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Hope Umd 0117E 21241.Pdf (10.43Mb) ABSTRACT Title of Document: Quantification of the Past and Future Anthropogenic Effect on Climate Change Using the Empirical Model of Global Climate, an Energy Balance Multiple Linear Regression Model Austin Patrick Hope, Doctor of Philosophy, 2020 Directed by: Professor Ross J. Salawitch, Department of Atmospheric and Oceanic Science, Department of Chemistry and Biochemistry, Earth System Science Interdisciplinary Center and Assistant Professor Timothy P. Canty, Department of Atmospheric and Oceanic Science, Marine Estuarine Environmental Sciences program The current episode of global warming is one of, if not the, biggest challenge to modern society as the world moves into the 21st century. Rising global temperatures due to anthropogenic emissions of greenhouse gases are causing sea level rise, extreme heat waves, droughts and floods, and other major social and economic disruptions. To prepare for and potentially reverse this warming trend, the causes of climate change must not only be understood, but thoroughly quantified so that we can attempt to make reasonable predictions of the future rise in global temperature and its associated consequences. The project described in this dissertation seeks to use a simple model of global climate, utilizing an energy balance and multiple linear regression approach, to provide a quantification of historical temperature trends and use that knowledge to provide probabilistic projections of future temperature. By considering many different greenhouse gas and aerosol emissions scenarios along with multiple possibilities for the role of the ocean in the climate system and the extent of climate feedbacks, I have determined that there is a 50% probability of keeping global warming beneath 2 °C if society can keep future emissions on the pathway suggested by the RCP 4.5 scenario, which includes moderately ambitious emissions reductions policies, and a 67% probability of keeping global warming beneath 1.5 °C if society can keep emissions in line with the very ambitious RCP 2.6 scenario. These probabilities are higher, e.g. more optimistic, than similar probabilities for the same scenarios given by the most recent IPCC assessment report. Similarly, we find larger carbon budgets than those from GCM analyses for any warming limitation target and confidence level, e.g. the EM-GC predicts a total carbon budget of 710 GtC for limiting global warming to 1.5 °C with 95% confidence. The results from our simple climate model suggest that the difference in future temperatures is related to an overestimation of recent warming by the IPCC global climate models. We postulate that this difference is partially due to an overestimation of cloud feedback processes in the global climate models. Importantly, though, I also reaffirm the consensus that anthropogenic emissions are driving current warming trends, and discuss both the effects of shifting the energy sector toward increase methane emissions and the timeline we have for emitting the remainder of our carbon budget – less than a decade if we wish to prevent global warming from exceeding the 1.5 °C threshold with 95% certainty. QUANTIFICATION OF THE PAST AND FUTURE ANTHROPOGENIC EFFECT ON CLIMAGE CHANGE USING THE EMPIRICAL MODEL OF GLOBAL CLIMATE, AN ENERGY BALANCE MULTIPLE LINEAR REGRESSION MODEL by Austin Patrick Hope Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2020 Advisory committee: Professor Ross Salawitch, Co-Advisor Assistant Professor Timothy Canty, Co-Advisor Professor Russell R. Dickerson Assistant Professor Jacob Wenegrat Associate Professor Ronald A. Yaros, Dean’s Representative © Copyright by Austin Patrick Hope 2020 Dedication I dedicate this body of work to my late grandfather, Dr. Hans Rath, MD, May 1928 – June 2017, whose commitment to education was only surpassed by his love for his grandchildren. My Papa (and my grandma) helped see all six of us grandkids through undergrad, and not only did my grandparents learn to acknowledge anthropogenic climate change thanks to hearing about my body of work here, they have high hopes for what my, my sister’s, and my cousins’ generation can do to fix the problem climate that previous generations have left for us. I’ll do my best, Papa. ii Acknowledgements I’d like to thank everyone who has supported me during my time at UMD. First, to my advisors Ross and Tim, who have taught me so much over the years and helped me grow as a scientist. Second, to Brian, Walt, Nora, and especially Laura, a.k.a. the other members in our little “climate club”, for providing support over the years and directly or indirectly helping develop the EM-GC. Third, to all other professors and students of the AChem group within AOSC, for the sense of community and regular feedback you have provided. Next, to my TA friends for camaraderie and to the AOSC office staff for incredible clarity and aid. And last, but far from least, to my family, who have always given me plenty of love and support, both emotional and financial, throughout my time at UMD. In particular, my sister has had to live with me for all three years of my “last year” as a grad student, and my parents has served as beta readers for several large writing efforts from our “climate club” over the past five years. I can’t thank you enough for everything, and I hope I’ve made you proud! I also thank NASA for funding this work under the NASA Climate Indicators and Data Products for Future National Climate Assessments (INCA) program (award NNX16AG34G). iii Table of Contents Dedication . ii Acknowledgements . iii Table of Contents . iv List of Figures . vi List of Tables . x Abbreviations and Acronyms . xi 1. Introduction . 1 1.1. Basics of Climate . 5 1.1.1. Radiative Forcing . 5 1.1.2. The Greenhouse Effect . 9 1.1.3. Other Considerations . 15 1.2. Previous Modeling Efforts . 20 1.2.1. History of GCMs . 20 1.2.2. The Representative Concentration Pathways . 24 1.2.3. Multiple Linear Regression Climate Models . 27 1.3. Goals and Accomplishments . 31 2. Advanced Physics and Chemistry of Climate . 36 2.1. Radiative Forcing by Species . 37 2.2. Human Fingerprints on Global Warming . 46 2.2.1. The “Hockey Sticks”: Multiple Strong Recent Correlations 46 2.2.2. Carbon Dioxide (CO2) . 49 2.2.3. Other GHGs . 55 2.2.3.1. Methane (CH4) . 55 2.2.3.2. Nitrous Oxide (N2O) . 60 2.2.3.3. Halogenated Gases . 61 2.2.4. Anthropogenic Aerosols . 62 2.2.5. Final Comments on Anthropogenic Fingerprints and the Evolution of RF Over Time . 66 2.3. Nonlinearity & Uncertainty of the Climate System . 69 2.3.1. Considerations for a Linear Model of a Nonlinear Climate . 72 2.3.2. Uncertainty in Future Projections . 78 3. Forecasting Global Warming . 83 3.1. Introduction . 83 3.2. Empirical Model of Global Climate . 91 3.2.1. Formulation . 93 3.2.1.1. Model Inputs . 100 iv 3.2.1.2. Model Outputs . 111 3.2.2. The Degeneracy of Earth’s Climate . 115 3.2.3. Equilibrium Climate Sensitivity . 119 3.3. Attributable Anthropogenic Warming Rate . 124 3.4. Global Warming Hiatus . 135 3.5. Future Temperature Projections . 139 3.6. Methods . 152 4. Examining the Human Influence on Global Climate Using an Empirical Model . 171 4.1. Introduction . 171 4.1.1. Previous Estimates of AAWR . 173 4.1.2. Prior Projections of Future Temperature . 174 4.1.3. Overview of This Work . 181 4.2. Model Construction . 181 4.2.1. EM-GC Core Equations . 182 4.2.1.1. Model Inputs, Natural Factors . 189 4.2.1.2. Model Inputs, Anthropogenic Factors . 195 4.2.2. EM-GC Ocean Components . 205 4.2.3. Climate Feedback and Sensitivity . 211 4.3. Results and Analysis . 213 4.3.1. AAWR from the EM-GC . 219 4.3.2. Comparison to Previous AAWR Estimates . 223 4.3.3. Comparison to AAWR from GCMs . 232 4.3.3.1. Extracting AAWR from GCMs . 242 4.3.4. The Effects of Aerosols and Climate Feedback on Future ΔT 245 4.3.5. Other RCPs and Comparisons to Projections from GCMs . 257 4.3.6. The effect of increased future emissions of CH4 . 265 4.3.7. Response to Cumulative Emissions . 267 4.4. Conclusions . 274 5. Conclusion and Future Research Opportunities . 278 5.1. Summary of Work Presented . 278 5.2. Potential Future Work . 280 5.2.1. Altering the Spatial and Temporal Resolution of the EM-GC 281 5.2.1.1. Other Ocean Temporal and Spatial Issues to Examine 283 5.2.2. Climate Relationships to Examine . 286 5.2.3. Public Modeling . 288 5.3. Final Comment . 289 Bibliography . 290 v List of Figures Ch1 Figure 1.1 – GMST vs ΔRF; Anthropocene . 8 Figure 1.2 – Schematic of Earth’s Energy Balance . 10 Figure 1.3 – GMST vs CO2; Deep Time . 13 Figure 1.4 – Component Development of GCMs . 22 Ch2 Figure 2.1 – Radiation Transmitted by the Atmosphere . 39 Figure 2.2 – Total ΔRF of Climate by Anthropogenic Species . 43 Figure 2.3 – ΔRF of Climate by Anthropogenic Species; Anthropocene . 44 Figure 2.4 – GMST, GHGs, and Population; Common Era . 47 Figure 2.5 – CO2 Yearly Emissions Rate vs Hemispheric Gradient . 50 Figure 2.6 – Atmospheric Chemistry Fingerprints of Anthropogenic Activity 51 Figure 2.7 – Global Methane Budget, 2000 to 2009 . 56 Figure 2.8 – CO2 Emissions by Source; Anthropocene . 68 Figure 2.9 – Comparison of GHG RF vs Aerosol RF Uncertainty . 71 Figure 2.10 – The Ice-Albedo Feedback in CERES Data . 75 Figure 2.11 – Rate of Temperature Change on Different Timescales .
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