclone Potential Intensity (TCPI) and how does the TCPI vary with the change in CO2 concentration are the two fundamental questions we investigated here. In the first part, a strong spatial correlation between the TCPI and the ocean temperature underneath was used to develop a statistical model to quantify the TCPI over the remote regions where the TC related observations are difficult to acquire. The model revealed an overall increase in the TCPI when the atmospheric CO2 concentration was doubled. Finally, the study examines the TCPI's sensitivity on the ocean temperature (at the spatial scales). Two independent models (HADCM3 from Met Office, UK and GFDL-CM3 from GFDL, NOAA, USA) on an average reveals an increase in the TCPI between 8 to 10 m/s per unit increase in the ocean temperature (in oC). The key finding to emerge from this study is that the increase in the TCPI responds comparatively weakly to the increasing ocean temperature when CO2 amount is increased. We call this observation as, \the sensitivity saturation effect”. According to our findings, the TCPI responds weakly (become less sensitive) to the ocean temperature on doubling the CO2 concentration. This effect was observed in all the ocean basins and in both the considered climate models. Though the TCPI show a rise on increasing the CO2 concentration but, its response to the SST decreases. This observation leads to a set of next level questions for instance, will there be a sensitivity saturation effect, analogous to the well-known \Band Saturation effect”, on increasing the CO2 levels and if it does, will the TCPI's sensitivity plateau? If it plateaus, at what cut-off CO2 levels would that happen? These emerging questions open up a new area of investigation for the climatologists and the enthusiasts in the related fields. In this manner, this part of the research provides a framework for the future exploration of the subject. Acknowledgements Firstly, I would like to express my gratitude to my advisors, Prof. Matthew Collins and Prof. David Stephenson for their continuous support. I am grateful to my academic mentor, Dr Chris Ferro for his insightful suggestions and encouragement throughout the endeavour. The highest thanks and praise to my mother Mrs Sneh Arora, the most inspira- tional person I've ever met. My deepest gratitude to my father, Late Mr Surendra Arora for his everlasting encouragement. I am deeply grateful to my siblings, Sachin and Anshuman for their support and encouragement. My heartfelt appre- ciation goes to my husband, Abhinav Sharma, for his support, understanding and patience throughout this long process. A very special gratitude goes out to my friend, Dr Prasanjith Dash (from NOAA) for sharing his insightful knowledge and experience on the subject and extending his warm encouragement and constructive comments. A warm thanks to my lovely friends from Church, Ruth, Christopher, Dorothy and Rachael for their moral and emotional support during my stay in Exeter. I particularly like to thank Chinna, Deepthi & jagadish (Reading), Asmi, Haroon, Shalini, Madhav, Harish, Varun and Anusha for making my stay in U.K. pleasant and lively. A special thanks to my colleagues and students at, INTO, Exeter. This is where I spent my spare time very productively. And finally, last but by no means least, I owe my deepest gratitude to my office colleagues for making the place delightful. iv Contents Declaration of Authorshipi Abstract ii Acknowledgements iv List of Figures ix List of Tables xiv 1 Introduction and Motivation1 1.1 Introduction and Motivation......................1 1.1.1 Challenges............................3 1.1.2 Classification..........................4 1.1.3 Formation............................4 1.1.3.1 Metrics for Intensity.................5 1.2 Effects of Tropical Cyclones on climate................5 1.2.0.1 Tropical Cyclogenesis................5 1.2.1 Warm ocean temperature, above 26:5 ◦C, extended 50 me- ters deep.............................6 1.2.2 Sufficient humidity in the troposphere.............7 1.3 Quantifying Tropical Cyclone Intensity Using Emanuel's Approach.8 1.3.1 Thesis Aims and Outline.................... 17 2 Methodology and Data Used 22 2.1 Model Output.............................. 22 2.1.1 Global Climate Model (GCM) an Introduction and Model Physics.............................. 23 2.1.2 GCMs and Tropical Cyclones................. 24 2.1.3 Perturbed Physics Ensembles (PPE) and Multi Model En- sembles (MME) Explained................... 24 2.2 Observational Data........................... 25 v Contents vi 2.2.1 IBTrACS Data......................... 25 2.2.1.1 Advantages and Limitations of IBTrACS Dataset. 25 2.3 HadISST Dataset............................ 26 2.3.0.1 Data Assimilation Technique, HadISST....... 26 2.3.0.2 Strengths and Weaknesses of HadISST Dataset.. 26 2.3.0.3 Climate Model Intercomparison Project version-5 (CMIP5) Dataset................... 27 2.3.1 Atmospheric Reanalysis Dataset................ 28 2.4 Tropical Cyclone Scales......................... 28 2.5 Methods Applied............................ 32 2.5.1 Pearson's Correlation coefficient and Bootstrap Methods.. 32 2.5.2 Regression Analysis....................... 33 2.5.3 Partial Least Square Regression................ 34 2.5.3.1 Goals of partial least square regression....... 35 2.5.3.2 Simultaneous decomposition of predictors and de- pendent variables................... 36 2.5.4 Ensemble Empirical Mode Decomposition (EEMD)..... 36 2.5.5 Bilinear Interpolation...................... 38 3 Simple Methods for Estimating Tropical Cyclone Intensities 40 3.1 Introduction............................... 40 3.1.1 Modeling TC Intensification.................. 41 3.2 Theoretical Background: A steady state analytical TC model.... 44 3.3 Data and Methods Used........................ 46 3.3.1 Methodology.......................... 46 3.3.1.1 Similarity and Differences between COBE and ERSST.V3b 47 3.3.1.2 Computation of sustained wind speeds....... 48 3.3.2 Data Used............................ 49 3.3.3 Tropical Distribution of SST From Three Different Datasets 51 3.4 Results and Discussion......................... 52 3.4.1 Development of a linear Vmax Model and its validation against an established model...................... 52 3.5 Spatial Distribution of SST...................... 58 3.6 Model Verification: ACE vs VmaxNw .................. 59 3.7 Model Limitation and Possible Explanation............. 61 3.8 Summary and Conclusions....................... 63 4 Saturation of Tropical Cyclone Strength under a doubled CO2 scenario 66 4.1 Introduction............................... 67 4.2 Data and Methods Used........................ 70 4.2.1 Data Used............................ 70 4.2.1.1 UKMO's HadCM3.................. 70 4.2.1.2 GFDL's ESM-CM3.................. 73 4.2.2 Methodology.......................... 74 Contents vii 4.2.2.1 Partial Least Square Regression (PLSR)...... 74 4.2.2.2 Sensitivity Analysis.................. 76 4.3 Results and Discussions........................ 77 4.3.1 Analysis Using Partial Least Square Regression....... 77 4.3.2 Analysis Using Histograms................... 80 4.3.2.1 Histogram of sensitivity to Vmax in the Tropical Ocean Basin..................... 81 4.3.2.2 Histogram of sensitivity to Vmax in the North In- dian Ocean...................... 82 4.3.2.3 Histogram of sensitivity to Vmax in the South In- dian Ocean...................... 82 4.3.2.4 Histogram of sensitivity to Vmax in the Northwest Pacific Ocean..................... 83 4.3.2.5 Histogram of sensitivity to Vmax in the Northeast Pacific Ocean and North Atlantic Ocean...... 85 4.3.3 Sensitivity Analysis....................... 88 4.3.4 Detailed Explanation of the Tables from Sensitivity Analysis section:............................. 90 4.4 Conclusions............................... 92 4.5 Weakness and Limitations....................... 93 5 Summary, Conclusions and Recommendations 95 5.1 Introduction............................... 95 5.2 The Questions.............................. 96 5.3 The Answers.............................. 97 5.4 Limitations & Prospective Suggestions................ 99 5.5 Contributions.............................. 100 A Supplimentary Figures, Tables and Explanations 102 A.1 Chapter 3...............................
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