border 2012: new mexico - study iib assessment of...
TRANSCRIPT
Study IIb
Assessment of Climatological and Meteorological Phenomena
for the
Assessment of Land-based Sources of Air Quality Contaminants in the
Binational Border Region of Southwestern New Mexico, Northwestern
Chihuahua and West Texas
Prepared for the Department of Health
Office of Border Health
1170 N. Solano Dr.
Las Cruces, NM 88001
Submitted by
Principal Investigators
Dave DuBois Erin Ward
Additional Contributors:
Raymond Carr Alma Pacheco
Janet Greenlee Max Bleiweiss
New Mexico State University
Las Cruces, NM 88003
June 30, 2012
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PREFACE
This project is being carried out by New Mexico State University in association with the
University of Texas El Paso, Autonomous University of Juarez (Chihuahua), the Desert
Research Institute, and the University of Arkansas for Medical Sciences.
This work is being funded under a MOA 13828 with the New Mexico Department of Health,
Office of Border Health.
Mr. Paul Dulin provided overall project management.
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Table of Contents Acronyms and Symbols .......................................................................................................................... 6
1 INTRODUCTION .............................................................................................................................. 8
1.1 Goals and Objectives .................................................................................................................. 8
2 STATE OF KNOWLEDGE OF CLIMATE IN THE REGION .................................................................... 8
2.1 Historical Climate of the Region ................................................................................................. 8
2.2 Regional Precipitation .............................................................................................................. 10
2.3 Temperature ............................................................................................................................. 14
2.4 Wind Patterns ........................................................................................................................... 16
2.4.1 Wind Rose Analysis ....................................................................................................... 18
2.5 Climate Variability .................................................................................................................... 21
2.5.1 El Niño Southern Oscillation ......................................................................................... 23
2.5.2 Arctic Oscillation ........................................................................................................... 24
2.5.3 Drought ......................................................................................................................... 25
2.6 Climate Extremes ...................................................................................................................... 28
2.6.1 Extreme Precipitation Events and Floods ..................................................................... 29
2.6.2 Heat Waves ................................................................................................................... 30
2.6.3 Winter Storms .............................................................................................................. 31
2.7 Human Impacts on Climate Observations ................................................................................ 32
3 EXISTING CLIMATE OBSERVATION NETWORK .............................................................................. 33
4 TEMPORAL TRENDS ...................................................................................................................... 35
5 DATA ACCESS ................................................................................................................................ 38
6 SUMMARY AND RECOMMENDATIONS ........................................................................................ 39
7 REFERENCES ................................................................................................................................. 41
Appendix A: Wind Rose Data and Processing ...................................................................................... 45
Appendix B: NWS’s Advanced Hydrologic Prediction Service (AHPS) .................................................. 52
Appendix C: THREDDS Data Portal at NMSU ........................................................................................ 55
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Table of Figures Page
Figure 2.1-1. Elevations and US climate divisions (dotted lines) in the study region .......................... 10
Figure 2.2-1. Annual precipitation based on the 1971 to 2000 PRISM database ................................ 11
Figure 2.2-2. Monthly precipitation at several NWS Cooperative stations in the region. ................... 12
Figure 2.2-3. El Niño Spring precipitation. Image courtesy of Ed Polasko, NWS ABQ ......................... 13
Figure 2.2-4. Impacts of La Niña on Spring (MAM) precipitation. Numbers show percent of normal
precipitation during La Niña events. Left shows impacts of 23 La Niñas and the right is
for the 7 strongest La Niña events. Image courtesy of Ed Polasko, NWS ABQ ............. 13
Figure 2.2-5. Trends of annual precipitation in climate division 8 from 1895 to 2011 ....................... 14
Figure 2.3-1. Mean temperatures over the period 1971 to 2000 from the PRISM database .............. 15
Figure 2.3-2. Minimum temperatures over the period 1971 to 2000 from the PRISM database ....... 16
Figure 2.4-1. Annual windrose for NMSU Las Cruces (top) and the La Union stations ........................ 18
Figure 2.4-2. Annual wind patterns in the Mesilla Valley .................................................................... 19
Figure 2.4-3. Wind streamlines on January 25, 2011 at 6 am MST. These were based on the 20-
kilometer resolution RUC model predictions. ............................................................... 20
Figure 2.4-4. Regional perspective of Wind Rose analysis for Paso del Norte (TCEQ sites, 2010
annual data). .................................................................................................................. 21
Figure 2.5-1. PDO time series from 1900 to 2011 (JISAO,2011) .......................................................... 22
Figure 2.5-2. AMO with 5-year running average from 1950 to 2009 based on data from Enfield et al.
2009 ............................................................................................................................... 22
Figure 2.5-3. Normal Pacific Ocean sea surface patterns and winds (top) and during an El Nino
(bottom) (graphic from NOAA CPC) .............................................................................. 23
Figure 2.5-4. Oceanic Niño Index (ONI) based on the Niño 3.4 region from 1950 to 2011 (NOAA data)
....................................................................................................................................... 24
Figure 2.5-5. Effects of the Arctic Oscillation on winter (DJF) temperatures ....................................... 25
Figure 2.5-6. Palmer Drought Severity Index for climate division 1 (top), climate divisions 3 and 7
(middle), and climate divisions 2,3,4,5,9 (bottom) for January 1945 to May 2012.
Image courtesy of D. Kann (NWS ABQ). ........................................................................ 27
Figure 2.5-7. VHI over the state of NM from 2005 to 2012. Plot courtesy of NOAA STAR. ................. 28
Figure 2.6-1. Number of days with precipitation more than 2 inches during each month. These sums
are for 21 stations with various start and end dates in climate division 8 (southern
desert) in NM. ................................................................................................................ 29
Figure 2.6-2. Record rain event of 1935 in Las Cruces from Leopold (1942) ....................................... 30
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Figure 2.6-3. Mean July temperatures from 1896 to 2011 in climate division 8 ................................. 30
Figure 2.6-4. Hourly temperature at the NMSU Cooperative station during the 2011 cold air
outbreak in February. .................................................................................................... 31
Figure 2.6-5. Map from Hardiman (2011) showing low temperatures during the event .................... 32
Figure 2.7-1. Night scene from Landsat 7 on February 25, 2012. Note that pixel values are raw sensor
counts and related to temperature. Landmarks are labeled on the image for
reference. ...................................................................................................................... 33
Figure 3-1. Station layout for the US Climate Reference Network (USCRN) ........................................ 34
Figure 3-2. Site layout for the US Regional Climate Reference Network (USRCRN) ............................ 34
Figure 4-1. Annual temperature trends across the region from the NWS Cooperative Observer
network. Top line in each plot is the annual highs, middle shows the mean annual, and
lower line is the annual lows. For those stations with more than 30 years a trend line
was calculated along with a linear regression. .............................................................. 35
Figure 4-2. Annual temperature trends across the region from the NWS Cooperative Observer
network. Top line in each plot is the annual highs, middle shows the mean annual, and
lower line is the annual lows. For those stations with more than 30 years a trend line
was calculated along with a linear regression. .............................................................. 36
Figure 4-3. Annual temperature trends across the region from the NWS Cooperative Observer
network. Top line in each plot is the annual highs, middle shows the mean annual, and
lower line is the annual lows. For those stations with more than 30 years a trend line
was calculated along with a linear regression. .............................................................. 37
Figure 4-4. Google Earth view of the Redrock 1 NNE station and the surroundings. Image is dated
July 24, 2011 .................................................................................................................. 38
Figure C-0-1. THREDDS server at NM Climate Center .......................................................................... 56
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Acronyms and Symbols
°C degrees Celsius
μm micron or micrometer (10-6 m)
ABL Atmospheric Boundary Layer
AGL Above Ground Level
AMO Atlantic Multi-Decadal Oscillation
AO Arctic Oscillation
ASOS Automated Surface Observing System
AWOS Automated Weather Observing Station
BLM Bureau of Land Management
CASTNet Clean Air Status and Trends Network
CEFA Program for Climate, Ecosystem and Fire Applications
CO carbon monoxide
CO2 carbon dioxide
CPC NOAA Climate Prediction Center
DOQ Digital Orthophoto Quadrangle
DRI Desert Research Institute
EDAS Eta Data Assimilation System
ENSO El Nino Southern Oscillation
GIS Geographic Information System
GOES Geostationary Operational Environmental Satellite
GPS Global Positioning System
HYSPLIT HYbrid Single-Particle Lagrangian Integrated Trajectory
model
IMPROVE Interagency Monitoring of PROtected Visual Environments
km kilometer (103 m)
m meter
m3 cubic meter
mb millibar
mm millimeter
m/s meter per second
MODIS MODerate Imaging Spectroradiometer
MSL Mean Sea Level
NAM North American Model
NAO North Atlantic Oscillation
NASA National Aeronautics and Space Administration
NCAR National Center for Atmospheric Research
NCDC National Climatic Data Center
NCEP National Center for Environmental Prediction
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NOAA National Oceanic and Atmospheric Adminstration
PDO Pacific Decadal Oscillation
RAMADDA Repository for Archiving, Managing and Accessing Diverse
Data
RAWS Remote Automated Weather System
THREDDS Thematic Real-time Environmental Distributed Data
Services
WRCC Western Regional Climate Center
WRF Weather Research and Forecasting model
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1 INTRODUCTION
This report summarizes Study IIb Assessment of Climatological and Meteorological
Phenomena for the NM Department of Health’s Assessment of Land-based Sources of Air
Quality Contaminants in the Binational Border Region of Southwestern New Mexico,
Northwestern Chihuahua and West Texas.
1.1 Goals and Objectives
The goal of this study is to better understand the links between climate and air quality
through a study of climate in the Binational Border Region of Southwestern New Mexico,
Northwestern Chihuahua and West Texas. The objectives of this study include
Summarize Climatological patterns of wind, temperature, precipitation
Determine the sources of data should be used to track climate
Investigate atmospheric transport pathways to the region
Review phenomena that affects long-term climate variability in the region
Provide recommendations to improve the knowledge and tracking of climate in the region
2 STATE OF KNOWLEDGE OF CLIMATE IN THE REGION
2.1 Historical Climate of the Region
Due to its location within the Chihuahuan Desert the study area has an arid to semi-arid
climate with mild winters, warm summers, large diurnal variations in temperature and 350
days of clear weather.
The earliest investigations of climate in the region have been from paleoclimatic proxy data
obtained at archeological sites. For example early to middle Holocene records from soil
carbon studies and packrat midden indicate a dry period in the southwest as the North
Atlantic warmed (Buck and Monger, 1999; Benson et al., 1997). Further paleoclimatic
research has shown that by 4,000 years before present the climate began to appear similar
to today’s climate (Van Devender, 1990; Turnbow et al., 2000).
As the expansion of the US in the 1800s drove settlers west into New Mexico, weather
observations and diaries started to appear. For example a commissioner of the U.S. and
Mexico Boundary Commission observed in May of 1851 (Bartlett, 1856) that the
southwestern plains of New Mexico as “barren and uninteresting in the extreme.” Later
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W.H. Emory (1857) writes that the land is “wholly unsusceptible of sustaining an agricultural
population, until you reach sufficiently far south to encounter the rains from the tropics.”
Tuan et al. (1973) mention that these observations would have reflected a particularly dry
period from 1850 and 1851.
Some of the earliest recorded climate data in the region were collected at the military
installations in the 1800s. The earliest weather observations at Fort Bayard were taken on
March 1, 1867 by US Army surgeons only one year after the fort was established (Grice,
2005). Weather observations at the fort ended in 1893. Weather data was again started
nearby the fort in February of 1897 and continued until June of 2012. The earliest weather
data collected at the New Mexico State University was in 1892. The station was originally
called the New Mexico Agricultural College and eventually was named “State University.”
Weather data from this and other stations were published in annual reports of the Weather
Bureau Office (1897). In the
1897 annual report, a table
indicated that 10 years of
weather records had
already been archived in Las
Cruces. However, most of
the climate stations in
southwest New Mexico do
not have century-long
records. Sources of
historical climate data used
in this report were obtained
at the NOAA National
Climatic Data Center, the
NOAA Applied Climate
Service (ACIS) web portal,
the Western Regional
Climate Center, USDA NRCS, and at the NM Climate Center. Table 2.1-1 provides a listing of
a few NWS cooperative stations that have more than 50 years of data. Those shaded are no
longer collecting data. Both NMSU and Lordsburg currently have been in existence for 120
years.
The introduction of climate divisions to delineate drainage basins and regions of climatatic
homogeneity were started in the mid-1950s (Guttman and Quayle, 1996). The state of New
Mexico has eight climate divisions with the majority of the study area in the Southern
Desert climate division. This climate division occupies an area of 18,919 square miles and is
Table 2.1-1. Cooperative station years of record of a select number in the region
Coop location Start/End
dates Years
Gage 1899-2007 108
Orogrande 1904-present 108
Hatch 1894-2008 114
Lordsburg 1892-present 120
El Paso 1942-present 70
La Tuna 1943-present 69
Columbus 1909-2011 102
White Sands NM 1939-present 73
Animas 1923-present 89
Deming 1892-2010 118
Hachita 1909-present 103
Jornada 1914-present 98
NMSU 1892-present 120
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shown in Figure 2-1 covering all of Hidalgo, Luna counties and parts of Dona Ana, Grant,
Sierra, and Otero counties.
Figure 2.1-1. Elevations and US climate divisions (dotted lines) in the study region
As figure 2-1 shows, the southern desert climate division captures most of the lower
elevation Chihuahuan Desert within New Mexico. To the northwest, the southwestern
mountain climate division covers the higher elevation ecotones and corresponding lower
temperatures and higher precipitation. Along the boundary of these climate divisions are
the Big Burro Mountains that rise to 2438 m MSL (8,000 feet) near White Signal. These
mountains lie in the basin and range physiographic province and not in the Chihuahuan
Desert. To the northeast we see the southernmost extent of the central valley climate
division.
2.2 Regional Precipitation
Annual precipitation ranges from 40 inches (102 cm) in the highest elevations of the
Mogollon Mountains in the Gila Wilderness to around 9 inches (23 cm) along the Rio
Grande River in southern Doña Ana county, the lower elevations in southeastern Luna and
El Paso counties, and in the Tularosa Basin east of the San Andres mountain range toward
the area of the White Sands gypsum dunes. The map in Figure 2-2 is based on the PRISM
model and shows annual precipitation averaged over the years 1971 to 2000 (Daly et al.,
1994; 1997). In general the higher elevations tend to receive more precipitation than the
lower elevations. The majority of the study area averages 10 to 15 inches of annual
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precipitation. At the higher elevations, sites receive most of their snow between December
and January with the highest accumulations measured in the Gila Wilderness. Little to no
snow accumulations occur at the lower elevations in southern New Mexico.
Figure 2.2-1. Annual precipitation based on the 1971 to 2000 PRISM database
In general, fall and spring are the dry seasons, with most of the precipitation occurring in
the summer from the North American Monsoon System. The numerous convective storms
from the monsoon are a very important event in defining the air quality in the study region.
High winds from thunderstorm downdrafts and gust fronts lofting dust into the air have
accounted for many exceedances of the PM10 NAAQS in the study region. Some of the
highest hourly averaged wind speeds recorded have been during these types of storms.
Overall, southwestern New Mexico receives approximately 50 percent of its annual
precipitation during the months of July, August and September, based on historical data
from the NOAA/NWS Cooperative Observer Network. Figure 2-2 shows the geographical
variation of monthly precipitation across the study region based on a few of the National
Weather Service Cooperative observer stations.
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Figure 2.2-2. Monthly precipitation at several NWS Cooperative stations in the region.
The contribution from the monsoon season to annual precipitation at individual sites
ranged from 48 percent (Tularosa and Lordsburg) to 58 percent (Florida in northeast Luna
County). During the monsoon season, the moisture can flow into the region from the Gulf
of Mexico, Gulf of California and the eastern Pacific (Douglas et al., 1993; Adams and
Comrie, 1997).
The El Niño Southern Oscillation (ENSO) maintains an effect on precipitation in the desert
southwest mainly in the winter and spring. For example, in Doña Ana County, during El Niño
events winter precipitation averages 1.9 inches while precipitation during La Niñas average
1.1 inches. During the spring, an El Nino favors above average precipitation for most
events in the 20th century. As Figure 2.2-3 shows, an El Niño tends to favor wetter than
normal spring precipitation. The numbers in black indicate the 16 events during the 20th
century in percent of normal. For the state as a whole the past 16 events resulted in 149
percent of normal precipitation on average. The numbers in red show that the last 3 El
Nino events before 2010 (2003, 2005, 2007) had less of an impact with statewide average
precipitation of 113 percent of normal.
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Figure 2.2-3. El Niño Spring precipitation. Image courtesy of Ed Polasko, NWS ABQ
La Niña events usually result in less than normal winter precipitation particularly for the
southern two-thirds of New Mexico. This is also true for spring precipitation as Figure 2.2-4
shows.
Figure 2.2-4. Impacts of La Niña on Spring (MAM) precipitation. Numbers show percent of normal precipitation during La Niña events. Left shows impacts of 23 La Niñas and the right is for the 7 strongest La Niña events. Image courtesy of Ed Polasko, NWS ABQ
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A La Niña event is a concern in the study region due to low soil moistures and storm tracks
bringing in winds but little to no precipitation. This is a recipe for potentially higher than
normal dust storms and storms that are intense.
Figure 2.2-5 shows how annual precipitation in climate division 8 varies year-to-year from
1895 to 2011. The 116 year mean is 10.85” and shown as the horizontal line in the plot.
Annual precipitation was as low as 4.27” in 1956 to a high of 19.75” in 1941. There are two
notable observations from this plot and deal with long term variations. The drought of the
1950s is clearly seen from roughly 1945 to 1957. During that time the area experienced the
least amount of precipitation. What is interesting about this dry period is how quickly the
annual precipitation went from the lowest to the 5th highest in two years. The second
significant observation is the wet period starting 1983 and lasted to 1994. Because of these
shifts from a drought to a wet period it is important to make generalizations from a short
record of observations. This period of above normal precipitation is also within our recent
memory and we should not expect rain and snow to be similar to those years in the years to
come.
Figure 2.2-5. Trends of annual precipitation in climate division 8 from 1895 to 2011
2.3 Temperature
Based on the NOAA National Weather Service Cooperative observations, average daily
maximum temperatures range from a high of 98 degrees Fahrenheit in July at White Sands
15
to 40 degrees Fahrenheit in January at White Signal (elev. 6,070 feet) in the Big Burro
Mountains northeast of Lordsburg. Average daily minimum temperatures range from
nearly 69 degrees in July in Anthony to 24 degrees at the White Signal site in January.
As Figure 2.3-1 shows, mean temperatures in our study region are driven mainly by the
elevation of the terrain. On a day to day basis, however temperatures can deviate from this
generalization due to patterns in airmasses, temperature inversions, and effects from
topography.
Figure 2.3-1. Mean temperatures over the period 1971 to 2000 from the PRISM database
Average low temperatures also are defined by elevation as shown in Figure 2-5 based on
the PRISM model. Lowest temperatures are found in the higher elevation of the Gila
Mountains north of Silver City and a few mountain tops in the Bootheel of New Mexico. An
interesting feature of Figure 2.3-2 is large area of low temperatures in the range from 44.7
to 50°F in the southern portions of the region. In a few locations, some of the higher
elevation terrain are at high temperatures than those at lower elevation nearby. This
feature is caused by the fact that he PRISM temperature estimation methodology uses
observational data. This is particularly evident in the Jornada del Muerto basin north of Las
Cruces the divides the southern desert and central valley climate divisions. In this basin the
foothills of the San Andres Mountains are warmer than the basin. A similar effect occurs in
the Sierra de las Uvas hill region northwest of Las Cruces and at the Florida Mountains
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southeast of Deming. During the morning hours a stable layer near the ground creates a
layer of air warmer aloft than near the ground. The temperature warming as a function of
height is called a temperature inversion and this behavior is common in low lying areas such
as valley floors and river channels. Since most of the climate observations in Dona Ana
county and El Paso are situated in valleys, this could bias the PRISM model algorithm to
create all areas with this type of lapse rate.
Figure 2.3-2. Minimum temperatures over the period 1971 to 2000 from the PRISM database
2.4 Wind Patterns
Winds in general are the result of pressure differences between two locations. These
pressure differences arise from large-scale variations in weather patterns, storms, and
those from variations in temperature over the landscape. The large scale patterns or
commonly called synoptic scale patterns manifest themselves as high and low pressure
systems at the surface. Boundaries between airmasses are defined fronts and show up on
weather maps as cold, warm, occluded or stationary. Synoptic scale patterns are large in
extent and can cover areas 1000s of kilometers and commonly include major portions of
the western US. Winds forced from landscape variations at smaller scales are called
mesoscale patterns. Wind patterns are driven by combinations of mesoscale circulations
and synoptic scale forcing. Physical mechanisms causing mesoscale circulations include
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land-water contrasts, elevation differences, urban-rural contrasts, gradients in soil moisture
content, gradients in snow cover, variations in cloud shadowing, and contrasts in ground
albedo and vegetation. In our study region local wind patterns develop due to differential
heating between the ground surface and the air above it. We commonly see large diurnal
temperature variations in the desert so that during the day the higher terrain becomes an
elevated heat source and at night it is an elevated heat sink. These temperature variations
cause two types of mesoscale wind patterns: slope flows and valley wind circulations. Slope
flows can be either caused by cool, dense air flowing down elevated terrain at night or
warm, less dense air moving toward higher elevation during the day. The night slope flow is
commonly called a katabatic wind and the daytime flow is called an anabatic wind. These
slope flows provide many mountainous areas a triggering mechanism for cloud formation
and afternoon showers. Up- and down-valley circulations develop from along-valley
horizontal pressure gradients in one segment of a valley. During the night, when there are
clear skies and light synoptic flows, the basin of a valley is sometimes characterized by the
accumulation of relatively cold, stable, and stagnant air. These cirucumstances lead to poor
air quality when the valley is subjected to low-level pollutant emissions. Haze or fog
occasionally forms at night if the air is moist. In the winter, the accumulation of cold air will
sometimes result in frost in the valley and is important in agricultural areas. When synoptic
scale winds are light, mesoscale circulations are dominant and depend on the terrain
surrounding the location.
Annually averaged wind speeds measured at the Air Quality Bureau meteorological stations
range from 4.9 to 8.5 miles per hour and record the highest hourly averaged wind speeds
during spring storms. Winds during these spring storms are generally from the west and
west-southwest and regularly exceed averages of 30 miles per hour or more and 50 mile per
hour gusts. Locally strong winds associated with summer thunderstorms may occur from
any direction and frequently exceed 30 miles per hour, but are usually brief in nature.
The various topographic features in the study area define the regional to microscale wind
patterns and pollutant transport pathways. Many of the basins and mountain ranges are
oriented north-south with a gradual slope of higher elevations to the north and lower
elevations in the south. Measurements taken by the Air Quality Bureau, RAWS and New
Mexico State University show terrain induced effects such as daytime upslope and up-valley
flows as well as night-time downslope and down-valley flows. Typical up and down valley
flows can be seen in the Las Cruces and La Union wind direction data. The Air Quality
Bureau maintains these sites and collects hourly wind data at the 10-meter height. Both of
these sites are situated in the Mesilla Valley in south central Doña Ana County. Figure 2-4-1
shows the annual wind rose for these two sites based on hourly wind data. These two sites
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are separated by 41 kilometers and show similar wind direction frequency distributions over
the six year period of data collection.
Figure 2.4-1. Annual windrose for NMSU Las Cruces (top) and the La Union stations
2.4.1 Wind Rose Analysis
A wind rose graphically depicts the frequency of occurrence of winds in each of the
specified wind direction sectors and wind speed classes for a given location and time
period. This time period is typically long for a climatological record of prevailing winds or
short to show wind character for a particular event or purpose. The most common wind
rose consists of a circular (polar) graphic plot from which, usually, eight or sixteen lines
emanate, one for each compass point. The length of each line is proportional to the wind
frequency from that direction over the period of record — the longest arms of the rose
corresponding to the most frequent wind directions. Commonly, the frequency of calm
conditions is shown in the rose's center. The wind rose built around a continuous 360-
degree circle provides a visual continuity not found on linear bar charts or line graphs.
Figure 2.4-2 shows the annual statistics of wind directions in the Mesilla Valley in relation to
the surrounding terrain features. Wind patterns in the study region during fairly calm
conditions have shown to exhibit down and upslope winds and along the Rio Grande River,
down and up valley flows. Wind roses covering all seasons in the Mesilla Valley show wind
patterns that follow the valley, along a northwest to southeast direction. An exception to
that is the in the Anthony station where it appears to be influenced by easterly winds
flowing through the Anthony Gap to the east. Stations out of the valley, on the east and
west mesas, show predominate westerly winds. The winds at the Holman Road station see
the majority of the winds from the southerly directions with the highest coming from the
19
southwest. The higher terrain along the Summerford Mountains and the Doña Ana Peak
appear to modify the westerly flow somewhat.
Figure 2.4-2. Annual wind patterns in the Mesilla Valley
During these low wind conditions, over the larger scale, winds tend to flow from higher to
lower elevations in many cases. An example of this was from January 25, 2011. On this
morning the NMSU low was 24°F. Our normal low at NMSU for today is 29F. The Las Cruces
Airport low was 23°F and 22°F at the Deming Airport. Looking at the winds this morning in
Figure 2.4-3 we saw the RUC model indicating downslope and downvalley wind patterns.
The map shows surface wind streamlines is for 13 UTC (6 am) in the morning. I highlighted
some examples of the wind flows from higher to lower terrain wind flows. It's not perfect
but it captures some of the basic flow from the most significant terrain. The implications for
network design are that wind flows will bring in pollutants or the lack of them from the
20
north toward the south. During the day the winds could to reverse, indicating an upslope
flow from lower to higher elevations. This is evident in the wind roses in Figure 3-8. During
these conditions it is important to have hourly wind data in order to track the directions of
the slope flows and when they reverse.
Figure 2.4-3. Wind streamlines on January 25, 2011 at 6 am MST. These were based on the 20-kilometer resolution RUC model predictions.
In the Paso del Norte winds are more complex and show winds channeled through the river
valley and modified by the higher terrain in the Franklin Mountains. Figure 2.4-4 shows
some of the complexities of the wind patterns. The northernmost site in the map is the
airport weather station and shows some wind flows from the north-northeast while the
stations along the river southwest of the airport do not show any flow from that direction
and primarily show winds from westerly directions.
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Figure 2.4-4. Regional perspective of Wind Rose analysis for Paso del Norte (TCEQ sites, 2010 annual data).
2.5 Climate Variability
The natural climate is variable with several short- and long-term oscillations. In the short
term, there are seasonal variations due to the tilt of the earth and resulting differences in
heating that varies according to latitude.
During periods of two weeks the Madden Julian Oscillation influences extreme precipitation
in tropics and subtropics and teleconnections exist that affect precipitation patterns in the
U.S. (Madden & Julian, 1974; 1994; Jones et al., 2011). At longer time scales from 1 to 3
years is the El Niño Southern Oscillation or ENSO. The Pacific Decadal Oscillation is a way to
describe patterns of Pacific Ocean sea surface temperature anomalies over time. The PDO
index has been created to track this oscillation over time (Zhang et al., 1997; Mantua et al.,
1997; Mantua and Hare, 2002). When the index is negative, it correlates well with drier than
normal conditions in the southern US. Cooler and wetter conditions have been tied to
positive phases of the PDO.
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Figure 2.5-1. PDO time series from 1900 to 2011 (JISAO,2011)
Based on observational and modeling studies the index has an oscillatory behavior with a
period around 50 years with one phase lasting about 25 years. A negative PDO phase
occurred in the 1950s starting around 1949 and lasted up to 1966. A second negative phase
started around 1999 and continues to the writing of this report.
Another oscillation with a slightly longer period that has affects climate in the continental
US is the Atlantic Multi-decadal Oscillation (AMO). This oscillation shows up in variations of
the North Atlantic Ocean sea surface temperatures in most of the Atlantic from the equator
to Greenland. This oscillation has a period between 60 to 80 years and influences North
American climate (Enfield et al., 2001; Schlesinger and Ramankutty, 1994; Kerr, 2000).
Figure 2.5-2. AMO with 5-year running average from 1950 to 2009 based on data from Enfield et al. 2009
23
Positive AMO indicies have correlated with dry conditions in the US and influences tropical
storm frequencies over the Atlantic (Trenberth & Shea, 2006). A positive phase has been
identified starting around 1948 and ending around 1965 correlating with the drought of the
1950s. Because of its fairly long period over several decades, it can mask the anthropogenic
contributions of climate change (Hegerl et al. 1997).
2.5.1 El Niño Southern Oscillation
The El Niño Southern Oscillation or ENSO is a natural cycle that affects sea surface
temperatures, global precipitation, and wind patterns and is an example of climate
variability. This cycle is described by the presence abnormally warm or colder sea surface
temperature along the equatorial region off the coast of South America toward the west to
Indonesia. Normally trade winds blow winds from the east toward the west due to an area
of high pressure off the coast of South America and a low over Indonesia as the top of
Figure 2.5-3 shows.
Figure 2.5-3. Normal Pacific Ocean sea surface patterns and winds (top) and during an El Nino (bottom) (graphic from NOAA CPC)
An El Niño event occurs when the sea surface temperatures are abnormally warm in the
eastern Pacific compared to the western Pacific Ocean. The warmer body of water creates
warmer air above it and an area of high pressure. Winds blow from the east toward the
24
west due to the lower air pressure from the cooler waters in the western Pacific. During La
Niña events the sea surface temperatures are below normal and the cold surface water
remains over central and eastern Pacific. The ENSO pattern is described using several
indices based on sea surface temperature anomalies over the equatorial Pacific Ocean.
Figure 2.5-4 shows one such index, the Oceanic Niño Index (ONI). The ONI is defined by a
three month running mean of NOAA ERSST.v2 SST anomalies in the Nino 3.4 region (5°N-
5°S, 120°-170°W), based on the 1971-2000 base period. In the figure the red shaded parts
are the El Niño events and the blue shaded portions are La Niña events. The stronger El
Niño events are shown with the larger positive ONI values. Conversely the stronger La Niña
events are shown with large negative ONI values. Note that there are multiple cases then
there were back to back La Niña events and back to back El Niño events in the past 60 years.
Figure 2.5-4. Oceanic Niño Index (ONI) based on the Niño 3.4 region from 1950 to 2011 (NOAA data)
2.5.2 Arctic Oscillation
The Arctic Oscillation or AO is a natural cycle that is detected by pressure anomalies
between the Arctic and those further south around 37°–45°N. The AO can have a negative
or positive phase depending on the direction of the pressure anomaly. While this oscillation
has profound impacts on winter storm tracks in the US, it has little effect on the study
region as shown in Figure 2-14. Most of the impacts of the AO are in the eastern half of the
US.
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
ON
I (N
ino
3.4
reg
ion
)
25
Figure 2.5-5. Effects of the Arctic Oscillation on winter (DJF) temperatures
2.5.3 Drought
Drought manifests itself in many different ways and because of such manifestation; drought
can be classified into four major “types” (McVicar and Jupp, 1998):
“Meteorological drought, which is generally regarded as being lower than average precipitation for some time period; in some cases air temperature and precipitation anomalies may be combined;
“Agricultural drought, occurs when plant available water, from precipitation and water stored in the soil, falls below that required by a plant community during a critical growth stage. This leads to below average yields in both pastoral and grain-producing regions;
“Hydrologic drought is generally defined by one or a combination of factors such as stream flow, reservoir storage, snowpack, and groundwater”; There is usually a delay between lack of rain or snow and less measurable water in streams, lakes and reservoirs. Therefore, hydrological measurements tend to lag other drought indicators and meteorological drought can be overcome by irrigation until the hydrological drought has progressed to the point that irrigation supplies are limited
26
“Socioeconomic drought is defined in terms of loss from an average or expected return. It can be measured by both social and economic indicators, of which profit is only one.”
The National Drought Policy Commission (2000) defined drought as “A persistent abnormal
moisture deficiency having adverse impacts on vegetation, animals, and people.” Because
there are these different types of drought and because they are all inter-related, different
methods are relevant for determining the existence and impact of drought. For example it
may be sufficient to monitor precipitation and air/surface temperature to determine the
extent and severity of meteorological drought while, for agricultural drought it is necessary
to monitor soil moisture and vegetation conditions. In addition to these “drought”
classifications, specific adverse effects of drought can be addressed. Some of the more
familiar effects are loss of forage for range animals and inadequate water supplies for
irrigation.
Table 2.5.3-1 Periods of Drought for New Mexico calculated from the Palmer Drought Index.
Drought Period
Approximate Duration (months)
1900-1904 60
1909-1911
36
1917-1918 24
1922 12
1934-1935 24
1945-1948 48
1950-1957 96
1963-1964 24
1976-1978 36
1989 12
1996 12
2000 12
2002-2003 24
2011-2012 24
In New Mexico researchers believe the massive die-off of piñon during 2002 and 2003
drought in New Mexico could be due to the effects of climate change. Tree deaths occurred
in areas that were relatively unaffected by a drier drought during the 1950s, but this
drought was warmer. Scientists have predicted that mountain snowpack would be reduced
in a warming world. Recent research indicates that warming in much of the west during
winter and spring has already produced declines in mountain snowpack earlier snowmelt
27
runoff and lower summer streamflow and creating hydrologic drought conditions. (Mote et
al., 2008; Mote et al., 2005).
The key to any drought response is drought monitoring. The current drought index’s
include the Palmer drought severity index (PDSI), Standardized Precipitation Index (SPI),
and the surface water supply I Index along with several other reviewed by Heim (2002). The
Palmer Drought Severity Index (PDSI) is a "meteorological" drought index that responds to
weather conditions that have been abnormally dry or abnormally wet. The PDSI is
calculated based on precipitation, temperature and Available Water Content of the soil. The
PDSI varies from values of +6.0 to -6.0 with a classification scale indicating relative
meteorological and hydrological development cycles. Figure 2.5-6 shows the PDSI from
1945 to 2012.
Figure 2.5-6. Palmer Drought Severity Index for climate division 1 (top), climate divisions 3 and 7 (middle), and climate divisions 2,3,4,5,9 (bottom) for January 1945 to May 2012. Image courtesy of D. Kann (NWS ABQ).
The major problem with the PDSI index is that only point measurement with over 15-20
years of record can be used in the calculation of the index and the spatial coverage of the
point meteorological measurement are scarce and some of the automated precipitation
data is questionable as to its accuracy. The Standardized Precipitation Index (SPI) is
another meteorological index that suffers from the same problems as the PSDI index except
28
that it overcomes the time lag problem associated with the PSDI index. The SWSI is used to
incorporate both hydrological and climatological features into a single index. It is intended
to be an indicator of surface water conditions where mountain snowpack is a major
component. Four inputs are required for the SWSI: snowpack, streamflow, precipitation,
and reservoir storage. Because it is dependent on the season, the SWSI is computed with
only snowpack, precipitation and reservoir in the winter months, with stream flow replacing
snowpack in the equation during the summer months. Again the problem with this index is
the spatial variability of the point measurement of rainfall and snow fall used in the index
calculations.
Satellite data have been used to monitor drought by calculating a satellite-based Vegetation
Health Index that has been used successful in detection drought-related vegetation stress
and estimation of crop losses in the US. The index is a combination of the Normalized
Difference Vegetative Index (NDVI) and the Temperature Condition Index (TCI). Using this
combined index a large-area drought can be detected up to two months in advance of other
drought index techniques. (Kogan, 1995; 2001). Figure 2.5-7 shows the VHI for New Mexico
from 2005 to 2012. The VHI appears to track well with both wet and drought conditions.
Figure 2.5-7. VHI over the state of NM from 2005 to 2012. Plot courtesy of NOAA STAR.
The Vegetation Health Index been used to estimate the impact of drought on wheat yield
(Salazar, et al., 2007 ) and corn yield (Salazar et al., 2008). This index does not measure daily
evapotranspiration (ET) directly but only infers the impact of drought on daily ET rate and
subsequent yield. Yield is linearly related to accumulative ET through the ET production
functions. (Doorenbos et al., 2007).
2.6 Climate Extremes
This section discusses precipitation, heat waves, and winter storms. These extremes can
last for an hour or may last more than a week. One example of an extreme was the
29
February 2011 cold outbreak throughout the state of New Mexico that brought in below
zero temperatures for much of the state.
2.6.1 Extreme Precipitation Events and Floods
As the area along the border received more than 50 percent of the annual precipitation
during the monsoon season, we would also expect that the majority of the extreme
precipitation events to also occur during that time. This is the case as shown in Figure 2.6-1.
Figure 2.6-1. Number of days with precipitation more than 2 inches during each month. These sums are for 21 stations with various start and end dates in climate division 8 (southern desert) in NM.
The month of August has the most number of days with over 2 inches of rain. The months
of July, August and September have 77 percent of these extreme rain days. The years 2006,
1999, and 2000 had more than 6 days where at least one NWS Cooperative station in the
region recorded more than 2 inches of rain. The year 2006 had the most with 10 days of
the year had rain more than 2 inches. The wet summer contributed to 2006 having the
wettest July-September on record. Over climate division 8, precipitation averaged 12.02
inches, which is 6.35 inches more than the 20th century average. Major flooding occurred in
Alamogordo, Columbus, Hatch, Silver City, El Paso, Hillsboro, Vinton, Canutillo, Santa
Teresa, Sunland Park, and numerous locations in Cd. Juarez in 2006. Historically notable
floods have occurred in Hatch in 1988, 2002, and 2006.
The largest amount of rain to fall in this climate division was 6.49 inches on August 30, 1935
and recorded at the State University weather station. This rain event started on the 29th at
11:06 pm and ended at 8:05 am on the 30th. Figure 2.6-2 estimates the extent of the record
rainfall during this event.
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12
Nu
mb
er
of
day
s w
ith
pre
cip
itat
ion
>2
inch
es
30
Figure 2.6-2. Record rain event of 1935 in Las Cruces from Leopold (1942)
2.6.2 Heat Waves
Even with the onset of the monsoon in that month, the warmest month of the year in
climate division 8 is in July with June coming in second. Figure 2.6-2 shows how each mean
July temperature varies from 1896 to 2011. Some notable warmest Junes include those in
1901, 1934, 1951, 1980, and 2003.
Figure 2.6-3. Mean July temperatures from 1896 to 2011 in climate division 8
Considering 12 Cooperative stations in climate division 8, the years 1994, 1951, and 1980
had the most number of days greater than 100°F. For example the Gage station had 73 days
31
in that 1994 that had highs of over 100°F. On average this station records 15 days per year
above 100°F. At the NMSU Cooperative station, on average 11 days have highs at or above
100°F, but in 2002 it recorded 34 days above 100°F. June of 2002 was particularly hot since
there were two weeks in a row where there were 5 consecutive days over 100°F.
2.6.3 Winter Storms
One example of an extreme was the February 2011 cold outbreak throughout the state of
New Mexico. Hardiman (2011) examined this episode and attributes this to an intense arctic
air mass moving into the region along with an upper-level trough. Figure 2.6-3 shows the
hourly temperature at the NMSU Cooperative station from January 31 to February 6, 2011.
The red areas are shown for temperature above freezing and the blue shaded areas are
those that are below freezing. The long term average high temperatures are shown as red
squares, the long term average low temperatures are shown as violet squares, the long
term extremes low temperatures are indicated by the violet circles. The figure shows that
there were 68 consecutive hours below freezing in this episode with a low of -3.6°C.
Figure 2.6-4. Hourly temperature at the NMSU Cooperative station during the 2011 cold air outbreak in February.
As the following map (Figure 2.6-5) shows the extent of the cold event across the region
with many below zero lows in the low Tularosa Basin and extending to Antelope Wells.
32
Figure 2.6-5. Map from Hardiman (2011) showing low temperatures during the event
2.7 Human Impacts on Climate Observations
An analysis of the long-term trend in temperature is important tool to assess the impacts of
climate change in the region. It is important to look at the trends over many decades rather
than over a few years since year to year variations in average temperature can be several
degrees F. Causes for changes in temperatures can include climate change, landuse change
near the site, undocumented site moves, urban heat island effects and operator errors. As
building, roads, and parking lots replace the natural landscape in populated areas, it
changes the heat capacity and the speed of cooling and heating. The urban heat island’s
effect is to warm the urban areas more than the surroundings and potentially skew
temperature records that are within the urban area. In large urban areas, the heat island
effect can alter the wind flow patterns and transport pollutants from industrial areas to
normally unpolluted neighborhoods. Figure 2.7-1 shows a night time infrared image of the
City of Las Cruces from the Landsat 7 satellite on February 25, 2012 at 11 pm local time. The
thermal infrared band 6 (10.4 - 12.5 µm) of Landsat provides a good measure of ground
temperature. This image clearly shows the effects of land use change such as agriculture on
temperature. The left side of the image is dominated by agriculture and open lots while the
center of the image is urbanized with a mixture of houses, businesses, parking lots, and
roads. Tops of large buildings are cool as well as golf courses and cropland and colored in
33
blue. The warmest objects are paved roads, concrete surfaces, and rocks and are colored in
red.
Figure 2.7-1. Night scene from Landsat 7 on February 25, 2012. Note that pixel values are raw sensor counts and related to temperature. Landmarks are labeled on the image for reference.
3 EXISTING CLIMATE OBSERVATION NETWORK
The primary source of historical climate data is from the National Weather Service
Cooperative Observer network. Tracking changes in precipitation and temperature in this
climate division depend on a stable observational network over time. In the past few years
there has been a decrease in the number of stations in southwest NM due to station
operators no longer able to take measurements or death of the operator. Of the 52 NWS
Coop stations in the database in climate division 8, only 14 are currently collecting data. The
number of operating stations will likely decrease over time. Some of the stations no longer
operating include Lordsburg, Columbus, Ft. Bayard, Alamogordo, Hatch, and Deming. As a
replacement of this network, NOAA has been installing two networks across the country for
the purpose of collecting long-term, high quality climate data.
The US Climate Reference Network or USCRN sites are designed to measure high precision
and accuracy air temperature, precipitation, solar radiation, wind speed, surface
temperature and relative humidity. This site is one of 82 USCRN stations in the United
States. Figure 3-1 shows the standard USCRN site layout with each instrument labeled.
34
Figure 3-1. Station layout for the US Climate Reference Network (USCRN)
As a way to upgrade the aging Cooperative network NOAA has been installing a network of
automated stations with similar sensors as the USRCRN but with just one rain gauge and the
one temperature sensor. This was mentioned earlier in a Study Ia report that inventoried
climatological monitoring stations but the name of the network has since changed from
HCN-M to USRCRN. There are 22 USRCRN stations in the state with one in the study area.
Figure 3-2. Site layout for the US Regional Climate Reference Network (USRCRN)
The Hachita 7ESE USRCRN station became operational on September 30, 2011 and is 11.5
km east of the village of Hachita.
Other networks that collect high quality meteorological data are useful in tracking climate
but were not designed for that purpose and can serve as secondary sources of climate data.
Such data can be found from the RAWS, Snotel, SCAN, and National Weather Service airport
networks.
35
4 TEMPORAL TRENDS
An analysis of the long-term trend in temperature is important tool to assess the impacts of
climate change in the park. It is important to look at the trends over many decades rather
than over a few years since year to year variations in average temperature can be several
degrees F. Causes for changes in temperatures can include climate change, landuse change
near the site, undocumented site moves, urban heat island effects, instrumental
degradation over time, and changes in operator. An increasing trend of annually averaged
minimum temperatures is a common occurance at many climate monitoring stations
throughout New Mexico, the U.S. as well as world-wide. Only stations that have been
operated in a consistent manner over time with documentation of changes at the site are
useful for this type of analysis. Figures 4-1, 4-2, and 4-3 show temperature trends over a
range of a few stations in the region.
Animas 3ESE
Antelope Wells
Figure 4-1. Annual temperature trends across the region from the NWS Cooperative Observer network. Top line in each plot is the annual highs, middle shows the mean annual, and lower line is the annual lows. For those stations with more
than 30 years a trend line was calculated along with a linear regression.
36
Columbus
Deming FAA Airport
Faywood
Glenwood
Hachita
Hillsboro
Figure 4-2. Annual temperature trends across the region from the NWS Cooperative Observer network. Top line in each plot is the annual highs, middle shows the mean annual, and lower line is the annual lows. For those stations with more than 30 years a trend line was calculated along with a linear regression.
37
Jornada Experimental Range
Orogrande
Redrock 1 NNE
NMSU State University
Figure 4-3. Annual temperature trends across the region from the NWS Cooperative Observer network. Top line in each plot is the annual highs, middle shows the mean annual, and lower line is the annual lows. For those stations with more than 30 years a trend line was calculated along with a linear regression.
From these plots we see both warming trends and cooling trends. Out of the 10 stations
shown here, 6 show a positive trend in morning lows, and 4 show a decrease in the morning
lows.
The Redrock 1 NNE shows the largest positive trend in the morning low temperatures of
0.049 degrees F per year. Figure 4-4 provides a view of the station from Google Earth. As
this image shows, the station is in a rural setting not surrounded by large building, concrete,
or parking lots. There does not show any signs of any major landuse change from a wildfire
or construction project. The station appears to be in an open area about 15 meters from
the closest building. An agricultural field is approximately 100 meters to the east and
southeast of the station.
38
Figure 4-4. Google Earth view of the Redrock 1 NNE station and the surroundings. Image is dated July 24, 2011
A simple view of the surroundings does not reveal the causes of the trends. These trends
are not well understood since most of the stations are outside of urbanized areas and the
effects of heat island and station siting cannot explain the trends.
5 DATA ACCESS
The primary data center for climate data is the National Climatic Data Center (NCDC) in
Asheville, North Carolina. NCDC remains as the final source of quality controlled and quality
controlled data for the country. However, not all data products are free and the process of
finding the data is cumbersome even for seasoned climatologists. NCDC has recently made
large strides in providing better access to databases though mapping user interfaces and
help files.
As an alternative to NCDC, the majority of the hourly climate data is also archived and
available for no cost at the Western Regional Climate Center (WRCC) at the Desert Research
Institute. A focus of their mission is to archive and provide access to climate data across the
western US. The WRCC provides a unique portal for hourly data from the airports and RAWS
that is not found elsewhere. WRCC also provides a brief climate summary of all NWS
Cooperative stations in the state to include climate normals, monthly averages, and daily
extremes.
39
The New Mexico Climate Center provides access to the state agricultural climate network in
addition to other networks across the state. The website provides a way to get both
graphical and tabular data.
6 SUMMARY AND RECOMMENDATIONS
Over the past century the desert region of Southwestern New Mexico, Northwestern
Chihuahua and West Texas has experienced a decade long drought in the 1950s and a
recent wet period in the 1980s and 1990s. These decade long variations in weather patterns
along with the year to year changes have shown the desert region climate to be dynamic.
The region responds to the ENSO cycle with strong El Niños providing above normal
precipitation particularly in the winter and spring and La Niña events depriving the region of
much needed precipitation.
Not all stations show the same trends of temperature over time. The majority of those in
this report show both a warming trend of morning low and daytime high temperatures. Still
there are stations that have decreasing trends in highs and lows. These trends are not well
understood since most of the stations are outside of urbanized areas and the effects of heat
island and station siting cannot explain the trends. The Redrock 1 NNE and NMSU campus
station exhibits the largest positive trends in the morning low temperatures.
For those living in this region in the 21st century drought is a way life with its impacts
affecting many sectors including the economy through the agricultural sector, the way we
build and landscape our homes, recreation, and our health. The use of data, modeling and
remote sensing to track the drought has become relatively mature over the past decade. A
sign of that are the groups of people from each state assessing the latest state of the
drought every week of the year through the Drought Monitor. Despite the efforts, there are
still deficiencies in the way drought is viewed. The extended drought of the 1950s has
shown what it could be like again in the future. The prediction of these types of droughts is
still in its infancy and we mainly rely on past records to understand the future. We have to
rely on multiple sources of data and impacts to assess drought and not on our short
memories.
An examination of extreme events should help us plan for the future. Extreme events can
be short term like a 5-minute hail storm, a flood, an intense dust storm or they can be
stretched over a longer period of time such as a peak dry period during an intense drought.
Despite the desert’s relatively calm weather, the main hazard is from flooding during
torrential monsoon rains during the warm months. Looking at past records, practically every
location where there is a NWS Cooperative station, there has been a flood.
40
Even though there are very few stations with more than 100 years of climate observations,
we can use these to understand how climate has varied in this region. Even though there
appears to be a sufficient number of climate monitoring stations in the climate division to
assess climate change however the coverage is insufficient for monitoring and tracking
health and the number of stations are likely to decrease over time. This forces the research
community to rely more and more on models and use of remote sensing to fill in the gaps.
If the density of precipitation gauges is not dense enough for use in tracking extreme
events, flood warning, and short term forecasting will suffer.
The highest priority recommendations include
Making access to climate data free and accessible to the public
Secure adequate funding to continue climate monitoring at the current number of stations or more
Make sure that climate data from our current monitoring network is of high quality and lessen data gaps
Expand the CoCoRaHS network into rural areas in the study area. A small amount of funding would be needed to purchase rain gauges to lessen the burden on the volunteer observers and offer training.
Continue funding for long-term climate monitoring at Columbus
Expand emergency roadway monitoring along major roads where blowing dust is a hazard. Here there needs to be collaboration with public safety, NM Department of Transportation, counties, and local roads authorities. These would require reliable telemetry to get data on sub-hourly basis. At these stations, there is also need for measuring dust levels.
Metadata to document the data and any changes to the data but also to the surrounding environment at the station and around the immediate location.
Seek partnerships between state/federal/local agencies to combine efforts
Some of the lower priority, but nevertheless important, recommendations include
Fill in gaps in climate between populated areas to better understand the dust emission process in the region. These would be sited at rangeland locations to monitor climate at disturbed and relatively undisturbed soils.
Review of networks at an annual basis to include an outside entity not those operating a network and agencies running the networks. A form of this can be a annual monitoring workshop for the desert southwest and border region.
Expanding climate monitoring network to Palomas, Mexico
Utilize satellite remote sensing data for climate and land surface monitoring and provide the data so it can be easily accessed and understood
Seek partnerships with schools to bring in awareness of climate to younger generation
41
Connect with Department of Interior Climate Science Centers and Desert Landscape Conservation Cooperative to communicate needs and recommendations
Increase connectivity with biological and hydrological science community for collaboration in monitoring
Include soil temperature and moisture at a select number of rangeland sites
Consider low cost, automated precipitation and temperature only stations to help with rainfall observations and drought assessment in data sparse areas
7 REFERENCES
Adams,D.K.; and Comrie,A.C. (1997). The North American monsoon. Bull. Am. Meteor.
Soc., 78:2197-2213.
Bartlett, J.R. (1856). Personal narrative of explorations and incidents in Texas, New Mexico,
California. New York, Appleton, Vol. 1, pp. 247-248.
Benson, L., J. Burdett, S. Lund, M. Kashgarian, and S. Mensing. (1997). Nearly synchronous
climate change in the northern hemisphere during the last glacial termination.
Nature 388, 263-265.
Buck, B.J. and H.C. Monger (1999). Stable isotopes and soil-geomorphology as indicators of
Holocene climate change, northern Chihuahuan Desert. J.Arid Env. 43(4): 357-373.
Daly,C.; Neilson,R.P.; and Phillips,D.L. (1994). A statistical-topographic model for mapping
climatological precipitation over mountainous terrain. J. Appl. Meteorol., 33:140-
158.
Daly,C.; Taylor,G.H.; and Gibson,W.P. (1997). The PRISM approach to mapping precipitation
and temperature. In In reprints: 10th Conf. on Applied Climatology, Reno, NV.
American Meteorological Society, Boston, MA, pp. 10-12.
Doorenbos, J. A.. H. Kassam C. L. M. Bentvelsen, V. Bransheid, J.M.G. A. Plusje,M.Smithe G.
O. Uittenbogaard , kH. K. Van Der Wal 2007. FAO 33 Irrigation and Drainage paper ,
Yield response to water. P 1-191
Douglas,M.W.; Maddox,R.A.; Howard,K.; and Reyes,S. (1993). The Mexican monsoon. J.
Climate, 6:1665-1677.
Emory, W.H. (1857). Report on the U.S. and Mexican Boundary Survey, Washington, DC.
Vol. 1, S. Doc. No. 108, p. 47.
42
Enfield, D. B., A.M. Mestas-Nunes & P.J. Trimble. (2001). The Atlantic multidecadal
oscillation and its relation to rainfall and river flows in the continental U.S.,
Geophysical Research Letters, 28(10), 2077-2080.
Grice, G.K. (2005). History of Weather Observations, Fort Bayard, New Mexico, 1867 - 1893.
Prepared for the Midwestern Regional Climate Center under the auspices of the
Climate Database Modernization Program, NOAA National Climatic Data Center,
Asheville, North Carolina, September 2005.
Guttman, N.B., R.G. Quayle. (1996). A historical perspective of U.S. climate divisions.
Bulletin of the American Meteorological Society, 77(2): 293-303.
Hardiman, T. (2011). Intense cold wave of February 2011, National Weather Service, Santa
Teresa Forecast Office. Available at
http://www.srh.noaa.gov/images/epz/Storm_Reports/Cold11/Feb2011ColdWx.pdf
Hegerl, G. C., and Coauthors, 2007: Understanding and attributing climate change. Global
climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et
al., Eds., Cambridge University Press, 663–745.
Heim, Jr., R. R., (2002). A review of Twentieth-Century drought indices used in the United
States. Bulletin of the American Meteorological Society, 83, 1149-1165.
Jones, C, J. Gottschalck, L. M. V. Carvalho, and W. Higgins (2011). Influence of the Madden–
Julian Oscillation on Forecasts of Extreme Precipitation in the Contiguous United
States. Monthly Weather Review, Volume 139, Issue 2 (February 2011) pp. 332-350.
doi: 10.1175/2010MWR3512.1
Kerr, R. A. A North Atlantic climate pacemaker for the centuries. (2000). Science 288, 1984–
1985.
Kogan, F.N., 1995: Droughts of the Late 1980s in the United States as Derived from NOAA
Polar Orbiting Satellite Data. Bull. Amer. Meteor. Soc. 76, 655-668.
Kogan, F.N. 2001: Operational space technology for global vegetation assessments Bull.
Amer. Meteor. Soc., 82(9): 1949-1964
Leopold, L.B. (1942). Areal extent of intense rainfalls, New Mexico and Arizona, Trnas.
Amer. Geophys. Union, Pt. II. pp.558-559
Madden, R. A., and P. R. Julian (1971), Detection of a 40-50 day oscillation in the zonal wind
in the tropical Pacific, J. Atmos. Sci., 28, 702-708.
43
Madden, R. A., and P. R. Julian (1994), Observations of the 40-50-Day Tropical Oscillation - a
Review, Monthly Weather Review, 122, 814-837.
Malm, N. (2003). Climate Guide, Las Cruces, 1892-2000. Agricultural Experiment Station,
Research report 749.
Mantua, N.J. and S.R. Hare (2002). The Pacific Decadal Oscillation, J. Climate, 58, 35-44.
Mantua, N.J., S.R. Hare, Y. Zhang, J.M. Wallace, and R. C. Francisi (1997). A Pacific
interdecadal climate oscillation with impacts on salmon production, BAMS, 78(6),
1069-1079.
McVicar, T.R. and D.L.B. Jupp (1998). “The Current and Potential Operational Uses of
Remote Sensing to Aid Decisions on Drought Exceptional Circumstances in Australia:
a Review”, Agricultural Systems, Vol. 57, No. 3, 399-468.
Mote, P.W., M. Clark, and A.F. Hamlet. (2008). Variability and trends in mountain snowpack
in western North America. In F. Wagner (ed.), Proceedings of the AAAS Pacific
Division Annual Meeting.
Mote, P.W., A.F. Hamlet, M.P. Clark, and. D.P. Lettenmaier(2005). Declining mountain
snowpack in western North America. Bulletin of the American Meteorological
Society, 86:39-49.
NDPC (2000). Preparing for Drought in the 21st Century. Report of The National Drought
Policy Commission. May 2000
NMOSE (2006). The impact of climate Change on New Mexico’s water supply and ability to
manage water resources (A. Watkins, lead author), New Mexico Office of the State
Engineer.
PRISM (2004). PRISM Climate Group, Oregon State University,
http://prism.oregonstate.edu, created 4 Feb 2004.
Salazar, L., F. Kogan and L. Roytman, 2007: Use of remote sensing data for estimation of
winter wheat yield in the United States. Int. J. Remote Sens., Vol 28, Nos 17-18;
3795-3811.
Salazar L., F. Kogan L. Roytman 2008. Using vegetation health indices and partial least
squares method for estimation of corn yield. International Journal of Remote
Sensing archive 29(1):175-189
44
Schlesinger, M. E. & N. Ramankutty. (1994). An oscillation in the global climate system of
period 65–70 years. Nature 367, 723–726.
Trenberth, K. E. & D.J. Shea. (2006). Atlantic hurricanes and natural variability in 2005.
Geophysical Research Letters, 33, L12704, doi:10.1029/2006GL026894, 2006.
Tuan, Y.-F., C.E. Everard, J.G. Widdison, and I. Bennett. (1973). The climate of New Mexico-
Revised Edition. State Planning Office, Santa Fe, 87501.
Turnbow, C.A., J.E. Van Hoose, L.S. Reed, L.W. Huckell, J.A. Railey, R.M. Reycraft, G.A.
Duncan, R.D. Holmes, J.C. Acklen, T.G. Baugh, G.D. Smith, C. Heyne, S. Bozarth, M.S.
Shakley, A.R. Nelson, A. Carbenter, J. Grant, and H. Neff. (2000). A highway through
time: Archaeological investigations along NM 90, in Grant and Hidalgo counties, New
Mexico. (US Forest Service report 03-06-98-005b) prepared for the New Mexico
State Highway and Transportation Department, Technical Report 2000-3.
Van Devender, T.R. (1990). Late quarternary vegetation and climate of the Chihuahuan
Desert, United State and Mexico. In Packrat middens: The last 40,000 years of biotic
change, edited by J.L. Betancourt, T.R. Van Devender, and P.S. Martin, pp. 104-133,
University of Arizona, Tucson.
Weather Bureau Office (1897). Annual Summary, New Mexico Section of the Climate and
Crop Service of the Weather Bureau. In cooperation with the New Mexico Weather
Service. Published by authority of the Secretary of Agriculture.
Zhang, Y., J.M. Wallace, and D. S. Battisti (1997), ENSO-like interdecadal variability: 1900-93,
J. Climate, 10, 1004-1020.
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Appendix A: Wind Rose Data and Processing
Wind Data Acquisition RAWS data were obtained from the Western Regional Climate Center (http://www.raws.dri.edu/). Formatting of the RAWS data was done following the procedures provided in Appendix D of the Fiscal Year 2011 Final Technical Report (i.e., the Excel left and mid functions). Dripping Springs Hachita Valley Burro Mountain NMED data were obtained from http://air.nmenv.state.nm.us/. Select from the pull-down menu for Monitoring Stations/South, and then select specific station. Download historical data for wind speed and wind direction; specify hourly, standard averaging, Excel format. Use year, month, day, and hour Excel functions with NMED datasets. Holman Road/Las Cruces West Mesa/Las Cruces Hurley Elementary School Desert View Sunland Park La Union Deming Airport Santa Teresa TCEQ data were downloaded from Texas Air Monitoring Information System (TAMIS), which can be accessed off our main TCEQ AIR public web pages or directly at http://www5.tceq.texas.gov/tamis/index.cfm?fuseaction=home.welcome. The recommended raw data report for this project is called a "JMP" report in which each
sample is a row with the parameters listed in columns. Use the following steps:
1. Website: http://www5.tceq.texas.gov/tamis/index.cfm?fuseaction=home.welcome 2. Click on Start Report 3. Select "Raw Data Report (JMP)" from Select Report drop-down menu 4. Click on Next to start the criteria selection wizard 5. Enter a Date Range, the beginning date is inclusive and the ending date is exclusive.
To get data for all of 2010, for example, enter "01/01/2010" under Start (inclusive) and "01/01/2011" under End (exclusive). Click Next
6. For Locations: Select Individual Sites (you could try Counties, Urban Areas, or TCEQ Regions, but that would probably be too large of a request and eventually timeout). Click Next
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7. Highlight Sites (holding ctrl key). You said you had 7 sites you were interested in, so select those using the ctrl key. Note that the more sites you select, the greater your chances of timing out with too large a request. Click Next.
8. In the Select Parameters list, highlight Wind Direction - Resultant and Wind Speed - Resultant (holding control key). Click Next
9. Under Select Duration, choose "1 HOUR" from drop-down menu. You can also choose Tab, Comma, or Semicolon under Select File Delimiter. Click Create Report.
10. To save the file to your computer, right click on the Report File link and choose “Save Target As” from the context menu that appears.
11. To view the file in your browser, left click the link. (if the file is not a fixed width format, the columns and data will not be aligned properly in the browser).
Sites that were included in the wind rose analysis include:
Ascarate Park (TCEQ) Cd. Juarez 20-30 Club (TCEQ) Cd. Juarez Advance (TCEQ) Cd. Juarez Delphi (TCEQ) Chamizal (TCEQ) El Paso Lower Valley Sounder (TCEQ) El Paso Sun Metro (TCEQ) El Paso UTEP (TCEQ) Ft. Bliss (TCEQ) Ivanhoe (TCEQ) Skyline Park (TCEQ) Socorro (TCEQ) Tillman (TCEQ)
NWS/FAA data were downloaded from Mesowest (http://mesowest.utah.edu/cgi-
bin/droman/mesomap.cgi?state=NM&rawsflag=3). Select NWS and RAWS networks. Parameters to download are SKNT (wind speed) and DRCT (wind direction). Alternatively use http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?laKLRU. Data is generally in 20-minute intervals and must be re-formatted for input into Lakes Environmental WRPlot software. The Deming Municipal Airport formatting is not complete at this time. KELP – El Paso International Airport
KLRU – Las Cruces International Airport KDMN – Deming Municipal Airport Soil Climate Analysis Network (SCAN)/NRCS: http://www.wcc.nrcs.usda.gov/nwcc/site?sitenum=2168&state=nm Jornada Experimental Range
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NMCC - http://weather.nmsu.edu/data/data.html Under Climate Data Printouts, choose Hourly for interpolated data and Raw Hourly for un-interpolated data. NMSU Wind Rose Processing A summary of the process used to convert the spreadsheet data to a GIS geodatabase is: .xls -> .sam -> .kml -> .gdb Excel, Wind Rose Plot, Google Earth, and ArcGIS 10 were used to obtain the final datasets. The process was detailed in the Fiscal Year 2011 Final Technical Report dated 30 June 2011. The Wind Rose Plot software allows the user to specify dates and times using the .sam file that is created after reading in the Excel data. Wind roses include annual, spring (20 Mar – 20 Jun) and summer (21 Jun – 22- Sep) for most of the specified areas. Diurnal (day-time and night-time) wind roses can be generated at a later time. Wind rose data has been packaged and delivered in the WindRose2010 geodatabase, as feature datasets. Additional files include screen shots of annual, spring, and summer wind roses from Google Earth and all the .sam and .kml files. Examples of these deliverables follow (Figures 1-9), using the El Paso International Airport (KELP), a NWS site.
Figure 1. El Paso International Airport (NWS) 2010 Wind Class Frequency (Annual)
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Figure 2. El Paso International Airport (NWS) 2010 Wind Rose (Annual)
Figure 3. El Paso International Airport (NWS) Wind Rose Plot in Google Earth (Annual).
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Figure 4. El Paso International Airport (NWS) 2010 Wind Class Frequency (Spring)
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Figure 5. El Paso International Airport (NWS) 2010 Wind Rose (Spring)
Figure 6. El Paso International Airport (NWS) Wind Rose Plot in Google Earth (Spring).
Figure 7. El Paso International Airport (NWS) 2010 Wind Class Frequency (Summer)
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Figure 8. El Paso International Airport (NWS) 2010 Wind Rose (Summer)
Figure 9. El Paso International Airport (NWS) Wind Rose Plot in Google Earth (Summer).
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Appendix B: NWS’s Advanced Hydrologic Prediction Service (AHPS)
This appendix reviews the development of the project database containing the AHPS precipitation product.
Daily precipitation was delivered as a geodatabase by the SpARC group at NMSU.
Precipitation data from NWS’s Advanced Hydrologic Prediction Service has been
downloaded on a regular basis from http://water.weather.gov/precip/download.php
starting 8/1/2010 through 4/30/2012. The WSR-88D rainfall algorithm generates a one-hour
rainfall product that has been remapped from a local, radar-centered, polar grid into the
national, quasi-rectangular Hydrologic Rainfall Analysis Project (HRAP) grid of nominal grid
size of 4 km x 4 km.
These datasets are available as shapefiles, and they contain the following fields:
1. id - a unique value for each grid bin 2. hrapx - column number of the HRAP grid cell (higher numbers are further north) 3. hrapy - row number of the HRAP grid cell (higher numbers are further east) 4. latitude of the HRAP grid point 5. longitude of the HRAP grid point 6. globvalue - 24-hour precipitation value in inches. "-2" values correspond to "Missing
Data", e.g. an incomplete dataset. 7. units - inches
The shapefiles were clipped to study area boundaries and integrated into an
NWSDailyPrecip geodatabase. Grids with no precipitation (i.e. 0.00") are not in the
observed data shapefiles, as those grid points are null. The eventual conversion (FY13) of
these point-based datasets to raster format (and possibly animation) will aid in the
visualization and analysis of the precipitation data. Additionally, project models will find the
raster format more compatible. These data will be used as the primary precipitation map
layer. Data can continue to be downloaded, and archival data (back to 2005) can also be
easily obtained. The team may explore a more automated download and conversion
procedure in FY13.
Figure 2 illustrates the precipitation data for October 22, 2010, displayed using quantities
with graduated colors and natural breaks for rainfall values greater than zero. The blue and
purple shades represent the highest amount of precipitation.
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Figure 2. Example of observed daily precipitation from NWS
Precipitation data are also available in Network Common Data Form (NetCDF), which is a
raster format that requires additional processing to obtain the correct geo-referencing.
Once processed, it is reasonably straightforward to animate a time sequence in ArcGIS
through a procedure found online.
http://sofia.usgs.gov/eden/edenapps/Quick_Guide_Using_EDEN_NetCDF_Files_ArcGIS.pdf
Spatial Analyst in ArcGIS provides several tools for surface interpolation (e.g., inverse
distance weighting, kriging, spline or natural neighbor). These tools can be used to create a
continuous spatial coverage of point data. This kind of analysis is applicable to the point
precipitation data from monitoring stations or point emissions data. Figure 3 shows an
example of using kriging, a geostatistical technique to interpolate the value of a random
field, to generate a precipitation surface. Input to the kriging algorithm was a set of gridded
precipitation points from NWS’s Advanced Hydrological Prediction Service.
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Figure 3. Precipitation Surface using Kriging
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Appendix C: THREDDS Data Portal at NMSU
Our community data portal is using the UNIDATA Thematic Real-time Environmental
Distributed Data Services (THREDDS) and Repository for Archiving, Managing and Accessing
Diverse Data (RAMADDA) server applications. The purpose of this portal is to make available
to the public, data sets that have been archived at NMSU’s Center for Applied Remote
Sensing in Agriculture, Meteorology and Environment (CARSAME) and New Mexico Climate
Center but not available to the public. The data portal increases the availability of near real-
time satellite, numerical weather prediction model output, and surface weather station
data to the environmental sciences community locally and throughout the region. Our data
portal is growing and currently we have archives of CFSv2 data, WRF initialized with NAM
data, and LANDSAT imagery.
Our original plan was to purchase a Dell PowerVault MD1000 storage server but we found
another machine at a lower cost and had more storage capacity. The Dell cost had increased
significantly from the proposal date to date of contract award and was no longer an option
for us. This custom machine similar to the Backblaze Storage Pod
(http://blog.backblaze.com/category/storage-pod/) was built from individual components
including power supplies, processors, memory chips, boot hard drives, RAID controller, CPU
cooling fan, 5-bay backplane case, 4U server enclosure, heat sinks, and miscellaneous cables
and hardware. Total storage amount for data and imagery is about 100 TB. UNIDATA funds
were also used to purchase a Supermicro 4 CPU machine with a total of 48 cores that we
are using for data processing and web service. The servers are installed in the New Mexico
Climate Center in Skeen Hall (see Figure C-1).
One of the primary purposes of the portal is to serve the education and research
community not only at New Mexico State University but regionally and across the border
into Mexico. For example the data served on the portal is used in a newly offered
Introduction to Air Pollution ES 460 course in our Environmental Sciences department at
NMSU. In this course we investigate the impacts of meteorology on air quality through the
study of past events. We visualize data using UNIDATA’s Integrated Data Viewer (IDV). The
RAMADDA application is being used to store case studies that can be viewed and used by
students and other interested researchers and at NMSU and by the community. Our data
portal will also be a key component of any climate related course we offer at NMSU.
A research group taking advantage of this archive is one studying wind erosion and air
quality in the southwestern US. Several faculty members at New Mexico State University,
the University of Texas El Paso, and Texas Tech have active research projects in the study of
the sources and transport of dust in the Chihuahuan Desert region. These projects have
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made extensive use of our infrared and visible NOAA AVHRR and GOES imagery to
determine dust plume boundaries.
Access to the THREDDS portal is located at: http://cirrus.nmsu.edu:8080/thredds/ and the
RAMADDA at http://cirrus.nmsu.edu:8080/repository. Links to this will also be from
http://weather.nmsu.edu.
Figure C-0-1. THREDDS server at NM Climate Center