Near-Surface Air Temperature Patterns in Complex Terrain, San Francisco Peaks, Arizona
Simeral, David, B., (1) (2) (3) and Albright, Thomas, P., (2)
(1) Desert Research Institute, Division of Atmospheric Sciences, Reno, NV, (2) University of Nevada, Reno,
Department of Geography, Reno, NV, (3) Western Regional Climate Center, Reno, NV
david.simeral@dri.edu
Introduction
Near-surface temperature is an important parameter for modeling local climate regimes and
understanding a variety of biological, ecological, glaciological, and hydrological processes.
Temperatures are often driven by finer-scale conditions where site-specific data are normally
not available. In such cases, climate mapping applications, such as Parameter–Elevation
Regressions on Independent Slopes Model (PRISM), are commonly used. However, these
applications may not always resolve air temperature fields and associated lapse rates on a scale
necessary to understand biological and ecological processes in mountainous terrain. To address
these issues, the San Francisco Peaks Mesonet (SFP Mesonet) was established in June 2010 in
an effort to measure and quantify air temperature patterns across elevation and slope aspect on
an isolated mountain peak.
Objectives
Results
Methods
Lapse Rate Comparison: Free-Atmosphere vs. Near-Surface
Instrumentation and Sampling Design
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HOBO Tidbit v2 Temperature Data Logger (Onset Computer Corp;
Bourne, MA) (Fig 3).
HOBO Tidbit Boot – thermoplastic elastomer boot to
to help minimize reflected shortwave radiation when
snowcover is present.
Sensor accuracy – 0.2°C over 0°C to 50°C.
Radiation shield – white polyethylene funnel.
Scan Interval – point observation every 15 minutes.
Fig. 3: Field deployment of the HOBO Tidbit
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Observational period – December 2010.
Data – FGZ sounding at 0Z (4:00 p.m.), SFP Mesonet south transect at 0Z.
Distance between FGZ launch site and SFP Mesonet south transect – ~ 15km (Fig 7).
Mean lapse rates were as follows: FGZ (-4.7 °C/km); SFP (-4.4 °C/km).
Daily lapse rates ranged from -9.2 °C/km to 6.5 °C/km under strong inversion conditions.
There was a weak, positive correlation between the free-atmosphere and the nearsurface lapse rates, r=0.46, n=30, p=.05.
sensor in a radiation shield.
Lapse Rate Calculations
1. Measure temperature variation on an isolated mountain in order to infer the roles of
elevation, slope, aspect, time period, and synoptic variability on observed patterns and
lapse rates on various temporal scales.
2. Calculate near-surface temperature lapse rates (the rate at which temperature decreases
with height) across the north, south, east, and west slopes of the San Francisco Peaks (SFP)
on various temporal scales.
3. Compare SFP Mesonet data with the PRISM 800-meter resolution dataset for the
contemporaneous period.
4. Compare lapse rate relationships between the free-atmosphere and the near-surface.
Hypotheses
 Response variable (yi) = air temperature; predictor variable (x) = elevation.
 Lapse rates were calculated using the following simple linear regression model:
Fig. 7: Map identifying the location of FGZ launch site and south
transect.
PRISM vs. SFP Mesonet Observations
Results
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Observational period - July 2010-December 2010.
Data – PRISM 800-meter monthly Tmin/Tmax, SFP Mesonet monthly Tmin/Tmax.
PRISM displayed an overall cold-bias for Tmin compared to the SFP obs(Fig 9A).
PRISM displayed a warm-bias for Tmax under cooler conditions (cooler conditions = ~0°C
to ~12°C range) and a cold-bias under warmer conditions (~12°C to ~25°C range) (Fig 9B).
Monthly Mean Air Temperature Across Elevation
Temp (Deg C)
1. Current climate and meteorological models do not adequately represent temperature
fields in complex terrain.
2. Near-surface temperature lapse rates vary significantly across slope aspect and seasonally.
3. Application of a spatially uniform lapse rate of 6.5°C/km is not representative of surface
conditions in complex terrain, especially on short temporal scales.
Temp (Deg C)
Methods
Fig. 1: SFP Mesonet station locations.
Site
Name
Latitude
(°N)
Longitude
(°W)
North 1
North 2
North 3
North 4
North 5
North 6
South 1
South 2
South 3
South 4
South 5
South 6
East 1
East 2
East 3
East 4
East 5
East 6
West 1
West 2
West 3
West 4
West 5
West 6
35.356
35.361
35.366
35.373
35.382
35.396
35.321
35.317
35.313
35.309
35.304
35.292
35.338
35.339
35.342
35.345
35.352
35.360
35.325
35.325
35.327
35.328
35.327
35.326
-111.680
-111.679
-111.677
-111.674
-111.675
-111.680
-111.679
-111.680
-111.682
-111.684
-111.686
-111.691
-111.677
-111.670
-111.660
-111.644
-111.627
-111.605
-111.684
-111.691
-111.699
-111.703
-111.722
-111.760
Elevation
(meters
above msl)
3498
3302
3102
2897
2700
2500
3502
3300
3102
2901
2700
2501
3461
3300
3098
2900
2696
2501
3521
3301
3107
2902
2700
2510
Slope
Aspect
(°)
338
22
43
44
52
357
217
218
237
224
192
205
112
63
118
121
87
120
233
230
240
345
250
250
Slope
Angle
(°)
28
25
25
15
12
5
30
32
35
30
10
10
10
15
10
10
5
15
15
24
25
18
10
10
Vegetation
Life Zone
Dominant
Tree Species
Hudsonian
Hudsonian
Canadian
Canadian
Transition
Transition
Hudsonian
Hudsonian
Canadian
Canadian
Transition
Transition
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Hudsonian
Canadian
Transition
Transition
PiEn
PiEn
AbCo
AbCo
PiPo
PiPo
PiEn
PiAr
PiAr
PiSt
PsMe
PiPo
PiEn
PiEn
PiEn
AbCo
PsMe
PiPo
PiEn
PiEn
PiEn
PiPo
PiPo
PiPo
Fig. 2: SFP Mesonet site characteristics.
Study Area
 This study was carried out on the San Francisco Peaks (35.35°N, -111.68°; 3851m), Southern
Colorado Plateau, Arizona from June 2010 – June 2012 (Fig. 1).
 The San Francisco Peaks are the remnants of an eroded stratovolcano and the place of
origin of C. Hart Merriam’s Life Zone concept (Merriam, 1889).
Network Design
 Transects design – avoidance of valleys/concavities
(except the east transect) which act to encourage local
microclimates (Fig. 1,2).
 Spacing - four evenly-spaced transects (200 m
intervals) comprising of six sites spanning in elevation
from 2500 m to 3500 m above mean sea level.
 Sensor placement – sensors were placed on the north
side of trees at approximate heights ranging from 2-5 m
taking into consideration the estimated maximum
snow depth.
Fig. 8: Time-series plot of instantaneous lapse rates calculated from FGZ
and the SFP Mesonet south transect.
A
Temp (Deg C)
B
Fig 9: Relationships between x and y for Tmin (A) and Tmax (B) between PRISM 800-meter gridded data and the SFP
Mesonet data for the contemporaneous. A one-to-one line is shown for clarity.
Conclusions
Fig. 4: Time-series plots of monthly mean air temperatures stratified by elevation.
Lapse Rates - Tmean
 Monthly lapse rates were calculated using the
monthly Tavg from each station in the transect.
 Mean lapse rates for the entire period of
record (June 2010-June 2012) were as follows:
North (-6.5 °C/km); South (-6.3 °C/km); East
(-6.6 °C/km); and West (-6.5 °C/km)(Fig. 6).
 Individual monthly lapse rates ranged from
-2.6 °C/km to -9.6 °C/km (Fig. 5).
 Steepest lapse rates occurred in April, May,
and June (Fig. 5).
Fig 5: Time-series plot of monthly lapse rates from the SFP Mesonet.
 Shallowest lapse rates occurred in November,
December, and January (Fig. 5).
 Highest degree of variability in lapse rates
 Anomalously shallow lapse rates occurred
occurred along the east transect and the
along the east transect during the summer
least along the north transect.
months (Fig. 5).
 Application of a uniform lapse rate of 6.5 °C/km is not representative for the greater San
Francisco Peaks region except on an annual time scale.
 Slope aspect, slope curvature, and seasonality play a significant role in seasonal
temperature patterns and associated lapse rate behavior.
 On the scale of a small mountain range (SF Peaks), the accuracy of PRISM is highly
dependent upon and limited by the available stations providing input data. Integration of
high spatial and temporal resolution datasets, such as the SFP Mesonet, will help to
facilitate improvements.
 A weak correlation was found to exist between lapse rates in the free-atmosphere and
near-surface.
Future Research
Future Research
 Development of a methodology to assess various data interpolation
techniques and subsequent application to the SFP Mesonet dataset.
 Creation of a monthly gridded datasets of Tmin, Tmax, and Tmean in
ArcGIS 10 to analyze spatial relationships between the four transects
on various temporal scales.
 Multiple regression modeling to investigate the influence of synoptic
patterns (synoptic typing), winds, humidity, slope aspect, slope angle,
and solar input on subsequent temperature patterns.
Acknowledgments
Fig 6: Box plots describing variability in monthly mean lapse rates by transect for the period of June 2010 – June 2012.
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Neil Cobb & Paul Heinrich, Northern Arizona University, Merriam Powell Center for Environmental Research
Connie Millar, USFS Pacific SW Research Station
Kelly Redmond, Western Regional Climate Center & DRI
Coconino National Forest, Peaks District
Arizona Snowbowl
Hopi Tribe
Navajo Nation
James Ashby, DRI
3-D rendering of monthly
data from the SFP Mesonet.