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University of Nevada, Reno University of Nevada, Reno Integrating urban heat island influences into statistically downscaled climate projections for the Truckee Meadows, Nevada A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Atmospheric Science by Benjamin J. Hatchett Dr. Darko R. Koraĉin/Thesis Advisor May, 2012 THE GRADUATE SCHOOL We recommend that the thesis prepared under our supervision by BENJAMIN JAMES HATCHETT entitled Integrating Urban Heat Island Influences Into Statistically Downscaled Climate Projections For The Truckee Meadows, Nevada be accepted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Darko Koracin, Advisor Michael Kaplan, Committee Member Scott Bassett, Graduate School Representative Marsha H. Read, Ph. D., Dean, Graduate School May, 2012 i i Abstract The Truckee Meadows is a narrow, semi-arid valley located in the lee of the Sierra Nevada and includes the cities of Reno and Sparks, Nevada. Cities are usually warmer than the surrounding countryside, especially at night, due to changes in the surface energy budget. This effect is known as the urban heat island (UHI) and results in a decreased diurnal temperature range, increased urban water usage and cooling costs during the warm season and exacerbates public health problems associated with heat waves and air quality. An examination of the Truckee Meadows’ trends in daily and monthly mean minimum temperatures during 1938-2010 identified an UHI. The maximum summer UHI exceeds the magnitude predicted as a function of population by the classical method of Oke (1976) by 2°C. The thermal perturbation of the UHI was not discernible in nearby upper-air rawinsonde sounding data which indicates the shallow, localized effect of this physical phenomenon. A synoptic climatology indicated that the North American Monsoon may provide favorable conditions for UHI development during the summer. Several methods for downscaling future temperature projections for the Truckee Meadows produced by global climate model data under three IPCC emissions scenarios (A1b, A2 and B2) for the future period 2041-2060 were examined. Results indicated that a bias correction and constructed analogs method with an additional bias correction step that incorporates the maximum UHI signal (1970-2009) is vital in producing robust results that include UHI effects for future urban resource planning and management. Further work is suggested focusing on the development of a ii ii fine spatial resolution observation network and physical sensitivity testing of the UHI via sub-kilometer scale numerical modeling efforts. iiii ii Acknowledgements I would like to thank my advisor Dr. Darko Koracin for his endless patience, instruction and direction he has provided me over the past several years of this work. I would like to thank each of my committee members, Drs. Michael Kaplan and Scott Bassett, for their time, knowledge and expertise that they have kindly shared with me throughout my work. The following other individuals provided substantial feedback, advice and general assistance during the course of my master’s work and I would like to send them all a shout out: Big ups! The discussions had with all those listed on this page have been instrumental in my development as a scientist and human being. Of course, I must also thank all of my friends and family throughout Planet Earth who forever provide the means to redline the fun-meter! Thank you, everybody! Dr. John F. Mejia, DRI Dr. Kelly Redmond, WRCC Dr. Ramesh Vellore, DRI Dr. John Abatzoglou Idaho Travis McCord, DRI Charles Morton, DRI Jim Ashby, WRCC Dr. Shawn Stoddard, TMWA Dr. Pat Arnott, UNR Michael Dolloff, UNR Greg McCurdy, WRCC Dr. Justin Huntington, DRI Laura Edwards, WRCC Nick Nauslar, DRI K.C. King, DRI Andrew Joros, DRI i iv Table of Contents Abstract…………………………………………………………………………………...i Acknowledgements………………………………………………………………………iii List of Tables……………………………………………………………………………..vi List of Figures………………………………………………………………………..…..vii List of Appendix Figures………………………………………………………………....xi 1. Introduction……………………………………………………………………………1 2. Study Area……………………………………………………………………………..9 2.1 The Great Basin………………………………………………………………………9 2.2 Change……………………………………………………………………………….18 2.3 The Planetary Boundary Layer……………………………………………................19 2.3.1 The Surface Energy Budget………………………………………………………..21 2.3.2 The Role of Water Vapor…………………………………………………………..23 2.3.3 The Effect of Cities on Climate……………………………………………………25 2.4 The Truckee Meadows………………………………………………………27 3. Global Climate Modeling……………………………………………………………..31 3.1 Future Climate Modeling…………………………………………………….34 3.2 Downscaling Global Climate Models………………………………………..37 3.2.1 Limitations of Downscaling………………………………………..39 3.2.2 Demand for Downscaled Future Climate Data…………………….42 4. Data and Methods……………………………………………………………………..42 4.1 Historical Climate Data……………………………………………………...42 4.2 Synoptic Climatology of the Truckee Meadows UHI………………………62 4.3 GIS and Remote Sensing Data………………………………………………73 v ii 4.4 Downscaled Climate Projection Data……………………………………….89 5. Results and Discussion………………………………………………………………..97 5.1 Surface and Upper Air Data…………………………………………………97 5.2 Seasonal Variability………………………………………………………….99 5.3 Urban Versus Rural Temperature Trends………………………………….102 5.4 Vertical Extent of the UHI…………………………………………………102 5.5 Influence of Water Vapor…………………………………………………..103 5.6 Diurnal UHI Variations…………………………………………………….106 5.7 Synoptic Climatology………………………………………………………109 5.8 Remote Sensing and Land Use……………………………………………..113 5.9 Downscaling Results………………………………………………………..119 6. Conclusions…………………………………………………………………………..123 7. References……………………………………………………………………………128 Appendix A: Monthly Mean Downscaled Results……………………………………..145 viii List of Tables Table 1: Historical locations of the Reno Airport weather station………………………46 iiivii List of Figures Figure 1: Idealized, conceptual example of the temperature perturbation induced by an Urban Heat Island (UHI). The city center is found at the origin………………………….3 Figure 2: The Truckee Meadows Region of Nevada. Significant topographic barriers exist on all sides of the city……………………………………………………………………..5 Figure 3: Seasonal mean maximum (top) and minimum (bottom) temperatures (derived from daily data) observed at KRNO between 1938 and 2010. The warming trend present in mean minimum temperatures beginning in the mid-1980s sparked the interest of O’Hara (2006) and Menne et al. (2009)…………………………………………………...6 Figure 4: The Great Basin and the western United States……………………………….11 Figure 5: Diurnal cycle of partitioning of surface energy budget heat flux components..22 Figure 6: Comparison of urban (left) and rural (right) energy flux partitioning. The size of the arrows represents the relative flux magnitudes. Figure from Oke (1988)…………...27 Figure 7: Typical cold season sounding for KRNO. Notice the deep inversion which can be extended a further 150m lower into the valley floor………………………………….30 Figure 8: Cartoon representation of the SRES Emissions Scenarios. Image courtesy Nakićenović et al. 2000…………………………………………………………………..35 Figure 9: A qualitative representation of the various changes in primary indices for the IPCC SRES scenarios. Data courtesy of IPCC (2007)…………………………………..36 Figure 10: Influence of IPCC SRES scenarios on multi-model average and ranges for global surface temperature. Data from Nakićenvoić et al. (2000)……………………….37 Figure 11: The 29 Regional COOP stations used in the analysis………………………..44 Figure 12: KRNO seasonal temperatures after removal of regional climate trend……...47 Figure 13: Standardized anomalies of KRNO minus the mean of 4 smaller cities (Minden, Carson City, Stead and Virginia City)…………………………………………………...49 Figure 14: KRNO seasonal temperature anomalies compared to 29 regional COOP stations…………………………………………………………………………………...50 Figure 15: The Dead Camel Mountain RAWS station; the most rural comparison site available to the study. Photo provided by the Western Regional Climate Center……….51 viviii Figure 16: Summertime 5-day averaged differences between urban (KRNO) and rural (Dead Camel) maximum (red) and minimum (blue) temperatures……………………...52 Figure 17: 700mb and 500mb 7-day running mean temperatures rawinsonde observations based on REV rawinsonde observations…………………………………………………53 Figure 18: Monthly mean column-integrated precipitable water, smoothed with a 10-day running mean, derived from REV rawinsonde data……………………………………...54 Figure 19: Power spectrum analysis on rawinsonde-derived precipitable water at NWS REV location……………………………………………………………………………..55 Figure 20: Monthly correlations of REV-derived precipitable water with KRNO observed minimum and maximum temperatures…………………………………………………..56 Figure 21: Monthly scatterplots of relative humidity (RH in %) versus minimum temperature for KRNO (1938-2010). Values exceeding the 90th percentile are highlighted in magenta…………………………………………………………………...56 Figure 22: Histograms showing number of days per year where 90% percentile minimum temperatures are observed………………………………………………………………..57 Figure 23: Wind rose of summertime nocturnal winds at KRNO (1975-2010)…………58 Figure 24: Wind rose of summertime nocturnal winds at KRNO (1950-1974)…………59 Figure 25: Histogram of U and V components comparing 1950-1974 period with 1975- 2010 period at KRNO……………………………………………………………………60 Figure 26: Comparison of hourly mean temperature differences between pre-heat island period (1950-1979) and modern period (1980-2009) at KRNO………………………....61 Figure 27: Seasonal lapse
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