Spatial and Temporal Variability in Winter Precipitation across ... - MDPI

remote sensing Article

Spatial and Temporal Variability in Winter Precipitation across the Western United States during the Satellite Era Deanna Nash 1, *, Hengchun Ye 1 and Eric Fetzer 2 1 2

*

Department of Geosciences and Environment, California State University, Los Angeles, CA 90032, USA; hye2@calstatela.edu Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA; Eric.J.Fetzer@jpl.nasa.gov Correspondence: dnash2@calstatela.edu; Tel.: +1-626-658-8643

Received: 31 May 2017; Accepted: 31 August 2017; Published: 7 September 2017

Abstract: The western United States is known for its water shortages due to large seasonal and inter-annual variability of precipitation as well as increasing demand. Climate change will impact the availability of water in the western United States through the modification of precipitation characteristics. Satellite data presents the opportunity to study these changes at a fine, continuous spatial resolution—particularly in places where no traditional ground observations exist. Utilizing the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data, this study examines the spatio-temporal changes in precipitation characteristics over the western United States between 1998 and 2015, and their connections with the atmospheric total column water vapor and the El Niño Southern Oscillation during the winter season. The results show that precipitation frequency in the western United States has been decreasing in general, precipitation totals and mean daily intensity are increasing in northwestern United States, but decreasing in the southwest United States during the 18 years of the study time period. Additionally, results show a strong relationship between total column water vapor and the precipitation characteristics, specifically in the southwestern United States. Keywords: precipitation; intensity; TRMM; AIRS; western United States

1. Introduction Precipitation, a significant variable in the water cycle, is predicted to change with the changing climate. As the air temperature increases its moisture, holding capacity will increase exponentially according to the Clausius-Clapeyron (C-C) equation [1]. Thus, precipitation characteristics such as frequency, total, and mean daily intensity will change [2–4]. Studies have suggested that precipitation extremes and daily precipitation intensity have been increasing in general, despite decreases in precipitation total and frequency of precipitation events in some places [3–6]. Studies have also suggested that increasing precipitation intensity and extremes are associated with increased surface air temperature and atmospheric water vapor [5,6]. Precipitation has decreased over the southwestern United States, but increased over the Midwest and the Great Plains between 1991 and 2012 (relative to the 1901–1960 average) [7]. This seems to be consistent with a general drying trend over the western United States since the 1970s, especially in the southwest, where mild to extreme drought has persisted in the past 4 decades [8]. The number and the intensity of heavy precipitation events have been increasing significantly across the United States between 1991 to 2012, especially in the Northeast, Great Plains, Midwest, and Southeast United States [7,9]. Even though the recent increases in heavy precipitation in the Northwest and

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Southwest United States between 1958 and 2012 are not larger than natural variations [10], projections of future climate suggest that the trend of increased heavy precipitation events will continue, even in the southwestern United States where total precipitation has been shown to decrease [7,9,11–13]. Previous studies have mostly relied on surface gauge data, ground radar data, or model reanalysis products for climatological analysis on precipitation trends and teleconnections [14]. While surface gauge data can have over 100 years of records on an approximately daily timescale, the gauges are often spread far apart, and not placed in a consistent spatial pattern [15]. There are only 640 stations in the Global Historical Climatology Network-Daily (GHCN-D) over the western United States that have rain gauges reporting at least 90% of the time [15]. Given the large spatial and temporal variation of precipitation and the complex topographical features of the western United States, information may be missed over the regions where there is no rain gauge [16]. The NCEP Stage IV ground radar product is available hourly in a gridded 4 km spatial product commonly used for many hydrological applications [15]. While Stage IV has a more consistent spatial and temporal resolution compared to rain gauges, it is not recommended for use in mountainous areas such as the western United States due to poor coverage [15]. Reanalysis precipitation products, while on a consistent spatial and temporal scale, can have a large bias and thus are not suitable for studies in examining climate variability and trend analysis [17]. Using observational satellite records to study regional precipitation over an extended period of time would remove these barriers and provide a more complete analysis, especially in the topographically complex western United States. This study will use two notable satellite products: Tropical Rainfall Measuring Mission (TRMM) for precipitation estimates, and the Atmospheric Infrared Sounder (AIRS) for total column water vapor and surface temperature estimates. Due to the nature of climate regimes over the western United States where a majority of precipitation occurs during the winter season, winter season precipitation is chosen for this study [18,19]. In northern California, extreme precipitation events contribute a large portion (30–50%) of annual precipitation [20], with 85% of the variance in annual precipitation resulting from the top 5% wettest days per year [21]. The high intensity winter season precipitation plays a primary role in water resource management and water supply across the western U.S. [20]. This study explores the possible changes in three major precipitation characteristics of winter season precipitation frequency, seasonal total, and mean daily intensity by examining the winter seasonal time series of these characteristics over the western United States. In addition, their possible connections to atmospheric water vapor, surface air temperature, and El Nino/Southern Oscillation are explored. This study will use fine scale remote sensing data to (1) calculate the winter season precipitation frequency, seasonal total, and mean daily intensity, (2) quantify the temporal changes in the winter season precipitation characteristics, and (3) explore the correlation between the winter season precipitation characteristics and atmospheric water vapor, surface air temperature, and ENSO for the most recent decades of the satellite era. Figure 1 shows the elevation of the study region, as well as the sub-regions that are used for the detailed analysis in Section 3.2.


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Figure 1. The study areaand andthe theterrain terrainrepresented represented at at aa 0.44 0.44°◦ resolution boxes Figure 1. The study area resolutionwhere wherethe thenumbered numbered boxes with white boundaries indicate the sub-regions in which the area-mean time series are evaluated. with white boundaries indicate the sub-regions in which the area-mean time series are evaluated. Region 1 (R1) westernMontana, Montana,(R2) (R2)isis central central Oregon, Oregon, (R3) Region 1 (R1) is is western (R3) isis aaportion portionofofnorthern northernCalifornia, California, (R4) is a northern Utah/Nevada, (R5) is a small area near Los Angeles, and (R6) is Arizona, and a (R4) is a northern Utah/Nevada, (R5) is a small area near Los Angeles, and (R6) is Arizona, and a portion of southern California and Nevada. portion of southern California and Nevada.

2. Materials and Methods 2. Materials and Methods 2.1. Data 2.1. Data Three major data sources are used in this study, two of which are satellite based (Table 1). The Three major data sources are used in this study, two of which are satellite based (Table 1). TRMM 3B42v7 daily precipitation from the time period 1998 to 2015 are from NASA’s Goddard TheEarth TRMM 3B42v7 daily precipitation from Services the timeCenter period(DISC). 1998 toTRMM 2015 are from aNASA’s Goddard Sciences (GES) Data and Information utilizes combination of Earth Sciences (GES) Data and Information Services Center (DISC). TRMM utilizes a combination sensors in order to improve the accuracy, coverage, and resolution of precipitation data at a 3-hourly of sensors in order to improve coverage, and resolution of precipitation data at aof 3-hourly time scale of 0.25° latitudethe byaccuracy, 0.25° longitude between 50°N to 50°S [22]. The availability over ◦ ◦ ◦ ◦ time scale of 0.25 latitude by 0.25 longitude between 50Mission N to 50(TRMM) S [22]. The availability of over 18-year 18-year records of the Tropical Rainfall Measuring allows for a climatological records Tropical Rainfall Measuring Mission allows for a climatological studyhas at aless high studyofatthe a high spatio-temporal resolution on the(TRMM) most recent decades. The TRMM dataset spatio-temporal resolution the most recentStates decades. dataset less biased precipitation biased precipitation overon western United than The overTRMM the eastern US,has despite underestimating over United States elevations than over[19,23]. the eastern US, despite underestimating the precipitation at thewestern precipitation at higher higher elevations [19,23]. Table 1. Sources of data used in this study. Dataset Name TRMM 3B42v7

Table 1.Temporal Sources of data used in this study. Parameter(s) Spatial Time Period 0.25° × 0.25°

Dataset Name

Spatial

TRMM 3B42v7 AIRS v6L3

◦ 0.25◦ ×1°0.25 × 1°

Multivariate AIRS v6L3 ENSO Index

Multivariate ENSO Index

1◦ × 1◦

-

-

Daily

January 1998– Time Period present

Daily Monthly

January2002– September 1998–present present

Temporal

Monthly

Monthly

Monthly

Precipitation

Parameter(s)

Total Column Water Precipitation Vapor, Surface Temperature

Reference [21,24]

Reference [21,24] [25,26]

September January 1950– 2002–present

Total Column Water Vapor, El Niño Oscillation Index Surface Temperature

[25,26]

January 1950–present

El Niño Oscillation Index

[27]

present

[27]


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The quality and accuracy of TRMM 3B42v7 over the continental US has been assessed by multiple studies and determined to be realistic [28–30]. When evaluated against ground-based gauge measurements, TRMM 3B42v6 had reduced biases on longer timescales, and therefore was recommended for long-term, retrospective climatological studies [30]. Maggioni et al. (2016) found that as compared to other satellite precipitation products, TRMM 3B42v7 is superior with a significant reduction in bias from Version 6 [28]. Over the continental US, TRMM has been found to have the lowest biases when compared to other satellite precipitation products because of the incorporation of the rain gauge correction [29]. However, TRMM can often underestimate the precipitation over the continental United States, with a low probability of detection rate (less than ~50%) [28]. TRMM 3B42 also has difficulty with orographic precipitation in high elevations as well as the underestimation of snow measurements because of the poor ability of the IR sensors to distinguish between raining and non-raining clouds [23,28]. So, precipitation in topographically complex regions in this study may have been systematically underestimated. Overall, TRMM 3B42v7 is an appropriate product for a long-term climatological study over an expansive region such as the western United States. The precipitable water vapor and surface air temperature data from the Atmospheric Infrared Sounder (AIRS) is also provided by NASA’s GES DISC [26]. The AIRS data products are derived from a hyperspectral infrared instrument (AIRS), and two multichannel microwave instruments (AMSU-A) [25]. The level 3 product has twice daily values at a spatial resolution of 1◦ × 1◦ [25]. Precipitable water vapor and air temperature products derived from the AIRS retrieval algorithm have been validated against numerous other sources, and have been used in many research studies [25]. The El Niño Southern Oscillation is represented by the multivariate El Niño Southern Oscillation (ENSO) index, provided by the National Oceanic and Atmospheric Administration (NOAA). The Multivariate ENSO Index (MEI) utilizes six variables (sea-level pressure, surface wind, sea surface and air temperature, and the total cloudiness fraction of the sky) and Principal Component Analysis to calculate an index that is used to determine El Niño and La Niña conditions [27]. Compared to other ENSO Indices, this index was chosen because it utilizes the full multivariate signal of ENSO, rather than a single ocean-atmospheric variable such as sea-surface temperature. An index that takes a multivariate approach is a more balanced and appropriate way to determine the ENSO state because ENSO is a multivariate phenomenon [27]. 2.2. Methods In order to calculate winter season precipitation frequency, seasonal total, and mean daily intensity over the western United States utilizing daily 3B42v7 TRMM data, some preprocessing was necessary. First, each year was temporally subset to the winter season, defined as 1 December to the end of February of the following year, or DJF. The study area of the western United States is defined as 30◦ N to 50◦ N and 110◦ W to 125◦ W. We define this region as the “western United States” since it falls roughly west of the continental divide. It encompasses the west coast, the great basin and range region, and is west of the Rocky Mountains. Then, an ocean mask was applied to remove analysis artifacts from the “bleeding” of coastal gauge information into adjacent waters. According to the TRMM update in 2014, coastal bleeding as far as 1◦ is seen [31]. Winter season precipitation frequency is the number of days where precipitation exceeds 0.1 mm within each winter season, a metric used as the standard for precipitation events in many studies [6,32,33]. The seasonal total is the sum of the daily precipitation values that occurred for all three months of the season. Mean daily intensity is the seasonal total divided by the seasonal frequency for each year, which results in millimeters per day [6,12,13]. The seasonal mean air temperature is the seasonal average of mean daily temperature, and the seasonal total column water vapor is the seasonal average of the daily total column water vapor, both averaged from two values of ascending and descending passes. The rate of change for frequency, total, and mean daily intensity of precipitation was calculated via a simple linear regression. For each grid point, the slope of the frequency, total, or mean daily

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intensity of precipitation was calculated with regard to time, or y = mx + b. In this example, y is the dependent variable (frequency, total, or daily mean intensity), time is the y-intercept, intensity of precipitation was calculated with regard to time, or yx =ismx + b.(year), In thisbexample, y is the and m is the slope, or (frequency, the rate of change. The slope, rate of change is (year), then multiplied by the time dependent variable total, or daily mean or intensity), x is time b is the y-intercept, period to is derive total or changes the study period, and of is displayed in the figures. by the time and m the slope, the rateduring of change. The slope, or rate change is then multiplied Pearson’s R correlation analysis is used to examine the relationships of total, frequency, and the period to derive total changes during the study period, and is displayed in the figures. intensity to the surface air temperatures and atmospheric total column water vapor. The significance Pearson’s R correlation analysis is used to examine the relationships of total, frequency, and the intensity to the surface air on temperatures and atmospheric total column water vapor. The significance of the relationship is based a 95% confidence level. of The the relationship is based onofa each 95% confidence level. averaged anomalies type of precipitation characteristics corresponding to El Niño The averaged anomalies of each type of precipitation characteristics corresponding to the El Niño and La Niña events were analyzed. For each calendar month, if the MEI index is above 0.5, month and La Niña weremonth, analyzed. each calendar month, if the MEI is above 0.5, the is considered as events an El Niño andFor if the MEI index value is below −0.5,index the month is considered is considered an example, El Niño month, and ifyears the MEI index1999 value is 2016 below month is as month a La Niña month [27].asFor the El Niño between and are−0.5, the the winter seasons considered as a La Niña month [27]. For example, the El Niño years between 1999 and 2016 are the that finished on years 2003, 2004, 2007, 2010, and 2016 (see Figure 2). Meanwhile, the La Niña years winter seasons that finished on years 2003, 2004, 2007, 2010, and 2016 (see Figure 2). Meanwhile, the between 1999 and 2016 are the winter seasons that finished on years 1999, 2000, 2001, 2008, 2009, 2011, La Niña years between 1999 and 2016 are the winter seasons that finished on years 1999, 2000, 2001, and 2012. For month considered to be either El Niño or La Niña, the averages of the anomalies of each 2008, 2009, 2011, and 2012. For month considered to be either El Niño or La Niña, the averages of the type of precipitation characteristic were analyzed. anomalies of each type of precipitation characteristic were analyzed.

Figure 2. The standardized departure of the Multivariate El Niño Southern Oscillation (ENSO) Index Figure 2. The standardized departure of the Multivariate El Niño Southern Oscillation (ENSO) Index values between 1999 and 2016 [26]. The red values indicate months with El Niño conditions, and the values between 1999 and 2016 [26]. The red values indicate months with El Niño conditions, and the blue values indicate months with La Niña conditions. blue values indicate months with La Niña conditions.

3. Results 3. Results 3.1. Changing Winter Precipitation Characteristics 3.1. Changing Winter Precipitation Characteristics The geographical distribution of the 18-year mean winter precipitation frequency, total, and Theintensity geographical distribution winter precipitation frequency, total,region and daily daily are shown in Figureof3.the The18-year highermean average frequency found along the coastal is intensity are shown in Figure 3. The higher average frequency found along the coastal region is around around 32 days per winter season, with a standard deviation of approximately 10 days (Figure 3a). 32 Areas days per winter season, with a standard deviation of approximately 10 with days an (Figure 3a).frequency Areas with with the lowest frequency of precipitation occur in the Southwest, average theoflowest precipitation occur indeviation the Southwest, with an average frequency of 8 days per 8 daysfrequency per winterofseason with a standard of 4 days. The largest of deviation precipitation occurred over the Pacific Northwest region with a winter season with aamount standard of 4 days. maximum value of about 420 mm (Figure 3b). The mean deviation region values with over athe study The largest amount of precipitation occurred over the standard Pacific Northwest maximum region is around mm. In general, the coastal regions have the highest daily mean with is value of about 420 60 mm (Figure 3b). The mean standard deviation values over theintensity, study region average between 4 to 14regions mm per daythe (Figure 3c). daily The standard deviation for average mean around 60 values mm. Infalling general, the coastal have highest mean intensity, with daily intensity ranges from 1.2 to 4.8 mm per day.


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values falling between 4 to 14 mm per day (Figure 3c). The standard deviation for mean daily intensity ranges 1.2 to 4.8 mm per day. Remotefrom Sens. 2017, 9, 928 6 of 15

Figure 3. The winter season precipitation (a) frequency, (b) seasonal (c) daily meanintensity daily Figure 3. The winter season precipitation (a) frequency, (b) seasonal total, total, and (c)and mean intensity with the corresponding standard deviation for the winter season precipitation (d) values withvalues the corresponding standard deviation for the winter season precipitation (d) frequency, frequency, (e) seasonal total, and (f) mean daily intensity between 1999 and 2016. (e) seasonal total, and (f) mean daily intensity between 1999 and 2016.

An overall decrease in precipitation frequency can be seen and is statistically significant across An overall decrease in precipitation frequency can be seen and is statistically significant across much of the study region (Figure 4a). In general, there has been about a half of a day decrease over much of the study region (Figure 4a). In general, there has been about a half of a day decrease over the western United States. A larger decrease of approximately one day can be seen in the southern theCalifornia western United States. larger (~35°N decrease of approximately oneperiod day can seen in the southern region near LosA Angeles 119°W) during the study of be 1999–2016. ◦ N 119◦ W) during the study period of 1999–2016. California region near Los Angeles (~35 There is an overall general decrease in seasonal total precipitation over the western United There is an overall significant general decrease in seasonal total precipitation over(~41°N the western United States, States, with localized areas over the coastal northern California 124°W), northern ◦ ◦ with localized areas over coastal northern California N 12440°N W),and northern Idaho Idaho (~47°Nsignificant 116°W), western edgethe of Wyoming, and northern Utah(~41 (between 45°N, and ◦ N 116◦ W), western edge of Wyoming, and northern Utah (between 40◦ N and 45◦ N, and 111◦ W) (~47 111°W) during 1999–2016. The values range between about −10 and −15 mm among these localized during 1999–2016. The period. values range between about −10 and −15 mm among these localized regions regions for the study for the study period. There is a statistically significant increase in precipitation mean daily intensity over the Great Basin Range region in central Washington and Oregon (between 42°N and 47°N and There is a statistically significant increase in precipitation mean daily intensity over~120°W), the Great ◦ ◦ ◦ western Montana (between 45°N and 50°N and between 110°W and 115°W), northern Nevada Basin Range region in central Washington and Oregon (between 42 N and 47 N and ~120 W), western ◦ N and ◦ N 116 ◦ W), (~40°N (between 116°W), and Arizona (~32°N 111°W). The average change in precipitation mean daily Montana 45◦central N and 50 between 110◦ W and 115◦ W), northern Nevada (~40 ◦ ◦ intensity approximately relativelychange large increase for a dry region 4-10 is and centralisArizona (~32 N0.24 111mm/day, W). Thea average in precipitation mean with dailyabout intensity mm/day average winter precipitation intensity (see Figure 3c). In general, there is also an increase in approximately 0.24 mm/day, a relatively large increase for a dry region with about 4-10 mm/day mean daily intensity over California, but only a few grid cells are statistically significant in the average winter precipitation intensity (see Figure 3c). In general, there is also an increase in mean daily northeast corner of the state (~41°N 120°W). intensity over California, but only a few grid cells are statistically significant in the northeast corner of ◦ ◦ the state (~41 N 120 W).


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Figure 4. The total change in the winter season precipitation (a) frequency, (b) seasonal total, and (c)

Figure 4. The total change in the winter season precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity over the western United States between 1999 and 2016. The hatches indicate mean daily intensity over the western United States between 1999 and 2016. The hatches indicate where changes statistically significant. Figurethe 4. The totalare change in the winter season precipitation (a) frequency, (b) seasonal total, and (c) where the changes are statistically significant. mean daily intensity over the western United States between 1999 and 2016. The hatches indicate

3.2. Relationships to Surface Air Temperature and Atmospheric Water Vapor where the changes are statistically significant.

3.2. Relationships to Surface Air Temperature and Atmospheric Water Vapor

Figure 5 shows that southern and coastal regions have warmer temperatures and more water 3.2. Relationships to Surface Air Temperature and Atmospheric Water Vapor vapor compared the northern inland regions regions have of the western US, which and havemore coolerwater Figureas5 shows thattosouthern and coastal warmer temperatures Figure 5 and shows that southern and coastal regions warmer temperatures and more water temperatures water vapor. Coastal regions a higher temperature withcooler values of 282 to vapor as compared toless the northern inland regions ofhave thehave western US, which have temperatures vapor compared to water the northern inland regions of the western which have cooler K, asaswell as a higher vaporhave content with values ranging from values 8 toUS, 11 mm. Inland, and 285 less water vapor. Coastal regions a higher temperature with of 282 to 285surface K, as well temperaturesrange less water vapor. Coastal regions values of 282 to from 268–276 K and water vaporhave content fromInland, 5 to 8 with mm. as a temperatures higher water and vapor content with values ranging froma higher 8 ranges to 11temperature mm. surface temperatures 285 K, as well as a higher water vapor content with values ranging from 8 to 11 mm. Inland, surface range from 268–276 K and water vapor content ranges from 5 to 8 mm. temperatures range from 268–276 K and water vapor content ranges from 5 to 8 mm.

Figure 5. The mean (a) surface temperature and (b) integrated water for the winter season between 2003 and 2016. The standard deviation from the mean for the (c) surface temperature and (d) integrated water vapor. Figure 5. The mean (a) surface temperature and (b) integrated water for the winter season between Figure 5. The mean (a) surface temperature and (b) integrated water for the winter season between 2003 and 2016. The standard deviation from the mean for the (c) surface temperature and (d) 2003 and 2016. The standard deviation from the mean for the (c) surface temperature and (d) integrated integrated water vapor.

water vapor.


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The temperature and and atmospheric atmosphericwater watervapor vaporare areshown shownininFigure Figure6.6. The rate rate of change for surface air temperature

Figure 6. 6. The total change in winter winter (a) (a) surface surface air air temperature temperature and and (b) (b) atmospheric atmospheric water water vapor vapor Figure over the the western western United United States States between between 2003 2003 and and 2016. 2016. Hatching indicates where the changes changes are are over statistically significant. significant. statistically

In the the western western United United States, States, there there is is aa general general increase increase in in surface surface temperature, temperature, especially especially over over In central California where changes range from 0.06 to 0.09 K and are statistically significant. There is central California where changes range from 0.06 to 0.09 K and are statistically significant. There is an an increase in water vapor the northwest United 0.03while mm,the while the southwest increase in water vapor in theinnorthwest United States States aroundaround 0.03 mm, southwest United United States has decreased at about 0.03 mm. States has decreased at about 0.03 mm. The correlation correlation between between total total column column water water vapor vapor and and precipitation precipitation characteristics characteristics is is shown shown in in The Figure 7. 7. There There is is aa statistically statistically significant significant positive positive relationship relationship between between total total column column water water vapor vapor Figure and all precipitation characteristics in the southwestern United States. It appears that the higher and all precipitation characteristics in the southwestern United States. It appears that the higher atmospheric water watervapor vaporisisdirectly directly linked to higher precipitation frequency, and intensity atmospheric linked to higher precipitation frequency, total,total, and intensity over over the southwestern States inseasons. winter seasons. a small area in Northwest Arizona the southwestern UnitedUnited States in winter There is aThere smallisarea in Northwest Arizona (Figure 7a) (Figurea statistically 7a) where asignificant statistically significant negative relationship is total found between total column where negative relationship is found between column water vapor and water vapor and precipitation frequency. The relationship between surface airand temperature and precipitation frequency. The relationship between the surface air the temperature precipitation precipitation frequency is mostly negative over central and southern western United States, with frequency is mostly negative over central and southern western United States, with scattered areas of scattered areas of significance (Figure 8a). Over northern 45°N 50°N, ◦ W, significance (Figure 8a). Over northern Montana, between Montana, 45◦ N and between 50◦ N, and 110◦and W and 115and 110°W and 115°W, there are areas of with a significant positive relationship between temperature there are areas of with a significant positive relationship between temperature and precipitation and precipitation frequency is a small Nevada area in that northeastern Nevada significant that has a frequency (Figure 8a). There is(Figure a small8a). areaThere in northeastern has a statistically statistically significant negative relationship between temperature and precipitation total, well as negative relationship between temperature and precipitation total, as well as a small area inas Montana a small in Montana that positive has a statistically relationship (Figure 8b). that has aarea statistically significant relationship significant (Figure 8b).positive Precipitation mean daily intensity Precipitation mean daily intensity and temperature have a statistically significant positive and temperature have a statistically significant positive relationship in scattered areas of California relationship (Figure 8c). in scattered areas of California (Figure 8c). In ordertotovisualize visualize these changes a temporal scale,series timewere series were constructed for In order these changes on aon temporal scale, time constructed for different different sub-regions with regard to the overall change in winter season precipitation frequency, sub-regions with regard to the overall change in winter season precipitation frequency, seasonal seasonal mean daily as intensity, as well as the changes in total column vapor. The total, andtotal, meanand daily intensity, well as the changes in total column water vapor.water The sub-regions, sub-regions, determined by areas of statistical significance in the regression analysis, are in determined by areas of statistical significance in the regression analysis, are shown in Figureshown 1. Figure 1.

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Figure The correlation coefficient between total water season Figure7.7. 7.The The correlation coefficient between total column column water vapor and winter winter season Figure correlation coefficient between total column water vapor andvapor winter and season precipitation precipitation (b) total, and mean over the United precipitation (a) frequency, (b) seasonal seasonal total,daily and (c) (c) mean daily daily intensity over the western western United (a) frequency, (a) (b)frequency, seasonal total, and (c) mean intensity over intensity the western United States during States season from 2003 Hatching areas of States during during winter winter season fromHatching 2003 to to 2016. 2016. Hatching indicates areas ofstatistical statistical significance. significance. winter season from 2003 to 2016. indicates areasindicates of statistical significance.

Figure Figure8.8.The Thecorrelation correlationcoefficient coefficientbetween betweenthe thesurface surfacetemperature temperatureand andwinter winterseason seasonprecipitation precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity over the western United States (a) (a)frequency, frequency,(b) (b)seasonal seasonaltotal, total,and and(c) (c)mean meandaily dailyintensity intensityover overthe thewestern westernUnited UnitedStates Statesbetween between December-January-February (DJF) 2003 and 2016. Hatching indicates areas of statistical significance. December-January-February (DJF) 2003 and 2016. Hatching indicates areas of statistical significance. December-January-February (DJF) 2003 and 2016. Hatching indicates areas of statistical significance.

Figure shows the mean time series the precipitation frequency, total, and intensity, as Figure999shows showsthe themean meantime timeseries seriesofof ofthe theprecipitation precipitation frequency, total, and intensity, as well well Figure frequency, total, and intensity, as well as as the total column water vapor over each sub-region. The dashed lines show the slope of change as the total column water vapor over each sub-region. The dashed lines show the slope of change the total column water vapor over each sub-region. The dashed lines show the slope of change based on based simple linear analysis. For there based on on simple linear regression regression analysis. For precipitation precipitation frequency, there is is aa decreasing decreasing trend in in simple linear regression analysis. For precipitation frequency, frequency, there is a decreasing trend in all trend regions, all regions, and are statistically significant in Regions 3–6. Precipitation total is showing all regions, and are statistically significant in Regions 3–6. Precipitation total is showing and are statistically significant in Regions 3–6. Precipitation total is showing a statistically significantaa statistically significant decreasing trend in increasing in 11 and statisticallytrend significant decreasing trend in Regions Regions 3–6, and and1is is increasing in Regions Regions and 2. 2. decreasing in Regions 3–6, and is increasing in 3–6, Regions and 2. Interestingly, precipitation Interestingly, precipitation intensity is increasing in Regions 1 and 2 with statistical significance, Interestingly, precipitation intensity in Regions 1 anddespite 2 withprecipitation statistical significance, intensity is increasing in Regions 1 andis2 increasing with statistical significance, frequency despite precipitation frequency and total not having a strong signal in these regions. Frequency is despite precipitation frequency and total not having a strong signal in these regions. Frequency is and total not having a strong signal in these regions. Frequency is decreasing in Regions 1 and 2 while decreasing in Regions 1 and 2 while precipitation totals are increasing. Overall, Figure 9 confirms the decreasing in Regions 1 and 2 while precipitation totals are increasing. Overall, Figure 9 confirms the precipitation totals are increasing. Overall, Figure 9 confirms the results of Figure 4. results results of of Figure Figure 4. 4.


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Figure 9. The solid lines indicate the mean time series of frequency (number of days), totals (mm),

Figure 9. The solid lines indicate the mean time series of frequency (number of days), totals (mm), mean daily intensity (mm/day), and total column water vapor (mm) within the sub-regions. Dashed mean daily (mm/day), and total column water vaporfor (mm) within the sub-regions. Dashed linesintensity are the slope of change based on linear regression analysis the solid lines. Figure 9. The solid lines indicate the mean time series of frequency (number of days), totals (mm), lines are the slope of change based on linear regression analysis for the solid lines.

mean daily intensity (mm/day), and total column water vapor (mm) within the sub-regions. Dashed For the correlation results between precipitation characteristics and total column water vapor lines are the slope of change based on linear regression analysis for the solid lines. (Figure 10), we see results consistent with those shown in Figure 6. Region 4 is showing a statistically For the correlation results between precipitation characteristics and total column water vapor significant negative relationship between frequency and water vapor (Figure 10a). Region 5 is (Figure 10), results consistent with those shown in Figure 6. Region 4 ischaracteristics showing a statistically Forwe thesee between precipitation and total column water vapor showing acorrelation statisticallyresults significant positive relationshipcharacteristics between all precipitation and (Figure 10), we see results consistent with those shown in Figure 6. Region 4 is showing a statistically significant negative relationship between frequency and water vapor (Figure 10a). Region 5 is showing water vapor. Region 6 shows a statistically significant positive relationship between precipitation significant negative relationship between frequency and water vapor (Figure 10a). Region is a statistically significant between all precipitation and5 water totals and mean dailypositive intensityrelationship and water vapor. Figure 10c shows the close characteristics relationship between showing a statistically significant positive relationship between all precipitation characteristics and water vapor and aprecipitation daily intensity at relationship the inter-annual time scale, especially totals for vapor. Region 6 shows statisticallymean significant positive between precipitation and water vapor. Region 6 water shows aisstatistically positive relationship between regions 5 and 6.and Water vaporvapor. significantly correlated precipitation frequency (rprecipitation = 0.502), mean daily intensity Figure significant 10c showswith thethe close relationship between water vapor totals = 0.615) andintensity intensityand (r =water 0.670)vapor. in Region 5. In10c Region water vapor is significantly and(rmean daily Figure shows6, the close relationship between and totals precipitation mean daily intensity at the inter-annual time scale, especially for regions 5 and 6. correlated with precipitation totals (r = 0.499), and mean daily intensity (r = 0.663). water vapor and precipitation mean daily intensity at the inter-annual time scale, especially for

Water vapor is significantly correlated with the precipitation frequency (r = 0.502), totals (r = 0.615) and regions 5 and 6. Water vapor is significantly correlated with the precipitation frequency (r = 0.502), intensity (r = 0.670) in Region 5. In Region 6, water vapor is significantly correlated with precipitation totals (r = 0.615) and intensity (r = 0.670) in Region 5. In Region 6, water vapor is significantly totals (r = 0.499), and mean daily intensity (r = 0.663). correlated with precipitation totals (r = 0.499), and mean daily intensity (r = 0.663).

Figure 10. The time series of (a) frequency in days (blue), (b) totals in mm (red), and (c) mean daily intensity in mm/day (green), with total column water vapor in mm (black) within the sub-regions 4–6.

3.3. Relationship with ENSO The composites of monthly anomalies of precipitation characteristics were compiled for the Figure 10. The time series of (a) frequency in days (blue), (b) totals in mm (red), and (c) mean daily frequency, total, and intensity for months based on the Multivariate ENSO Index that indicated an El Figure 10. The time series of (a) frequency in days (b)mm totals in mm (red), and (c) mean intensity in mm/day (green), with total column water(blue), vapor in (black) within the sub-regions 4–6.daily

intensity in mm/day (green), with total column water vapor in mm (black) within the sub-regions 4–6. 3.3. Relationship with ENSO

3.3. Relationship with ENSO

The composites of monthly anomalies of precipitation characteristics were compiled for the frequency, total, and foranomalies months based on the Multivariate ENSO Indexwere that indicated El the The composites ofintensity monthly of precipitation characteristics compiledanfor

frequency, total, and intensity for months based on the Multivariate ENSO Index that indicated an


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El Niño month wellasasLaLaNiña Niñamonths. months.The TheElElNiño Niñowinters wintersduring during the the study study period period were 2003, Niño month asas well 2004, 2007, 2010, and 2016. The La Niña winters during the study period were 1999, 2000, 2001, 2008, 2004, 2007, 2010, and 2016. 2009, 2009, 2011, 2011, and and 2012. 2012. In theEl ElNiño Niñoprecipitation precipitation anomalies to the La Niña anomalies, appears In comparing the anomalies to the La Niña anomalies, therethere appears to be to be an alternating pattern of positive and negative precipitation anomalies along the west coast an alternating pattern of positive and negative precipitation anomalies along the west coast (Figure (Figure For example, the anomalies during El Niño events show frequency precipitation 11). For11). example, the anomalies during El Niño events show thatthat the the frequency of of precipitation is ◦ N–50◦ N and is higher than average along the northwest coast (approximately +1.5 days between 40 higher than average along the northwest coast (approximately +1.5 40°N–50°N and ◦ W–125◦ W), while the frequency is less than average (approximately −1 days) during La Niña 122 122°W–125°W), while the frequency is less than average (approximately −1 during La Niña events events in in the the same same region. region. In In southern southern California, California, the the precipitation precipitation frequency frequency is is higher higher than than average average ◦ N 120◦ W). Similar (approximately +1 day) for La Niña events, especially in the Los Angeles area (~35 (approximately +1 day) for La Niña events, especially in the Los Angeles area (~35°N 120°W). patterns are apparent for total precipitation: the northwest sees higher of precipitation Similar patterns are apparent for total precipitation: the coast northwest coast amounts sees higher amounts of during El Niño (~15 mm), and(~15 central and southern California seeCalifornia higher amounts of precipitation precipitation during El Niño mm), and central and southern see higher amounts of during La Niña (~9 mm). Interestingly, precipitation intensity is less than average for El Niño events, precipitation during La Niña (~9 mm). Interestingly, precipitation intensity is less than average for El but higher thanbut average during Niña events theNiña Washington areas. Specifically, western Niño events, higher than La average duringinLa events coastal in the Washington coastal areas. Washington less than normal precipitation intensity during El Niño events (−1.8 during mm/day) Specifically,experiences western Washington experiences less than normal precipitation intensity El and precipitation during La Niña events (+1.2during mm/day). However, Niñohigher eventsthan (−1.8normal mm/day) and higherintensity than normal precipitation intensity La Niña events further south around southern Oregon and around northernsouthern California, precipitation intensityCalifornia, is higher (+1.2 mm/day). However, further south Oregon and northern than normal during El Niño events (+1.8 mm/day) and lower for La Niña events ( − 1.2 mm/day). precipitation intensity is higher than normal during El Niño events (+1.8 mm/day) and lower for La There is also (−1.2 a positive anomaly intensity during El Niño events over during southern Niña events mm/day). Thereinisprecipitation also a positive anomaly in precipitation intensity El California (+1.8 mm/day) that is typical(+1.8 of this region, with the exception of the 2015–2016 El Niño [34]. Niño events over southern California mm/day) that is typical of this region, with the exception These are consistent that precipitation in the western of theresults 2015–2016 El Niño with [34]. other Thesestudies resultsthat areshow consistent with other intensity studies that show that United States intensity is higher in than Niñoisevents Despiteduring these broad patterns, precipitation theaverage westernduring UnitedElStates higher[35–37]. than average El Niño events on a gridDespite by grid basis, the patterns, relationship ENSO andthe precipitation is and not [35–37]. these broad on abetween grid by grid basis, relationshipcharacteristics between ENSO significant in characteristics any region (see 12). In in theany Pacific Northwest, is the a positive precipitation is Figure not significant region (see Figurethere 12). In Pacific correlation Northwest, between precipitation frequency andthe ENSO (r = 0.24).frequency It is possible no significant there is athe positive correlation between precipitation andthat ENSO (r = 0.24). Itrelationship is possible was found because 18 years of data is not enough to reveal the correlation between ENSO that no significant relationship was found because 18 years of data is not enough to revealand the precipitation characteristics. correlation between ENSO and precipitation characteristics.

Figure 11. 11. The The averaged averaged anomalies anomalies for for El El Niño Niño events events (top) (top) and and La La Niña Niña events events (bottom) (bottom) for for winter winter Figure season precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity. season precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity.


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Figure 12. 12. The Thecorrelation correlationcoefficient coefficient between Multivariate ENSO Index and between Multivariate ENSO IndexIndex (MEI)(MEI) ENSOENSO Index and winter winter precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity the season season precipitation (a) frequency, (b) seasonal total, and (c) mean daily intensity over the over western western United States between DJF 1999 and 2016. The hatched areas indicate areas of statistical United States between DJF 1999 and 2016. The hatched areas indicate areas of statistical significance. significance.

4. Discussion 4. Discussion Overall, the results of this research tell us that the precipitation total and frequency have been Overall, results ofthe thiswestern research tell usStates that the precipitation and frequency been decreasing in the general over United during the past 18total years (Regions 3–6).have However, decreasing in general over the western United States during the past 18 years (Regions 3–6). there has been a statistically significant increase in the mean daily intensity, specifically in western However, there has a statistically increase(Region in the mean daily intensity, in Montana (Region 1) been and central Oregonsignificant and Washington 2). This suggests thatspecifically precipitation western Montana (Region 1) and central Oregon and Washington (Region 2). This suggests that has become less frequent but more intense over Regions 1 and 2 during the study period. These findings precipitation less frequent but more intense overClimate RegionsAssessment 1 and 2 during the study are consistenthas withbecome the general trends revealed in the National Report 2014 [7]. period. These findings are consistent with the general trends revealed in the National Climate A strong positive relationship between the total column water vapor and the daily precipitation Assessment Report [7]. A strongUnited positive relationship column vapor intensity found over 2014 the southwestern States (Regions 5between and 6) isthe alsototal supported bywater the findings and the daily precipitation intensity found over the southwestern United States (Regions 5 and 6) is in other parts of the world including Europe and northern Eurasia using historical observational also supported the findings in other partsproducts of the world records, and at aby global scale using reanalysis [3–6].including Europe and northern Eurasia usingThis historical observational records, and at a global scale using reanalysis [3–6]. study has a variety of implications, particularly for Regions 1 and products 2 where the mean daily This is study a variety implications, particularly forof Regions 1 and 2days where theprecipitation mean daily intensity seenhas to have beenofincreasing while the number precipitation and intensity is been seen to have been increasing the number precipitation days precipitation totals have decreasing over the pastwhile 18 years. A more of in-depth study of thisand region’s climate totals have been decreasing over the past 18 years. A more in-depth study of this region’s climate and characteristics would be necessary to draw more specific conclusions, however, an increase in the and characteristics would be necessary to draw more specific conclusions, however, an increase in mean daily intensity could increase flood risk and soil erosion, as well as decrease the predictability the mean daily intensity could increase flood risk and soil erosion, as well as decrease the of water resources. An overall decrease in precipitation frequency and totals could cause a decline predictability of water resources. overall decrease precipitation andconsequences totals could in water availability in the westernAn United States where in water is already frequency scarce. These cause decline in water in the western UnitedofStates where water is already scarce. shouldabe examined more availability in depth to determine the breadth impact on this particular region. TheseAnother consequences should be examined more in depth to determine the breadth of impact on interesting result is the strong correlation between the atmospheric water vapor andthis the particular region. precipitation intensity in the southwestern United States (Regions 5 and 6). Since most of this region Another interesting result is precipitation the strong correlation the atmospheric vapora and is considered a desert, increased intensity between and decreased frequency water have quite few the precipitation intensity in the southwestern United States (Regions 5 and 6). Since most of this implications regarding floods, soil erosion, drought, and water resource management. region is considered desert, increased precipitation intensity andand decreased frequency haveStates quite is a Another area ofa research related to precipitation intensity the western United few implications regarding floods, soil erosion, drought, and water resource management. Atmospheric Rivers (ARs). ARs are long, narrow, water vapor rich regions of the atmosphere of vapor research related to precipitation intensity the western United States is that Another transportarea water from the tropics to the mid to highand latitudes [38,39]. Much of AR Atmospheric Rivers (ARs). ARs are long, across narrow, vapor richUnited regionsStates of the[40]. atmosphere that research focuses on precipitation patterns thewater coastal western Some studies transport water vapor from the tropics to the mid to high latitudes [38,39]. Much of AR research show that the intensity of ARs are increasing with a warming climate—potentially increasing the focuses the coastal western States [40].inSome studies show intensityon of precipitation precipitation patterns overall inacross the western United States, United more specifically California, Oregon, that the intensity of ARs are increasing with a warming climate—potentially increasing the intensity and Washington [41–43]. of precipitation overall in the western United States, more specifically in California, Oregon, and Washington [41–43].


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5. Conclusions This study examined the possible changes in three major precipitation characteristics of total, frequency, and daily intensity over the western United States during the winter season by using TRMM 3B42 version 7 gridded daily precipitation records during 1999–2016. In addition, their possible connections to atmospheric water vapor, surface air temperature, and El Nino/Southern Oscillation were explored. Precipitation frequency, total, and mean daily intensity are decreasing in Regions 3–6. In Regions 1 and 2, precipitation frequency is decreasing, while precipitation totals are increasing, causing a significant increase in the mean daily precipitation intensity during the study period. Atmospheric water vapor is strongly positively correlated with the mean daily precipitation intensity in the southwestern United States (Regions 5 and 6). In addition, there is a positive correlation between atmospheric water vapor and precipitation frequency and total over the southwestern United States (Regions 5 and 6) for the winter seasons during the study period. There is a dramatic, alternating pattern of precipitation characteristics in response to El Nino/La Nino conditions, but they are not statistically significant during the study period. Considering the short time period of the availability of TRMM and AIRS data, some of these changes may be connected to various large-scale decadal variability. In future research, a longer dataset will be more appropriate to explore these connections. Acknowledgments: This research was funded by the NASA MIRO grant #NNX15AQ06A at California State University Los Angeles and the first author is also partially supported by the Earth Sciences Division at the Jet Propulsion Laboratory in Pasadena, CA. Part of the work described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Author Contributions: Deanna Nash and Hengchun Ye conceived and designed the experiments; Deanna Nash performed the experiments; Deanna Nash and Hengchun Ye analyzed the data; Deanna Nash wrote the paper; Hengchun Ye and Eric Fetzer assisted in the preparation of the manuscript. Conflicts of Interest: The authors declare no conflicts of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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