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Sensitivity of Summer Climate to Land Cover Change over the Greater Phoenix,
AZ, Region
Matei Georgescu
Center for Environmental Prediction, and
Department for Environmental Sciences
Rutgers University
Gonzalo Miguez-Macho
Nonlinear Physics Group
Universidad de Santiago de Compostela
Louis T. Steyaert
Formerly U.S. Geological Survey Bethesda, MD 20816
Christopher P. Weaver
Center for Environmental Prediction, and
Department for Environmental Sciences
Rutgers University
1

1 ABSTRACT
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We investigated the first-order impact of land-use/land-cover change (LULCC) on the summer climate of one of the nation’s most rapidly expanding regions, the Greater
Phoenix , AZ, region. We used high-resolution Regional Atmospheric Modeling System simulations based on two different land cover reconstructions for the region: a circa 1992 representation, and a hypothetical land cover scenario, where the anthropogenic landscape consisting of irrigated agriculture and urban pixels was replaced with current semi-natural vegetation. In order to quantify the impact of changing landscape during diverse atmospheric conditions, three “wet” and three “dry” summer seasons were selected, and simulations were performed for one summer month with each landscape representation. Results suggested that development of extensive irrigated agriculture adjacent to the urban area, has dampened any regional-mean warming due to urbanization. Effects of LULCC on precipitation suggested a noticeable increase in precipitation amounts to the east of the city during the three “dry” months.
2

1 1.
Introduction
2 In addition to global-scale climatic forcings, anthropogenic land-use and land-cover
3 change (LULCC) has the potential to be a highly significant driver of climate change at
4 regional scales [ NRC , 2005; Feddema et al ., 2005; Davin et al ., 2007]. The potential
5 effects of human-induced conversion of the landscape in urban/suburban complexes are
6 particularly important, because the majority of the world’s population resides in urban
7 areas (and the percentage is growing). For example, the Greater Phoenix region is
8 located in the semi-arid Sonoran Desert, has a regional population of nearly 4 million,
9 and was recently ranked as the 14 th
largest metropolitan city in the U.S. [ GP Regional
10 Atlas , 2003]. Throughout the 20 th
century, Maricopa county (Arizona’s most populous
11 and home to Phoenix) has undergone extensive modifications to its pre-1900 landscape as
12 a result of the rapid population growth and territorial expansion in which semi-natural
13 shrublands were replaced by irrigated agriculture and a widespread urban/residential
14 development [ Knowles-Yanez et al., 1999]. The potential impacts of this development
15 raise concerns about future water supplies [ Grimm and Redman , 2004] and anthropogenic
16 air pollution issues [ Lee at al.
, 2003].
17 The urban fabric has long been known to exert a warming influence on the local
18 environment [e.g., Bornstein , 1968], also known as the urban heat island (UHI) effect.
19 The UHI occurs due to the alteration of the thermal, radiative, moisture, and aerodynamic
20 characteristics of the land surface [ Oke , 1987]. Hedquist and Brazel [2006] collected
21 observations along mobile transects during the summer season to identify the strength of
22 the summertime Phoenix UHI and to measure its variability from urban to residential to
23 rural locations. Their measurements indicated the presence of a strong UHI, with a mean
3

1 evening urban-to-rural temperature difference of 7.3 °C. They also found a greater than 3
2 °C increase in dew-point from the urban to rural locations as a result of fewer water
3 sources and less evapotranspiration (ET) from vegetation in urban areas, in contrast to
4 increased evapotranspiration from irrigated agriculture in rural areas.
5 In addition to temperature, urban areas are thought to affect precipitation ( Huff and
6 Changnon , 1973; Chagnon , 1979a; Bornstein and Lin , 2000; Burian and Shepherd ,
7 2005). There are at least three widely accepted mechanisms for this influence
8 [ Changnon, 1992]. First, changes in surface roughness affect forward propagation of
9 precipitating systems with modifications in low-level convergence over and downwind of
10 an urban area [ Loose and Bornstein (1977)]. Second, urban areas typically consist of
11 impermeable, lower albedo, heat-retaining surfaces lacking in vegetation, so enhanced
12 warming relative to adjacent rural areas creates a deeper, more turbulent boundary layer
13 and promotes increased vertical lift [ Lemonsu and Masson , 2002]. Third, increased
14 amounts of polluting material supply cloud condensation nuclei (CCN) which can alter
15 cloud droplet concentrations, thereby affecting cloud formation and downwind
16 precipitation [e.g., Van den Heever and Cotton , 2006].
17 In this paper, we investigated the potential first-order impact of LULCC over the last
18 century on the summertime climate of the Greater Phoenix region. To accomplish this,
19 we combined simulations using the Regional Atmospheric Modeling System (RAMS)
20
21 with two different LULC reconstructions: one based on a circa 1992 representation of the area’s landscape, and the other a pre-1900 condition where the anthropogenic influence
22 (in terms of irrigated agriculture and urbanization) is removed. The focus is on
4

1 summertime because it is during this season that the public and city infrastructure
2 experience the greatest climate-related strains (i.e., extreme high temperatures, with
3 associated heat stress and limited water availability, combined with periodic, intense
4 convective precipitation, with associated flash flooding).
5
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2. Model Description and Methods
We used RAMS version 4.3 [ Walko and Tremback , 2000] to conduct sensitivity
7 experiments that quantify the impact of hypothetical LULCC over the Greater Phoenix ,
8 Arizona, region. RAMS is a non-hydrostatic model that solves the full nonlinear
9 equations of motion and includes a comprehensive soil and vegetation component, the
10 Land Ecosystem-Atmosphere Feedback Model (LEAF-2 [ Walko et al.
, 2000b]).
11 We use three nested grids centered on the Phoenix Sky Harbor International Airport
12 (33.43N, -112.01 W). The parent grid covers a 1600 X 1600 km area with a horizontal
13 grid spacing of 32 km while the intermediate grid covers a 592 X 592 km area with a
14 horizontal grid spacing of 8km. The fine grid (Grid 3) encompasses a 204 X 204 km
15 domain with a 2 km grid mesh. Convection is parameterized using the Kain-Fritsch
16 convective scheme [ Kain and Fritsch , 1992] on the outer two grids, while convection is
17 explicitly resolved on Grid 3.
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The circa 1992 land cover data on Grid 3 was derived from the U.S. Geological
Survey’s (USGS) 30-m 1992 National Land Cover Dataset (NLCD) ( Vogelmann et al .,
20 2001). We aggregated the 21-class NLCD dataset to derive an alternate LULC
21 classification (henceforth termed NLCD92) for LEAF-2. We also derived a LEAF-2
22 biophysical parameter table for the NLCD92 land cover classes with parameter values
5

1 and characteristics appropriate for the Sonoran Desert and the Greater Phoenix region
2 (see Table 1). LEAF-2 permits the division of each RAMS grid cell into multiple
3 patches, so we divided each 2 km cell into five patches of decreasing LULC fractional
4 area (four plus water), thus retaining much of the information available in the high-
5 resolution 30-m NLCD data. Figure 1[a] shows the dominant LULC category used in the
6 representation of our fine grid. To replicate the effects of irrigation, we forced the
7 irrigated agricultural land cover type to saturation at each model time step. (We discuss
8 the sensitivity of our results to this assumption in more detail later in the paper.) We
9 compared these simulations to an equivalent set carried out for a pre-1900, “Pre-
10
Settlement” case, constructed by replacing that part of the altered landscape attributed to
11 anthropogenic influence (defined as irrigated agriculture and urbanized pixels) with
12 semi-natural shrubland (Figure 1[b]). For the outer two grids in both sets of simulations,
13 we used the standard RAMS LULC dataset that is based on the USGS 1-km Advanced
14 Very High Resolution Radiometer (AVHRR) Olsen Global Ecosystem (OGE) land cover
15 data [ Olson, 1994a].
16 We initialized RAMS with surface (soil moisture and temperature) and lateral
17 boundary (geopotential height, relative humidity, temperature, and horizontal winds)
18 conditions extracted from the North American Regional Reanalysis (NARR) data
19 [ Mesinger et al.
, 2006] (available at http://www.emc.ncep.noaa.gov/mmb/rreanl/ ). All
20 simulations were initialized on June 30, 00Z, forced at the lateral boundaries by NARR
21 data, and continued through July 31, 12Z. The initial 30 hours were discarded prior to
22 analysis. We simulated six different Julys for both the NLCD92 and Pre-Settlement
23 LULC scenarios. Within the Greater Phoenix region, a significant amount of the annual
6

1 precipitation (about 40%) falls during a three-month period each year associated with the
2 North American Monsoon System (NAMS) [ Adams and Comrie , 1997]. Increased
3 atmospheric instability results from large-scale moisture transport (from the tropical
4 eastern Pacific, Gulf of California, and to a lesser extent, the Gulf of Mexico) and intense
5 heating of the land-surface produces convective precipitation that is highly variable in
6 space and time. We selected the three wettest and three driest monsoon seasons during
7 the time period for which NARR data is available (1979-present) according to the
8 UNIFIED Precipitation dataset [ Higgins , et al., 2000] (see Table 2 for a summary of all
9 experiments performed). This allowed us to investigate the possibility of greater or lesser
10 sensitivity to LULCC given different hydro-meteorological regimes.
11
12
3. Results
Before assessing the impact of hypothetical LULCC, we briefly present a
13 comparison between RAMS-simulated 2-meter temperatures and observed
14 temperatures from five stations in and around the Greater Phoenix area. Four of the
15 stations are available courtesy of the Arizona Meteorological Network (AZMET) (the
16 interested reader is referred to http://ag.arizona.edu/azmet/ ) while the fifth station -
17 Phoenix Sky Harbor Airport – was available courtesy of the National Climatic Data
18 Center (NCDC). The simulation used to validate the performance of RAMS made use
19 of the NLCD92 landscape.
20 Figure 1[c] shows the comparison between observed, three-hourly air temperatures
21 and RAMS simulated 2-meter air temperatures, while Table 3 shows mean observed
22 and model-simulated air temperatures, for each station, for July 1990. Overall, RAMS
23 captured the roughly weeklong warm and cool periods, as well as the daily maxima and
7

1 minima with reasonable fidelity, though with a -2.35 °C cool bias in the simulated
2 minima (see Table 3), for the entire month, averaged across all 5 stations. We suggest
3 three possible reasons for this disagreement. First, surface observations are gathered
4 using thermistors situated at 1.5 meters above the ground, while the post-processing
5 routine of RAMS calculates 2 meter air temperatures instead. This difference is
6 expected to explain only a small fraction of the noted cool bias. Second, prior to 1994,
7 the Sky Harbor station was located at the airport’s runway premises ( Brazel et al.
,
8 2000), possibly contributing to an observed warm bias at that station due to a lower
9 surface albedo (resulting in higher daytime temperatures) and enhanced night-time heat
10 loss from an underlying paved surface (resulting in higher night-time temperatures).
11 Finally, several studies have noted that urban longwave cooling is reduced in
12 comparison to rural localities, a characteristic largely attributed to the reduction in sky
13 view factor (SVF) (e.g., Oke , 1987). The SVF, recognized as a fundamental control on
14 night-time development of the UHI, is not taken into account by LEAF-2.
15 RAMS simulated ensemble differences in first atmospheric level air temperature
16 and dew-point, for the WET and DRY years are presented in Figure 2[a - d]. A dipole
17 pattern of differences is visible for both temperature and dew-point. For the WET
18 years, the largest positive differences [> 0.5 °C] in temperature are over the central
19 urban area, with lower magnitude cooling [-0.1 to -0.2 °C] located south of the metro
20 area, primarily over the plots of irrigated agriculture. This pattern is reversed for dew-
21 point. These findings are in qualitative agreement with previous transect measurements
22 over this area [ Hedquist and Brazel , 2006]. The same patterns, but with more
23 pronounced maxima and minima, are evident for the DRY year simulations for both
8

1 temperature and dew-point (compare Figure 2[c, d] to Figure 2[a, b]). Maximum
2 warming for the DRY years exceeds 0.7 °C over urban areas while irrigation induced
3 cooling is enhanced and covers a greater fractional area of the domain. Due to the
4 increased surface-to-air water vapor concentration gradient experienced during dry
5 atmospheric regimes, enhanced urban drying and increased moistening over irrigated
6 agriculture occurs. In a regional-mean sense, the competing impacts of irrigated
7 agriculture and urbanization largely tend to counteract each other for both the WET and
8 DRY sets of runs.
9 To assess whether the Greater Phoenix landscape possesses a discernible influence
10 on modeled precipitation, we next present simulated differences in accumulated
11 monthly precipitation for both the WET [Figure 3a] and DRY [Figure 3b] simulation
12 years. Mean differences for the WET years show a patchwork of increases and
13 decreases in precipitation and do not indicate a systematic alteration in rainfall pattern
14 or magnitude related to our imposed change in landscape. Additionally, there is no
15 uniformity of trend or pattern across individual simulation years. This may be because
16 the sensitivity of convective precipitation to near-surface thermodynamic perturbations
17 can be reduced in moist compared to dry atmospheric regimes [e.g., see Findell et al .,
18 2003; Koster , 2004]. This possible explanation is consistent with the fact that
19 differences between the pair of ensembles for the DRY years present a much more
20 coherent and consistent signal, both in ensemble mean and across individual years. For
21 all DRY years, the member differences show an increase in total domain precipitation
22 for the NLCD92 landscape, with regions of most pronounced rainfall enhancement (at
23 least 20-40 mm) situated generally east and north of the urban area (not shown).
9

1 These results are also broadly consistent with recent observational analysis
2 suggesting a Greater Phoenix -imposed enhancement of precipitation. Shepherd (2006)
3 used a 108-year precipitation data record and noted that positive precipitation
4 anomalies, during monsoon season (July-September [JAS]), exist to the northeast of the
5
Phoenix metro area in a “post-urban” period defined as 1950-2003 as compared to a
6 1895-1949 “pre-urban” period. Despite this qualitative agreement between the two
7 studies, we advise caution on a quantitative comparison of precipitation differences.
8 First, Shepherd (2006) compared monsoon season averages of two periods, each
9 consisting of in excess of 50 years, while our analysis is based on two relatively short-
10 term, single-month periods each consisting of 3 years (as the WET year results show no
11 discernible signal). Second, our work isolates the impact of LULCC alone, whereas the
12 Shepherd (2006) results include an assortment of effects due to a shifting landscape
13 during each time segment.
14 Finally, to gain insight on the mechanism responsible for the simulated rainfall
15 enhancement during DRY years, we focused our attention to the area of observed
16
17 increase. Here we show (Figure 3[c]) vertical profiles of θ e
difference averaged over a
1º X 1.2º region (see rectangular sub-domain in Figure 3b]) for three selected pre-storm
18 cases. We selected one case, for each year, which best illustrates the degree of impact
19 on total static energy for the area of interest. For each case subsequent rainfall
20 accumulation for NLCD92 was significantly enhanced relative to Pre-Settlement. The
21 trio of profiles exhibit considerable increase in conditional instability extending
22 upwards of the lowest kilometer of the atmosphere. The net difference in θ e for all
23 cases is large, on the order of 5-10 K. Figure 3[d] shows that the net impact of LULCC
10

1 is systematic and that the chief influence is a destabilization of the layer between the
2 surface and 1.5 km. The monthly averaged net difference in θ e for all 3 simulated Julys
3 is on the order of 1 K.
4 4. Discussion and Conclusions
5
6
Using high-resolution simulations conducted with RAMS, we investigated the firstorder impact of LULCC on the summer meteorology and climate of one of the nation’s
7 most rapidly expanding regions, the Greater Phoenix , AZ, region. Results illustrate a
8 distinct dipole pattern for simulated temperature and dew-point, with positive
9 temperature differences evident over urban areas and cooling over the plots of irrigated
10 agriculture. In qualitative agreement with previous work, this pattern is reversed for
11 dew-point. DRY year simulations show a similar pattern but with more pronounced
12 maxima and minima. The opposing impacts of irrigated agriculture and urbanization
13 tend to offset each other in a regionally-averaged sense. Methodological factors that
14 may affect these results include an underestimate of urban warming as RAMS does not
15 account for some characteristics of urban complexes (e.g., SVF). Additionally, cooling
16 resulting from irrigated agriculture, due to our assumption of soil saturation, may be
17 overestimated. To test the sensitivity of this assumption, we performed a repeat of a
18 previous experiment (NLCD92; see Table 2) whereby we forced the irrigated
19 agricultural land cover type to field capacity at each model time step. The domain-
20 averaged impact of LULCC on temperature resulted in net warming, cooling was
21 restricted in size considerably, and widespread drying over a majority of the domain
22 occurred. Our assumption of irrigated agriculture as soil saturation, however, accounts
23 for additional sources of water not available in our field capacity experiment (e.g.,
11

1 urban mesic landscaping, surface water reservoirs) which may explain why temperature
2 time-series comparisons to both station data and qualitative transect measurements of
3 dew-point are superior for the experiment assuming soil saturation as irrigated
4 agriculture.
5 While LULCC effects on simulated precipitation are not clear for WET years, the
6 DRY years present a more coherent picture. The WET simulation years consist of a
7 combination of precipitation scales from the regional (e.g., Gulf of California “surges”)
8 to the local, making the unambiguous identification of a landscape induced signal
9 difficult. Because the sensitivity of convective precipitation to near-surface
10 thermodynamic perturbations can be reduced in moist compared to dry atmospheric
11 regimes, the identification of local land-induced forcing is possible during DRY years.
12
13
Finally, the relative coverage of irrigated agriculture and extent of urbanization has shifted significantly since the 1970’s. Diagnosing regional climate in response to this
14 landscape alteration in this rapidly expanding region is essential when considering
15 current land use projections encompass a wide range of future growth scenarios, with
16 population values reaching between 10 and 30 million by the year 2050 [ GP Regional
17 Atlas , 2003].
18
12

1 References
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14 Changnon, S. A. (1992), Inadvertent Weather Modification in Urban Areas: Lessons for
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1 Feddema, J. J., K. W. Oleson, G. B. Bonan, L. O. Mearns, L. E. Buja, G. A. Meehl, and
2 W. M. Washington (2005), The Importance of Land Cover Change in Simulating
3 Future Climates, Science , 310 (5754): 1674 – 1678.
4 Findell, K. L., and E. A. B. Eltahir, (2003a), Atmospheric controls on soil moisture-
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6 boundary layer interactions. Part I: Framework development. J. Hydrometeorol .,
4 , 552-569.
7 Greater Phoenix Regional Atlas (2003), A Preview of the Region’s 50-Year Future,
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Greater Phoenix 2100 , Arizona State University, Tempe, AZ, USA
[www.gp2100.org].
10 Grimm, N. B., and C. L. Redman (2004), Approaches to the study of urban ecosystems:
11 The case of Central Arizona – Phoenix, Urban Ecosystems , 7 , 199-213.
12 Hedquist, B. C., and A. J. Brazel (2006), Urban, residential, and rural climate
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14 comparisons from mobile transects and fixed stations: Phoenix, Arizona, J.
Arizona-Nevada Academy of Scienc e, 38 , 77-87.
15 Higgins, R. W., et al. (2000), Improved US Precipitation Quality Control System and
16
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Analysis. NCEP/Climate Prediction Center ATLAS No. 7.
18 Huff, F. A., and S. A. Changnon Jr. (1973), Precipitation Modification by Major Urban
19 Areas, Bull. Amer. Meteor. Soc., 54 , 1220-1232.
14

1 Kain, J. S., and J. M. Fritsch (1992), The role of the convective trigger function in
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4 Knowles-Yanez, K., C. Moritz, J. Fry, C. L. Redman, M. Bucchin, P. H. McCartney
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Arizona-Phoenix Long-Term Ecological research Contribution No.1., Arizona
State University, Tempe, pp. 21.
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9 precipitation, Science , 305 , 1138-1140.
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Anderson (2003), Transport and Diffusion of Ozone in the Nocturnal and
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14 Lemonsu, A., and V. Masson (2002), Simulation of a Summer Urban Breeze over Paris,
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17 Movement through an Urban Area, Mon. Wea. Rev.
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18 Mesinger, F., et al. (2006), North American Regional Reanalysis, Bull. Amer. Meteor.,
19 Soc.
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15

1 NRC (2005), Radiative Forcing of Climate Change: Expanding the Concept and
2 Addressing Uncertainties. National Research Council, Washington, DC,
3 http://www.nap.edu/catalog/11175.html
4 Oke, T. R. (1987), Boundary Layer Climates, 2nd Edition. Methuen, London.
5 Olson, J. S., 1994a, Global ecosystem framework-definitions: USGS EROS
6 Data Center Internal Report, Sioux Falls, SD, 37 p.
7 Shepherd, J. M. (2006), Evidence of urban-induced precipitation variability in arid
8 climate regions, J. Arid. Env.
, 67 , 607-628.
9 Van den Heever, S. C., and W. R. Cotton (2006), Urban aerosol effect on convective
10 storm characteristics. Pre-prints, Sixth Symp. on the Urban Environment, Atlanta,
11 GA, Amer. Meteor. Soc., 1-10.
12 Vogelmann, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel
13 (2001), Completion of the 1990’s National Land Cover Data Set for the conterminous
14
15
United States, Photogr. Eng. Rem. Sens.
, 67 , 650-662.
16 Walko, R. L., and C. J. Tremback, Regional Atmospheric Modeling System User’s
17
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Guide: Version 4.2MRC, ASTeR internal report, ASTeR Div., Mission Research
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19
20 Walko, R. L., et al. (2000b), Coupled-atmosphere-biophysics-hydrology models for
21 environmental modeling, J. Appl. Meteorol.
, 39, 931–944.
16

3
4
1
2
Table 1.
Biophysical parameters used in LEAF-2 land-use class description. α = albedo; ε = emissivity; LAI = Leaf Area Index; vfrac = vegetation fraction; zo = roughness length (m).
5
LEAF-2
Class
Water
Barren
Shrubland
Urban Low
Intensity
Urban High
Intensity
Grassland
Irrigated
Agriculture
Evergreen
Needle-leaf
α
0.14
0.21
0.19
0.16
0.16
0.18
0.18
0.11
ε
0.99
0.86
0.95
0.90
0.88
0.96
0.95
0.96
LAI
0.0
0.5
0.5
0.4
0.2
0.5
6.0
5.0 vfrac
0.0
0.33
0.19
0.40
0.26
0.43
0.75
0.68 zo
0.00
0.05
0.18
0.50
0.50
0.13
0.06
1.00
17

1 Table 2.
Summary of all 12 experiments performed. For each experiment, the analysis
2 time consists of the period lasting from July 1, 12Z through July 31, 12Z. ** denotes
3 experiment used as Control simulation which was validated against observations.
4
LULC Year from which Initial and Boundary Conditions were used to force
RAMS
WET Years DRY Years
5
NLCD92
Pre-
Settlement
NLCD92
Pre-
Settlement
NLCD92
Pre-
Settlement
1990**
1990
1984
1984
1983
1983
1994
1994
1989
1989
1979
1979
18

1 Table 3.
Mean monthly temperature comparison between RAMS-simulated control
2 experiment and station-observations for July 1, 12Z - July 31, 12Z, 1990.
3
4
Station
Phoenix –
Sky Harbor
Phoenix -
Encanto
Phoenix -
Greenway
Waddell
Maricopa
Agricultural
Station
Regional
Average
July 1 12Z – July 31, 12Z [1990] Mean Temperatures [°C]
Observed
34.37
32.08
31.98
32.76
30.13
32.27
Simulated
30.88
30.62
28.65
27.56
31.91
29.92
19

1 FIGURE 1
2
3
4
5
6
7
8 Figure 1.
Dominant LULC representation for fine grid employing (a) NLCD92 and (b)
9 Pre-Settlement landscape used as surface boundary conditions in ensemble simulations.
10 (c) Observed three-hourly 1.5 meter air temperatures ( black ) and RAMS simulated 2-
11 meter temperatures from the control run simulation ( red ) during the period July 1 st
-
12 12Z to July 31 st -12Z, 1990. Units are in [°C]. The time series represent temperatures
13 averaged over five stations: (1) Sky Harbor International Airport, (2) Phoenix Encanto,
14 (3) Phoenix Greenway, (4) Waddell, and (5) Maricopa Agricultural Station.
15
20

1
2
3
4
5
FIGURE 2
6
7
8 Figure 2.
RAMS simulated ensemble differences (NLCD92 - Pre-Settlement) in (a)
9 first atmospheric level [24.1 m] air temperature [°C] and
(b) dew point [°C], for the
10 WET years; (c) first atmospheric level [24.1 m] air temperature [°C] and (d) dew point
11
[°C], for the DRY years. Each calculation is for the analysis period: July 1, 12Z – July
12 31, 12Z.
13
21

3
4
1
2
FIGURE 3
5
6
7 Figure 3.
RAMS simulated ensemble differences (NLCD92 - Pre-Settlement) in (a)
8 total accumulated precipitation [mm] for all 3 WET years; (b) total accumulated
9 precipitation [mm] for all 3 DRY years; (c) domain-averaged [lat: 33 to 34/lon: -112.2
10 to -111.0] vertical profile of equivalent potential temperature difference (NLCD92 -
11 Pre-Settlement) for selected DRY year cases; (d) as (c), but averaged over each DRY
12 year simulation for the analysis period: July 1, 12Z – July 31, 12Z.
22