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 ivi

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 rates for minimum and maximum temperatures between Slide Mountain and KRNO (1983-2009)……………………………..…………...…………...62

Figure 28: Composite mean 300mb geopotential heights (m).……………...…………..64

Figure 29: Composite mean 300mb wind vectors (arrows) and velocities (shading, in m/s)…………………………………………………………………………...…………65

Figure 30: Composite mean 500mb geopotential heights (m)…………………………..66

Figure 31: Composite mean 700mb geopotential heights (m).………………………….67

Figure 32: Composite mean 700mb temperatures (C). ……………………...………….68 ixv

Figure 33: Composite mean 700mb wind vectors (arrows) and velocities (shading, in m/s)………………………………………………………………………...…………….69

Figure 34: Composite mean 700mb specific humidity (Kg H20/Kg Air)……………….70

Figure 35: Summertime (JJA) composite mean 300mb vector winds (m/s) and velocities (m/s, shaded) for 1948-2011……………………………………………………………..71

Figure 36: Summertime (JJA) composite mean 500mb geopotential height (m) for 1948- 2011……………………………………………………………………………………...72

Figure 37: Summertime (JJA) composite mean 700mb vector winds (m/s) and velocities (m/s, shaded) for 1948-2011……………………………………………………………..73

Figure 38: Comparison of changes in NDVI and Surface Temperature between 1980s and 2000s for the Truckee Meadows. The red star represents the downtown region and the blue square with the white airplane indicates the KRNO weather station and airport. The thermal signature of the airport is clearly visible as a cross with runways oriented north- northeast/south-southwest and east-west………………………………………………...76

Figure 39: Close-up view of the spatial structure of the relation between changes in NDVI and surface temperature…….………………………………………………….....77

Figure 40: Decadal changes in urban footprint (1987-2007)…………………………….79

Figure 41: Land use type areal coverage by year for the Truckee Meadows (1979- 2009)……………………………………………………………………………………..80

Figure 42: Example of TMWA high resolution land use dataset. Image provided by Michael Dolloff.…………………….…………………………………………………...82

Figure 43: Historical and future projections of population in the Truckee Meadows (1900-2050)……………………………………….……………………………………..83

Figure 44: Oke (1973) urban heat island prediction for the Truckee Meadows.………..84

Figure 45: Change in urban heat island in the Truckee Meadows from the base (1939) value of 3.28C. .…………………………………………………………………………85

Figure 46: Remaining mean temperature anomalies at KRNO after removal of the regional WRCC trend and the predicted influence of the UHI….………………………86

Figure 47: Comparison of population with mean seasonal temperature anomalies at KRNO. Data includes the period 1939-2009……....…………………………………….87 vix

Figure 48: Comparison of seasonal mean temperature anomalies with urban area land uses. Data is presented from 1979-2009...……………………………………………….88

Figure 49: Seasonal correlations of population and land use with minimum and maximum temperatures at KRNO. Note the correlation coefficient scale differences between maximum and minimum temperatures for population and land use……………………..89

Figure 50: The cumulative distribution function (CDF) provides examples of GCM model biases in BCCA 12km downscaled historical data (MOD) compared to observations at KRNO (OHD) between 1961-2000.…………………………………….91

Figure 51: Downscaling results for monthly minimum temperatures under IPCC SRES A1b....…………………………………………………………………………………….92

Figure 52: Downscaling results for monthly minimum temperatures under IPCC SRES A1b……………………………………………………………………………………….93

Figure 53: Downscaling results for monthly minimum temperatures under IPCC SRES A2. ……………………………………………………………………………………….94

Figure 54: Downscaling results for monthly maximum temperatures under IPCC SRES A2. ……………………………………………………………………………………….95

Figure 55: Downscaling results for monthly minimum temperatures under IPCC SRES B1. ……………………………………………………………………………………….96

Figure 56: Downscaling results for monthly maximum temperatures under IPCC SRES B1. ……………………………………………………………………………………….97 viixi

List of Appendix Figures

A.1: Monthly mean downscaled results for January 2041-2060 A1b, A2 and B1 scenarios.…………………………………………………………………………...... 159 A.2: Monthly mean downscaled results for February 2041-2060 A1b, A2 and B1 scenarios.………………………………………………………………………………..160 A.3: Monthly mean downscaled results for March 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...161 A.4: Monthly mean downscaled results for April 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...162 A.5: Monthly mean downscaled results for May 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...163 A.6: Monthly mean downscaled results for June 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...164 A.7: Monthly mean downscaled results for July 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...165 A.8: Monthly mean downscaled results for August 2041-2060 A1b, A2 and B1 scenarios.. …………………………………………………………………...………….166

A.9: Monthly mean downscaled results for September 2041-2060 A1b, A2 and B1 xii scenarios. ……………………………………………………………………………….167 A.10: Monthly mean downscaled results for October 2041-2060 A1b, A2 and B1 scenarios. ……………………………………………………………………………….168 A.11: Monthly mean downscaled results for November 2041-2060 A1b, A2 and B1 scenarios. ……………………………………………………………………………….169 A.12: Monthly mean downscaled results for December 2041-2060 A1b, A2 and B1 scenarios………………………………………………………………………………...170 1

1. Introduction

Emissions of greenhouse gases and biogeophysical changes associated with land use, i.e. urban growth and agriculture, represent the most important anthropogenic perturbations to climate (Kalnay and Cai 2003, Kueppers et al. 2007). Urban growth creates a positive thermal perturbation known as the 'urban heat island' (hereafter UHI) that is commonly represented in three dimensions as a bell-shaped volume with warmer temperatures centered about the core urban area and cooler temperatures in the surrounding rural areas (Figure 1; Howard 1833; Oke 1987). Classic definitions of the

UHI are based upon the temperature difference between an urban weather station and a rural weather station (e.g., Oke 1982). The center of the city is located at the origin and is collocated with the greatest thermal perturbation. As the distance from the center increases and the landscape becomes increasingly rural, the influence of the UHI decreases. In reality, the UHI perturbation is not a smooth surface as it is strongly forced by localized concentrations of heating whose distribution through the city is a function of geographic factors (and may be located at the urban-rural interface) as well as thermal advection by local or regional winds which serve to displace the influence of the UHI to locations that are dependent upon flow conditions occurring at that time.

In arid and non-arid regions alike, urban growth dramatically changes the surface characteristics and alters the moisture, energy and momentum budgets thus leading to changes in climate (Crutzen 2004; Warner 2004). Urbanization affects local, regional and global climate at diurnal, seasonal and longer term scales (Zhou et al. 2004; Zhang et al.

2005). The UHI effect is manifested primarily during the night and the signal is captured 2

in daily minimum temperatures and the magnitude of the UHI ranges from 2-5°C. (Lo et al. 1997; Banta et al. 1998). It is at this time that surface winds and atmospheric turbulence are minimized (Balling and Brazel 1989) thus limiting downward mixing of higher potential temperature air from aloft. Studies of UHI generally focus on one of three themes: physical studies, biophysical impacts and sustainable mititgation (Chow et al. 2012). Physical studies include the description and spatial mapping of the UHI (Brazel et al. 2007), time series analysis and land use change (Lee and Ho 2010) and multiscale urban climate modeling (Grossman-Clarke et al. 2010). Biophysical impact studies focus on human discomfort (Baker et al. 2002), urban water and energy usage (Balling and

Gober 2007) and UHI vulnerability (Harlan et al. 2006). Sustainable mitigation studies include green landscaping (Gober et al. 2010) and modification of urban building material thermodynamic characteristics (Emmanuel and Fernando 2007). Detailed reviews of recent progress in urban climatology can be found in Arnfield (2003), Voogt and Oke (2003), Shepherd (2005), Kanda (2006), Souch and Grimmond (2006) and

Chow et al. (2012). 3

Figure 1: Idealized, conceptual example of the temperature perturbation induced by an Urban Heat Island (UHI). The city center is found at the origin. The magnitude of the urban heat island is a function of many factors including the local topography and proximity to hydrologic features, urban fabric and geometry, partitioning of latent and sensible heat fluxes, thermal radiation due to human activities such as heating buildings and operating vehicles, and finally the region’s characteristic mesoscale and synoptic weather conditions (Landsburg 1981; Oke 1976; Bonan 2002;

Arnfield 2003). The highly heterogeneous urban fabric is constituted by various types of land uses (e.g. residential, commercial, and industrial) and the materials which compose them (brick, asphalt, wood, concrete, etc.). This fabric, combined with varying densities of development and vegetation coverage create a myriad of microclimates that feed back upscale to influence the urban thermal landscape (Bonan 2002). Urban geometry refers to 4

the size, shape and orientation of buildings and streets as well as the thermodynamic nature of the urban surfaces, e.g. albedo, heat capacity and thermal conductivity (Bonan

2002). Grimmond and Oke (1995) show that as urban growth increases a decreased latent heat flux and increased sensible heat flux is observed due to decreasing wind speeds, evapotranspiration and convective heat loss by bulk fluid transport of thermal energy. An enhanced sensible heat flux to the urban boundary layer thus occurs (Wilby 2008).

Hence, an increased Bowen ratio B (where B=Qh/Qe with Qh representing sensible heat flux and Qe the latent heat flux, respectively) is observed for cities. Bottyan et al. (2005) discuss the strong connection between land use parameters (e.g. built-up area and urban areal extension) and UHI influence. Despite seemingly overwhelming complexity at transcendent spatial scales, population is used as a general forcing term in the calculation of the UHI effect (Oke 1976) and is as follows:

UHI perturbation (°C) = .73 * log10 (population) (Equation 1)

Three recent studies (Arndt and Redmond 2004, O’Hara 2006 and Menne et al.

2009) have speculated upon the presence of a UHI in the Truckee Meadows region of

Nevada (Figure 2 and Figure 3) as indicated by increasing trends in summer (defined as

June, July and August, hereafter JJA, other seasons include winter; December, January and February or DJF; spring; March, April and May or MAM; and fall; September,

October and November or SON) minimum temperatures during the 1985-2009 period as compared to the total period of record for the Reno area (which dates back to 1939). Prior to 1942, the Reno weather station was located in the downtown region. On September 1,

1942 the airport, located 4 km to the southeast, became the official observation site. Upon this move, a cooling trend was observed compared to the 1930s as the airport was located 5

in a relatively rural region (O’Hara 2006). In the decades since being relocated to the airport, urban growth has engulfed the site and average temperatures have gradually increased. Arndt and Redmond (2004) and O’Hara (2006) attribute this observation to increased urbanization of the area surrounding the airport.

Figure 2: The Truckee Meadows Region of Nevada. Significant topographic barriers exist on all sides of the city. 6

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). Urban population growth is occurring at a faster rate than that of the Earth's total population (World Resources Institute 1996). By 2030, 60% of the world's population is expected to reside in urban centers (United Nations 2003). Urban areas in developed countries are trending towards decreased building density (Brown et al. 2005; European 7

Environment Agency 2006) and as such, urban expansion continues to go beyond the city periphery into agricultural and native undeveloped landscapes (Trusilova et al. 2009). As increasing numbers of the world's population make their residence in urban areas, it is extremely important to evaluate the potential impacts of climate change on cities. Urban areas may be considered both drivers and receivers of climate change (Wilby 2008).

While they drive perturbations to the local and regional climate, they also are highly susceptible to climate change-related extreme weather events. The risks associated with these impacts are economic, environmental, physical, social and fiduciary (Rosenzweig

2007). The most often recognized impacts include: reduced winter season cold spells, increased warm season heat spells, changes in the timing, frequency and severity of urban floods, air and water pollution episodes, strain on water supply, sewer, power and wastewater systems and sea level rise (IPCC 2007; Rosenzweig et al. 2007; Wilby 2007;

Wilby 2008). The primary effects of climate change on cities with regards to UHI influence will be related to increased severity of air pollution and heat wave events. The number one weather-related cause of mortality in the United States is heat (Johnson and

Wilson 2009; Anderson and Bell 2009) and seasonal increases in mortality and morbidity are often associated with extreme weather events such as heat waves even in healthy individuals (Loughnan et al. 2010).

Despite the uncertainty associated with future climate predictions, it is critical to establish planning, management and investment adaption strategies and decisions that are robust across the variety of possible futures (Rosenzweig 2007; Dessai et al. 2009). Such climate change adaption strategies have been implemented in major cities such as New 8

York (Major et al. 2005), London (LCCP 2002) and Toronto (CDNR 2005) but there is critical need to do similar work in both smaller modern cities as well as rapidly growing mega-cities in the developing world (Rosenzweig 2007).

Recently, methods to include urban effects in climate models have been developed (Jin and Shepherd 2005; Jin et al. 2007) and estimates of historical regional climate changes forced by urban land have been made in the northeastern United States

(Lamptey et al. 2005) and Europe (Trusilova 2008). Future estimates of urban growth on climate have been made for Europe as well (Trusilova et al. 2009). All studies indicate that local modifications of land cover from vegetated to urban or impervious surfaces significantly alter temperature and precipitation regimes in areas which extend beyond the urban landscape. The numerical simulations of expanding urban areas by Trusilova et al. (2009) showed that the proportion of land over which the diurnal temperature range was significantly affected by a factor of two occurred in response to a 40% increase in urban land cover. From these findings it is apparent that urban growth has significant nonlinear effects on local and regional scales and thus must be accounted for in regional climate forecasts by climate scientists and policy makers, especially when future urban growth of cities is expected (Trusilova et al. 2009). These findings are in agreement with

Xu et al. (2009) who showed using remotely sensed satellite imagery (Landsat TM) that increases in urban area can exponentially accelerate the increase in land surface temperature.

The intent herein is to closely examine the forcing mechanisms of the Truckee

Meadows UHI and offer insights towards incorporating the influence of the UHI into downscaled future climate predictions for the area. It is hypothesized that the conversion 9

of both agricultural and historical floodplain grasslands and marshes in the eastern and southern portions of the Truckee Meadows to urban landscapes have increased the regional Bowen ratio and that significant growth of industrial and commercial facilities in the near proximity of the airport have enhanced the UHI without the dramatic increase in population required to satisfy the prediction of the Oke method. Stations which remain at a constant location undergo changes in local land use and land cover; however adjustments to ensure high quality and regionally representative data are rarely made to rectify the problem (Pielke et al. 2007). Here we will show how incorporating the UHI signal into the downscaling process is instrumental in generating more realistic representations of urban climate as compared to using future climate predictions that are either not-downscaled or do not include this signal. The improvement in downscaled future climate predictions for the Truckee Meadows area represent an effort to increase the spectrum and quality of future climate scenario possibilities available to water and energy resource managers and land use planners. This will provide an enhanced ability to propose robust climate change adaption strategies across the various alternative futures as well as identify potential vulnerabilities within current and future adaption strategies

(Dessai et al. 2009).

2. Study Area

2.1 The Great Basin

Edmund Jaeger described Nevada and the Great Basin Figure 4) as “a great expanse of monotony against waterless horizons” (1957:v). In reality, the Nevada landscape exists as a complex mosaic of microclimates, vegetation regimes, and geologic features. Geology has a central role in Nevada’s ecology, climate, human history, 10

economy and future (Price 2004). Cordilleran tectonics during the past 20 million years resulted in regional extension and generated uplifted relief throughout Nevada that exceeds 2300m and averages nearly 1800m (Grayson, 1993:19). The same forces created the 4000m high Sierra Nevada and 3000m high Cascade mountain ranges that form the western boundary of the Great Basin and act to shadow Nevada from moist Pacific storms, making it the driest state in the nation (Price 2004). 11

Figure 4: The Great Basin and the western United States. 12

The state of Nevada lies entirely within the Intermountain West region and almost entirely within the hydrographic Great Basin. The Great Basin is characterized by a unique basin and range topography consisting of alternating, predominately north-south oriented block-faulted mountain ranges interspersed with slightly tilted down-dropped alluvial basin floors (Fiero 1986). Nevada’s unique geography of isolated physical features and microclimates create environments known as “sky islands”. These mountaintop forests lie “within seas of aridity, harboring on their summits a peculiar boreal biota” (Jaeger, 1957:145) and contribute to the many endemic species of flora and fauna, including the Sand Mountain Blue butterfly (Euphilotes pallescens arenamontana) and White Mountain Skypilot (Polemonium chartaceum).

The climate of the Nevada is a function of the complex nature of its topography and is characterized by its high degree of variability and climatic extremes. Annual average precipitation can vary by nearly an order of magnitude within 20 km and ranges from 120mm in basin bottoms to greater than 1400mm in higher elevation mountains

(Hammer 1986:37). Single station annual precipitation variability is high as well and can range between 565mm (1975-76) and 2462mm (1982-83) (data for Tahoe Meadows, elevation 2600m) (Hidy and Klieforth 1990:34-35). For much of Nevada, the precipitation maximum occurs in the winter and the precipitation minimum occurs during summer (Billings, 1949). Temperature varies widely, with diurnal ranges up to 38° C and seasonal averages ranging from well below 0° C in winter to over 40° C in summer

(Hammer 1986:37). Despite the low latitude of the Great Basin, the high elevation relative to sea level keeps winter temperatures low (Whiteman, 2000:21). The high 13

variability of temperature is attributed several factors: the isolation of the region from moderating oceanic influences, the strong insolation during long summer days, low relative humidity and the strong heat loss by terrestrial radiation during long winter nights (Hidy and Klieforth 1990:28). A high degree of spatial variability also exists due to the large variation in topographic parameters such as elevation, aspect and range orientation. Local temperature changes on the order of 15°C or more within the hourly scale result from mesoscale air flow patterns such as rapid warming due to downslope winds forced by mountain lee waves (Whiteman 2000:151) or rapid cooling due to evaporation from thunderstorm-induced downbursts (Houze 1994:324). Humidity in

Nevada is also accompanied by considerable variability, e.g., during precipitation events relative humidity will reach 100% and during Chinook wind events relative humidity will drop as low as 2% (Hidy and Klieforth 1990:29). During winter, cold, dry arctic air masses cause the dew point to fall to several degrees below freezing while during summer, rare incursions of warm, moist air (from the Gulfs of California and Mexico) may achieve dew points in excess of 10° C (Hidy and Klieforth 1990:29).

Synoptic scale circulation in the Intermountain Region is driven by contrasting thermal properties and the motions of large air masses across the North American continent that form a weak high pressure system called the Great Basin High (Whiteman

2000:8). Air flows over this region predominately from the west with a winter wind velocity maximum of 50 m s-1 which is associated with the polar jet/front system (Hidy and Klieforth 1990:23-24). During wintertime, these winds are responsible for the formation and advection of snow over mountain ridgelines and creating deep snowdrifts on leeward slopes, thus influencing local patterns of runoff and playing an important role 14

in vegetation composition, character and survival patterns in upper montane zones

(Billings 1973; Hadley and Smith 1987) due to soil moisture variations. Wind-driven snow drifting during the Pleistocene epoch led to the formation of permanent ice fields and glaciers on eastern slopes of the highest ranges of the Intermountain Region and generated Nevada’s only remaining glacier, found on Wheeler Peak in the Snake Range

(Billings 1990:49; Heald 1956a,b). During summer, a convergence zone develops over the region between the southeasterly flow of moist subtropical air and dry southwesterly flow from the mid-latitudes of the eastern Pacific due to a thermal lee trough predominantly over the western part of the state (Hidy and Klieforth 1990:25). Localized wind patterns are modulated by orographic forcing, diurnal differential diabatic heating and surface friction (Whiteman 2001:171). Thermally driven circulations create anabatic flows during the morning due to insolation of east facing slopes while in the afternoon and evening katabatic winds drain cold air into valleys, creating a local valley nocturnal inversion and thermal belt of warm air above the inversion (Whiteman 2000:173-175).

The relative elevation of the thermal belt on Nevada mountain ranges is indicated by the presence of pinyon-juniper forests (Billings 1954).

Houghton (1979) analyzed the temporal and spatial distribution of precipitation in the Great Basin and found there to be three primary precipitation regimes: winter frontal cyclones from the Pacific Ocean which result in winter to be the wettest season in western and northern Nevada, springtime cold continental cyclones (“Tonopah Lows”) that involve moist Pacific air and develop east of the Sierra during spring and autumn and cause a springtime precipitation maxima in central and eastern Nevada and summer thunderstorms resulting from the advection of low level, moist, subtropical air masses 15

from the Gulfs of Mexico and/or California that produce a summer precipitation maxima in southern Nevada. Snow is the dominant form of precipitation in the western Great

Basin while rain becomes increasingly important for the regions further to the east and south (West 1988; Billings 1990:75). Hidy and Klieforth (1990:35) argue that because the Great Basin is represented by three different precipitation regimes it is less susceptible to regional-scale drought than regions such as the neighboring Sierra Nevada, as decreases in precipitation during one season may be compensated by increased precipitation during another season of that year.

Orography plays a significant role in the generation of precipitation during frontal systems and is a major contributing factor to the precipitation events in Nevada. As moist air is lifted over mountains, it is adiabatically cooled until the point of condensation which then generates precipitation. Orographic enhancement and subsequent precipitation distribution is governed by several factors including: proximity to moisture sources, terrain relief and terrain aspect relative to prevailing wet season wind (Whiteman

2001:105). Because the majority of Nevada’s moisture originates from Pacific air masses and has a predominantly westerly wind component which is orthogonal to the north-south trend of nearly all of Nevada’s high relief mountain ranges, favorable conditions often exist for orographic precipitation events. Orographic effects during winter storms commonly produce precipitation ratios between the summit of Mount Rose Highway

(2700m) and the 16km leeward (to the east) Carson City (1510m) between 10:1 and 20:1

(Hidy and Klieforth 1990:34). Tonopah Lows generate the largest snowstorms in the majority of Great Basin ranges and are characterized by the presence of nimbostratus clouds and/or rain on the eastern slopes of the Sierra Nevada and ranges to the east, 16

reduced windspeeds and reduced orographic enhancement (peak to valley precipitation ratios of approximately 2:1) (Hidy and Klieforth 1990:34).

Summer convective storms are also often orographically enhanced, however their high degree of spatial variability and relatively small scale of influence lead to more localized effects on hydrology. Heavy rain may fall over one canyon with no rain falling a short distance away. Large, long duration storms result in localized flash flooding and act as a primary geomorphic driver as well as causes of lightning strike-induced forest and range fires. Wallace (1975) describes the pronounced diurnal pattern in western mountain convective storms and Hidy and Klieforth (1990:35) note that the heaviest rain in thunderstorms often occurs in valleys in contrast to winter storms.

From a hydrologic point of view, Duffy and Al-Hassan (1988) describe “the single most important feature of the Great Basin landform is that it collectively captures all rivers and subsurface flows originating in the region”. This condition of internal drainage created expansive freshwater pluvial lakes during the Pleistocene within basin areas that now exist as saline lakes or playas whose levels are found to be sensitive to very small variations in climate (Snyder and Langbein 1962; Mifflin and Wheat 1979).

Alluvial fans occupy up to 75% of the basin areas (Shreve 1942) and basin fill deposits may accumulate up to thicknesses exceeding 1000m (Duffy and Al-Hassan 1988).

Hydrologic conditions in the Great Basin are primarily controlled by physiography

(Dobrowolski 1990:249) though vegetation can also influence water allocation through stem and leaf interception and evaporation (West and Gifford 1976), drop attenuation and infiltration promotion by plant litter (Thompson and James 1985) and litter decomposition which improves water absorption and storage through improved soil 17

structure (Oades 1984). Mountains of the Great Basin have a poor natural water storage ability (Snyder 1962) while the basins have a water storage capacity that exceeds the quantity of direct annual precipitation (Dobrowolski et al. 1990:247). Because the mountains receive the bulk of annual precipitation and provide the principal source of water in the Great Basin, low elevation evaporation from playas and saline lakes is the principal source of water loss next to human diversion and use (Duffy and Al-Hassan

1988).

Deserts represent one of the most extreme environments for vegetation on Earth

(Smith and Nowak 1990:181). The Great Basin desert is the coldest and northernmost desert in North America (Brooks and Pyke 2001). Low elevation vegetation consists of sagebrush steppe communities dominated by sagebrush (Artemsia spp.), mountain mahogany (Cercocarpus ledifolius) and perennial grasses (e.g. Elymus cinerus,

Agropyron sertorum) and salt desert shrublands dominated by saltbush (Atriplex spp.), greasewood (Sarcobatus vermiculatus) and saltgrass (Distichlis spicata stricta)

(Chambers and Miller 1993; Brooks and Pyke 2001). Higher elevation vegetation is primarily pinyon-juniper woodlands and composed of Pinus spp. and Juniperus spp. while at the highest elevations whitebark pine (Pinus albicaulis), bristlecone pine (Pinus longaeva) and Ponderosa Pine (Pinus ponderosa, Western Nevada only) (Billings

1990:75) predominate. Riparian areas comprise less than 1 percent of the Great Basin, however they serve as the foundation for much of the region’s biodiversity and provide vital ecosystem services (Chambers and Miller 1993).

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2.2 Change

It is well established that complex changes in the global environment are occurring due to the growing and developing human population (Vitousek 1994). Rapid climatic change disturbs the web of environmental factors upon which biodiversity depend (Lovejoy 1997:12). Drought conditions in the Great Basin tend to favor invasive annual grasses while suppressing perennials (Brooks and Pyke 2001), increase strain on urban water resources (Rosenzweig et al. 2007), increase forest and also increase range fuel loads by reducing fuel moisture (Deerning et al. 1978). Drought can additionally lead to ecotonal shifts in woodlands (Allen and Breshears 1998).

Ample evidence exists for the ecological impacts of recent climate change

(Walther et al. 2002). Clear evidence also exists for nonlinear, synergistic interactions within the driving forces of global environmental change, however the strength of interactions on ecosystems is largely unknown (Sala et al. 2000). Climate change impacts on regional scales are difficult, if not impossible to reproduce due to the local influences of chaotic weather and climate dynamics (Kalnay 2003:260) The Great Basin has been identified by Maggs (1989) as a highly sensitive region to climatic change due to its unique environmental characteristics. It is thus the hope of the author that this comprehensive overview of the influences of global environmental change acting on a sensitive system such as the Great Basin and its many smaller microclimate systems will highlight the myriad uses of downscaled climate models in local and regional studies for the purposes of forecasting future changes as well as gaining a better understanding of the coupled Earth system. 19

2.3 The Planetary Boundary Layer

In order to explain more ideas of this work, we will introduce the nexus of the

Earth and the atmosphere, commonly referred to as the planetary boundary layer (PBL).

The UHI is a direct forcing agent on the PBL due to its perturbation of the surface energy budget which govern the PBL processes of transfers of energy, mass and momentum; as such an introduction focused on the desert PBL is critical. It is within the PBL that the

Earth’s biological inhabitants interact with atmosphere and feel the effects of incoming solar radiation which drive motions on the Earth from the microscale turbulent eddies we feel as gusts of wind to the planetary circulations, such as the Hadley and Ferrell Cells, which develop due to the differential heating of the tropics as compared to the polar regions. The roughness of the Earth surface is due to vegetation, topography and anthropogenic creation of structures and acts to retard and alter the direction of the airflow in the PBL via frictional drag. The PBL is where many transformations of energy take place; incoming shortwave solar radiation (considered to be the fraction remaining compared to the top of the atmosphere after attenuation by gases, aerosols and clouds in the free atmosphere in accordance with the Beer-Lambert-Bouguer Law) is converted into thermal energy. This thermal energy is then transferred to the atmosphere via bulk fluid motions, or convection and via molecular collisions, or conduction and acts to create local and regional temperature gradients which then drive regional pressure gradients (via the ideal gas law). The resulting airflow serves to force larger scale 20

reorganizations of mass and momentum which results in changes in the local temperature gradients and allows the cycle to repeat ad infinitum.

Within this cycle, water provides a key energy transfer pathway and the PBL acts as the primary medium through which water is transferred from the Earth’s surface to the atmosphere. First, the basic principle of energy conservation shall be introduced and the surface energy balance shall be addressed in the context of an urban climate. The importance of water in the overall system will also be discussed. These discussions will prove invaluable in untangling the effects of urban growth and land use change in the

Truckee Meadows on temperature; Munn (1966) lists three ways by which humanity can alter the local weather patterns:

1. Introducing or removing local obstacles to the airflow (e.g. building structures on flat surfaces, deforestation2. By changing the radiation properties of the land surface (e.g. deforestation, urbanization).

3. By changing the water balance of the surface (draining swamps, irrigation of crops or non-native landscaping).

Since the modification of local weather by anthropogenic influences is a reality, addressing the above-listed factors as they pertain to the study area will be of considerable importance.

21

2.3.1 Surface Energy Budget

Virtually all natural processes in a closed system are characterized as following the principle of conservation of energy, i.e., for the Earth surface, the net gain and net loss of energy observed at the surface must be equal. The key ingredients in this conservation equation include short-wave radiation from the sun, long-wave radiation from the Earth and sky, heat transfer through the soil and the absorption or release of latent heat via evaporation and condensation, respectively. Equation 2 presents a simple representation of this balance (after Munn 1966), called the surface energy budget. Figure presents a conceptual diagram of the surface energy budget.

QN = QT - QR + QL↓ – QL↑ = +/- QG +/– QH +/– QE (Equation 2)

QN represents the net` radiation at the surface. If QN > 0, the surface is considered to be gaining energy; if QN < 0, the surface is losing energy to space. QT represents shortwave energy from the sky and sun, at night QT = 0. QR represents reflected shortwave energy from the Earth, at night, QR = 0 as well. QL↓ is the long-wave radiation received by the surface from the atmosphere while QL↑ is the long-wave radiation emitted by the surface.

QG represents the transfer of heat through the ground, if QG > 0 the heat flow is considered to be downward, if QG < 0 the flow of heat is upwards. QH is the turbulent transfer of sensible heat to the atmosphere if QH > 0 the heat flow is considered to be upward, if QH < 0 the flow of heat is downwards. Finally, QE represents the contribution of latent heat of evaporation and evapotranspiration. If QE > 0 the heat flow is considered to be upward and water vapor is being evaporated and transferred to the atmosphere, if 22

QE < 0 the flow of heat is downward as condensation transfers water vapor to the surface and releases heat.

Figure 5: Diurnal cycle of partitioning of surface energy budget heat flux components.

Equation 2 can be summarized in the context of a typical diurnal cycle which is shown above in Figure . During the daytime, the surface gains energy, thus QN is positive. The energy that contributes to the surplus of radiant energy is partitioned as follows: some energy is transferred downward into the soil (QG > 0), some is transferred upwards to the air (QH > 0) and what remains is evaporated at the surface (QE > 0). It is important to note that QH and QG are actual flows of heat while the transfer of water 23

vapor in either direction causes heat flow to be manifested as compensational reductions or enhancements of the magnitudes (or even signs) in QH and QG. The arid land case is of particular importance, for it is possible for evaporational cooling to be greater than available radiant energy QN when irrigation practices are performed; this results in a sign reversal of QH and/or QG (Munn, 1966). During the nighttime, the surface undergoes a net loss of radiation, thus QN is negative. Heat flows upward from the ground (QG < 0), downward from the air (QH < 0) and if air temperatures fall to the dew point temperature, condensational heating will occur (QE < 0).

2.3.2 The Role of Water Vapor

Let us now turn our attention to a most curious, common and unusual substance that plays a vital role in climate and local surface energy budgets. Dihydrogen monoxide, or water, is a triatomic molecule formed by covalent bonds between two hydrogen atoms and one oxygen atom. On Earth, water can be found in any one of three states, solid, liquid and gaseous; these states are determined via the Clausius-Clapeyron relationship for given pressures and temperatures. Due to the unequal sharing of electrons between each hydrogen and oxygen bond, water is a polar molecule with a slightly negative electronic charge on the opposite end of the oxygen molecule and slightly positive charges on each hydrogen atom. As a result, water molecules will readily form many hydrogen bonds with other water molecules. The relative strength of the hydrogen bonds causes water to have high specific and latent heats as compared to air and thus significant energy can be stored in water and transported on planetary scales through the processes of advection. The release of heat via condensation and absorption of heat by evaporation 24

will act to dramatically alter thermodynamic characteristics of local, regional and global air masses. Though water composes merely 0.4% by volume of Earth’s atmosphere (this value increases to 4% over tropical oceans) and is found virtually completely within the troposphere, roughly 75% of the heat transfer in the atmosphere occurs due to phase transformations and the subsequent heat exchanges with the environment. Water thus serves as a first order, scale transcendent climate forcing term.

Water is present along the Earth’s surface and sub-surface in many guises; hydrologic features such as lakes, rivers, glaciers, seasonal snowpack and oceans are perhaps the most obvious to the human observer. However a keen observer will realize that significant water exists at small scales (to the naked eye) as well, i.e., within the soil saturated zones (groundwater) and unsaturated (vadose) zones. These multi-scale localizations of water provide sources for evaporation as well as transport mechanisms for water to move across the Earth’s surface. Vegetation acts as both a moisture reservoir and as a conduit for water stored below ground to reach the PBL; the high surface tension exhibited by water (another result of hydrogen bonding) allows plant roots to extract soil water from the ground, transport it throughout their tissues and expel it through stomata, where it evaporates. This process is known as evapotranspiration and is governed by transpiration rates through the plant and humidity gradients between the plant and the environmental air. Arid climates tend to have high evapotranspiration rates and high temperatures. The plants’ evolutionary adaptation mechanism to such conditions is that of microphylly (succulents notwithstanding), or having small leaves thereby lowering the leaf area index and reducing convective heat exchange with the environment and 25

similarly reducing the requirement of evapotranspirational cooling. Water and vegetation play important roles in the arid Great Basin and in the urban setting these roles are magnified since their presence generates a strong oasis (cooling) effect due to the absorption of heat during the evaporation process. Changes in the water balance in an urban, arid environment due to irrigation of agricultural fields and urban landscapes

(effects of urban landscapes will change in time due to the maturation of landscape vegetation), removal of native vegetation, coverage of exposed soil with asphalt, concrete and structures all will act to alter the diurnal behavior of QE and consequently change the heat flux magnitudes and perhaps signs of QH and/or QG.

2.3.3 The Effect of Cities on Climate

The urban climate may be the best example of anthropogenic weather modification (Munn 1966). As briefly discussed previously, there are several mechanisms which contribute to the anthropogenic effect on the city; here a deeper examination is prudent prior to discussion and analysis of the observed effects of changes in the thermal regime and Truckee Meadows urban landscape. The primary physical mechanisms that influence the urban climate include: disturbance of the natural radiation balance by changing the surface energy budget, e.g., replacing native vegetation and soil with concrete and asphalt or via the maturation of well nurtured vegetation in older residential neighborhoods, the disturbance of wind regimes by the building of structures, the alteration of the water vapor balance as impermeable surfaces replace native vegetation and soils and the emission of heat, water vapor and pollution to the atmosphere (Munn

1966). As the net heat storage is increased in the urban area by the addition of buildings 26

and roads as well as changing the vegetation characteristics, more energy can be absorbed and released into the environment by the land surface and the behavior of the land surface with respect to incoming solar and incoming as well as outgoing longwave radiation will be altered (i.e. changing the albedo and emissivity of the surfaces). The end result is a new system that responds differently to perturbations than did the old system, of course the added challenge revolves around the inherent nonlinearity of the total system. These mechanisms are coupled and interact nonlinearly by alteration of fluxes of mass, momentum and energy. The interactions may change the local wind regimes as well. As the urban area stores more heat and radiates energy into the environment, the local boundary layer air is warmed and becomes less dense than the surrounding air. The positively buoyant air rises in an effort to regain thermal equilibrium with its surroundings and a convergence zone is formed around the region where surface air is being evacuated. A local pressure gradient is formed with lower pressure being centered about the core of rising air and mass continuity demands that pressure gradient-forced advection replace air in this location. Another example includes the emission of primary atmospheric pollutants from motor vehicle traffic (e.g., carbon monoxide, hydrocarbons, or nitrogen dioxides) which influence the atmospheric and surface radiation balances and alter the temperature structure of the PBL and surface. These emissions can also undergo photochemical transformations which generate more radiatively-active secondary pollutants such as tropospheric ozone. A conceptual example of the effect of cities on climate is presented in Figure 6. 27

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). 2.4 Truckee Meadows Overview

The Truckee Meadows Metropolitan area is made up of the twin cities of Reno and Sparks and is located in west-central Nevada (Figure 2) 39.5N, 119.8W. In 2009, the city covered 280 km2 and its population was 398,300 as compared to 108 km2 and a population of 178,757 in 1979 (population figures provided by the Nevada State

Demographer and land use area provided by the Truckee Meadows Water Authority).

Lying in the floodplain of the Truckee River and bounded on the west by the 2800 m

Sierra Nevada Range and the east by the 1600 m Virginia Range, the Truckee Meadows lies at the western extent of the cold desert basin and range landscape known as the Great

Basin (Figure 4). The Truckee Meadows has a mean elevation of 1300m and the climate is typical of a high desert with an annual precipitation of 19cm (NCDC 1971-2000 normals) and snow being the dominant precipitation form (West 1988). The Köppen 28

climate classification for the Truckee Meadows is BSk, or a cool, semi-arid, steppe climate.

Winter is characterized by small diurnal temperature ranges while summer demonstrates the largest diurnal temperature range; these observations are characteristic of the BSk climate. Historically, the Truckee Meadows was primarily composed of sagebrush steppe vegetation communities. Ephemeral flooding by the Truckee River created marshlands in the southern regions of the valley, many of which were converted to agricultural lands during human settlement in the middle 1900s. In the past 30 years, many of these agricultural lands have been purchased by private entities and subsequently developed into low and medium density residential and commercial zones.

The Truckee Meadows offers a unique, if not extraordinarily challenging, location to study the impacts of anthropogenic urbanization on the local climate. It is located in a landscape that has been shown to be sensitive to climate change; thus minimal buffering by the landscape can be expected to occur. As the location of the city is in a narrow valley and surrounded by relatively high topographic features, there will be an influence of diurnal, terrain-forced flows on the local boundary layer meteorology. Stable nocturnal inversions commonly develop in the Truckee Meadows due to oft-occurring conditions of clear skies and low dew points due to dry desert air masses allowing longwave radiation emission processes to transfer significant thermal energy to space. Cool air drains into the valley from the surrounding high mountains through many large canyons located along the valley as differential cooling due to reduced air mass density drives faster cooling rates at elevated regions. The cool, dense air draining into the valley helps displace warm, 29

less dense air at the valley floor that is heated by solar radiation and anthropogenic UHI forcing. The descending air undergoes adiabatic compression and warming but this warming is not enough to be offset by loss of energy via thermal emission.

During the winter and spring months, the presence (absence) of snow on the valley floor can greatly enhance (inhibit) the establishment of a stably stratified valley air mass. Figure 7 shows a typical morning wintertime sounding for Reno. It should be noted that the inversion extends below the shown rawinsonde launching elevation as the true valley floor is located 180m lower. The sounding indicates a deep inversion extending from the surface at 850mb to 750mb, very dry air and very light winds from the surface until well into the mid-troposphere. Light winds facilitate the establishment of a stably stratified air mass by limiting mixing. Under conditions of snow coverage in the valley, little surface heating occurs due to the high albedo of snow which reflects incoming shortwave solar energy while the high emissivity (ability to emit electromagnetic radiation) of the snow allows significant longwave radiation to be emitted to space during nocturnal hours. Cloud cover is most favored during the winter and spring months and its presence will serve to reduce nocturnal surface cooling and aid the establishment of stable inversions by absorption of longwave radiation emitted from the surface which is re-emitted back to the surface. 30

Figure 7: Typical cold season sounding for KRNO. Notice the deep inversion which can be extended a further 150m lower into the valley floor.

Low level clouds (such as cumulus and stratus) serve to reduce surface heating during the day via reflectance of incoming solar energy and thus reduce the overall diurnal temperature range, though localized enhancement of energy received at the surface can occur due to the ability of partly cloudy skies to focus solar energy similar to how a magnifying glass functions. Low level clouds will absorb and re-emit longwave radiation during the night; this acts to further decrease the diurnal temperature range.

Clouds located at higher elevations in the atmosphere are more transparent to incoming 31

solar radiation but still act to absorb and emit longwave radiation. Thus high clouds allow surface heating to occur but will moderate nocturnal low temperatures. The probability of high velocity wind regimes is greatest during the winter and spring months as the southerly shift in the polar jet stream brings high winds aloft which can mix momentum downward to the surface and drives the passage of transient eddies and the associated cold fronts which cause gusty winds at the surface and strong downslope wind events.

Wind velocities during these months tend to be an order of magnitude greater than the warm months. During the summer and fall months, diurnal, terrain-forced flows are the key sources of wind along with the occasional establishment of a synoptic scale westerly wind referred to as the Washoe Zephyr which is driven by a regional pressure gradient between the Central Valley of California and the Great Basin (Zhong et al. 2008). The variance in winds impacts the degree of mixing occurring in the Truckee Meadows boundary layer and can serve to enhance or inhibit stable inversion formation. These meteorological factors will serve to influence the magnitude of the UHI.

3. Global Climate Modeling

Global Climate Models (hereafter GCMs) are the best current tools to study the behavior of the Earth’s climate system and the components and interactions that compose this complex, nonlinear geophysical system (IPCC 2007). Modern GCMs represent the current state of the art modeling system for coupled climate system modeling and are designed to study climate processes, natural variability and the influence of anthropogenic forcing on the system (IPCC 2007). In the simplest terms, GCMs are mathematical representations of the primary components of the climate system, the 32

cryosphere, atmosphere, land surface and hydrosphere (IPCC 2007). Systems of differential equations (namely the Navier-Stokes equations of motion with included terms to represent thermodynamic variables) controlling the conservation of mass, energy and momentum are solved on a rotating sphere with horizontal resolutions of 100-300km2 and vertical resolutions of 10-30 levels from the surface to beyond the stratopause (IPCC

2007). As the horizontal and vertical resolution improves, so does the ability of the models to capture and simulate regional and global scale climate phenomena (e.g.,

Kimoto et al. 2005). Due to the nonlinear nature of these equations, well-accepted numerical methods of solution must be utilized. More than half of the IPCC AR4 GCMs are using spectral numerical techniques, meaning that parameters are calculated using spherical harmonics and semi-Lagrangian advection schemes instead of Eulerian finite- volume and finite-difference advection schemes. The reasons for employing spectral techniques are increased computational accuracy, ability to use longer time steps and maintain positive values of scalar variables such as water vapor, though some models do not formally conserve mass. At this point in time no consensus exists on which numerical approach is superior (IPCC 2007). Computational time steps are generally 30 minutes with outputs of major variables (e.g., 500mb heights, surface temperature, sea surface temperature) being reported at intervals between 6 hours and 1 day (IPCC 2007).

Beyond the basic physical laws that are integrated in GCMs, many other complex very fine scale processes influencing the climate state of the Earth must be represented in the simulations as well, such as clouds and precipitation, radiative processes and effects of aerosols, sea-ice distributions, deep ocean temperatures, land surface and 33

biogeochemical cycling and boundary layer processes (IPCC 2007). However, these processes occur at scales that are smaller than the model’s resolution and thus must be represented via a parametric formulation (IPCC 2007). These representations or formulations, called parameterizations, are different for each GCM and serve to generate some of the variability in model outputs for historical and future time periods. Their differences stem from the inherent empirical nature of cloud and aerosol physical processes. Perhaps the most important facet of GCMs is a realistic simulation of the coupling between the atmosphere and oceans, as oceans are instrumental in governing the global energy budget (due to their tremendous heat capacity), the hydrologic cycle (via latent heat transfer) and play a major role in the biogeochemical cycling of CO2 and other greenhouse gases. These ocean-atmosphere coupling physical processes ultimately influence clouds more than anything else. Thus the coupling between the atmosphere and ocean is of first-order importance in governing the energy budget of the climate system.

Difficulties have arisen in modeling this coupling. In an effort to prevent unrealistic model climate divergence from observations an artificial correction to the coupling must be applied, referred to as a “flux adjustment”, although some recent models rely on increasingly less adjustment or none at all due to very recent improvements in the air-sea coupling mechanisms and parameterizations (IPCC 2007).

In recent years, significant increases in computer power have allowed for finer resolution GCMs and increasingly detailed parameterizations to be developed. A detailed analysis of the performance and improvement of GCMs can be found in Chapter 8 of the

Intergovernmental Panel for Climate Change’s 2007 Synthesis Report: “Climate Models 34

and their Evaluation”. While representations of key climate forcing phenomena such as the El Nino Southern Oscillation Index (ENSO) and the Madden Julian Oscillation

(MJO) have improved substantially in recent years, the GCMs are still unable to skillfully simulate major atmospheric and oceanic circulation features such as ENSO, the Pacific

Decadal Oscillation, North Atlantic Oscillation and South Asian Monsoon (Annamalai et al. 2007; Pielke 2010). Because these large-scale features determine weather and climate for both large and small regions, efforts remain underway to address these deficiencies; however they remain in the developmental phases (Chase et al. 2006; Hurrell et al. 2009).

3.1 Future Climate Modeling

The IPCC has advised future projections of climate as resolved by GCMs to be done under the standard A2, A1B and B2 SRES scenarios and run until the year 2100

(Nakićenović et al. 2000; IPCC 2007). These scenarios of projected emissions are based upon possible directions of global economic growth, technological change and environmental consciousness thus attempting to estimate magnitudes of the emission of radiatively active species (CO2, CH4, N2O, chloroflourocarbons (CFCs) and SO2. A simple cartoon of the basic premises behind the SRES scenarios is shown in Figure 9. 35

Figure 8: Cartoon representation of the SRES Emissions Scenarios. Image courtesy Nakićenović et al. 2000. Figure 9 demonstrates a more in-depth perspective on particular changes and the expected rates at which these key forcing agents will change. Each arrow gives an indication of the rate of change with respect to time, which may not be linear, of each forcing agent. Inclusion, magnitude and temporal evolution of other forcing agents, such as tropospheric and stratospheric ozone, non-sulfate aerosols, indirect effects of aerosols, land use and solar variability, as well as black carbon are decided upon by each individual modeling group and, similar to the case of parameterizations, serve as possible reasons for model solution divergence as the models are integrated into the future (IPCC

2007). The globally-averaged surface temperature projections for all models included in the IPCC suite are presented in Figure 10. The present analysis utilizes nine GCMs under all three primary (A1B, A2 and B2) SRES scenarios. 36

Figure 9: A qualitative representation of the various changes in primary indices for the IPCC SRES scenarios. Data courtesy of IPCC (2007). 37

Figure 10: Influence of IPCC SRES scenarios on multi-model average and ranges for global surface temperature. Data from Nakićenvoić et al. (2000).

3.2 Downscaling Global Climate Models

Currently, land-ocean-atmosphere coupled GCMs are the most sophisticated tool available to understand and predict future climate variability on seasonal to multi-decadal timescales (Widmann and Bretherton 2000). While GCM simulations indicate large-scale patterns of change associated with natural and anthropogenic climate forcing, they lack the spatial resolution to capture the effects of complex terrain (e.g., narrow mountain ranges), land/water interactions, or regional-scale patterns of land use (e.g., Salathé et al.

2007). The spatial resolutions of GCMs are generally 200-300km (Widmann and

Bretherton 2000), while regional studies require a resolution of less than 50km (Salathé et al. 2007). For the purposes of hydrologic forecasting and climate change impact studies, 38

such as urban-climate interactions, the key parameters to acquire from GCMs are surface temperature and precipitation (Salathé et al. 2007). Global climate models (GCMs) represent an important tool to evaluate the estimated future response of the atmosphere- land-ocean system to changing atmospheric composition (Fowler et al. 2007). The use of

GCMs fortifies most climate change impacts studies (Wilby and Harris 2006). However, they are relatively course in spatial resolution (typical grid size 100-500km) and are unable to resolve significant subgrid features such as topography, clouds and land use

(Fowler et al. 2007). Climate change environmental impacts studies typically require resolutions of less than ten kilometers (Salathe 2003; Hidalgo et al. 2008) and the problem created by the spatial mismatch of GCMs to local features needed for impact assessment has been long recognized (Kim et al. 1984; Lamb 1987; Lettenmaier et al.

1999; Wood et al. 2002). Finally, it is well established that output from GCMs cannot be used to force environmental impact models without some method of prior bias correction to ensure meaningful results (Wood et al. 2004; Feddersen and Andersen 2005; Piani

2010).

The process of ‘downscaling’ seeks to bridge the gap in resolution by transforming information provided by course resolution GCMs to fine spatial resolutions needed for impact assessment. Downscaling is necessary because the underlying processes captured by environmental impact models are highly sensitive to nuances of local climate that are parameterized by course scale GCMs (Wilby 2004; Hidalgo et al.

2008). Two primary downscaling methods exist. First is statistical downscaling, a computationally efficient process that transforms course-scale climate predictions to finer scales based on observed relationships between the climate at the model grid point and 39

measurement locations. Secondly there is dynamical downscaling, a computationally intensive nesting of a fine scale regional climate model within a course scale GCM which incorporates large scale forcing from the GCM into the regional model’s more accurate simulation of topography and mesoscale thermodynamical and hydrodynamical process that neither the global model nor the statistical downscaling method can incorporate

(Christensen et al. 2007). The main disadvantage of the dynamical approach is in its computational expense which makes its use in impact studies limited or impossible for multiple-decade and multiple GCM simulations under varying atmospheric greenhouse gas scenarios (Hidalgo et al. 2008). Statistical techniques are most limited by nonstationarity in climate (Ramage 1983; Huth 1997; Milly et al. 2008). A modern review of downscaling methods for use in impacts studies can be found in Fowler et al.

(2007). The present study employs the statistical downscaling bias correction technique of Wood (2004) based on the quantile mapping technique described in Panofsky and

Brier (1968) that is applied to a 12km spatial resolution gridded dataset that has been downscaled from the native GCM grid scales using the bias correction and constructed analog (BCCA) technique of Hidalgo et al. (2008).

3.2.1 Limitations of Downscaling

Statistical downscaling techniques are limited by several factors. A stationary process is a random process whose joint probability distribution remains constant despite temporal or spatial shifts, i.e., the statistical moments, e.g. mean and variance, also do not change. Conversely, transient processes have constantly varying statistical moments due to non equilibrium characteristics of the forcing functions for the process. It is well 40

established that climate is a transient feature (Lorenz 1963) and that relationships (e.g., correlations) that may be established for a given period of time will not hold indefinitely, despite the relatively stationary behavior on long time scales. ENSO, a quasi-periodic oscillation, is a prime example of this and as Ramage (1983) discusses in detail, the fact that statistical relationships change in magnitude and even sign over time, suggests an absence of physical understanding of the phenomena. All statistical downscaling techniques rely upon a historical period to use as a predictor for future periods and since historical periods of record for neither weather stations nor gridded datasets extend infinitely long into the past, it can be assumed that not all possible climate states are captured in these periods. It entails that future climate predictions that have been calibrated to the past observations will also not be inclusive of all possible climate states and thus fall victim to the stationarity assumption as they will be assuming that future climate states will have the same statistical relationship as was observed in the historical record.

A station’s period of record is a critical juncture in the downscaling process. As discussed above, the length of the record is instrumental in ensuring that many oscillating climate phenomena (e.g. ENSO, the Arctic Oscillation Index, North Atlantic Oscillation, etc.) and their manifestations on local climate are captured in the record. While the length of the historical data is an important facet of the station’s record, the quality of the data kept at the station is of equal if not greater importance. Missing or incorrectly reported values upset the temporal continuity of the dataset and generate questions as to how representative the station actually is for the period of record. Stations with substantial 41

missing data (> 15%) have been excluded from the present analysis. A further challenge with many weather stations has to do with the actual physical location of the station relative to the area that is being assumed to be sampled. This issue is at the forefront of the global surface temperature record as many observation stations have been found to be non-representative of their location. For instance, consider a low altitude desert environment. If a weather station is located on the north side of a building and near or in an irrigated field or modern landscaped environment, the data will be far less representative than if the station was located in a native ecotone away from the influence of structures and other anthropogenic forcing.

A further problem with many stations that may have long periods of record and good quality data is the fact that these stations may have moved location while retaining the same station name and number over the course of their record history. These stations may appear excellent candidates for downscaling at first glance, but under closer examination they may have to be treated as separate entities or completely disregarded.

This is not always the case, but proper examination of station metadata is always required in order to ensure the meaningfulness of results for stations that have not maintained spatial heterogeneity. Arndt and Redmond (2004) utilized a double mass technique, first developed by Kohler (1949) to decipher undocumented station moves. This technique is often used in hydroclimatology work to detect station moves. This technique is very useful when station metadata is absent or incomplete. The present analysis includes detailed metadata for the KRNO station and thus does not need to employ the double- mass technique. Table 1 presents a summary of the station moves and changes undergone by the KRNO station since 1938. 42

3.2.2 Demand for Downscaled Future Climate Data

The range of ecological and societal impacts due to natural and anthropogenic climate variation are enormous (Widmann and Bretherton, 2000). Some of the most important possible impacts of climate change in the western United States are expressed through spatial and temporal changes in hydrologic processes including streamflow, snowpack and flooding (Salathé et al. 2007). The current state of climate modeling is adequate for many applications in hydrology (Salathé et al. 2007) but downscaling is still required to produce reasonable results in local regions. The scenarios produced by statistical downscaling have been of considerable value for climate change impact assessments, especially in areas of complex topography, such as coastal or mountainous regions (Widmann and Bretherton 2000; Leung 2004; Leung et al. 2005; Salathé et al.

2007). Nevada is the epitome of a mountainous region thus requiring very high resolution information from simulations. By better resolving orographic influences, improved skill in seasonal climate and hydrologic forecasting can be achieved (Roads 2004). Such improvements will lead to enhanced abilities for resource managers to estimate demand and adapt strategies that are robust under possible future climate scenarios.

4. Data and Methods

4.1 Historical Climate Data

MathWork’s MATLAB ® (www.mathworks.com, Natick, MA) software was used for all analysis of climate data in the current study. Historical climate data for 29 stations in the state of Nevada and California was acquired from the Western Regional

Climate Center (http://www.wrcc.dri.edu/, hereafter WRCC). Figure presents a map of the Cooperative National Weather (COOP) stations included in this analysis. Climate 43

parameters used in the current downscaling study include daily minimum temperature and maximum temperature from all stations and when available, hourly winds and temperature data. For the current study precipitation was ignored because the focus centers about the thermal effects and climatic perturbations resulting from UHI influences. Future work should include the effects of atmospheric moisture to examine the influence convective instability may have in the vertical transport of heat with regards to the heat island perturbation. The climate data undergoes a quality assurance/quality control process by the WRCC as well as the National Climatic Data Center to ensure minimal missing data and erroneous data. Any data that appeared to have outliers was reexamined and if the data was beyond +/- 5 standard deviations from the climatological mean, the data was considered erroneous and was ignored in the analysis. COOP stations have limitations and benefits that affect the quality of the data as well as the applicability to climate modeling efforts. As the period of record increases, so does the probability that historical extremes in temperature and precipitation will be captured as well as the effects of complex interactions between synoptic and hemispheric teleconnection patterns which influence climate on scales from individual events to multiple decades. On average,

COOP stations have long periods of records (>80years) which is useful to capture multiple cycles of low-frequency oscillations such as the El Nino Southern Oscillation,

North Atlantic Oscillation, Pacific Decadal Oscillation and Arctic Oscillation, all of which have been shown to influence local temperature and precipitation regimes on interseasonal and interdecadal scales. Measurements of temperature and precipitation are taken at daily intervals, so high temporal resolution measurements are unavailable and 44

limit the ability of COOP stations to verify the daily evolution of temperatures generated by numerical weather and climate models.

Figure 11: The 29 Regional COOP stations used in the analysis.

For UHI studies it is of particular importance when selecting stations to try to eliminate extraneous effects due to topography, water bodies and downwind effects of the UHI itself (Oke 1987). The stations chosen for the current analysis were done in a manner that best represents the complex topography of the study area and can thus capture both the background conditions and urban conditions sufficiently. In an ideal situation to quantify and evaluate the UHI, an extensive series of pre-urban measurements would be available for direct comparison to present day observations (Lowry 1977; Oke 1987; Stewart and Oke 2009). However, this is often not the case (e.g. Saaroni and Ziv 2010) and as such, stations must be selected to be as representative as possible, as has been done in the current work. Due to the short period of record for all local weather stations located in the Truckee Meadows with the exception of the Reno Airport in the Truckee Meadows area, the historical analysis of the Truckee Meadows is based solely upon data from this station. Figure 3 presented a time series of seasonal 45

mean maximum and minimum temperature for the period of record (1938-2009). The airport station has moved multiple times during its history and these moves have had significant effects on the measurements (Arndt and Redmond 2004).

Table 1 presents the historical station locations and the length and directions of the station movements through time. Comparisons in this study make use of all available data, usually ranging from 1938-2010, though actual years used in each portion of the analysis will always be noted to maintain a frame of reference. Seasons will be referred to in their conventional manner with respect to the northern hemisphere’s tilt relative to the plane of revolution.

Distance from Direction Previous from Location Previous Elevation Station Location Occupied From Occupied To (km) Location (m) Post Office Building at Mill and Virginia 3/1/1934 8/31/1942 0.00 1369.82 Hubbard Field (KRNO) 9/1/1942 5/31/1949 6.44 SSE 1371.04 C.A.A. Building (KRNO) 6/1/1949 10/23/1959 0.01 SSE 1369.82 Federal Facilities Building (KRNO) 10/24/1959 11/15/1963 1.29 NNW 1369.82 Due east of Federal Building (KRNO) 11/16/1963 5/11/1978 0.72 N 1369.82 More north of building (KRNO) 5/12/1978 8/12/1980 0.00 N 1369.82 North end of aviation building (KRNO) 8/13/1980 9/1/1995 0.97 SE 1369.82 Runway (KRNO) 9/2/1995 3/1/1998 2.00 SSW 1369.82 North end of aviation building (KRNO) 3/2/1998 present 2.00 NNE 1369.82

Table 1: Historical locations of official Reno weather observation station.

In an effort to reduce the influence of large-scale climate forcing from both long- term and interannual effects, the monthly state-wide Nevada climate trend for maximum and minimum temperatures produced by the Western Regional Climate Center (hereafter

WRCC) based on normals generated from the PRISM dataset (Daly et al. 1994) and referred to as the “Nevada Climate Tracker” (Data and information for the climate tracker 46

can be found at: http://www.wrcc.dri.edu/monitor/nev-mon/index.html) was subtracted from the observed KRNO monthly means in order to isolate the local scale perturbations to the KRNO values. These values are reported as anomalies of KRNO minus the Nevada mean temperature for the given variable of maximum and minimum temperature, respectively. Figure 12 presents the seasonal maximum and minimum anomalies for

KRNO after removal of the regional trends.

Figure 12: KRNO seasonal temperatures after removal of regional climate trend.

Long-term, daily station data (maximum and minimum temperatures) from three stations in similar leeside basins east of the Sierra Nevada (Minden, Carson City and 47

Stead) and one upland station to the east of Reno (Virginia City) in the Virginia Range was acquired from the WRCC and standardized by the full period of record-derived monthly means for minimum and maximum temperature to create a standard score or anomaly for each daily value for the period 1940-2010 (Figure 13). Equation 3 shows how the standard score (anomaly) ‘z’ is derived from the daily value ‘μ’ which is subtracted from the monthly mean ‘x’ and divided by the monthly standard deviation ‘σ’ in an effort to better measure differences in the various stations.

(Equation 3)

Figure presents comparisons of standardized temperatures between Reno and the

4 nearby cities of Minden, Carson City, Virginia City and Stead. The standard deviation is taken over the entire period of record so as to hold it constant. Because this is not a stationary quantity and dependent upon the length of time period chosen, this assumption represents a degree of freedom in the analysis which could be a source for error. If the standard deviation was calculated for each month of each year, the anomaly would be scaled by the respective standard deviation i.e., a small value of standard deviation would create a large anomaly whereas a larger standard deviation would reduce the anomaly.

Such results would change the results and subsequent outcome of the analysis; for the purpose of simplification we use the full period of record standard deviation to maintain consistency in calculation of the anomaly. 48

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. Direct, i.e., non-standardized comparisons of seasonal means as computed from daily values between the KRNO temperature time series and averaged over 29 COOP network stations in the northern-central California and Nevada region was performed for the period 1938-2009 and the results are presented in Figure 14. The purpose of this exercise is to offer a quasi-independent measure of change relative to KRNO to compare signals to those derived when computing anomalies via the WRCC Nevada Climate

Tracker. PRISM values are calculated using COOP station data as well as a multitude of other data sources, therefore this technique is referred to as ‘quasi-independent’. 50

A fundamental measure of UHI is the temperature difference between an urban station and a rural station located in a similar physical environment, anthropogenic influences notwithstanding. In the case of the Truckee Meadows, the nearest station that could be classified as a truly rural station is the RAWS site of Dead Camel (Figure 15), located 70km due east of the Truckee Meadows in an adjacent, albeit more arid basin.

The coordinates for Dead Camel are 39.2619N, -118.9431W and its elevation is 1368m above sea level. Daily maximum and minimum temperature differences between KRNO and Dead Camel were calculated for summer (JJA) during 1987-2009 and smoothed to 5- day means to reduce noise (smoothing was bounded by each year of interest to prevent overlap between years) and are presented in Figure 16.

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

Figure 16: Summertime 5-day averaged differences between urban (KRNO) and rural (Dead Camel) maximum (red) and minimum (blue) temperatures. In order to gain an improved understanding of the vertical structure of the UHI, an examination of upper air data using radiosonde measurements was undertaken. The

National Weather Service office in Reno (REV), located 7km NNW of KRNO and 180m above the valley floor (39.57N, -119.801W), launches twice-daily (0Z and 12Z) radiosonde balloons to collect observations of wind speed and direction, temperature, pressure and relative humidity from the surface to the lower stratosphere. The period of record for this observational network is 16 years and spans January 1995- December

2010. The historical observations are housed at the University of Wyoming

(http://weather.uwyo.edu/upperair/sounding.html). The data was downloaded and monthly climatologies of temperature and geopotential height were generated for 700mb and 500mb levels to examine whether a UHI signal can be extracted. The data are 52

presented in Figure 17 with a 7-day running mean. The presence of water vapor influences the radiation budget of the atmosphere due to the ability to excite vibrational modes in water molecules with infrared radiation. Therefore an examination of monthly mean column-integrated precipitable water measurements, based on the REV rawinsonde data, is attempted and the data presented in Figure 18 with a 10-day running mean for smoothing purposes. A Welch power spectrum analysis following the method of Trauth

(2006) was performed to understand the frequency that increased moisture surges occur in the Truckee Meadows region; the results are presented in Figure 19. The steps involved in the power spectrum analysis included removing a linear trend from the data, which can be mis-interpreted as a long period oscillation, computing a Fast Fourier

Transform (FFT) using the periodogram function in Matlab with an empty window, FFT length of 1024 and a frequency scaling factor of one.

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. Further examination of the behavior of the KRNO UHI with respect to the influence of water vapor is presented in Figure 20. Pearson’s correlation coefficient was calculated for each month between daily minimum and maximum temperature values observed at KRNO and the precipitable water measurements from the REV sounding data. Correlations between all three variables are presented at monthly intervals. An extension of the examination between surface temperatures and water vapor concentrations (using the relative humidity values provided by the station) for KRNO are presented as monthly scatterplots with those values exceeding the 90th percentile highlighted in magenta in Figure 21. Figure presents histograms of the 90th percentile temperature values by the frequency that they occur in each month by year. 55

Figure 20: Monthly correlations of REV-derived precipitable water with KRNO observed minimum and maximum temperatures.

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 at KRNO. Wind roses of nocturnal winds (2200Z-0600Z) at KRNO between 1975-2010 and

1950-1974 are presented in Figure 23 and Figure 24 and histograms of U and V wind components for each time period are presented in Figure 25. A 2 sample t-test comparing both periods of time for wind direction and the respective U and V components was performed. A comparison of seasonal hourly mean temperature differences between two periods, a pre-urbanized period (1950-1979) and a modern period (1980-2009) at KRNO were calculated and are shown in Figure 26. Daily maximum and minimum temperature data from the Slide Mountain WRCC weather station located at 2942m atop Slide

Mountain, approximately 22km southwest and 1500m above KRNO, was used with the 57

KRNO daily maximum and minimum temperatures to calculate lapse rates by taking the difference of the stations divided by the topographic height separating them. The values were calculated at daily intervals and averaged seasonally. Figure 27 presents the seasonal average maximum and minimum temperature lapse rates for the period 1983-

2009.

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

Temperature Mean

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 rates for minimum and maximum temperatures between

Slide Mountain and KRNO (1983-2009).

4.2 Synoptic Climatology of the Truckee Meadows UHI

Based upon the lists of highest minimum and maximum temperature days in Reno provided by O’Hara (2006), eight of the highest minimum temperature days since 1948 were selected and used to generate composite synoptic maps from the 2.5° x 2.5° spatial resolution NCEP/NCAR Reanalysis dataset (Kalnay et al. 1996) using the NOAA/ESRL

Physical Sciences Division Map Room (http://www.esrl.noaa.gov/psd/map/) graphic user interface. The purpose is to identify large scale features of the western North American 62

region that are characteristic during Reno’s highest minimum temperature events to enhance both short term (less than seven days) and longer term (multi-week to seasonal) forecasts. Short term forecast improvements can gain from better recognition of large scale flow patterns that are favorable to high minimum temperature events using data from operational numerical weather models, such as those used by the National Weather

Service. Longer range forecasts may improve via the use of probabilistic schemes for determining if there exist greater or lesser chances of flow regimes conducive to high minimum temperatures in Reno at the seasonal scale. Furthermore, examination of dynamically downscaled results for the region at future time periods under various IPCC greenhouse gas emissions scenarios can be examined and the likelihoods of such flow regimes calculated to offer perspectives on how whether such events will become more or less likely under future climate scenarios.

In order to better understand the synoptic scale forcing patterns that coincide with anomalously high minimum surface (KRNO) temperatures, the following composite synoptic maps were generated: Jet-stream level (300mb) geopotential heights are calculated in Figure 28. Figure 29 shows the associated 300mb wind direction and velocity via overlaying unit vectors specifying the wind direction with color shading to indicate the velocity magnitude. Figure 30 presents the 500mb geopotential heights during the composite period, Figure 31 shows the 700mb (roughly mountain-top elevation or 3050m) geopotential heights and Figure 32 demonstrates the 700mb temperatures. 700mb wind vectors and velocities are presented in Figure 33 and 700mb specific humidities are shown in Figure 34. 63

For comparison to long term climatological mean states, composite synoptic maps for three key atmospheric pressure levels were generated using the NCEP/NCAR

Reanalysis data for the entire summer period and spanning the years 1948-2011. The

300mb wind vectors and velocities are shown in Figure 35. The 500mb geopotential heights are presented in Figure 36. Finally, Figure 37 shows 700mb wind vectors and velocities.

Figure 28: Composite mean 300mb geopotential heights (m). 64

Figure 29: Composite mean 300mb wind vectors (arrows) and velocities (shading, in m/s). 65

Figure 30: Composite mean 500mb geopotential heights (m).

66

Figure 31: Composite mean 700mb geopotential heights (m). 67

Figure 32: Composite mean 700mb temperatures (C).

68

Figure 33: Composite mean 700mb wind vectors (arrows) and velocities (shading, in m/s).

69

Figure 34: Composite mean 700mb specific humidity (Kg H20/Kg Air). 70

Figure 35: Summertime (JJA) composite mean 300mb vector winds (m/s) and velocities (m/s, shaded) for 1948-2011. 71

Figure 36: Summertime (JJA) composite mean 500mb geopotential height (m) for 1948- 2011. 72

Figure 37: Summertime (JJA) composite mean 700mb vector winds (m/s) and velocities (m/s, shaded) for 1948-2011. 4.3 GIS and Remote Sensing Data

Landsat Thematic Mapper (hereafter LTM) data was acquired from the United

States Geological Survey (USGS) Glovis server (http://glovis.usgs.gov/) during the summer of 2010. The USGS project description summarizes the LTM mission as follows:

“Landsat represents the world’s longest continuously acquired collection of space-based moderate resolution land remote sensing data” (USGS 2011). The Landsat satellite collects 185km wide images through a descending orbit along the illuminated surface of the Earth (USGS 2011). The satellite passes the same point over Earth every 16 or 18 days (depending on the altitude of orbit) and circles the Earth approximately every 99 73

minutes (USGS 2011). Landsat 5 carried a Thematic Mapper (TM) sensor with 30m resolution in several spectral bands in the shortwave infrared and visible and 120m resolution in the thermal infrared channel while Landsat 7 uses the Enhanced Thematic

Mapper (ETM+, reflecting the improvements made upon the TM sensor) with 30m visible and infrared bands, a 60m spatial resolution thermal band and a 15m panchromatic band (USGS 2011). The high temporal and spatial resolution of Landsat data, coupled with a nearly 40-year long database of imagery, provides a unique and highly applicable resource for global, regional and local change research with regards to land surface evolution. 12 images from cloudless days in July every odd year (three for

1985-1989, five for 1991-1999 and four for 2001-2009) were obtained and included

Landsat 5 data (1984-1998) and Landsat 7 (1999-present) mission data. Charles Morton pre- and post-processed the images using the METRIC methodology (Allen et al. 2005;

Morton 2011) to generate images of surface temperature (this refers to skin of the surface temperature or the emission temperature of the surface, not to be confused with 2-m air temperature) and normalized difference vegetation indices (NDVI). NDVI is calculated from the reflectance ratio of visible red light absorbed by chlorophyll and near-infrared energy scattered from mesophyll leaf structures of terrestrial vegetation (NDVI = NIR-

RED/(NIR + RED)) and offers a method to quantify vegetation density from satellite observations (Myneni et al. 1995). NDVI values range from -1 to 1; negative values correspond to the absence of vegetation (Myneni et al. 1995). A complete description of the METRIC algorithm and the pre- and post-processing steps involved is given in Allen et al. (2005). The completed images were analyzed using ESRI’s ArcGIS version 10. The

Reno-Sparks municipal boundary was used to crop the images to focus the analysis on 74

the urban region. The calculations of NDVI and surface temperature were averaged by decade (1980s, 1990s and 2000s) and differences from the 2000s to 1980s were calculated. The NDVI changes that result in negative (positive) values imply a decrease

(increase) in pixel vegetation coverage. The results of these calculations are shown for the entire Truckee Meadows in Figure 38 and for a sub-sampled neighborhood in the southeastern region of the Truckee Meadows in Figure 39 (shown for clarity). 75

Figure 38: Comparison of changes in NDVI and Surface Temperature between 1980s and 2000s for the Truckee Meadows. The red star represents the downtown region and the blue square with the white airplane indicates the KRNO weather station and airport. The thermal signature of the airport is clearly visible as a cross with runways oriented north- northeast/south-southwest and east-west. 76

Figure 39: Close-up view of the spatial structure of the relation between changes in NDVI and surface temperature. 77

The Truckee Meadows Water Authority (TMWA) maintains a highly detailed

(20m spatial resolution) land parcel dataset that originates in 1979 and is current through

2009. The original dataset contains over 15 categories of parcels, or land use types. To simplify the analysis, parcels were grouped into basic land use categories: Single family, multi-family, commercial, industrial, agricultural and parks. Roads are excluded from the

TMWA parcel dataset. Decadal changes in the urban footprint of the Truckee Meadows are presented in Figure 40. Figure presents a time series of the growth by major land use category. The high resolution of this dataset as well as the exclusion of roads is highlighted by a map of the downtown Reno are generated by Dolloff (2011) and presented in Figure 41.

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Figure 40: Decadal changes in urban footprint (1987-2007). 79

Figure 41: Land use type areal coverage by year for the Truckee Meadows (1979- 2009). TMWA, in conjunction with the Nevada State Demographer and Washoe County, kindly provided historical population data and future growth estimates. The Demographer provided two scenarios of growth, a ‘High’ and a ‘Low’ growth scenario. The historical period spans 1900-2010 and the future estimates are for the period 2010-2050. Between the period of 1900 and 1979, data was only available on the decadal (census) scale. A simple linear interpolation was performed to create data at 1-year intervals during this period to generate smooth graphs. From 1979 onwards to the future projections, population data is given at 1-year intervals. The historical and future population data are presented in Figure 42. During the post-WWII era, population growth occurred at a nearly exponential rate. Rapid growth continued until the modern economic downturn ca.

2009 where a slight decrease in population was observed. The future growth predictions have a range of approximately 300,000 people, with the lowest growth scenario (Nevada 80

Demographer’s Low Scenario) resulting in an increase of population by approximately

30,000 and the highest growth scenario (Nevada Demographer’s High Scenario) resulting in an increase of population by approximately 320,000 (compared to 2009 values). The growth predictions are based upon varying degrees and timescales of economic recovery and development as well as expected inflow and outflow of domestic and international persons. These predictions are often used by regional planning and resource management agencies to estimate demand on resource systems and plan for development of future urban infrastructure.

The population data was used to calculate a UHI estimate using Equation 1 for the primary historical period of interest (1940-2009) and under the future population scenarios (2010-2050). This estimate is presented in Figure 42 and is also calculated as a difference from the base UHI effect in 1939 of 3.28°C in Figure to highlight the increase since the weather station was relocated to the airport from the downtown location. The

UHI estimate is removed from the KRNO time series in the same way that the WRCC climate trend was removed with the remaining anomaly is presented in Figure 43. The temperature anomalies versus population and urban land use coverage are presented in

Figure 44 and Figure 45. Correlations at the seasonal time scale were calculated between minimum and maximum temperature at KRNO for the 1979-2009 time period and are shown in Figure 46. 81

Figure 42: Example of TMWA high resolution land use dataset. Image provided by Michael Dolloff. 82

Figure 43: Historical and future projections of population in the Truckee Meadows (1900-2050). 83

Figure 44: Oke (1973) urban heat island prediction for the Truckee Meadows. 84

Figure 45: Change in urban heat island in the Truckee Meadows from the base (1939) value of 3.28C. 85

Figure 46: Remaining mean temperature anomalies at KRNO after removal of the regional WRCC trend and the predicted influence of the UHI. 86

Figure 47: Comparison of population with mean seasonal temperature anomalies at KRNO. Data includes the period 1939-2009. 87

Figure 48: Comparison of seasonal mean temperature anomalies with urban area land uses. Data is presented from 1979-2009. 88

Figure 49: Seasonal correlations of population and land use with minimum and maximum temperatures at KRNO. Note the correlation coefficient scale differences between maximum and minimum temperatures for population and land use. 4.4 Downscaled Climate Projection Data

The present study uses a unique approach in downscaling future climate data.

Nine GCMs from the World Climate Research Programme's (WCRP's) Coupled Model

Intercomparison Project Phase 3 (CMIP3) multi-model dataset were selected. A collaborative effort between the Lawrence Livermore National Laboratory (LLNL),

United States Bureau of Reclamation and Santa Clara University has downscaled climate projections derived from the WCRP's CMIP3 multimodel dataset, stored and served at the LLNL Green Data Oasis (https://computing.llnl.gov/resources/gdo/), to a gridded 89

12km resolution dataset over the contiguous United States using the Bias Correction and

Constructed Analog (BCCA) method of Hidalgo et al. (2008). The selected GCMs were acquired from the LLNL Green Data Oasis for the A1b, A2 and B2 IPCC SRES scenarios. A simple bilinear interpolation from the nearest four GCM gridpoints of each of the nine models calculated the predicted 2-meter minimum and maximum air temperature for each day of each month for the historical or hindcast 20th century experiment or 20C3M period 1961-2000 (hereafter referred to as the model observed historical dataset or MHD). The 20C3M experiment is used to represent the post- industrial time period from 1851-2000. The second step uses the cumulative distribution function mapping technique developed by Wood et al. (2004) to bias correct future climate projections from the 12km gridded data to the station scale, in this case the Reno

Airport station (KRNO). The bias correction scheme works as follows: cumulative distribution functions (CDFs) of the two observed historical data periods (OHD for the period 1961-2000 and OHD_X for the period 1970-2009) from the Reno Airport were generated. Figure presents the CDFs of the OHD and MOD and the associated systematic bias of the MODs. The purpose of using two historical periods is to test the sensitivity of the future projections to the historical calibration period; in this case the period between the years 1961-2000 does not carry the strong UHI signal while the period between the years 1970-2009 does. A simple i-th percentile transfer function following the method of Wood et al. (2004) was utilized by Abatzoglou (2009, Pers.

Comm.) and performed by the author to shift the first and second order statistical moments from each MOD to match the respective OHD (OHD and OHD_X), thereby removing the GCM bias in the MOD and preserving the variability of the OHD in the 90

respective bias corrected output. The same transfer function was used to then map the bias corrected MOD to the respective 2041-2060 emissions scenario and model future dataset (MFD). The MFD now includes the variability of the OHD and no longer retains the initial GCM bias. The results of the future climate downscaling procedure are presented in Figures 51-56. The nine-GCM average of maximum and minimum time series for 2041-2060 with respect to each scenario is presented in Appendix A.

Figure 50: The cumulative distribution function (CDF) provides examples of GCM model biases in BCCA 12km downscaled historical data (MOD) compared to observations at KRNO (OHD) between the period 1961-2000. 91

Figure 51: Downscaling results for monthly minimum temperatures under IPCC SRES A1b. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI). 92

Figure 52: Downscaling results for monthly minimum temperatures under IPCC SRES A1b. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI).

93

Figure 53: Downscaling results for monthly minimum temperatures under IPCC SRES A2. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI). 94

Figure 54: Downscaling results for monthly maximum temperatures under IPCC SRES A2. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI).

95

Figure 55: Downscaling results for monthly minimum temperatures under IPCC SRES B1. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI). 96

Figure 56: Downscaling results for monthly maximum temperatures under IPCC SRES B1. The Raw GCM results show the 12km BCCA dataset while the DS 1961-2000 and DS 1970-2009 results represent the 12km BCCA data that was further bias corrected, or downscaled, using the KRNO historical periods of 1961- 2000 (less developed UHI) and 1970-2000 (more developed UHI).

5. Results and Discussion

5.1 Surface and Upper Air Data

Interannual variability is observed in all seasons at KRNO (Figure 4), with winter and spring showing the greatest oscillations in maximum temperatures and winter showing the greatest oscillations in minimum temperatures. Interannual oscillations are the result of complex, nonlinear interactions of the coupled ocean-atmosphere circulation 97

pattern (e.g., the El Niño Southern Oscillation). The effects are manifested through large scale forcing mechanisms such as the position of mid-latitude jet stream locations and intensities and strongly influence weather and short-term climate variability during all seasons by virtue of changing temperatures, winds and precipitation observed at a location. Interannual effects generally perturb climate at spatial scales on the order of 106 km2 though the effects may be experienced at both larger and smaller spatial scales. Of particular interest is the asymmetric warming of minimum temperatures compared to maximum temperatures which are most notable for the summertime; such results are consistent with an UHI signal (e.g., Landsberg 1981).

A strong warming trend beginning in the middle to late 1970s is the most obvious feature of the minimum temperature anomalies at KRNO even after the removal of the

WRCC regional climate trend (Figure 11). The figure indicates several interesting behaviors. During the early 1980s it appears that the influence of the urban area on the thermal regime has reached a critical point where the anomaly, which has remained relatively constant until this point, now has shifted regimes. This urban thermal threshold may be generated by a variety of influences including but not limited to: a critical urban spatial extent has been achieved, a critical change in surface energy budget forcing parameters such as the Bowen ratio, or a change in the ratio of growth rates of Reno as compared to the other cities involved. Further study involving detailed observational data over a dense spatial network coupled with numerical modeling involving an urban canopy boundary layer scheme may prove fruitful in developing insight into this dramatic change in the relationship of the standardized anomalies between Reno and the rural cities. The fact that a significant increasing trend in minimum temperatures remains in the 98

data despite the removal of the WRCC trend clearly demonstrates the impact of the UHI signal on the KRNO temperature observations. If the regional effects of increasing temperatures were dominant, there would be little to no signal remaining after the removal of this trend. The data shows that the UHI impact is the dominant forcing mechanism in the KRNO urban climate system. Finally, the importance of station moves is indicated by the anomalously ‘cool’ year of 1997-1998, when the measurement station was moved a significant distance (see Table 1). The station was replaced following the realization that such a move would serve as a major discontinuity in the dataset and result in major complications for future climatological analysis efforts.

5.2 Seasonal Variability

The seasonal dependence on minimum temperature anomalies is also changing with time (Figure 11). Once again, it is clear that a shift in the urban thermal regime takes place in the late 1970s to early 1980s and the same station move is clearly visible in the data. Up until the 1970s, the KRNO anomaly was relatively constant for each season, but concurrent with the initiation of the warming trend the seasonal anomalies begin to converge towards warmer values. The major change in the relationship between KRNO and the rural cities takes place in the middle portion of the 1980 decade. It may be speculated that a thermal critical point was reached at this time, perhaps due to the various UHI forcing mechanisms and this point represents the transition to a new thermal state which would then be interpreted as a physical singularity which separates two distinct statistical populations. Given that the coupled land-atmospheric system, especially with regards to major anthropogenic thermal perturbations, may be considered 99

an almost intransitive system based on the definition given by Lorenz (1968), the need for greater mathematically-based physical understanding is highlighted.

Summer shows the greatest warming of approximately 4°C in the 2000s as compared to the 1940-1970 average with spring and fall warming approximately 2.8°C and 3.2°C respectively (Figure 11). Winter temperature increases were the smallest of the four seasons with an increase of approximately 2°C (Figure 11). The seasonal dependence of the asymmetric warming as well as the convergence to a nearly constant state of anomaly relative to the larger scale region suggests the role of complex thermodynamical and hydrodynamical interactions between the urban area and shorter, synoptic weather timescales as well as longer decadal climate timescales. Properly addressing these interactions will require the use of extremely high resolution nested numerical models such as a large eddy simulation model nested within a mesoscale numerical model such as the Weather Research and Forecasting Model (Skamarock et al.

2005). However, we still can attempt to gain insight into the problem using more qualitative methods and, perhaps more importantly, help frame appropriate questions which can be approached via the above-mentioned numerical modeling techniques.

The UHI influence is apparent in the time series comparisons of KRNO to the 29

COOP stations as shown in Figure 13. As mentioned above, the trend is evident in the minimum temperatures during all seasons and the two statistical populations centered around 1985 are significantly different as computed by a two sample t-test with p < .05.

However, while no trend is apparent in the maximum temperature data, it is of interest that the variability of the maximum temperature anomalies has increased and the average anomaly for each season is different for the post-UHI (after 1985) period as compared to 100

the pre-UHI (pre-1985) period. Wintertime anomalies in maximum temperature have remained relatively constant in time while it is observed that during summertime the mean anomalies have increased. The increased seasonal dependence of the anomalies indicates that the UHI impacts are closely related to the surface energy budget and therefore land use patterns as changes in the energetic balance of the land surface will feedback to alter the boundary layer thermal state.

The increasing UHI in the Truckee Meadows is well-illustrated by the histograms shown in Figure 22. The strong negative skew of the histograms is due to the increasing frequency of events exceeding the 90th percentile of minimum temperature in the past ten years. Consistent with other results discussed herein, the summer season exhibits the strongest negative skew and the shifted thermal regime singularity that occurred during the mid-1980s is evident. During the winter season no skew exists and an even distribution is shown which is consistent with the results shown in Figure where the winter season exhibits the smallest UHI influence. Several factors may be responsible for this result and includes but is not limited to, variability of snow cover presence (or lack thereof) at the surface, cloudiness and higher mean windspeeds associated with transient synoptic scale waves (i.e. mid-latitude cyclones and thermal fronts) which induce greater boundary layer mixing via turbulence generation from downward mixing of momentum under greater mean wind velocities in the free atmosphere and the associated advective processes with the higher wind speeds.

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5.3 Urban Versus Rural Temperature Trends

The urban to rural difference is an oft-used method to calculate the UHI intensity

(Oke 1982, Saaroni and Ziv 2010). Inspection of the urban-rural difference between

KRNO and Dead Camel during the summertime (Figure 15) indicates that the minimum temperature difference between Reno and Dead Camel has changed over time, signifying the warming of minimum temperatures in Reno while the temperature trend at Dead

Camel has remained stationary. Comparing the 1987-1995 period to the 1996-2010 period using a two-sample t-test indicates two separate populations (p < .05). The maximum temperature difference was reduced during the late 1980s into the early 1990s, but appears to have reached a steady state as it has not changed significantly in the past

15 years, though a gradual increase is observed from 2005-2010. The significant changes in the urban to rural differences indicated in this figure highlight the increasing magnitude of the Truckee Meadows UHI and agree with the results shown in Figures 11-

13. In this comparison, neither station had the WRCC trend removed, as the large scale regional climate trend is assumed to be the same at each site and therefore will cancel out in the analysis. As a result, the hypothesis that the Truckee Meadows is characterized by an UHI that is increasing in time is supported.

5.4 Vertical Extent of the UHI

The upper air data supplied by the REV rawinsonde observations indicates seasonal oscillations in temperature at both 700mb and 500mb pressure levels (Figure

16); such findings are in agreement with Peixoto and Oort (1992). The transition from boreal winter to summer (and vice versa) is observed to occur rapidly in the spring (fall) as the mean temperature at the level of interest shows two distinct regimes of higher and 102

lower temperatures dominating with a short (2-3 week) period separating the regimes. No significant trends were produced at these levels with respect to the UHI in comparisons of

90th percentile minimum temperatures at KRNO with the associated upper air data; therefore these results are not shown. It can thus be inferred that the UHI influence on the boundary layer does not extend to the launch site and represents a relatively shallow, but important, thermal perturbation to the boundary layer.

5.5 Influence of Water Vapor

The rawinsonde-derived precipitable water data (Figure 18) indicates the clear role of the North American Monsoon (e.g., Adams and Comrie 1997) in generating higher values of precipitable water during the summer months. Though the bulk of the precipitation in the western Great Basin occurs during the winter and spring months and is due to dynamically forced upward vertical motions associated with synoptic-scale divergence generated by baroclinic midlatitude extratropical cyclones, significant convective precipitation can result from the ample moisture supply during the summer.

The power spectrum density results (Figure 19) indicate the temporal roles of annual variability (peak at 0.002) seasonal variability (peak at f = 0.01) and synoptic variability

(peaks between .03 and .08). The seasonal peak expresses the highest power while the synoptic timescale range indicates the variety of types of advective situations for the region, such as single shortwave trough events, wintertime Rossby wave trains and summertime monsoonal moisture surges.

Of interest with regards to the UHI is the fact that during the summertime, the high sun angle and high frequency of cloudless days allows for large quantities of 103

incoming solar radiation to be absorbed at the surface and re-emitted as longwave radiation during the nocturnal hours. As discussed previously, water vapor is strongly absorbing in the longwave spectrum and outgoing longwave radiation from the surface may be then absorbed by the higher concentrations in the atmosphere which is then re- radiated back to the surface and acting to warm the boundary, in the same manner that nocturnal cloud cover will minimize surface temperature drops as outgoing longwave radiation is absorbed and re-emitted as well as reflected (Bohren 1987). However, the results of the daily correlations between REV observations of precipitable water and minimum and maximum temperatures at KRNO (Figure 19) indicate that the highest correlations (~0.6 for minimum temperature, ~0.3 for max temperature) occur during the winter months and the lowest correlations (~0.3 for minimum temperature, ~ -0.05 for max temperature) occur during the summer, which is contrary to expectation. All correlations between precipitable water and minimum temperature were positive as were the correlation for maximum temperature with the exception of during the summer months when the correlations were negative but very small (-0.05). Relatively high positive (~0.5) correlations between minimum and maximum temperature were found with the strongest correlations found during the spring months. While a positive correlation of water vapor with minimum temperature exists, which is in agreement with our basic tenets of thermodynamics, the seasonal dependence remains unanswered.

Perhaps clouds have a significant role as they may be masking the influence of water vapor under clear sky conditions. The correlation between precipitable water and minimum and maximum temperatures increases steeply as the warm season transitions to the cold season from the end of summer into winter. Future analyses using satellite 104

remote sensing imagery at the appropriate time and space scales (hourly imagery with

1km resolution, e.g. GOES http://www.goes.noaa.gov/) to identify cloudiness are recommended as a measure to isolate clear sky conditions to allow direct comparisons of surface temperature to precipitable water. The level at which clouds occur may either have a warming (high clouds) or cooling (low clouds) effect, therefore a detailed image analysis algorithm would be required for robust results (Manabe and Strickler 1964).

Regarding the scatterplots of relative humidity and minimum temperature (Figure

20), a generally negative slope of relative humidity with respect to temperature is observed during the warm season and is in agreement with the dependence on atmospheric water vapor saturation on temperature as given by the Clausius-Clapeyron relation. Summer air masses tend to be drier than those in other seasons given the overall higher temperatures which result in less probabilities for water molecules to exist in the vapor phase and due to large scale subsidence under the dominant anticyclonic synoptic flow regime which causes adiabatic warming and drying of the regional airmass. The summer months are characterized by a heavier distribution of high temperatures and corresponding low relative humidity values while the presence of increased moisture during the winter, spring and fall months is evident by the more uniform distributions.

The temperature dependence of relative humidity is evident during the summertime but decreases gradually until the cold (winter) season as shown by the negative slopes observed during the warm season months and the nearly non-existent slopes of the other season.

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5.6 Diurnal UHI Variations

The KRNO-derived nocturnal wind roses shown in Figure 22 and 23 indicate that changes in mean wind velocity magnitude and direction distributions were observed between the two time periods. Decomposition of the raw wind data into U and V vector components (Figure 24) shows similar results. Two-sample t-tests were performed comparing the total wind vectors and independent U and V components for the two periods (pre-UHI and post-UHI) to test whether or not the time periods are different.

Results for all three tests were statistically significant at the 95% level (p>.05), indicating that the two periods of time are indeed statistically different. Northerly flow regimes dominate both periods, but there is a slight increase in the frequency of northerly winds in the post-UHI period. The observed wind patterns during the post-UHI development period have become more concentrated into westerly to northwesterly flow regimes as southwesterly, southerly and southeasterly flow regimes are observed less frequently compared to the pre-UHI period. Bornstein (1987) demonstrates that urban areas can act to modify boundary layer wind fields. It is hypothesized that the altered flow regime may be due to the establishment of a thermal low as a direct consequence of the heat storage by the urban area. However, neither surface pressure observations nor modeling results currently exist to test this hypothesis. The varying direction of winds during the pre-UHI period which are no longer observed may indicate that the thermal perturbations are now overwhelming the random nature of winds which in the past may have been more characteristic of diurnal terrain forced wind patterns formed by the canyons and hills located in the Pah Rah and Virginia Range along the eastern boundary of the Truckee 106

Meadows. This phenomena represents an ideal experiment to test using a very high resolution (<500m grid spacing) numerical model with an urban canopy model and very high resolution terrain. The interaction of the nocturnal boundary layer with urban land uses amidst a complex terrain environment represents a fruitful area of future research.

The hourly mean temperatures for each season during the pre-UHI (in this case

1950-1979) and post-UHI (1980-2009) time periods are shown in Figure 25. Marked changes in the thermal diurnal cycle are observed for all seasons between the time periods. Consistent with results mentioned previously, the summer season exhibits the most dramatic increases in temperature throughout the diurnal cycle with fall, the warmer of the two transitive seasons, showing a similar but less magnified change. The asymmetric warming of greater increases in minimum temperature versus maximum temperature is observed in both cases. Springtime exhibits a cooling of maximum temperature but the nocturnal influence of the UHI remains evident. No change in wintertime maximum temperatures is observed while the minimum temperatures have increased, albeit the increase is the least observed of the four seasons. The figure provides compelling evidence for a net decrease in diurnal temperature range and the asymmetric warming of minimum temperatures agrees well with Landsberg (1981). These results offer further evidence for the increased heat storage of the urban area during the daytime, particularly during the summer, thereby increasing the available energy to warm the boundary layer. The greater warming of the fall versus spring season suggests that the thermal storage capability of the urban area may behave similar to soil as a seasonal time scale repository of heat due to its relatively large thermal heat capacity (Bonan 2002). 107

While derivatives of the slopes of the respective temperature-time curves (i.e., dT/dt) were not calculated explicitly, it is observed that all slopes of the post-UHI period are smaller than the pre-UHI period which may indicate that the greater extent of the urban area has a greater quantity of heat to release by virtue of its greater capability to store heat, which reduces the time rate of change at which the boundary layer cools as the urban landscape emits longwave radiation. The extent of which this phenomena is due to the thermal heat capacity and thermal conductivity of the urban area as well as turbulent bulk convective mixing within the boundary remains beyond the scope of this study and again offers another interesting avenue of future study.

Minimum temperature lapse rates between KRNO and Slide Mountain (Figure

26) have changed during the past 27 years, while maximum temperature lapse rates show little difference. Averaging for the periods 1983-1987 and 2005-2009 of minimum temperature lapse rates shows that the lapse rates (dT/dZ) have increased by 0.8C/km in winter, 1.5C/km in spring, 1.85C/km in summer and 0.3C/km in fall. During the summer, where the increase is most pronounced, Slide Mountain minimum temperatures were on average 2.4C warmer during 2005-2009 as compared to 1983-1987. This indicates increased asymmetric warming of KRNO versus Slide Mountain, if the warming were the same at both stations, no difference in the minimum temperature lapse rate should be observed. However, since the lapse rates have increased in spite of warming at Slide

Mountain, it can be inferred that KRNO minimum temperatures have increased by a greater quantity. A climatological decrease in stability (dT/dZ > 0) of the atmosphere during the time when minimum temperatures are observed and longwave emission of radiation has maximally cooled the surface is consistent with the UHI thermal 108

perturbation acting as a heat source (+Q) to warm the surface and decrease atmospheric stability. That the effect is most pronounced in the summer is in agreement with UHI effects being manifested most strongly during summer due to the largest absorption of solar diabatic heating by the urban environment. It is also in agreement with the dominance of local scale versus synoptic scale forcing of Reno’s weather. Other seasons, especially winter and spring, will not have as clear of a relationship due to transient snow and cloud cover that will act to alter surface energy budget characteristics.

5.7 Synoptic Climatology

The eight highest minimum temperature days which were composited into synoptic maps show several features of interest with regards to high minimum temperature predictability in the Truckee Meadows. A well-developed anticyclone tends to be present over the intermountain west region with its axis centered over Arizona,

Utah, eastern Idaho and western Montana (Figure 29). Large-scale subsidence and adiabatic warming of the middle troposphere is expected in the region of a ridge of high pressure. Common weather conditions under large-scale ridges include clear skies, a dominance of gusty, diurnal, terrain and thermally driven low-level winds compared to free tropospheric winds since the upper level wind velocities are low and greater diurnal temperature ranges (Whiteman 2002). It is important to note that spatially differential diabatic heating of terrain can produce local convective clouds and generate precipitation as well as drive gusty winds during the morning and afternoon hours as buoyancy perturbations develop in response to localized heating.

Assuming a force balance between the pressure gradient and Coriolis forces (the geostrophic flow regime), we expect higher velocity winds where the pressure gradient is 109

the greatest; this occurs over North Dakota, eastern Montana, southern Saskatchewan and southern Manitoba. Conversely, very low velocities of wind are to be expected in a region of a small pressure gradient such as is the case over the Intermountain West. Both

Figure 29 and Figure 30 are consistent with typical conditions for the northern hemisphere summer season as the northerly displacement of the 300mb velocity maximum is clearly evident.

Examinations of Figures 28-34 all lend credence to the establishment of a large- scale ridge in the area of interest during the composited dates. Reno tends to be located on the upstream, western half of the continental-scale ridge, but due to the nature of generating composite images from a variety of different dates, it is not fair to proclaim that Reno is always in such a position, rather, it must be stated that the location of Reno relative to the synoptic scale features is a tendency or average state, not an absolute certainty. In agreement with the location of the 300mb and 500mb ridge axes (Figure 28 and Figure 30, respectively) the highest 700mb geopotential heights (Figure 31) and corresponding temperatures (Figure 32) are observed in the central intermountain west with the core heating region over the eastern Great Basin and far-western Rocky

Mountains. Such conditions are representative of the climatological mean state for the western United States during boreal summer.

A closer examination of Figure 29 brings up an interesting consideration that requires further elucidation, namely, the small core of northerly directed, upper-level

(300mb) momentum tends to be present along the southern California and northern Baja coastal regions. This core of momentum extends downwards to 700mb, as shown in 110

Figure 33 and may play a role in advecting warm, moisture-rich air from the eastern central Pacific Ocean northwards into the Great Basin. Since the momentum exists from the mountain-top level (700mb) to deep into the upper troposphere (300mb), advection of moisture over the Sierra Nevada mountain range is possible and may play a role in enhancing nocturnal temperatures by absorption and re-emission of longwave radiation

(Bohren 1987, Wilcox 2011). This finding prompted the examination of the precipitable water data from rawinsonde observations discussed previously and shown in Figure 17.

However, this feature must be examined using a sub-sampling technique similar to a

Monte Carlo approach to ensure that one time period within the composite is not forcing this feature and therefore necessitating its removal. The relatively moist lower troposphere with respect to normal JJA conditions is shown in Figure 33. The synoptic patterns shown in the composite images are characteristic of North American Monsoon conditions in which an upper level high pressure center develops over the U.S. Southwest and the anticyclonic rotation helps to advect warm, moist air from the eastern central

Pacific and Gulf of Mexico into the U.S. southwest (Adams and Comrie 1997).

Comparisons of the anomalously high minimum temperature composited figures

(hereafter composites) to the long-term (1948-2011) summertime means (Figures 35-37, hereafter climatology) shows differences that may help to better understand the average climate state versus synoptic conditions conducive to higher minimum temperatures in the urban area. The climatological upper-level (300mb, Figure 35) flow regime exhibits more zonal flow whereas the composite (Figure 28) is characterized by well-developed planetary waves leading to the development of an anticyclonic ridge over the

Intermountain West. The subtropical flow in the climatology indicates the presence of a 111

strongly developed anticyclone over the southern United States, Mexico and the Gulf of

Mexico while the composite imagery indicates enhanced easterly flow in the tropics and subtropics, consistent with a westward migration of the continental ridge. The northerly displaced polar jet stream is evident in both images. The displacement of the continental scale ridge is very evident when comparing the composite 500mb geopotential heights

(Figure 30) with the climatological average (Figure 36). The climatological ridge axis tends to be located over the United States Great Plains region with a weak trough forming along the west coast. The composite image reveals a ridge that extends into

Saskatchewan and the northern plains of Canada with its axis shifting westward to Utah and Arizona. This indicates significant subsidence in the Intermountain West and the potential for the enhancement of North American Monsoon conditions which bring subtropical air masses into the southwestern United States. Comparing the composite

700mb wind vectors and velocities (Figure 33) with the climatological means (Figure 37) indicates very different flow regimes along the eastern Pacific. The composite image indicates northeasterly flow originating over Mexico and a northward extension of the low level easterly flow, or ‘trade winds’, associated with the planetary general circulation, namely the rightward directed low level return branch of the Hadley cell. The westward and northward migration of the planetary ridge allows the subtropical flow to migrate into the midlatitudes and drive warm, moisture-rich air into this region. The contrast with the climatological mean state is marked, as the average 700mb flow regime is characterized by the general westerly flow originating from the northern Pacific with very light winds in the eastern Pacific. The climatological 700mb trade winds are much weaker and do not have sufficient momentum to overcome the westerly flow, contrary to 112

what is observed in the composite image. Thus it is apparent that the synoptic conditions associated with the composites appears to favor the northward advection of warm, moist air from the central eastern Pacific and have a well developed ridge located over the

Intermountain West while the climatological conditions are more favorable for cool, dry onshore flow from the North Pacific. The synoptic discussion presented above indicates that more research is necessary to categorize conditions favorable to higher minimum temperatures in the Truckee Meadows and perhaps throughout the Great Basin. Detailed climatological analyses of NCEP/NCAR Reanalysis data coupled with regional weather and climate modeling efforts are recommended for further efforts.

5.8 Remote Sensing and Land Use

The Landsat-derived fields of NDVI and surface (skin) temperature changes between the 1985-1989 and 2005-2009 periods for the Truckee Meadows (Figure 38) and at a neighborhood scale (Figure 39) showcase the impacts that anthropogenic urban development makes on the land surface thermal and vegetative landscape. The colors represent the change in NDVI with blues (reds) showing increases (decreases) in NDVI and green (yellow to red) contours representing the average decrease (increase) in surface temperature. The spatial relationships of decreases (increases) in NDVI with increases

(decreases) in surface temperature are clear and Figure provides a detailed view of these relationships. Such a relationship is expected as increasing vegetation density will enhance the latent heat absorption by evapotranspiration as well as intercept incoming short wave solar radiation with the converse being true due to an increased sensible heat flux and absorption of incoming short wave solar radiation. As the urban area grew outwards from the core region (Figure 40) and began to extend into the native, 113

undisturbed landscapes, the land surface characteristics of albedo, emissivity, evapotranspiration and surface roughness were changed. Urban development occurring in the sagebrush-steppe environments, such as in the western and southwestern regions of the Truckee Meadows, resulted in net cooling due to replacing the native landscapes with irrigated residential vegetation and golf courses. As the vegetation matures, further increases in land surface cooling via evaporational absorption of latent heat at the expense of demand on urban water resources will be experienced. Older urban regions, particularly in the core area (near the red star on Figure 38) exhibit spatial heterogeneity with respect to NDVI changes with no macroscopic net change in surface temperature; this is due to the small-scale changes in vegetation coverage due to maturation and urban infilling.

The western south-central and particularly the eastern portion of the Truckee

Meadows demonstrate the influence of replacing historical floodplains composed of grasslands and ephemeral marshlands with urban impervious surfaces (Figure 38).

Spatially coherent decreases in NDVI are observed with consequent increases in surface temperature as the Bowen ratio (Equation 1) increases as less latent heat transfer from the surface to the boundary layer occurs and the net energy exchange is compensated by an increase in sensible heat transfer. Recent urban development in these regions replaced grasslands and marshlands with concrete, asphalt and non-native immature vegetation, all of which result in greater absorption of incident solar energy and reductions in net latent heat transfer by reducing the potential for evapotranspirational cooling. The preparations for urban development in the form of bulldozing appears to strongly influence the surface 114

temperature (Figure 38 and 39) as large areas of native vegetation and soils are compacted and removed thereby reducing evapotranspiration rates (Bonan 2002).

Construction of large asphalt roads results in a net increase in sensible heat transfer to the boundary layer and little vegetation is replaced prior to the construction of residential and commercial structures. The vegetation that is established as part of urban development may serve to offset the warming of this region in the future once maturation occurs.

The results of this simple satellite remote sensing study agree with the findings of

Xu et al. (2009) that increases in urban area may exponentially accelerate change land surface temperature (and subsequently the boundary layer temperature) and weakly supports the hypothesis expressed previously that increasing heat transfer to the boundary layer aids in the development of a thermal low in this region of the Truckee Meadows.

Provided the changes in land surface characteristics are indeed leading to the development of a thermal low in this region which persists throughout the night, it would be expected that convergent flow from the north, west and south, i.e., where no terrain boundary exists, would increase. This may in part explain the increased northerly and north-northwesterly flow at KRNO as low level flow increases to replace buoyant air being evacuated from the surface.

While the land use coverage data (Figure 40) only dates back to 1979, it can be imagined how the urban footprint increased dramatically after the late 1940s in a concentric manner. However, the increase in urban footprint during the earlier period

(1950-1979) can be assumed to have occurred at a smaller rate than what has been observed during the 1979-2009 period since the average area or lot size of various land 115

use parcels, mainly residential, were smaller during that time as compared to modern

(i.e., 1990s) lot sizes. The periphery of the Truckee Meadows has experienced the majority of the urban growth in all four cardinal directions. Residential and commercial land use development (Figure 41) comprises the majority of total urban land use increases during the past 30 years while few increases in parks or multifamily residential land uses are observed. The thermal perturbations are greatest for the single family residential and commercial land uses due to the high proportion of total area which is composed of impervious asphalt and or concrete (e.g., parking lots and residential streets) and thus greatly lowers the surface albedo, has minimal vegetation established to replace the native vegetation and has the greatest capability to store incident energy and subsequently re-radiate this energy to the boundary layer. The land use dataset (Figure

40) did not include roads, as Figure 42 exemplifies, but does include parking lots, therefore a complete description of the thermal alterations due to land use changes cannot be made at the present time.

Historical and future projections of population are shown in Figure 43 and demonstrate the exponential growth of the Truckee Meadows during the past 100 years.

Rapid growth followed the end of WWII and can be attributed to extensive post war economic development and a westward migration which continued until the 2009 recession. The recent economic downturn is shown by the small decrease in population following 2009. Considerable spread in future population estimates exists (Figure 43) and population estimates for the Truckee Meadows in 2050 range from 400,000 inhabitants

(Nevada Demographer’s Low estimate) to over 700,000 inhabitants (Nevada 116

Demographer’s High estimate), with the Washoe County Consensus and Truckee

Meadows Water Authority estimates showing just less than 700,000 and 600,000 inhabitants, respectively. The Nevada Demographer estimates are more conservative in forecasting the duration before growth is expected to resume in comparison to the

Truckee Meadows Water Authority and Washoe County Demographer. The UHI perturbation based on the classical Oke (1973) prediction (Equation 1) for the period spanning 1940-2050 is shown in Figure 44 and the change in UHI base value (3.28°C as calculated based on population in 1939) is shown in Figure 45. The underestimation of the actual summertime UHI influence by the Oke (1973) method is on the order of 2°C, calculated by subtracting minimum JJA temperatures in 1980-2009 from 1940-1979 and then subtracting the UHI estimate. The Oke (1973) estimate was developed for an idealized urban area situated in flat terrain and under presumably homogeneous land surface conditions, so it is not surprising that it is insufficient to predict the UHI influence in a city that occurs in a highly complex terrain location with great spatial homogeneity of land surface types and greatly varying, seasonally dependent weather phenomena.

The predicted UHI impact is removed from the KRNO climatology after the

WRCC trend is removed in an effort to totally isolate the effect of the Truckee Meadows

UHI. It is clear that a trend remains even after removal of the predicted UHI influence

(Figure 46). This result therefore indicates that the observed trend in increasing temperatures is due to a greater heat island influence than the classical (Oke 1973) method predicts. Since a trend remains despite removing the influences of both regional and urban trends, the classical method must therefore be modified in some manner to 117

better represent the observed behavior of the urban-environment system interactions. The generation of an improved, empirically-based and physically reasonable UHI prediction algorithm will certainly require improved spatial resolution of surface, boundary layer and upper air observations as well as the use of a numerical model that includes an urban boundary layer scheme; such work is beyond the scope of this preliminary, exploratory study and is suggested as a future direction of research for the arid, urban-environment interface.

Relationships are apparent between the minimum temperature values and both population and urban land use coverage (Figure 48, bottom) but not between maximum temperature values and population or urban land use coverage (Figure 48, top). Seasonal correlations between these two factors were produced and demonstrate stronger positive correlations with minimum temperature versus maximum temperature as would be expected based upon current UHI understanding (Figure 49). The correlations are the strongest in the summertime in further agreement with the UHI understanding. The high sun angle and plentiful solar insolation will cause maximum heat storage by the urban environment and serve to most strongly force the boundary layer thermal regimes during this season. Despite these obvious relationships, sufficient information does not exist to generate causality with any degree of certainty; we are left only with faint clues about the interaction of the built environment with the physical processes of the coupled land- atmosphere interface. The relationship between population and UHI effect in the Truckee

Meadows do not follow the estimates provided by Oke (1973) and are indicative of more complex interactions between the land uses, urban areal coverage and population. As 118

such we do not attempt to utilize future urban growth estimates to forecast future UHI impacts in an effort to avoid producing results which are not physically based and may mislead downstream users of the results such as resource managers. If the reader is so inclined, the future UHI factor based on the future population estimates could be calculated and added to the temperatures generated by the statistical downscaling results discussed below.

5.9 Downscaling Results

The statistical downscaling results presented in Figures 51-56 display a multitude of results that require discussion. The figures are showing the differences between future

(2041-2060) and historical (1961-2000) minimum and maximum temperatures for each of three IPCC emissions scenarios (A1b, A2 and B1) for three downscaling approaches.

The first approach utilizes the 12km data provided by the LLNL that has been downscaled using the BCCA method (Hidalgo et al. 2008) from the native GCM grid

(100-200km spatial resolution) with the intent of being immediately usable for climate change impact analyses. This dataset is referred to as the ‘Raw GCM’ data. The Raw

GCM data was further downscaled to the KRNO station using the bias correction method of Wood (2004) for the historical periods of 1961-2000 and 1970-2009 and is referred to as DS61’ and ‘DS70’, respectively. The Raw GCM displays small differences when compared to the historical observations for all scenarios, on the order of -0.5 to 2 °C, whereas the DS61 displays differences on the order of 1.5 to 4°C and the DS70 indicates differences ranging between 1.8 and 7.9°C.

The greatest differences are unanimously expressed during the summer season and the smallest differences are expressed during the spring and fall seasons. The A1b 119

scenario, which represents the most fossil fuel intensive (Figure 8) future with the greatest predicted increases in global mean temperature (Figure 9), resulted in the greatest increases in downscaled minimum and maximum temperatures with the A2 scenario indicating moderate increases and the B1 scenario indicating similar results as the A2 scenario. These results are consistent with the corresponding predictions of global mean temperature increases shown in Figure 9. The Raw GCM results indicate slight cooling during the winter and spring for the A2 and B1 scenarios; however these results will likely be ignored by those interested in climate change impacts for reasons to be discussed in the following paragraph. The Raw GCM data does provide us with a basic signal of projected large scale changes for the region that can aid in estimating the magnitude of potential future climate change for the area. In this case, the greatest increases in temperature for all scenarios are observed to occur during the summer season.

The societal implications of maximum warming during the summer period are severe for an arid urban region. Demand on the electrical grid for air conditioning will be increased as will water demands for irrigation and recreation. Evapotranspiration rates will be increased as the saturation vapor pressure of the environment is increased and a greater moisture gradient will be established that will result in higher demand for water by vegetation. The increases in ambient air temperature and reduced moisture availability will increase plant physiological stress. Human heat stresses, particularly with regards to the elderly or health-compromised population, due to prolonged and increased severity of hot episodes during the summer will result in increased rates of heat-related mortality and morbidity and thereby place a greater strain on health care resources. Increased 120

temperatures during the winter months will reduce costs for heating and reduce the likelihood of prolonged periods where snow is on the ground. This will reduce the likelihood of establishing a stable valley inversion since snow coverage enhances the surface cooling by longwave emission. This in turn would lead to improved winter valley air quality conditions by allowing improved mixing and ventilation of the Truckee

Meadows basin. This does assume that the relative probabilities of scenarios involving flow blocking and poor ventilation remain the same.

Let us now discuss the importance of downscaling the 12km data to the local urban station. Significant (p<.05) differences exist between the Raw GCM and the data generated using the KRNO historical observations, DS61 and DS70. They are independent of the IPCC emission scenario chosen. The differences by month between the Raw GCM data and the DS61 and DS70 datasets ranged between 2-5°C and tended to be maximized during the spring, though consistent differences exist independent of the season. The large differences are indicative of the improvement in downscaling results offered by downscaling to the local station when the region of interest is of a spatial scale that is smaller than the 12km resolution offered by the Raw GCM. Such a resolution cannot be expected to capture the local climatology of valleys with a relative width that is less than the resolution of the downscaled grid cell and bounded by mountainous terrain on all sides and particularly one with a demonstrated significant UHI impact. The downscaling scheme used in this work clearly exhibits its ability to capture the natural and anthropogenic variability that exists in the observed historical calibration period and thus improves the robustness of the future climate projection. However, the improvements over the Raw GCM method are still subject to the stationarity limitation of 121

all statistical downscaling techniques. With this caveat in mind, the importance that these future climate projections exist primarily as tools for examining possible future scenarios of climate should not be forgotten. The primary contribution is allowing decision makers to generate adaptation strategies that are robust under a variety of alternative possible future outcomes (Dessai et al. 2009). Despite significant, if not extreme, uncertainty in future climate projections, sufficient information exists regarding the range of possible climatic outcomes to allow decision makers to systematically examine present adaptation strategies, understand the inherent current and future vulnerabilities and make the necessary adjustments to ensure their strategies will be successful.

Finally it is important to discuss the need to select a historical calibration period that captures the UHI signal. The differences between the DS61 and DS70 results ranged from -0.2 to 3°C and were the greatest for minimum temperatures for all IPCC scenarios and smallest for maximum temperatures. The greatest differences between scenarios were found to occur for the A1b, A2 and B1 scenarios in order of decreasing differences. The differences were maximized during the summer season, indicating the importance of capturing the UHI signal which has been previously shown to be greatest during the summer season and manifested in the minimum temperatures (e.g. Figure 11). It is therefore apparent that statistical downscaling efforts for urban areas should be undertaken using an observed dataset for historical calibration that includes the maximum

UHI signal. The statistical downscaling algorithm causes the future downscaled data to inherit the variability observed in the historical observations and thus the UHI-driven signal of the urban location will be captured by this method. There is great need to examine how various treatments of the historical data will influence the future 122

downscaled results, for example, removal of large scale, i.e., regional trends, any and all trends and incorporating growth scenarios of population and land use change into the future projections.

6. Conclusions

This work has served to identify a variety of important aspects related to the UHI development in the Truckee Meadows and offers insight into future work which can approach many of these aspects in a detailed manner. Furthermore, implications of the

UHI and statistical downscaling methods with respect to future urban climate projections have been examined. An asymmetric, seasonally dependent signal of local climate change has been identified in the Truckee Meadows. Minimum temperature anomalies and the frequency with which anomalously warm nocturnal temperatures occur compared to nearby stations and the larger scale mean Nevada climate state have increased during the past 30 years, most notably in the summer season and least so during the winter season with spring and fall showing similar increases on the order of less than those observed in summer but greater than winter. The primary reasons for this are thermal heat storage by the urban environment, synoptic weather conditions and solar insolation which are most favorable to impact minimum temperatures during the summer season. The thermal perturbations of the Truckee Meadows due to human influence have been identified via the removal of regional climate trend signals, though the influence of the complex terrain continues to be not well understood. The predicted influence of UHI as a function of population was removed as well and the remaining perturbation to the temperature anomalies indicates that such a simple correction factor is insufficient to account for the UHI as a function of population growth alone. Examinations of 123

summertime remotely sensed data indicates land use and land cover change as having an important role in driving surface energy budget characteristics. The primary limiting factors in this analysis include: (1) a dearth of long period of record climate stations located in and around the Truckee Meadows which would enable a clearer picture of the spatial and temporal characteristics of the UHI development, (2) the lack of available high resolution numerical models to capture, verify and understand key driving factors in the UHI development as functions of land use change and anthropogenic development and how the thermodynamic and hydrodynamic properties and behaviors of the planetary boundary layer are altered and (3) the lack of vertical profiles of the urban planetary boundary layer.

Increases in temperature in the Truckee Meadows region of Nevada due to UHI effects as well as changes in regional and global climate will have profound implications for the urban ecosystem dynamics. Some of the main effects are: (1) increased minimum daily temperatures throughout the year but particularly in summer, (2) decreased diurnal temperature range with longer warm period and shorter cool period within a given day and (3) a lengthened warm season. It has been shown herein that land use change due to expansion of the urban periphery is indeed altering the temperature characteristics of the

Truckee Meadows at daily, seasonal and annual scales independent of larger scale warming. Larger scale, i.e., regional warming signals will only exacerbate UHI effects and vice versa.

Results of future climate downscaling for the Truckee Meadows for the 2041-

2060 time period under three IPCC emission scenarios (A1b, A2 and B1) indicates that the minimum temperatures may increase on the order of 2-5°C and maximum 124

temperatures may increase by 1-2°C. The increases in temperature will be the greatest during the warm season, particularly the summer season, though due to an increased thermal mass of the urban environment these effects will be maintained into the fall season as well. As a result, energy requirements to maintain comfortable indoor temperatures during the summer will be increased as will water requirements to maintain landscaping; both of which will contribute to increased costs of living during the warm seasons with some offset during the cold season due to reduced heating costs, though winter and spring temperatures have not changed significantly. Increases in heat-related mortality and morbidity may also be expected to occur as a result. These costs will continue to affect the population of Reno into the future and may be confounded by other factors which govern energy and water pricing. The increases in temperature during the cold season may help to improve air quality conditions by decreasing the probabilities for formation of persistent stable valley inversions which limit pollutant mixing and reducing the cold season energy costs for heating, however the winter season wind regimes must be better examined.

The downscaling approach used to create future climate predictions for the

Truckee Meadows under various IPCC emissions scenarios and growth scenarios has produced improved ranges of uncertainty in the various outcomes compared to pure

GCM-derived values. It has demonstrated the importance of downscaling GCM data to the urban station scale versus using pre-downscaled data marketed as useful for climate change impact studies. Such data does not capture important signals of the UHI and thus will limit the ability of future projections to incorporate this critical signal. The downscaling approach used herein has also demonstrated the importance of using 125

observations that include the maximum UHI signal to calibrate historical GCM data which ensures the UHI signal is translated to the future projections, thus enhancing the robustness of these results by improving their physical reasonability. While the statistical downscaling approach suffers from limitations such as the assumption of stationarity, the improvements made over using pure GCM solutions or even readily available pre- downscaled data at finer resolutions (e.g., the 12km Raw GCM data) via the BCSD method will enable policy makers at local, state and federal levels, urban planners and urban scientists the capability to better assess and mitigate impacts of future urban climate on public health, air pollution, water and energy resources. In turn, these users will be more successful in better assessing and developing current and future mitigation and adaption strategies that reduce vulnerability of vital infrastructure and natural resources to the nearly infinite suite of possible future climate outcomes.

This study has opened many doors for future work regarding the UHI in the

Truckee Meadows as it has been largely speculative in nature and is not defended by elegant mathematical proofs or clear, irrefutable physical evidence (as it is lacking in observational density). Numerous opportunities using numerical models for sensitivity studies can therefore be employed. Some examples include: inclusion of an urban canopy model with various land surface parameterizations to test the role of the urban area in modifying the boundary layer structure, namely via alteration of surface wind, pressure and temperature regimes; the influence of terrain-forced flows and synoptic weather conditions on UHI magnitude and the radiation impacts of atmospheric physical characteristics such as aerosols and water vapor. Detailed analysis of the remote sensing data from Landsat TM as well as other readily available platforms and land use datasets 126

using spatial analysis techniques are strongly recommended. A long-term field campaign utilizing a high density of temperature and wind sensors would provide useful data for understanding the spatial and temporal characteristics of the UHI at resolutions that the remote sensing techniques are unable to do. Such a campaign would provide useful data for model calibration and validation. These approaches would serve to provide significant information towards understanding the UHI in a relatively small urban area located in complex terrain characterized by many land uses and land surface types. This information would provide great improvement towards understanding the physical linkages of the

UHI on a historical basis and would greatly assist in generating future predictions of UHI impacts that could be assimilated into future downscaled climate projections under various climate change, population growth and land use change scenarios. 127

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Appendix A: Monthly Mean Downscaled Results (Averaged over nine GCMs) for A1b,

A2 and B1 scenarios.

ix

A.1: Monthly mean downscaled results for January 2041-2060 A1b, A2 and B1 scenarios.

145

A.2: Monthly mean downscaled results for February 2041-2060 A1b, A2 and B1 scenarios.

146

A.3: Monthly mean downscaled results for March 2041-2060 A1b, A2 and B1 scenarios.

147

A.4: Monthly mean downscaled results for March 2041-2060 A1b, A2 and B1 scenarios.

148

A.5: Monthly mean downscaled results for May 2041-2060 A1b, A2 and B1 scenarios.

149

A.6: Monthly mean downscaled results for June 2041-2060 A1b, A2 and B1 scenarios.

150

A.7: Monthly mean downscaled results for July 2041-2060 A1b, A2 and B1 scenarios.

151

A.8: Monthly mean downscaled results for August 2041-2060 A1b, A2 and B1 scenarios.

152

A.9: Monthly mean downscaled results for September 2041-2060 A1b, A2 and B1 scenarios.

153

A.10: Monthly mean downscaled results for October 2041-2060 A1b, A2 and B1 scenarios.

154

A.11: Monthly mean downscaled results for November 2041-2060 A1b, A2 and B1 scenarios.

155

A.12: Monthly mean downscaled results for December 2041-2060 A1b, A2 and B1 scenarios.