Trends in indices for extremes in daily temperature and precipitation over Utah, USA

Authors

  • Carlos A. C. dos Santos,

    Corresponding author
    1. Department of Atmospheric Science, Federal University of Campina Grande, Avenida Aprígio Veloso, 882, Bodocongó, Campina Grande, PB, CEP: 58109-970, Brazil
    • Department of Atmospheric Science, Federal University of Campina Grande, Avenida Aprígio Veloso, 882, Bodocongó, Campina Grande, PB, CEP: 58109-970, Brazil.
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  • Christopher M. U. Neale,

    1. Civil and Environmental Engineering Department, Utah State University, 4110 Old Main Hill, Logan, UT 84322-4110, USA
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  • Tantravahi V. R. Rao,

    1. Department of Atmospheric Science, Federal University of Campina Grande, Avenida Aprígio Veloso, 882, Bodocongó, Campina Grande, PB, CEP: 58109-970, Brazil
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  • Bernardo B. da Silva

    1. Department of Atmospheric Science, Federal University of Campina Grande, Avenida Aprígio Veloso, 882, Bodocongó, Campina Grande, PB, CEP: 58109-970, Brazil
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Abstract

The main objective of this study was to analyse the trends in 20 annual extreme indices of temperature and precipitation for Utah, USA. The analyses were conducted for 28 meteorological stations, during the period from 1930 to 2006, characterized by a long-term and high-quality dataset. The software used to process the data was the RClimdex 1.0. The analyses of extreme temperature indices have identified an increase in air temperature in Utah during the last century. Meanwhile, the analyses of precipitation indices showed a large variation throughout the studied area and time period, and, in general, with few statistically significant trends. Thus, it was not possible to conclude that significant changes in precipitation have occurred in this region over the last century. Copyright © 2010 Royal Meteorological Society

1. Introduction

There is growing evidence that the global changes in extremes of climatic variables observed in recent decades can only be accounted when anthropogenic and natural factors are considered (Alexander et al., 2007; IPCC, 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005; Toreti and Desiato, 2008; Choi et al., 2009). It is widely conceived that with the increase of air temperature, the water cycling process will be accelerated, resulting in an increase of precipitation amount and intensity. Karl and Knight (1998) found that the 8% increase in precipitation across the continuous USA since 1910 is reflected primarily in heavy and extreme daily precipitation events. These results were confirmed in Kunkel et al. (1999) that found national trend in short-duration (1–7 days) extreme precipitation events for the USA increasing at a rate of 3% decade−1 for the period between 1931 and 1996. While evidences of increasing trends are presented in the USA and many other regions, statistically significant decreasing trends in extreme rainfall events have also been found, including the Sahel region of Nigeria (Tarhule and Woo, 1998), Australia (Haylock and Nicholls, 2000), Asia and central Pacific (Griffiths et al., 2003), UK (Osbom et al., 2000) and some parts of India (Roy and Balling, 2004). Therefore, the spatial patterns of temperature and precipitation are complex and vary over the world (Wang et al., 2008). Many studies have investigated climate change and extremes on different scales (Easterling et al., 2000; Brunetti et al., 2004; Vincent et al., 2005; Brunetti et al., 2006; Haylock et al., 2006; Santos and Brito, 2007). However, IPCC (2001, 2007) in its reports evidenced the need for more detailed information about regional patterns of climate change.

The climate features of Utah in the USA are determined by its distance from the Equator, elevation above sea level and distance from the Pacific Ocean and Gulf of Mexico (main moisture sources for the region). Also, the mountain ranges over the western USA have a marked influence on the climate of Utah. During the past four decades, precipitation near the central Intermountain Region has experienced a pronounced increase in temporal variability (Wang et al., 2008). The prevailing westerly air currents reaching Utah are comparatively dry, resulting in light precipitation over most of the State (Moller and Gillies, 2008). To gain an understanding of Utah climate, a study using weather station data to analyse the local climate variability is warranted, to establish which areas of this region are being more affected by climate change.

The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCI) and the Climate Variability and Predictability project (CLIVAR) has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate. This study attempts to provide new information on trends, in regional scale, using long-term records of daily air temperature and precipitation over Utah, USA, through the analysis of different indices based on observational data from multiple stations in the region. This analysis is important for Utah since any change in climate can have large impacts on the daily life of the population and environment dependent on scarce water resources for agricultural and municipal use.

2. Material and methods

2.1. Data and quality control

Daily maximum and minimum surface air temperatures and daily precipitation data were taken from 28 meteorological stations across Utah, USA, between 37–41°N latitude and 109–114°W longitude and, in general, for the period between 1930 and 2006. This period has been chosen because it characterizes a long-term dataset for each station. The map of station locations is shown in Figure 1; the numbers indicating the stations with their names and coordinates shown in Table I. The dataset was provided by Utah Climate Center at Utah State University.

Figure 1.

(a) Map of the USA with the state of Utah highlighted and (b) map of Utah with the locations of the stations used in this study. The numbers relate to the name, latitude, longitude and elevation of the selected stations given in Table I. In general, the time series used for these stations was from 1930 to 2006

Table I. Meteorological stations used for the analysis of maximum and minimum daily air temperature and daily precipitation in Utah, USA
IDStationLatitude (N)Longitude (W)Elevation (m)
1Bryce Canyon37.633−112.1832412
2Alton37.440−112.4822146
3Blanding37.613−109.4851855
4Laketown41.825−111.3211823
5Escalante37.769−111.5981771
6Manti39.258−111.6311750
7Castle Dale39.174−111.0291713
8Cedar City37.709−113.0941703
9Heber40.491−111.4261716
10Duchesne40.168−110.3951682
11Levan39.554−111.8671612
12Fillmore38.966−112.3281561
13Morgan Power & Light41.043−111.6721551
14Canyonlands38.150−109.7821523
15Kanab37.027−112.5371506
16Black Rock38.708−112.9531492
17Tooele40.527−112.2981545
18Logan41.745−111.8031460
19Jensen40.364−109.3451448
20Spanish Fork40.079−111.6041439
21Deseret39.267−112.6521399
22Hanksville38.371−110.7151313
23Ogden41.244−111.9461326
24Callao39.899−113.7131320
25Mexican Hat37.149−109.8681254
26Salt Lake City40.778−111.9691288
27Zion's National Park37.208−112.9841234
28Moab38.574−109.5461241

An exhaustive data quality control (QC) was conducted because indices of extremes are sensitive to changes in station location, exposure, equipment and observer practice (Haylock et al., 2006). Data QC is a prerequisite for determining climatic indices. The QC module of the RClimdex software performs the following procedures: (1) replaces all missing values (currently coded as −99.9) into an internal format that the software recognizes (i.e. NA, not available) and (2) replaces all unreasonable values into NA. Those values include: (a) daily precipitation amounts less than zero and (b) daily maximum temperature less than daily minimum temperature. In addition, the QC also identified outliers in daily maximum and minimum temperature. The outliers are daily values outside a range defined by the user. Currently, this range is defined as n times standard deviation (STD) of the value for the day, that is (mean − n× STD, mean + n× STD), where STD for the day and n is an input from the user (Zhang and Yang, 2004; Vincent et al., 2005). Initially, data from 50 meteorological stations were available, and after the QC, only stations with less than 10% of missing data for a period of at least 50 years were considered, resulting in the 28 stations used in the analyses (Table I).

2.2. Methodology

The RClimdex 1.0 software developed by Xuebin Zhang and Feng Yang from Canadian Meteorological Service (Zhang and Yang, 2004) was used in this study to obtain the climatic extremes indices, following methodologies of Zhang et al. (2005a,b) and Haylock et al. (2006). RClimdex provided 20 extreme climate indices, which were chosen for discussion here: 11 indices based on air temperature data and nine based on precipitation data (Table II) because they better explain the climate behaviour of Utah (Moller and Gillies, 2008). The resulting series were analysed through trends. The slopes of the annual trends of the climate indices were calculated based on a least square linear fitting. Trends were obtained for each index at the 28 locations and the statistical significance of the trends were assessed through the Student's t-test and the number of degrees of freedom was obtained based on the length of the dataset, i.e. 76 for the 1930–2006 (Haylock et al., 2006; Santos and Brito, 2007; Dufek and Ambrizzi, 2008). The Student's t-test for 76 degrees of freedom gives a threshold of 1.67 for the identification of statistically significant trends at the 95% confidence level. Hence, the trends considered significant in this study are those for a threshold ≥ 1.67.

Table II. Definition of extreme air temperature and precipitation indices used in this study
IndicesNameDefinitionUnits
SUSummer daysAnnual count when TX(daily maximum) > 25 °CDays
IDIced daysAnnual count when TX(daily maximum) < 0 °CDays
TRTropical nightsAnnual count when TN(daily minimum) > 20 °CDays
FDFrost daysAnnual count when TN(daily minimum) < 0 °CDays
TXxMax TmaxMonthly maximum value of daily maximum temp °C
TNxMax TminMonthly maximum value of daily minimum temp °C
TXnMin TmaxMonthly minimum value of daily maximum temp °C
TNnMin TminMonthly minimum value of daily minimum temp °C
WSDIWarm spell duration indicatorAnnual count of days with at least six consecutive days when TX > 90th percentileDays
CSDICold spell duration indicatorAnnual count of days with at least six consecutive days when TN < 10th percentileDays
DTRDiurnal temperature rangeMonthly mean difference between TX and TN °C
RX1dayMax 1-day precipitation amountMonthly maximum 1-day precipitationmm
Rx5dayMax 5-day precipitation amountMonthly maximum consecutive 5-day precipitationmm
SDIISimple daily intensity indexAnnual mean precipitation when PRCP > = 1.0 mmmm
R10 mmNumber of heavy precipitation daysAnnual count of days when PRCP > = 10 mmDays
CDDConsecutive dry daysMaximum number of consecutive days with RR < 1 mmDays
CWDConsecutive wet daysMaximum number of consecutive days with RR > = 1 mmDays
R95pVery wet daysAnnual total PRCP when RR > 95pmm
R99pExtremely wet daysAnnual total PRCP when RR > 99pmm
PRCPTOTAnnual total wet-day precipitationAnnual total PRCP in wet days (RR > = 1 mm)mm

To run the RClimdex 1.0 software, the format of the input data file has several requirements: (1) ASCII text file; (2) Column sequence: year, month, day, PRCP, TMAX and TMIN. (Note: precipitation units = millimeters and temperature units = degrees Celsius); (3) the format as described above was space delimited (e.g. each element was separated by one or more spaces) and (4) for data records, missing data were coded as −99.9 and data records were in calendar date order (Zhang and Yang, 2004). The spatial distribution of the indices trends was represented using the symbols (tabfigure1000) for positive and (●) for negative trends, statistically significant at 95% level, i.e. p < 0.05. The representation of the trends which are statistically nonsignificant at the 95% level used the symbols (+) for positive and (○) for negative trends.

3. Results

3.1. Temperature indices analyses

Table III shows the decadal trends, i.e. trends for 10 years, of the extreme indices of air temperature in Utah, obtained by using the software RClimdex 1.0, for 28 stations. The bold and highlighted values represent significant level of 5% (p < 0.05) and values only highlighted represent significant level of 10% (0.05 < p < 0.1). The discussions presented in this study are only for those trends that showed significant level of 5%. The index Summer Days (SU) showed with positive trend at ten stations and negative trend at three, evidencing an overall increase in the annual number of days when the maximum air temperature was higher than 25 °C. The spatial distribution trend of this index is shown in Figure 2(a). The index Iced Days (ID) showed seven stations with negative trends, one station with positive trend and one station (Laketown) that did not present any trend (trend = 0), showing that the annual number of days when the maximum air temperature was less than 0 °C is decreasing. These results are consistent with those shown by SU index. Figure 2(b) shows the spatial distribution trends of ID index; in general, the significant values are in the southern portion of the studied area. It is possible to identify the heterogeneous behaviour of the indices presenting positive and negative trends. These results are in agreement with those of Karl et al. (1996).

Figure 2.

Spatial distribution of temperature extreme trends for Utah, USA. The symbol (equation image) for positive and (equation image) for negative trends, statistically significant at 95% level (p < 0.05), while the symbols (+) for positive and (○) for negative trends statistically nonsignificant

Table III. Decadal trends of the extreme indices of air temperature for Utah, USAThumbnail image of
  • The bold and highlighted values represent significance at 5% level (p < 0.05), and values only highlighted represent significance at 10% level (0.05 < p < 0.1).

  • Tropical Nights (TR) index shows eight stations with positive trends and one station with no trend. These results show that the annual number of days when the minimum air temperature is higher than 20 °C is increasing; the spatial distribution is shown in Figure 2(c), presenting predominant increase in the northern and eastern areas of Utah. The Frost Days (FD) index presented only negative trends (15 stations) as shown in Figure 2(d) evidencing the homogeneous behaviour of this index in the studied area. The Max Tmax (TXx) index, i.e. monthly maximum value of daily maximum temperature, presented 13 stations with positive trends and three stations with negative trends, showing a predominant increase in the monthly maximum value of daily maximum temperature in this area (Figure 2(e)). It is possible to observe in Min Tmax (TXn) index a similar behaviour, with only positive trends (nine stations), showing that the monthly minimum value of daily maximum temperature is increasing as well. The spatial distribution is shown in Figure 2(f) and these results indicate an increase in the temperature in the studied area. The Max Tmin (TNx) index, i.e. monthly maximum value of daily minimum air temperature, shows nine stations with positive trends and one station with negative trend (Figure 2(g)). The Min Tmin (TNn) index presented a similar behaviour with only positive trends (15 stations), indicating that the minimum temperature is also increasing in this region (Figure 2(h)). The increase of the air temperature in the study area was also previously identified by Karl et al. (1996) and Alexander et al. (2006).

    The Warm Spell Duration Indicator (WSDI) index, that represents the annual count of days with at least six consecutive days on which TX is more than the 90th percentile, showed nine stations with positive trends and two stations with negative trends (Figure 2(i)), evidencing the increase of warm spell duration. Figure 2(j) shows the spatial distribution of Cold Spell Duration Indicator (CSDI) index that represents the annual count of days with at least six consecutive days where TN is less than the tenth percentile. Table III shows that the CSDI index presented only negative trends (eight stations) evidencing that cold spell durations are decreasing; this result is in agreement with the result presented by the WSDI. In addition, Diurnal Temperature Range (DTR) index shows negative trends at 14 stations and positive trends at four stations (Figure 2(k)), indicating that the monthly mean difference between maximum and minimum temperature is decreasing in the studied area. These results are in agreement with the results obtained for TXx, TXn, TNx and TNn indices and are similar to the results obtained by Alexander et al. (2006).

    To assist with the interpretation of figures that involve analyses of temperature extreme indices, the percentage of stations with statistically significant and insignificant trends at the 5% level were calculated and are shown in Table IV. It can be observed that 35.7% of the stations show a significant increase in SU, as well as 32.1% in TR, 46.4% in TXx, 32.1% in TXn, TNx and WSDI and 53.6% in TNn, indicating an increase of temperature. While there is a significant decrease of 25% in ID, 53.6% in FD, 28.6% in CSDI and 50% in DTR, indicating a decrease in these indices consistent with the results previously shown.

    Table IV. The percentage of stations showing significant and not significant trends at the 5% level for the temperature and precipitation indices for Utah, USA
    IndicePositive significant trend (%)Positive not significant trend (%)Negative significant trend (%)Negative not significant trend (%)
    SU35.742.910.710.7
    ID7.121.42546.4
    TR32.146.4021.4
    FD014.353.632.1
    GSL14.360.7025
    TXx46.439.310.73.6
    TXn32.153.6014.3
    TNx32.153.63.610.7
    TNn53.646.400
    WSDI32.139.37.121.4
    CSDI010.728.660.7
    DTR14.314.35021.4
    RX1day7.164.3028.6
    RX5day10.778.6010.1
    SDII7.142.917.932.1
    R10mm21.446.43.625
    CDD017.917.964.3
    CWD32.142.93.621.4
    R95p14.353.67.125
    R99p7.132.13.653.6
    PRCPTOT2560.73.610.7

    3.2. Precipitation indices analyses

    Table V presents the decadal trends of the extreme indices of precipitation obtained for 28 locations in Utah. The bold and highlighted values represent significant level of 5% (p < 0.05) and values only highlighted represent significant level of 10% (0.05 < p < 0.1). Only the trends of precipitation indices that are significant at 5% level are discussed. As the precipitation indices have a large variation, the number of trends with statistical significance is less than those of temperature indices. The index Max 1-Day precipitation amount (RX1day) showed only positive trends (two stations) with statistical significance, but positive trends were predominant (20 stations). The spatial distribution of this index is shown in Figure 3(a). The index Max 5-Day precipitation amount (RX5day) showed also positive trends (three stations); however, the trends of two out of the three stations coincide with each other (Figure 3(b)), evidencing the increase of precipitation in one and five consecutive days at these locations. Kunkel et al. (1999) and Alexander et al. (2006) found similar increases of precipitation in this area of the USA.

    Figure 3.

    Spatial distribution of precipitation extreme trends for Utah, USA. The symbol (equation image) for positive and (●) for negative trends, statistically significant at 95% level (p < 0.05), while the symbols (+) for positive and (○) for negative trends statistically nonsignificant

    Table V. Decadal trends of the extreme indices of precipitation for Utah, USAThumbnail image of
  • The bold and highlighted values represent significance at 5% level (p < 0.05) and values only highlighted represent significance at 10% level (0.05 < p < 0.1).

  • Simple Daily Intensity Index (SDII) shows five stations with negative trends and one station with positive trend and its spatial distribution is shown in Figure 3(c). The Number of Heavy Precipitation Days (R10 mm) index presented only two stations with negative trends and one station with positive trend, as shown in Figure 3(d). The Consecutive Dry Days (CDD) index (maximum number of consecutive days with daily precipitation less than 1 mm) presented only negative trends (five stations) (Figure 3(e)), showing a predominant decrease and corroborating an increase of days with precipitation greater than 1 mm as seen in Consecutive Wet Days (CWD) index that presented nine stations with positive trends and only one station with negative trend (Figure 3(f)). The agreement between these two indices shows that the wet conditions in this region are increasing as discussed in Karl et al. (1996). The Very Wet Days (R95p) index presented four stations with positive trends and two stations with negative trends, as shown in Figure 3(g). The spatial distribution of Extremely Wet Days (R99p) index is presented in Figure 3(h) and shows two stations with positive trends and one station with negative trend. The last index to be analysed is Annual Total Wet-day Precipitation (PRCPTOT) that showed positive trends at seven stations and a negative trend at only one station, which evidences an increase in total annual precipitation in the studied area, as presented in Figure 3(i). These results are in agreement with Karl et al. (1996), Karl and Knight (1998) and Alexander et al. (2006).

    Table V helps the interpretation of the figures that show the analyses of extreme precipitation indices. The percentages of locations with statistically significant and insignificant trends at the 5% level are shown in bold. It can be seen in Table IV that 7.1% of the stations show an increase in RX1day, as well as 10.7% in RX5day, 21.4% in R10 mm, 32.1% in CWD, 14.3% in R95p, 7.1% in R99p and 25% in PRCPTOT. These results indicate an increase of precipitation. There is a decrease of 17.9% in SDII and CDD, indicating the agreement in behaviour between different precipitation indices.

    4. Discussion and conclusions

    Studies have shown that one of the most important questions regarding extreme events is if their occurrence is increasing or decreasing over time, characterized by the frequency of these events and if they are changing significantly. This study presents analyses of the trends in 20 annual extreme indices of air temperature and precipitation for Utah, USA. The analyses were conducted using long-term and high-quality datasets for 28 meteorological stations, in general, for a period between 1930 and 2006.

    An increase in the number of days with maximum temperature higher than 25 °C was found, and in southern Utah, a decrease in the number of days with maximum air temperature less than 0 °C was observed. The annual number of days with minimum temperature higher than 20 °C is increasing, predominantly in the northern and eastern areas of the state. The number of days, with the daily minimum temperature below zero, showed only negative trends with homogeneous behaviour in the study area. A predominant increase was identified in the maximum air temperature and the warm spell duration increased while the cold spell duration decreased. The difference between maximum and minimum temperatures is decreasing, indicating that the minimum temperature is increasing faster than the maximum temperature.

    Extreme events of precipitation are a random signal in the climate record and a time series of 76 years is important for a robust trend analysis. Some of the indices in this study can be good indicators for climate extremes in Utah. The precipitation indices showed a large variation for the studied time series and, in general, with few statistically significant trends. An increase of the precipitation in one and five consecutive days was observed a possible indication that conditions in the region are becoming wetter. Though total precipitation has increased across the region, not enough cases were statistically significant for a definite conclusion.

    Agriculture (crop production and livestock) is an important economic activity in Utah accounting for 13.9% of the total when considered with the associated processing sector. Crops such as hay, alfalfa, corn, wheat and barley are grown in the arid climate, mostly under irrigation practices. Irrigated agriculture is dependent on winter snowpack accumulation and storage as the main water supply for the summer months. Warming winter trends will lead to more of the precipitation falling as rain, requiring the construction of additional storage capacity to control the runoff hydrograph and match agricultural needs. Rapid population growth will exacerbate the pressure on the water resources. Continuing changes in climate will affect water supply and soil moisture availability, making it less feasible to grow crops in certain regions. Increases in extreme events such as floods, droughts and heat waves predicted by IPCC (2007) will pose further challenges to farmers.

    IPCC (2007) predicts an increase of about 20% in precipitation at high latitudes, and decrease in most subtropical land regions, as well as, an increase in the number of extreme precipitation events. It is difficult to forecast regional changes and their potential effects on agriculture by using global climate models. Studies have shown that changes in the frequency and severity of drought, floods and heat waves, are the key uncertainty in future climate change (EPA, 2010). Some important effects of an increase in temperature, especially in regions where agricultural production is seasonally limited by temperature as in Utah, can be the extension of the growing season, increase soil evaporation rates and severe droughts (Linderholm, 2006). Others important factors that influence crop yields are soil erosion rates and soil moisture; both can be affected by changes in rainfall patterns.

    The analysis has identified that the temperature has increased in Utah during the last century. The evaluation of the extreme temperature indices and their trends may help to a better understanding of the possible regional- and local-scale impacts of climate change on agriculture and human health. The analysis also showed an increase in total annual precipitation in Utah but few results of precipitation indices with significant trends. Thus, it is not possible to conclude that during the last century the precipitation has shown a significant pattern change throughout the studied area.

    Tourism and outdoor recreation have been an important, growing sector of Utah's economy. During the winter months, the ski and snow sport industry provides the main activities. Changes in precipitation and temperature patterns could have significant impacts on season lengths and the quality of the snow in the future, affecting the economic viability of this industry.

    Trends in regional temperature and precipitation extremes and their indication of climate change are of interest to Utah and the rest of the world. The trends obtained herein corroborate the general idea that during the last century the globe has warmed and as a consequence the precipitation level increased due to increased convective activity. Additionally, the experience acquired with this study for Utah, USA, can be extended to other regions. Future research should incorporate more regional stations in surrounding states, possibly stratifying the analysis by elevation and regional microclimates with a closer look to weather station location and possible urbanization effects.

    Acknowledgements

    The authors are grateful for the PhD scholarship provided by National Council for Scientific and Technological Development (CNPq) to the first author, the Utah Climate Center of Utah State University that provided the dataset, Dr Xuebin Zhang and Dr Feng Yang from Canadian Meteorological Service to provide the RClimdex software. To Dr Lawrence E. Hipps from the Department of Plants, Soils and Climate, as well as, the Remote Sensing Services Laboratory in the Civil and Environmental Engineering Department at Utah State University for additional support. This research was partially supported by the Utah Agricultural Experiment Station. The authors thank the anonymous reviewers whose suggestions were helpful for improving the quality of this paper.

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