THE INFLUENCE OF ATMOSPHERIC RIVERS ON EXTREME PRECIPITATION IN THE

CONTINENTAL UNITED STATES

By

Christian Landry

B.S., Texas A&M University, 2018

A Thesis Submitted in Partial Fulfillment of the Requirements for the Master of Science Degree

School of Earth Systems and Sustainability in the Graduate School Southern Illinois University Carbondale December 2020

THESIS APPROVAL

THE INFLUENCE OF ATMOSPHERIC RIVERS ON EXTREME PRECIPITATION IN THE

CONTINENTAL UNITED STATES

By

Christian Landry

A Thesis Submitted in Partial

Fulfillment of the Requirements

for the Degree of

Master of Science

in the field of Geography and Environmental Resources

Approved by:

Dr. Justin Schoof, Chair

Dr. Trent Ford

Dr. Jonathan Remo

Graduate School Southern Illinois University Carbondale October 20, 2020

AN ABSTRACT OF THE THESIS OF

Christian Landry, for the Master of Science degree in Geography and Environmental Resources, presented on October 6, 2020, at Southern Illinois University Carbondale.

TITLE: THE INFLUENCE OF ATMOSPHERIC RIVERS ON EXTREME PRECIPITATION

IN THE CONTINENTAL UNITED STATES

MAJOR PROFESSOR: Dr. Justin Schoof

The purpose of this study was to evaluate the influence of horizontal moisture fluxes from Atmospheric Rivers (ARs) on extreme precipitation (EP) events in the continental United

States (CONUS). Climatological results for both EP, objectively defined using a peaks-over- threshold and block maxima approach, and ARs were processed and analyzed for co-occurrence.

EP analyses produced a positive linear trend in magnitude, determined through the block maxima approach, in the Central US and a positive linear trend in frequency, determined by the peaks- over-threshold approach, predominantly for the Northern half of the CONUS. AR results show over 70 AR days throughout the country, and a linear trend of 10 less days per decade in the

Central US. Results of the co-occurrence analysis suggest an increasing trend of about one instance of co-occurrence per decade throughout much of the Eastern Coast, Midwest and Pacific

Northwest, with a corresponding negative linear trend of about one instance of co-occurrence per decade for much of the Southwest US to Louisiana. Throughout the world, the study of EP, and the careful analysis of its behavior, and possible amplification sources such as ARs, at the national and regional scale is imperative to obtain a comprehensive understanding of hydrometeorological impacts.

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TABLE OF CONTENTS

CHAPTER PAGE

ABSTRACT ...... i

LIST OF FIGURES ...... iii

CHAPTERS

CHAPTER 1 – Introduction...... 1

CHAPTER 2 – Literature Review ...... 5

CHAPTER 3 – Data and Methodology ...... 13

CHAPTER 4 – Results...... 20

CHAPTER 5 – Discussion ...... 59

CHAPTER 6 – Conclusion ...... 62

REFERENCES ...... 65

APPENDIX ...... 72

VITA ...... 81

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LIST OF FIGURES

FIGURE PAGE

Figure 1 – Average Annual Precipitation ...... 3

Figure 2 – Sample Instance of Atmospheric River ...... 9

Figure 3 – Demonstration of Extreme Precipitation Identification Methods ...... 16

Figure 4 – Average Annual Block Maxima Precipitation ...... 22

Figure 5 – Coefficient of Variation of Annual Block Maxima Precipitation ...... 22

Figure 6 – Linear Trend of Annual Block Maxima Precipitation ...... 24

Figure 7 – Seasonal Average Block Maxima Precipitation ...... 26

Figure 8 – Seasonal Coefficient of Variation of Block Maxima Precipitation ...... 27

Figure 9 – Linear Trend of Seasonal Block Maxima Precipitation ...... 28

Figure 10 – Average Annual Number of PoT Events for 15, 25, and 50 mm/day ...... 29

Figure 11 – Coefficient of Variation of Annual PoT Events for 15, 25, and 50 mm/day ...... 30

Figure 12 – Linear Trend of Annual PoT Events for 15, 25, and 50 mm/day ...... 31

Figure 13 – Seasonal Average Number of Daily 15 mm/day Exceedances ...... 33

Figure 14 – Seasonal Average Number of Daily 25 mm/day Exceedances ...... 34

Figure 15 – Seasonal Average Number of Daily 50 mm/day Exceedances ...... 34

Figure 16 – Seasonal Coefficient of Variation of Daily 15 mm/day Exceedances ...... 35

Figure 17 – Seasonal Coefficient of Variation of Daily 25 mm/day Exceedances ...... 36

Figure 18 – Seasonal Coefficient of Variation of Daily 50 mm/day Exceedances ...... 36

Figure 19 – Seasonal Linear Trend of Daily 15 mm/day Exceedances ...... 37

Figure 20 – Seasonal Linear Trend of Daily 25 mm/day Exceedances ...... 38

Figure 21 – Seasonal Linear Trend of Daily 50 mm/day Exceedances ...... 38

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Figure 22 – Annual Average AR Frequency ...... 40

Figure 23 – Coefficient of Variation of Annual AR frequency ...... 41

Figure 24 – Linear Trend in Annual AR Frequency ...... 41

Figure 25 – Seasonal Average AR Frequency ...... 43

Figure 26 – Seasonal Coefficient of Variation of AR Frequency ...... 43

Figure 27 – Seasonal Linear Trend of AR Frequency ...... 44

Figure 28 – Percentage of Annual Block Maxima Precipitation Co-occurring ARs ...... 45

Figure 29 – 3-Panel of Co-occurrence Statistics Using 15 mm/day EP Classification ...... 46

Figure 30 – 3-Panel of Co-occurrence Statistics Using 25 mm/day EP Classification ...... 46

Figure 31 – 3-Panel of Co-occurrence Statistics Using 50 mm/day EP Classification ...... 48

Figure 32 – Average Percent of Co-occurrences of AR and Precipitation ...... 49

Figure 33 – 3-Panel of Average Percent of Co-occurrence ...... 49

Figure 34 – 4-Panel of Percentage of Co-occurrences...... 50

Figure 35 – 4-Panel of Seasonal Average Number of Co-occurrences (15 mm/day) ...... 51

Figure 36 – 4-Panel of Seasonal Coefficient of Variation of Co-occurrences (15 mm/day) ...... 52

Figure 37 – 4-Panel of Seasonal Linear Trend of Co-occurrences (15 mm/day) ...... 52

Figure 38 – 4-Panel of Seasonal Average Number of Co-occurrences (25 mm/day) ...... 53

Figure 39 – 4-Panel of Seasonal Coefficient of Variation of Co-occurrences (25 mm/day) ...... 54

Figure 40 – 4-Panel of Seasonal Linear Trend of Co-occurrences (25 mm/day) ...... 55

Figure 41 – 4-Panel of Seasonal Average Number of Co-occurrences (50 mm/day) ...... 57

Figure 42 – 4-Panel of Seasonal Coefficient of Variation of Co-occurrences (50 mm/day) ...... 57

Figure 43 – 4-Panel of Seasonal Linear Trend of Co-occurrences (50 mm/day) ...... 58

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CHAPTER 1

INTRODUCTION

Observations, theory, and modeling all indicate increases in extreme precipitation (EP) in North America (Kirchmeier-Young and Zhang, 2020; Zobel, 2018). Understanding historic changes in EP events and their future evolution is paramount for prosperity (Ralph, 2011). EP events resulting in flooding alone were responsible for over $85 Billion in 2014 (Smith and

Matthews, 2015). Records show an increasing trend thereafter with a record-breaking season in

2017 that included EP events such as Hurricane Harvey totaling approximately $125 Billion

(NCEI, 2018) in damages alone. EP events can result in catastrophic loss of agricultural assets, cause damage to property and loss of life (Janssen, 2013). The definition of EP also introduces considerable complexity, as “extreme” is characterized by three distinct aspects: magnitude, timescale, and spatial scale (Barlow et al., 2019). Variations in these characteristics may result in different associated EP impacts such as flash flooding, for high temporal intensity, riverine flooding, expected with high magnitude events, and agricultural or water resource management impacts, which would be a consideration regarding the spatial footprint of an extreme precipitation event (Barlow, et al., 2019). EP events have been studied in a variety of ways such as examining maxima within fixed time periods (block maxima approach) or exceedances above absolute or percentile-based thresholds (peaks-over-threshold approach). Innate complexity is also expected regarding the interactions between EP, and its mechanisms, with geographical features associated with topography. For example, differences in elevation over short distances can lead to dramatic changes in precipitation distributions due to the interaction between topography and atmospheric flows (Gao et al. 2013).

Extreme precipitation is typically the result of a few specific factors, such as a lifting

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mechanism (often a synoptic-scale low pressure system), a source of atmospheric moisture to provide ample amounts of “fuel” in the form of latent heating (often poleward moisture transport within an approaching synoptic-scale low pressure system), and perhaps some upstream or downstream blocking phenomenon to halt the advancement of a precipitating system and transform a mild rain event into an EP event. As the world warms, the amount of moisture in the air will increase, resulting in changes in moisture transport and convergence, along with associated changes in EP.

Currently, as much as 90% (Trenberth, 2011) of the poleward atmospheric moisture transport occurs in atmospheric rivers (ARs), long filamentary structures of concentrated tropospheric water vapor (Newell et al. 1992), which have been linked to extreme precipitation trends in the Western US (Lavers, 2013). ARs, and their associated moisture fluxes, are also frequent in other US regions, but differ from those in the West in terms of orientation, magnitude, and persistence.

The spatial distribution of annual precipitation in the contiguous United States is consistent with strong continentality effects (Figure 1). However, differences in AR characteristics in Western and Eastern US environments raise questions about the relationship between ARs and EP events in other US regions. This is especially true in regions like the

Midwest, where the Great Plains low-level jet (GPLLJ) is often associated with low-level moisture transport. However, as noted by Gimeno et al. (2016), ARs are always associated with strong winds at low levels, whereas low-level jets are positioned ahead of the maximum moisture flux. For that reason, distinction is made here between ARs and other co-occurring potential transport mechanisms.

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Figure 1. Average nnual precipitation (mm) derived from nClimGrid-D, 1979-2017.

The purpose of this study is to evaluate the role of horizontal moisture fluxes from ARs on changes in EP events throughout the continental US. This is accomplished through a set of analyses asking and answering the following questions:

1) Is extreme precipitation changing in the continental US?

To answer this question, a newly developed, homogenized, gridded, high resolution data

set (nClimGrid-D) is used to establish annual and seasonal EP climatologies and conduct

trend analyses for annual and seasonal EP events identified using the block maxima and

peaks-over-threshold approaches.

2) Is AR frequency changing in the continental US?

To answer this question, an algorithm was developed to identify ARs in 6-hourly

atmospheric reanalysis data. Once identified, the ARs are subjected to a trend analysis to

identify regions where they may be contributing to increases in EP event frequency or

magnitude.

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3) Are observed AR changes contributing to observed EP changes?

To answer this question, a “co-occurrence analysis” is undertaken to assess the frequency

of co-occurring AR presence and EP occurrence. The frequency of co-occurrences is

considered in terms of annual and seasonal climatologies as well as trends.

Understanding the relationship between ARs and EP events across the diverse climates of the continental US, including their seasonal variability, will provide key information regarding recent and potential future impacts across multiple sectors. Since ARs can often be forecasted in advance, understanding their role in EP events could also improve lead times on EP forecasts.

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CHAPTER 2

LITERATURE REVIEW

This chapter reviews the existing literature on extreme precipitation (EP; Section 2.1) and atmospheric rivers (ARs; Section 2.2). As shown, many studies have considered these phenomena separately, but few have considered interactions between them. Among studies considering interactions, comparisons are difficult due to varying approaches to identify EP events and ARs. For example, Lavers and Villarini (2013) compared and analyzed ARs in the context of flood events, which are not likely to be as objectively defined as EP events.

2.1 Extreme Precipitation

Extreme precipitation (EP) events have been one of the top economic impactors for society throughout history. EP events are dynamic, are influenced by a variety of factors (Kunkel et al., 2013), and their impact remains costly. These impacts are the motivation for research in extreme precipitation; to improve detection, analysis, and understanding of precipitation extremes. Within extreme precipitation, inland flooding events are the most common and most expensive events followed by tropical cyclones and snowmelt events (NCEI, 2019). It has also been documented that an increase in the proportion of the area of the US affected by extreme precipitation is expected, with around 10% of US land surface affected by extreme precipitation and a linear regression analysis suggesting further increase (Karl & Knight, 1998). The above examples illustrate that flooding from excessive precipitation is a natural hazard for many parts of North America.

In practice, EP has been classified in variety of methods ranging from block maxima, thresholds to station specific thresholds and percentile-based classifications (Janssen, 2013;

Kunkel, 2012), each with their advantages and disadvantages. The block maxima approach, for

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example, considers the maximum event within a block of time (e.g., a year or season) and then considers the distribution of those extreme values across all blocks. Threshold-based approaches, on the other hand, provide information about the frequency of events within a block of time based on a pre-defined threshold that can be defined locally or globally. Specifications of the time scale of extreme precipitation also need to be set. Because daily data is common, most studies have focused on daily total precipitation (e.g., Allen, 2008; Anderson, 2015; Griffiths,

2007; Kunkel, 2013; Rajeevan, 2008; Todd, 2006), although there is also tremendous interest in sub-daily extremes.

Extreme precipitation is generally identified as the most intense, least frequent, precipitation events for a given region, location, or station, ideally including the entire spectrum of precipitation types as well. Many studies have found a statistically significant increase in the number and intensity of extreme precipitation events of durations ranging from hourly to a few days (Karl et al. 1996; Karl and Knight 1998; Groisman et al. 2004, 2005, 2012; Kunkel et al.

2003). As expected from theory, extreme precipitation has been increasing more rapidly than total precipitation as a consequence of large-scale warming (Pendergrass, 2019). A recent study

(Janssen et al. 2014) indicate positive increases in EP for the continental US as a whole, with rapid recent increases in the Northeast region (linked to both natural and anthropogenic forcing by Griffiths (2012)), positive trends in most of the eastern US, and weaker or negative trends for most areas in the western US. In parts of the central US, EP accounts for as much as 70% of total precipitation (Groisman 2012). In the Northwest region, for example, positive changes in EP are reported but they decrease rapidly toward lower latitudes (Janssen et al., 2014). Studies have linked some EP events in this region to the influence of ARs. Todd (2006) demonstrated that at small scales there is a preferential positive trend in EP in urbanized areas.

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The frequency of extreme precipitation has increased the most in summer and autumn, yet the intensity of extreme precipitation events trended most positively during winter and spring

(Karl & Knight, 1998). Some of the reported differences are due to differences in regional climatology that include differences in mechanisms producing precipitation extremes. For example, midlatitude cyclones contain and transport massive quantities of water vapor (Brubaker et al. 1994) and represent one of the most prevalent EP contributors for US regions east of the

Rocky Mountains.

Daily precipitation records are available for US locations back to the early 20th century.

However, there are large variations in the quality of these data due to changes in location, instrumentation, and other issues. Variations in the availability of long-term high-quality precipitation series has made comparison of regional studies difficult. The newly released nClimGrid-D data product from NOAA NCEI (see Section 3.2) is a high-resolution (4 km) gridded precipitation product that provides daily values of precipitation. Importantly, these data have been carefully checked for inhomogeneities and are appropriate for assessment of trends.

2.2 Atmospheric Rivers

An atmospheric river (AR) is defined to be a long filamentary structure of tropospheric water vapor (Newell 1992). Typically, ARs have lengths many times their widths and can persist for days while being translated through the atmosphere (Newell, 1992). An example AR, from

April 7, 2017 00:00 UTC is shown in Figure 2. ARs can be identified through the filamentary structure of the river itself, with the vertically integrated water vapor at a 2.5 cm liquid water equivalent and a flow of about 10 meters per second (Liepert, 2013). Partition of high intensity water vapor transport events is important in the identification of ARs, and the primary method to do so is the filamentary structure of the river itself (Zhu & Newell, 1998). ARs have been

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defined in some studies as an anomalously high, water vapor flux located within extratropical cyclones (Eiras-Barca et al., 2018). Certain patterns have also been discussed related to ARs that assist in the identification of such events, for example in the Southern hemisphere ARs are highly related and correlated with long-wave circulations found in the geopotential height at

1000 hectopascals (hPa). There is also a general tendency for ARs to flow eastward and poleward, a pattern which is often associated with AR-related precipitation events (Ralph, 2017) and reflected by the AR depicted in Figure 2. ARs are typically sourced and supplied in tropical regions, transport poleward and can lead to extreme precipitation when influenced by topography, lower tropospheric boundaries, or warm conveyor belt-related isentropic upward air motion.

The role of the orientation of an AR in relation to topography has been integral in the effects on flooding (Gimeno et al., 2016). This poleward motion of ARs has been quantified and estimates now show over 90% of all poleward water vapor transport in the extra-tropics are due to ARs. It has been suggested to move away from a more traditional definition of ARs being based upon the shape and structure, to one of more accuracy being all episodic poleward-moving moisture plumes (Gimeno et al., 2016). This definition would remove some of the limitations upon identification as well as skepticism to the widely accepted paradigm of the structural definition (Dacre, 2015; Newman, 2012).

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Figure 2. Sample instance of an atmospheric river (AR): April 7, 2017 00:00 UTC: a) integrated 푘𝑖푙표𝑔푟푎푚 vapor transport, or IVT ( ) and b) atmospheric river derived from ERA-Interim (푚푒푡푒푟)(푠푒푐표푛푑) reanalysis data as described in Chapter 3.

Source regions of ARs are primarily in the tropics and subtropics, particularly 20°N to

40°N, over primarily marine environments such as the Pacific Ocean and even warm water regions of Europe such as Mediterranean (Sodemann, 2013). The typical source region for ARs making landfall and impacting the US is the subtropical Pacific. Furthermore, anomalously warm sea surface temperatures in this source region have been found to correlate to increased precipitation trend in their respective AR landfalling regions (Sodemann, 2013). Another source region for US landfalling ARs has been the Caribbean Sea and Yucatan Peninsula. ARs making landfall from either direction are understood to have the capacity to persist into the Central US

(Miller et al., 2018).

Globally, seasonality is also known to play a role in AR dynamics, with a prominent winter and spring presence and less activity in the summer and fall. Intense ARs with a particular meteorological relevance in terms of precipitation, are more frequent in winter. Although integrated water vapor is greater during summer, vapor fluxes are stronger in the wintertime due to stronger flows associated with extratropical cyclones (Gimeno et al., 2016). In a similar

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analysis from 1979-2011, Lavers et al. (2013) found a total of 451 ARs in the Central US

(defined as 80 degrees to 100 degrees West and 40.35 degrees North) with 143 in winter, 144 in spring, 40 in summer, and 124 in autumn. This outlines the need for seasonal analyses of ARs and their associated impacts on EP events.

ARs can transport large quantities of vertically integrated water vapor but can also augment other precipitation catalysts such as topography and mid-latitude cyclones and their associated cold fronts. Compositionally, an AR can be dissected through detection and evaluation of trace constituents, such as ozone which was used in the initial identification of ARs by Newell (1992) and Nieman (2008). This traditional classification and identification of AR regions of genesis has been challenged by recent studies utilizing Lagrangian methods (e.g.

Gimeno et al., 2016) that indicate an important role of atmospheric dynamics as well as the origin of moisture being found anomalously from tropical and subtropical areas between the

Caribbean Sea and Northern Africa. ARs approaching the West Coast of the US are undoubtedly associated with extreme precipitation in the West (Lamjiri, 2017), particularly in the winter months. However, the ARs approaching the US from the Gulf of Mexico and Caribbean serve as a stark contrast with a strong presence in spring and summer. To understand precipitation extremes, a detailed knowledge of evaporative moisture sources and the relation to atmospheric water vapor transport is necessary (Trenberth, 2011).

Detection of ARs has been previously aimed at the two-dimensional, filamentary structure of warm low-level air masses with elevated concentrations of total column water vapor utilizing satellite imagery (Dacre, 2014). These filamentary structures are routinely used as proxies for identifying regions of strong water vapor transport. A common set up for AR influenced precipitation events is the presence of the filamentary structure providing increased

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moisture influx to a prefrontal region associated with a mid-latitude cyclone. These mesoscale atmospheric setups have proven to be quite conducive of extreme precipitation and associated flooding (Dacre, 2014). However, it is important to also differentiate between an AR which provides strong influx of high atmospheric water vapor concentrations, idealized as potential energy for a precipitation event, from a catalyst event which serves as the mechanism of formation for precipitation events. Typical catalyst systems have been identified as orographic influence, and frontal passages from mid-latitude cyclones. Mid-latitude cyclones as well as tropical cyclones have shown a positive response in extreme precipitation with the influence of an atmospheric river (Sodemann, 2013), ergo, ARs in the presence of catalyst systems have proven to be more conducive of intense precipitation. Furthermore, AR influence can persist through the duration of multiple passing mid-latitude cyclones and can contain enough vertically integrated water vapor to amplify precipitation extremes for all the influenced mid-latitude cyclones.

The most prevalent alternative atmospheric moisture transport system besides that of

Atmospheric Rivers is the Great Plains Low Level Jet (GPLLJ). The GPLLJ is a fast-flowing southerly airstream located in the lower troposphere and is one of the most important atmospheric phenomena that influence the central US (Tang, 2017). This maximum moisture flux is also often associated with that of the AR, however, as found by Gimeno et al. (2016), ARs are always associated with strong winds at low levels, whereas low-level jets are positioned ahead of the maximum moisture flux. This distinction allows for the safe discrimination between these two atmospheric phenomena.

As discussed previously MLCs also represent another atmospheric vapor transport that is quite prominent in the Eastern half of the CONUS. These large-scale rotating atmospheric

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regions of water vapor can transport and deposit large amounts of precipitable water across

Central, Midwest, and Southern US. These atmospheric phenomena, unlike AR are self- precipitating, serving as a catalyst for moisture in transit through divergence aloft. Associated frontal boundaries also serve as a catalyzing mechanism of precipitation across the Southern

Plains. Frontal boundaries are defined as a boundary between air masses, typically cold and warm, and in the case of more intense precipitation, cold air pushing onto and cutting underneath warmer air. This is denoted as a cold front with a minimum gradient of 6 degrees Celsius over

500km (Gimeno et al., 2016). This difference of temperature gives a rising motion to air and can catalyze precipitation events. Frontal boundaries as well as MLCs can interact with and amplify coincidental precipitation events, as well as provide a precipitation outlet to moisture sourced by transports such as an AR.

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CHAPTER 3

DATA AND METHODOLOGY

3.1 Introduction

To meet the stated research objectives, several types of data and methods were utilized.

The methods can be divided into three subgroups focused on characterization and analysis of (1) extreme precipitation, (2) atmospheric rivers, and (3) co-occurrence of extreme precipitation and atmospheric rivers. Following a description of the types of data utilized in this thesis, the methods associated with each subgroup are detailed.

3.2 Data

3.2.1 Precipitation Data

The climatological precipitation data used in this study has been obtained through the

NOAA National Centers for Environmental Information (NCEI). Specifically, a new 5km, daily gridded dataset (nClimGrid-D) based on the US climate divisions is utilized. Unlike other high- resolution gridded products, nClimGrid-D is homogenized (accounting for station moves, changes in instrumentation, etc.) and is therefore ideal for estimating multi-decadal trends. This data is monitored and improved through additional station networks (e.g., the monthly and daily

Global Historical Climatology Network data) for quality assurance reviews, and time and temperature bias adjustments. However, some error is to be expected when considering gridded data as opposed to station data as error is introduced through the processes of interpolation.

Nevertheless, given relatively high spatial resolution, sufficient temporal coverage, and data homogenization, nClimGrid-D provides the best available option for precipitation data for this analysis.

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3.2.2 Reanalysis Data

Reanalysis data are generated by an atmospheric model that assimilates observations from a myriad platform, providing spatially and serially complete representations of many atmospheric variables. In places where observations are plentiful reanalyses tend to perform well, while the products need to be used more carefully in data sparse regions. Throughout the study region, there are radiosonde sites that provide important inputs for reanalyses.

Furthermore, tremendous strides have been made in the ability to detect atmospheric moisture from satellites, thus improving moisture fields in reanalysis data substantially (Trenberth, 2011).

The primary use of reanalysis data in this thesis was identification of atmospheric rivers

(ARs). For this purpose, the European Center for Medium-Range Weather Forecasting

(ECMWF) ERA-Interim reanalysis (Dee et al. 2011; hereafter ERA-I) was used. The AR identification algorithm (see Section 3.4) requires multiple variables from atmospheric levels ranging from just above the surface to the mid-upper troposphere. The variables included in this analysis are specific humidity, and zonal (u) and meridional (v) components of wind. Pressure levels include 1000 mb to 750 mb at 25 mb intrevals, and 750 mb to 300 mb at 50 mb intrevals.

ERA-I data are available at approximately 80 km resolution at 6-hr intervals beginning in 1979.

The ER and AR analyses therefore cover the period from 1979 to 2017.

3.3 Identification and Analysis of Extreme Precipitation (EP) Events

Extreme precipitation (EP) is generally defined as the most intense, least frequent, precipitation event for a given location or region, where all precipitation types are considered in terms of their liquid water equivalent. Given this general definition, EP can be quantified in a number of different ways. The most common approaches involve extreme value theory (EVT) which focuses on the statistical characteristics of the most extreme events. The extreme events

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are typically identified using one of two approaches: block maxima (BM) or peaks-over- threshold (PoT), which are described in more detail below. The advantages of methods based on extreme value theory include providing information in a form useful for decision and policy makers (Kunkel et al., 2013).

The block maxima (BM) approach for analyzing extremes simply considers the largest event within a block of time (Figure 3). Statistical analysis can then be performed on the collection of block maxima which form a time series. The advantage of the BM approach is ease of application and focus on the most extreme events. The disadvantage is that if two or more large events occur within a block, the BM approach will only identify the largest event. In this study, the block maximum is identified for each year, and for each climatological season, at each nClimGrid-D data point in the contiguous United States.

As the name suggests, the peaks-over-threshold (PoT) approach identifies extreme events by counting events that exceed pre-specified thresholds, which can be defined in several ways. The most common are absolute thresholds, but studies have also considered absolute or percentile-based thresholds that are station-specific. In this study, there is interest in exploring both temporal and spatial aspects of extreme precipitation, leading to consideration of several daily absolute thresholds: 15 mm/day, 25 mm/day, and 50 mm/day. As with the BM approach, separate analyses were conducted for the full calendar year and for each climatological season.

An example for 2017 is provided in Figure 3 and shows five daily totals in exceedance of the 15 mm/day threshold, two exceedances for the 25 mm/day threshold and no exceedances for the 50 mm/day threshold.

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Figure 3. Demonstration of extreme precipitation (EP) identification methods, including the block maxima (BM) and peaks-over-threshold (PoT) approaches. The precipitation data are from the nClimGrid-D grid point at 103.85° W, 32.86° N during 2017.

The seasonal analyses outlined above are a critical component of the study. Extreme precipitation exhibits seasonality at most US locations. Whether using block maxima or peaks- over-threshold methods, seasonality must be considered as the processes controlling North

American EP vary accordingly (Barlow et al., 2019). Previous studies have indicated that ARs exhibit seasonality with a winter maximum (see Chapter 2). Seasonality is also present in other atmospheric drivers of EP, such as the GPLLJ, which is most prevalent during the spring and summer. While this study is primarily concerned with the relationship between ARs and EP, variations in the results across seasons may provide information about other physical drivers.

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Using both the BM and PoT approaches provides a novel and more comprehensive representation of EP than that of each approach used in isolation, primarily because it embodies information about both magnitude and frequency. Both approaches were applied to nClimGrid-D data at the level of individual grid cells, both annually and seasonally, for the period of 1979-

2017. The resulting series are then examined in terms of means, coefficient of variation (CV) chosen to represent interannual variability controlling the mean, and temporal trends derived using ordinary least squares (OLS). Analysis of statistical significance is conducted with consideration of autocorrelation in the series with an alpha value of 0.1 chosen. Maps containing all trend values, not just significant ones, will be included in the Appendix. For each EP event, the date of the event was also record for subsequent analysis.

3.4 Identification and Analysis of Atmospheric Rivers

As ARs are regions of elevated atmospheric moisture content, humidity is a key variable for AR identification. Identification of ARs begins with the calculation of integrated vapor transport (IVT) (Eq. 1), a measure that combines specific humidity (q) with information from the wind field (u, v) to produce a flux. Integration from near the surface (1000 mb) to 300 mb (IVT300, hereafter IVT) then provides a measure of moisture transport by the prevailing atmospheric circulation across vertical levels.

2 2 1 300ℎ푃푎 1 300ℎ푃푎 (1) √ 퐼푉푇300 = ( ∫ 푞 ∗ 푢 푑푝) + ( ∫ 푞 ∗ 푣 푑푝) 푔 1000ℎ푃푎 푔 1000ℎ푃푎 Equation 1 has been used in a large number of previous studies evaluating ARs (Lavers et al.,

2013; Eiras-Barca et al., 2018; Zhu and Newell, 1998; Gimeno et al., 2014; Lorente-Plazas et al.,

2018; Mundhenk et al., 2016; Nash et al., 2018; Newell et al., 1992; Rivera et al., 2014) as the basis for AR identification algorithms. Different identification techniques were considered,

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however not implemented, opting for consistency with previous studies’ identification methodologies.

The initial criteria for presence of an AR is an IVT value exceeding the locally defined

85th percentile value of IVT (Lavers and Villarini, 2013). However, ARs must also exhibit a filamentary structure, so two additional requirements are imposed: (1) the region exceeding the

85th percentile IVT value must have a length scale of at least 30 grid points, corresponding to a distance of approximately 2500 km and (2) the region exceeding the 85th percentile IVT value must have a length that is at least twice the average width, following Guan and Waliser (2015).

The AR identification algorithm described above was applied to 6-hr output from ERI-I for the period of 1979-2017. If at least one AR instance was recorded for a given day, that day was recorded as an AR day. As with the analysis of EP events, ARs are summarized in terms of the average number of AR days, the CV of days, and the trend in occurrences with separate analyses conducted for the full calendar year and individual climatological seasons. Analysis of statistical significance is conducted with consideration of autocorrelation in the series with an alpha value of 0.1 chosen.

3.5 Identification and Analysis of EP/AR Co-occurrence

To link the analyses described in the previous sections, the co-occurrence of EP events and ARs was explored across EP classifications and seasons. Implications of co-occurrences were drawn under the assumption that co-occurrence inferred association and possible augmentation. Because the ERA-I data is provided on an 80-km grid, while nClimGrid-D is provided on a 5-km grid, the ERA-I data was re-gridded to the nClimGrid-D resolution by simply using the ERA-I value from the nearest grid point. This method preserves, to the best of our ability, the spatial representation of the ERA-Interim AR climatology, while also making it

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possible to explore co-occurrence of ARs and EP events. Since the ERA-I data and corresponding AR files are 6-hr, but the nClimGrid-D data are daily values, if at one or more time steps in the day an AR was present on the day of an EP event for a given grid point, it was recorded as an co-occurrence.

As with EP and AR events, it is possible to count the number of events per season or year. A similar approach is therefore used in which the average, coefficient of variation (as an indicator dispersion about the mean) and the temporal trend in co-occurrences are considered.

Combined with the analysis of ARs, this analysis is aimed at addressing the final research question, “Are observed AR changes contributing to observed EP changes?”. The average number of EP/AR co-occurrences per year will indicate the frequency of such events, while CV of EP/AR co-occurrences will indicate the interannual variability of days of co-occurrence.

Finally, the trend analysis of EP/AR co-occurrences will provide a look into how changes in AR frequency are related to changes in EP frequency. Analysis of statistical significance is conducted with consideration of autocorrelation in the series with an alpha value of 0.1 chosen.

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CHAPTER 4

RESULTS

4.1 Introduction

This chapter presents the results of the analyses of extreme precipitation (EP) events, atmospheric rivers (ARs) and their co-occurrence. As described in Chapter 3, the analysis of EP events includes two approaches, block maxima (BM) and peaks-over-threshold (PoT). Within both approaches, a climatology is first established by examining the mean and coefficient of variation (representing interannual variability) of the number of EP events by grid point.

Changes in EP were then assessed using a trend analysis. These findings are presented in Section

4.2. A similar approach is adopted for analysis of ARs in Section 4.3 in which a climatology is established by exploring the mean coefficient of variation of occurrences, followed by a trend analysis to indicate where AR occurrence is becoming more, or less common. Finally, in Section

4.3, an analysis of the co-occurrence of ARs and Eps is presented with the goal of determining whether changes in EP frequency may be attributable to changes in AR frequency. All analyses are conducted at the annual timescale and for each climatological season.

4.2 Extreme Precipitation (EP) Analysis

The extreme precipitation (EP) climatology analysis is presented the two sections corresponding to the two EP classification methods used for this study: block maxima (BM) and peaks-over-threshold (PoT) approaches.

4.2.1 Block Maxima (BM) Extreme Precipitation (EP) Climatology:

By definition, the EP events identified by the BM approach represent the most extreme case of the given grid cell over a given time period. The annual average BM value is shown in

Figure 4 and indicates considerable spatial variability in EP. The West experiences relatively low

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magnitude EP events, with values not exceeding 50 millimeters, except for the Pacific West, on the West side of the Sierra and Cascade Mountain ranges. A stark “rain shadow” boundary can be seen along the mountainous boundary with values exceeding 100-150 mm. In the Midwest region, a gradient can be found from North to South, with higher values in the southern regions.

The values of the Midwestern region range from about 50-100 millimeters of precipitation. The

Southeast US demonstrates the most uniformly high magnitude of precipitation, with a peak along the Louisiana coastline, stretching into parts of Eastern Texas and Mississippi/Alabama coastline. The East Coast, like the Midwest US, demonstrates more uniform values, but with a slight gradient from West to East approaching the coast. Values here center about 100 millimeters again with little variation from North to South. The highest values for the CONUS include the Southeast US and the West Coast with the smallest values located in the Western

United States East of the Coast, reflecting the underlying precipitation climatology.

The interannual variability of annual BM precipitation shown in Figure 5 displays the coefficient of variation of the magnitude of block maxima precipitation events. Much of the

CONUS shows a relatively low, about 0.2, CV throughout which demonstrates that the annual

BM has low interannual variability and represents the region having precise estimations. East of the Rockies, small pockets or streaks of higher CV values, up to 0.5, is observed. This is to be expected as these regions are impacted by mesoscale and synoptic scale atmospheric systems, with heavy precipitation dependence upon low pressure centers spun up in the lee of the Rocky

Mountains (Schultz and Doswell III, 2000).

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Figure 4. Mean Annual Block Maxima Precipitation, derived from nClimGrid-D daily precipitation (mm), 1979-2017

Figure 5. CV of Annual Block Maxima Precipitation, derived from nClimGrid-D daily precipitation (ratio), 1979-2017

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This finding agrees with previous studies (e.g. Todd et al. 2006), which found that the

Midwest region demonstrated significant interannual variability, with Illinois having the highest values of the region. The trend of higher CV streaks along the Southeast could be representative of tropical cyclone influence in those regions where the heaviest precipitation event could range from a strong afternoon shower to extreme EP associated with tropical systems. The highest

CVs, values of 0.5, include the streaks scattered about the CONUS, with highest values located along the coast of North Carolina. This spatial trend is affirmed by previous studies (Groisman and Easterling, 1993) with some of the highest values of standard deviation appearing along the

Gulf Coast.

The trend in EP characterized using the BM approach (presented in mm/decade; Figure

6) produces a noisy spatial pattern, which could be indicative of multiple processes affecting the trend. In general, in the Western US, significant trends tend to be negative (lower annual BM

EP) while those in the eastern US tend to be positive. These findings agree with Janssen et al.

(2014) who also identified a significant negative trend in the Western states using a different period of analysis. These findings are in contrast with those concluding that EP trends in the

Western US are not statistically significant (Kunkel et al., 2013). Todd et al. (2006) used in situ precipitation data from over 100 years and 100-year 1-hour, 6-hour, 12-hour, and 24-hour precipitation found increasing trends in the majority of all stations in Illinois, Indiana, Kentucky and Ohio.

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Figure 6. Linear trend in annual block maxima precipitation based on nClimGrid-D precipitation, 1979-2017 (mm per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series.

Barlow et al. 2019 found that approximately 75% of stations are experiencing an increasing trend in extreme precipitation, although only about 15% of those trends were found to be statistically significant. Without accounting for autocorrelation in the grid point time series, around 30% of grid points exhibit significant trends, which tend to be small on average. Once autocorrelation is considered, the number of grid points with significant trends is reduced, but the resulting trends are larger.

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Table 1. Nationally averaged trend values for Block Maxima (BM) and the 15, 25, and 50 mm peaks-over-threshold (PoT) approaches. Note that trends in BM represent changes in magnitude of the most extreme events, while PoT results represents changes in EP frequency. “Unfiltered Trends” refers to OLS trends computed without consideration of autocorrelation in the series. Block Maxima POT 15 mm POT 25 mm POT 50 mm Unfiltered Trends Average Trend 0.0451 0.00013 0.0018 0.0018 % of Grid Points with Positive Trend 29.75 28.01 30.26 28.71 Autocorrelation Accounted Trends Average Trend 0.2737 0.0098 0.0141 0.0138 % of Grid Points with Positive Trend 2.19 3.81 4.34 1.38

The spatial variability present in the annual BM maps suggests that different physical processes could be at play. To investigate this possibility, the analysis was repeated using data for each climatological season. Figure 7 displays the average of the largest event of each season.

The largest seasonal averages occur in the NW region during winter and exceed 200 mm while the lowest values occur across the northern Great Plains during winter and along the West Coast during summer. Values tend to be more consistent across seasons in the Eastern US and more variable in the Western US.

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Figure 7. Seasonal averages (means) of block maxima precipitation, derived from nClimGrid-D daily precipitation (mm), 1979-2017. Results are shown for a) winter, b) spring, c) summer, and d) autumn.

Figure 8 show the seasonal CVs (representing interannual variability) of BM precipitation. Seasonally, a higher CV can be seen on average throughout much of the region than seen previously in Figure 5. For example, most of the seasons see an average value of CV of approximately 0.5 with higher values of up to 0.8. The highest values of up to 2 can be seen along the west coast in California in Summer. This shows that the standard deviation in this region is up to 2 times the magnitude of the average. This region shows the highest values in the other seasons as well however to a lower degree of up to 1.

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Figure 8. Seasonal CV of block maxima precipitation derived from nClimGrid-D daily precipitation (ratio), 1979-2017. Results are shown for a) winter, b) spring, c) summer, and d) autumn.

Figure 9 displays the seasonally based trends of block maxima precipitation in mm per decade. Positive trends can be found in the Northeast US in the Summer and Fall seasons, while more mixed in the Winter and Spring seasons. The highest positive trends can be found in the southern Midwest in the Spring months with a 10 mm/decade increase. This feature, and other positives in the eastern CONUS may be linked directly to extratropical cyclones as track density of such systems mirrors the growing trend found in Figure 9 (Bentley et al., 2019). Figure 9c and

9d also show a local positive trend along the southern coast of the US. Given the same annual or seasonal precipitation totals, it has been empirically demonstrated that areas of warmer climates generate more EP events than cooler climates (Kunkel et al., 2013).

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Figure 9. Linear trends in seasonal block maxima precipitation based on nClimGrid-D precipitation, 1979-2017 (mm per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series. Values are shown for a) winter, b) spring, c) summer, and d) autumn.

4.2.2 Peaks-Over-Threshold (PoT) Extreme Precipitation (EP) Climatology

To complement the analysis of block maxima, an analysis of peaks-over-threshold

(PoT) was conducted using three distinct thresholds: 15 mm per day, 25 mm per day, and 50 mm per day. As with the BM approach, results are presented for the annual time scale and for each climatological season. The average number of exceedances for each threshold are shown in

Figure 10. The corresponding CVs, representing interannual variability, are shown in Figure 11 and an analysis of trends is presented in Figure 12.

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a)

b)

c)

Figure 10. Average annual number of peaks-over-threshold events for thresholds of a) 15 mm/day, b) 25 mm/day, and c) 50 mm/day, derived from nClimGrid-D precipitation, 1979-2017 (peaks)

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a)

b)

c)

Figure 11. CV of annual number of peaks-over-threshold events for thresholds of a) 15 mm/day, b) 25 mm/day, and c) 50 mm/day, derived from nClimGrid-D precipitation, 1979-2017 (ratio)

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a)

b)

c)

Figure 12. Linear trends in annual peaks-over-threshold precipitation based on nClimGrid-D precipitation, 1979-2017 (peaks per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series. Values are shown for a) 15 mm/day threshold, b) 25 mm/day threshold, and c) 50 mm/day threshold.

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As expected, the number of occurrences decreases as the threshold increases (Figure 10).

Apart from the intermountain West, most of the contiguous US receives 15 mm daily precipitation events regularly with the greatest values occurring along the Pacific NW coast (>60 exceedances per year on average) and in the SE region. The NW results agree with previous studies such as Lorente-Plazas et al. (2018) who studied the effects of ARs on low threshold precipitation in the Pacific Northwest. The 25 mm daily events are most frequent in the Pacific

NW (40 events per year) and the SE region (30 events per year). For the highest threshold considered, 50 mm/day, frequent occurrences are limited to the Pacific NW and SE region.

Figure 11 displays the CV of the annual peaks-over-threshold results. Highest values of up to 2 in Figure 11a, 4 in Figure 11b, and 5 in Figure 11c, can be seen in the Rocky Mountains region, east of the West coast and west of the central plains. Outside of this region, there is little spatial variability in the CV in the eastern US. Additionally, more extreme thresholds are characterized by higher CVs in the west.

Finally, the trends in annual counts of peaks-over-threshold are shown in Figure 12. As with the BM trends reported in the previous section, the PoT trends shown in the figure are highly variable over space. When significant, trend values tend to be negative across the southern

US and positive across the northern US with a break at approximately 35N latitude. However, at the higher threshold, 50 mm/day, there is a greater tendency toward positive trends, especially in the central US. As with the BM results, additional insight is sought by considering seasonal analyses.

The seasonal PoT averages for 15 mm/day, 25 mm/day, and 50 mm/day thresholds are shown in Figures 13, 14, and 15, respectively. For all thresholds, seasonality is clear in EP across regions. For example, EP events in the Pacific NW, generally occur outside of summer.

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Likewise, a 15 mm/day maximum along the southeast coast during summer is likely related to development of sea-breeze thunderstorms. Because such storms tend to be short lived, they do not often produce precipitation sufficient to exceed the higher thresholds. Average exceedances greater than one are limited to the Pacific NW and eastern US for the higher thresholds of 25 mm/day and 50 mm/day. The eastern US tends to have the greatest northern extent of exceedances during the summer months when moisture transport is maximized.

Figure 13. Seasonal average number of daily exceedances of 15 mm for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (peaks)

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Figure 14. Seasonal average number of daily exceedances of 25 mm for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (peaks)

Figure 15. Seasonal average number of daily exceedances of 50 mm for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (peaks)

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The CVs of the seasonal PoT values are presented in Figures 16, 17, and 18, corresponding to the 15 mm/day, 25 mm/day, and 50 mm/day thresholds, respectively. As with the previously reported results, the greatest variability tends to be in the Rocky Mountains region. As such the interannual variability is greatest in winter months scaling positively with thresholds leading to values of up to 8 in the Rocky Mountains at the 50 mm/day threshold.

Values to the east are much lower, even less than one for all seasons even at higher thresholds.

Figure 16. CV of seasonal number of daily exceedances of 15 mm for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (ratio)

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Figure 17. CV of seasonal number of daily exceedances of 25 mm for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (ratio)

Figure 18. CV of seasonal number of daily exceedances of 50 mm/day for a) winter, b) spring, c) summer, and d) autumn, derived from nClimGrid-D, 1979-2017 (ratio)

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Finally, an trend analysis was conducted for PoT for each season and each threshold

(Figures 19-21). While the BM results provide insight about changes in the magnitude of EP, the

PoT approach counts exceedances and therefore provides information about EP frequency. As the with BM results, there is a lot of spatial variability, but also some clear signals that emerge within seasons. Winter is characterized by relatively little change with the exception of negative trends in PoT across thresholds in the central eastern US and some positive trends in the southern

Plains. Spring trends tend to be strong and positive for parts of the Great Lakes region and northern Plains and negative in the SW region. Summer trends exhibit a strong west-east dipole with positive trends in the east and negative trends in the west. Autumn trends tend to be negative, especially for the lowest threshold.

Figure 19. Linear trends in seasonal peaks-over-threshold for 15 mm based on nClimGrid-D precipitation, 1979-2017 (peaks per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series. Values are shown for a) winter, b) spring, c) summer, and d) autumn.

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Figure 20. Linear trends in seasonal peaks-over-threshold for 25 mm based on nClimGrid-D precipitation, 1979-2017 (peaks per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series. Values are shown for a) winter, b) spring, c) summer, and d) autumn.

Figure 21. Linear trends in seasonal peaks-over-threshold for 50 mm based on nClimGrid-D precipitation, 1979-2017 (peaks per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series. Values are shown for a) winter, b) spring, c) summer, and d) autumn.

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In Sections 4.2.1 and 4.2.2, two different approaches to quantifying extreme precipitation were implemented and the results were evaluated. For both the BM and PoT approaches, most grid points did not exhibit EP trends that were statistically significant. However, in all cases, there were some grid points for which the trends did attain statistical significance. In general, the analysis indicates that trends in annual metrics tend to have positive trends in the eastern US and negative trends in the western US. The resulting trends also exhibit seasonal variability. The seasonal BM results are in general agreement with the annual results. The seasonal PoT results indicate negative trends in the SE region outside of the summer months, with positive trends stretching across the northern Plains.

4.3 Atmospheric River Climatology and Analysis

As described in Chapter 3, ARs were identified for each 6-hr time step within the ERA-

I reanalysis. Following Mundhenk et al. (2016), the numbers of AR occurrences listed herein represent the total number of 6-hr periods during which an AR was detected rather than counts of independent AR events. This form of presentation preserves potential sub-daily periods which is favored for the study of features that may exist at or near their maximum intensity for less than one day and should not be construed as analogous to independent AR events.

In an average year, the number of days in which are AR is occurring ranges from a minimum in the SW region to a maximum value of more than 80 days north of the Great Lakes

(Figure 22). Much of the northern and eastern US experiences approximately 70 days of ARs a year.

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Figure 22. Annual Average AR frequency, derived from ERA-I, Lagrangian AR ID algorithm described in Chapter 3, 1979-2017 (number of AR days)

The interannual variability of AR frequency is presented in Figure 23 and indicates very low values of CV throughout the CONUS. This suggests that the maxima seen in Figure 22 in the Northern Great Lakes is potentially a relic of the semi-permanent low pressure in that region (e.g., the Hudson Bay Low) and may therefore not be driven by variations in climate that occur on interannual time scales. Similarly, the AR events in the Pacific NW are a reliable result of the passage of low-pressure systems and their accompanying moisture plumes. Relatively higher variability in the southern Plains suggests that some years experience large numbers of

AR events while other years experience relatively few. However, throughout the region, CV is relatively low with values of about 0.2 or less in most areas. Linear trends in AR frequency are presented in Figure 24 and show a large of area of significantly decreasing AR frequency in the southern Plains, with scattered significant positive trends in the Pacific NW and northern Plains as well as just off of the US east coast.

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Figure 23. CV of annual AR frequency, derived from ERA-I, Lagrangian AR ID algorithm described in Chapter 3, 1979-2017 (ratio)

Figure 24. Linear trends in annual AR frequency based on ERA-I, 1979-2017 (AR days per decade). Values are shown only for grid points with trends that are statistically significant with =0.1 after accounting for autocorrelation in the grid point time series.

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Figure 25-27 represent the seasonal averages, CVs, and trends in AR frequency, respectively. As demonstrated in Figure 25, there is strong seasonal variability in AR counts. In the western US ARs occur primarily outside of summer. The eastern US has relatively stable counts of ARs among seasons, while the summer is characterized by a large maximum over the south-central US. Figure 26 indicates higher values of CV than in the annual case (Figure 23), with a cluster of high CV values in the northern US, representing high interannual variability, during the winter. The trends in AR frequency (Figure 27) indicate substantial differences among seasons. The largest increases in AR frequency have occurred in the Pacific NW during autumn, the NE region in Spring, and the extreme SE region during summer. The largest negative trends in AR frequency have occurred over the SW region during spring and over the central and southeastern US in summer and autumn.

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Figure 25. Seasonal average AR frequency for a) winter, b) spring, c) summer, and d) autumn, derived from ERA-I, 1979-2017 (instances)

Figure 26. CV deviation of AR frequency for a) winter, b) spring, c) summer, and d) autumn, derived from ERA-I, 1979-2017 (ratio)

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Figure 27. Seasonal Linear Trend of AR frequency for a) winter, b) spring, c) summer, and d) autumn, derived from ERA-I, 1979-2017 (AR days per decade)

4.4 Atmospheric River (AR) and Extreme Precipitation (EP) Co-Occurrence Analysis

Analyses conducted so far indicate that both EP and ARs exhibit coherent trends in some regions during some seasons. However, it is also clear that there is not always direct correspondence in the resulting trends. To better understand the relationship between EP and

ARs an analysis of EP/AR co-occurrence was conducted.

The average percentage of precipitation events associated with an AR is shown in

Figure 28, while the proportion of block maxima EP events co-occurring with an AR is shown in

Figure 29 and those for each PoT threshold are presented in Figure 30. Taken together, these figures indicate that a substantial proportion of rain events are associated with ARs, but that the

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overwhelming majority of block maxima and PoT EP events co-occur with an AR, especially for the higher magnitude events, suggesting a greater influence of ARs on the most extreme EP events. The role of ARs in extreme precipitation in the Northwest region has been well documented and studied. However, the Midwest and Northeast US also demonstrate more than

20 percent of precipitation is collocated with an AR structure. This figure demonstrates the importance of ARs in the regional climatology and exemplifies the results of Figure 29-31. To better understand how these AR occurrences relate to different precipitation thresholds, an analysis of co-occurrences was conducted using the PoT results (Figure 31-33). As with previous analysis, results are presented first for the annual time scale followed by seasonal results.

Figure 28. Average percentage of annual precipitation events associated with an AR based on ERA-I AR identification algorithm and NclimGrid-D precipitation, 1979-2017 (percent of all events).

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Figure 29. Percentage of block maxima (BM) precipitation events associated with an AR based on ERA-I AR identification algorithm and NclimGrid-D precipitation, 1979-2017 (percent of all events).

Figure 30. Percentage of peaks-over-threshold (PoT) extreme precipitation events associated with an AR based on ERA-I AR identification algorithm and NclimGrid-D precipitation, 1979- 2017 (percent of all events). Results are shown for a) 15 mm/day threshold, b) 25 mm/day threshold and c) 50 mm/day threshold.

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Figures 31-33 present the average number, CV, and decadal trend of EP/AR co- occurrence instances respectively, based on the 15 mm/day, 25 mm/day, and 50 mm/day PoT approach. The patterns of the mean number of co-occurrences are qualitatively similar with the highest values corresponding to areas receiving the most precipitation (the Pacific NW and the

SE US). This affirms the highly influential role that mass atmospheric water vapor transports such as ARs have on the EP climatology. CV values are low across the thresholds in the eastern

US and high in the Rocky Mountains, indicating high standard deviation and interannual variability for the region that grows with the higher percentiles, up to 5 at the 50 mm/day threshold (Figure 33b). The trend map components of Figures 31-33 are slightly more dynamic, with strong negative trends in the southern US for co-occurrences of ARs and exceedances of the lowest threshold, 15 mm/day. Trends throughout much of the south-central region of the US are also negative for higher thresholds, but with a smaller spatial footprint. Conversely, there are significant positive trends across the north central US that extend into the SE region for the highest threshold (50 mm/day).

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Figure 31. Average number of co-occurrences (a)(number), CV of number of co-occurrences Instances (b)(ratio), and decadal trend of co-occurrences (c) (number per decade) based on nClimGrid-D derived 15 mm/day PoT and ERA-I derived ARs, 1979-2017.

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Figure 32. Average number of co-occurrences (a)(number), CV of number of co-occurrences Instances (b)(ratio), and decadal trend of co-occurrences (c) (number per decade) based on nClimGrid-D derived 25 mm/day PoT and ERA-I derived ARs, 1979-2017.

Figure 33. Average number of co-occurrences (a)(number), CV of number of co-occurrences Instances (b)(ratio), and decadal trend of co-occurrences (c) (number per decade) based on nClimGrid-D derived 50 mm/day PoT and ERA-I derived ARs, 1979-2017.

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To complement the results presented for the annual extremes, a similar analysis was conducted at the seasonal scale to determine if changes in annual co-occurrences of ARs and EP events are being driven by changes in particular seasons. Consistent with the annual results, the

BM in each season tend to be associated with ARs (Figure 34) and the maps of co-occurrence in therefore partially reflect AR climatology (see Figure 25). Because the PoT events are defined for different thresholds, they provide an opportunity to explore that role of ARs in extreme precipitation across both seasons and thresholds. For each threshold, the number of EP events co- occurring with an AR are quantified. These are then summarized according to the average number of co-occurrences, the CV of the number of co-occurrences (representing interannual variability) and the trend in the number of co-occurrences.

Figure 34. Percentage of block maxima (BM) extreme precipitation (EP) events with a co- occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn (percentage).

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Figures 35, 36, and 37 show the average number of seasonal co-occurrences of EP events and ARs for 15 mm/day, 25 mm/day, and 50 mm/day thresholds, respectively. For all three thresholds, the greatest number of co-occurrences is in the Pacific NW outside of summer, consistent with Gimeno et al. (2016). The maps also show many winter co-occurrences in the SE

USA that extends eastward and northward during the spring and summer. In many cases these patterns reflect underlying seasonal variations in the AR climatology (Figure 25), reflecting the important role that ARs play in seasonal EP variations. Taken together, Figures 35-37 indicate a strong seasonality in the location of co-occurring ARs and EP events.

Figure 35. Average number of peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 15 mm/day precipitation threshold, 1979-2017 (number).

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Figure 36. Average number of peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 25 mm/day precipitation threshold, 1979-2017 (number).

Figure 37. Mean number of peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 50 mm/day precipitation threshold, 1979-2017 (number).

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Figures 38, 39, and 40 show the CV of the average number of seasonal co-occurrences of

EP events and ARs for 15 mm/day, 25 mm/day, and 50 mm/day thresholds, respectively, reflecting the interannual variability of co-occurrences. The maps show low values of CV in the eastern US, higher in the west, suggesting strong scaling between the mean and the interannual variability of the EP/AR co-occurrences. Seasons and regions with high variability in EP/AR co- occurrences generally coincide with seasons and regions with low AR counts, such as the Rocky

Mountain west with CV values of up to 5 for the 15 mm/day threshold (Figure 38) and 25 mm/day threshold (Figure 39).

Figure 38. CV of number of seasonal peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 15 mm/day precipitation threshold, 1979-2017 (ratio).

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Figure 39. CV of number of seasonal peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 25 mm/day precipitation threshold, 1979-2017 (ratio).

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Figure 40. CV of number of seasonal peaks-over-threshold (PoT) extreme precipitation (EP) events with a co-occurring atmospheric river (AR) for a) winter, b) spring, c) summer, and d) autumn. Maps are based on the 50 mm/day precipitation threshold, 1979-2017 (ratio).

The final set of analyses for seasonal EP/AR co-occurrences is a trend analysis to detect possible changes in the contributions of ARs to EP events. The results, presented in Figures 41-

43, corresponding to precipitation thresholds of 15 mm/day, 25 mm/day, and 50 mm/day, respectively, indicate that there are, in some seasons and locations, changes in the frequency of co-occurring ARs and EP events. Interestingly, the Pacific NW region, characterized by the greatest number of EP/AR co-occurrences during the winter months does not exhibit a trend.

This suggests that ARs are a very important component of EP climatology in this region, but that their role in seasons EP events did not change significantly over the course of the last few decades. During the spring, the lower threshold EP events are becoming more commonly associated with ARs across the northern US and less commonly associated with ARs across the southern US. However, for the 50 mm threshold, the pattern is limited to the eastern US with 55

very few significant changes in the west. Summer is characterized by an increase in EP/AR co- occurrences at locations in the eastern US for all thresholds and a decrease in EP/AR co- occurrences in the western US for the lowest threshold. In autumn, the strongest trend in EP/AR co-occurrences is for the lowest threshold events (15 mm/day) in the south-central US. At higher thresholds, the pattern of significant trends is spatially noisy and may result from variations in tropical cyclone tracks.

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Figure 41. Trend in the co-occurrence of atmospheric rivers (ARs) and precipitation events exceeding 15 mm/day for a) winter, b) spring, c) summer, and d) autumn (events per decade)

Figure 42. Trend in the co-occurrence of atmospheric rivers (ARs) and precipitation events exceeding 25 mm/day for a) winter, b) spring, c) summer, and d) autumn (events per decade)

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Figure 43. Trend in the co-occurrence of atmospheric rivers (ARs) and precipitation events exceeding 50 mm/day for a) winter, b) spring, c) summer, and d) autumn (events per decade)

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CHAPTER 5

DISCUSSION

EP events and associated means, CV, and trends demonstrated how EP is dynamic and complex, with consistencies and deviations between classifications, regions, and annual versus seasonal timescales. Results show that within, however not throughout, all regions of the

CONUS, EP demonstrates significant trends on both annual and seasonal timescales through four separate EP classifications. EP was found to be mostly variable in seasonal cases, with annual values of CV remaining relatively low for all classifications. Interannual variability as well as seasonal variability did show to be strongest in the Southeast US and Midwest US, demonstrating how these regions are the most dynamic (Mahoney et al., 2016). EP trend was found to be the most interesting of the EP statistical results with high spatiotemporal variability.

The eastern half US demonstrated the most positive trends with areas of up to 10 mm increase per decade in the block maxima annual analysis, in most of all classifications including seasonal which is in agreeance with Janssen et al. (2014). The block maxima approach Figures 4, 6, 7, and

9, shows how the Southeastern portion of the CONUS exhibits larger block maxima events, except for the Northwest coast which still exhibits the highest in the study area. The peaks-over- threshold approach, seen in Figures 12, 19, 20, and 21, resulted in mostly the northern half of the

US seeing a positive trend of up to 2 additional peaks over the 15, 25, and 50 mm/day thresholds per decade, while the southern US mostly saw a decline of about 2 peaks a decade.

AR results show that ARs produce significant trends within, however not throughout, all regions of the US. Average AR instances in the annual case shows a blanket presence throughout the CONUS, however more dynamic when dissected into the seasonality. Annual AR statistical outputs demonstrated the reliable presence of ARs in the CONUS climatology with

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more than 70 AR days and a CV of about 0.2 observed for much of the CONUS. The corridor feature seen in Figure 25 spanning from Texas to the Great Lakes is a known frequent path of

ARs observed in previous studies that is reflected within the findings here (Lavers and Villarini,

2013; Gimeno et al., 2016). However, a declining trend, of about 10 instances less per decade is expected for much of the inner CONUS, with a hot spot of up to 20 instances less per decade in the Texas panhandle, perhaps suggesting a shift away from AR presence in the future. Figure 27 shows Summer and Spring to experience a decline in AR instances of up to 15 instances per decade in the Central US with highest values of CV, up to 1, in Winter, while Fall shifts the declining trend to the Southeast US.

On average, using the peaks-over-threshold approach, approximately 50 % of EP events were co-occurrences, with an AR instance, which is in agreeance with Mahoney et al. (2016) that found approximately 60 % of heavy precipitation to be associated with ARs. In addition, approximately 20 % of all precipitation in the CONUS was found to be an EP and AR cooccurrence. It is shown that a significant linkage of EP and ARs is present in the CONUS, and most prominent in the Northwest, Midwest, Northeast, and Southeast US (Lavers, 2013). ARs have been seen to reach up to 0.5 on the Spearman correlation coefficient scale in the western

US, demonstrating the significance of AR impacts on daily extreme precipitation (Steinschneider et al., 2018). AR EP co-occurrences show largest presence in the Southeastern US on average seen on Figures 31-33 and 35-37, however when compared to Figure 28 the region with highest percentage of AR co-occurrences with all precipitation is in the Northeast and Midwest. Areas of the highest percentage of co-occurrences when considering the BM approach to co-occurrences, include the Midwest, Northeast, Northwest, and parts of the West Coast with 80-90 % of the most extreme annual precipitation events being associated with an AR.

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This corridor feature described previously spanning from Northwest Texas to the Great

Lakes resembles features understood through general atmospheric dynamics, specifically a semi- permanent high pressure in the southeast, rotating air masses in Texas towards the Great Lakes.

This rotation of this phenomena funneling air against the Rocky Mountains and pushing it northward may produce a “moisture belt” feeding into the central US and produce a signal like that of an AR. This possibility gives rise to the questioning of the current, Lagrangian, AR identification technique. Alternative AR identification techniques have been adopted for global

AR studies such as Guan (2015). Perhaps, similar to that of a global study, different criteria need to be met relative to a certain region (i.e. US West Coast versus Southeast US), such as shifting and/or adjusting the vertical range boundaries currently thought to be 1000 mb and 300 mb, or considering a different percentile threshold instead of the 85th percentile to qualify as an AR candidate grid cell.

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CHAPTER 6

CONCLUSION

The purpose of this study was to evaluate the role of horizontal moisture fluxes from

ARs on changes in EP events throughout the continental US. This was accomplished through a set of analyses asking and answering the questions found in Chapter 1 restated below.

1) Is extreme precipitation changing in the continental US?

To answer this question, a newly developed, homogenized, gridded, high resolution data

set (nClimGrid-D) is used to establish annual and seasonal EP climatologies and conduct

trend analyses for annual and seasonal EP events identified using the block maxima and

peaks-over-threshold approaches.

2) Is AR frequency changing in the continental US?

To answer this question, an algorithm was developed to identify ARs in 6-hourly

atmospheric reanalysis data. Once identified, the ARs are subjected to a trend analysis to

identify regions where they may be contributing to increases in EP event frequency or

magnitude.

3) Are observed AR changes contributing to observed EP changes?

To answer this question, a “co-occurrence analysis” is undertaken to assess the frequency

of co-occurring AR presence and EP occurrence. The frequency of co-occurrences is

considered in terms of annual and seasonal climatologies as well as trends. Through the

assumption of co-occurrence being indicative of contribution, this study shows that there

is indeed a relationship between AR activity and EP frequency, defined by the PoT

approach, and EP magnitude, defined by the block maxima approach.

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There are some caveats for this study to be aware of. This study did not recognize AR magnitude, opting for a binary approach to AR identification to better obtain a sense of AR frequency and the temporal role of ARs throughout CONUS. This study also did not account for long-scale meteorological or climatological set-ups such as ENSO, MJO, or the NAO. These features may have a direct role in the dynamics of both EP and AR interaction and dissected trends from this study must be understood with these possible influences understood as well.

Additionally, AR identification was taken from several previous studies of North America to remain consistent as the methodology is tried and tested. However, it should be considered that this identification technique was verified mostly on AR activity on the West Coast of the US, and the question of AR anatomy arises when considering the consistency of this phenomena in a different part of the nation. As found by Guan (2015), different AR identification techniques are used around the globe, such as studies in Britain focusing mainly on 900 mb specific humidity, or studies in Antarctica using solely integrated water vapor, not the flux. Finally, it is well understood, however should be mentioned, that this study of trends was taken using gridded data instead of station-based data. This results in a dampening of results, indicating any trend results taken from this study may be larger due to this effect.

For future studies with access to more powerful hardware, the ERA-5 dataset would produce the least erroneous integration results, using Equation 1. Another preference for future work that would have produced more significant trend results would have been to expand the temporal range of the study. Expanding the temporal scope of this study may increase the amount of area that produces significant trend results. Future studies may explore the partitioning of trends in EP, ARs, or co-occurrences, linking trends directly to the source of influence. Future work may explore the strength of ARs in relation to EP events, as this study took a binary

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approach to AR recognition. Future work may also explore the linkage between ARs and overall precipitation, exploring the possibility of higher influence of ARs on EP or overall precipitation.

Finally, future work may include a study of AR anatomy across the world and establish any possible alternative identification techniques to be used in different regions. EP represents an ever-present threat to socioeconomic assets, such as people, infrastructure, and agriculture.

This study provides insight not previously attained by alternative studies. This study’s novel approach of including two different EP classification techniques explores both frequency and magnitude within one study area and timespan. This provides consistency within results and ensure comparability between classifications. This study also shows EP dependence upon ARs through the co-occurrence analysis and with the unique approach, provides a scope not previously explored. This study provides trends of AR and their contribution, not previously found in previous studies within the CONUS. This study explores both EP and ARs at an incredibly fine scale of 5 km, evaluating hundreds of cases of ARs. This study provides the statistical background necessary for a more complex AR/EP analysis such as those found in the future work section. As found, with this strong of an association between EP and ARs in the

CONUS, future studies such as analysis of projections, delineations with alternative transports and climatological mechanisms, analysis of those transports and implications are paramount to understand future risks from EP.

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APPENDIX A

TREND MAPS WITHOUT AUTOCORRELATION REMOVAL

Figure A1. Trend of Annual Block Maximum Precipitation, derived from nClimGrid-D daily precipitation (mm/decade), 1979-2017

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Figure A2. Seasonal Trend of Block Maxima Precipitation, Winter (a), Spring (b), Summer (c), Fall (d), derived from nClimGrid-D daily precipitation (mm/decade), 1979-2017

Figure A3. Trend of Peaks over 15 mm threshold, derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

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Figure A4. Trend of Peaks over 25 mm threshold, derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

Figure A5. Trend of Peaks over 50 mm threshold, derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

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Figure A6. Seasonal trend of peaks over 15 mm threshold precipitation, Winter (a), Spring (b), Summer (c), Fall (d), derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

Figure A7. Seasonal trend of peaks over 25 mm threshold precipitation, Winter (a), Spring (b), Summer (c), Fall (d), derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

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Figure A8. Seasonal trend of peaks over 50 mm threshold precipitation, Winter (a), Spring (b), Summer (c), Fall (d), derived from nClimGrid-D daily precipitation (events/decade), 1979-2017

Figure A9. Decadal Trend of AR days, derived from ERA-Interim, Lagrangian AR ID (events/decade), 1979-2017

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Figure A10. Seasonal decadal trend of AR Instances, Winter (a), Spring (b), Summer (c), Fall (d), derived from ERA-Interim, Lagrangian AR ID, (events/decade), 1979-2017

Figure A11. Decadal Trend of co-occurrences between nClimGrid-D derived 15mm POT and regridded ERA-Interim derived AR data

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Figure A12. Decadal Trend of co-occurrences between nClimGrid-D derived 25mm POT and regridded ERA-Interim derived AR data

Figure A13. Decadal Trend of co-occurrences between nClimGrid-D derived 50mm POT and regridded ERA-Interim derived AR data

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Figure A14. Four Panel of Decadal Trend (events/decade) of co-occurrences in Winter (A), Spring (B), Summer (C), Fall (D), comparison between nClimGrid-D derived 15mm POT and regridded ERA-Interim derived AR data, 1979-2017

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Figure A15. Four Panel of Decadal Trend (events/decade) of co-occurrences in Winter (A), Spring (B), Summer (C), Fall (D), comparison between nClimGrid-D derived 25mm POT and regridded ERA-Interim derived AR data, 1979-2017

Figure A16. Four Panel of Decadal Trend (events/decade) of co-occurrences in Winter (A), Spring (B), Summer (C), Fall (D), comparison between nClimGrid-D derived 50mm POT and regridded ERA-Interim derived AR data, 1979-2017

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VITA

Graduate School Southern Illinois University

Christian K. Landry [email protected]

Texas A&M University, College Station, Texas Bachelor of Science, Meteorology, May 2018

Thesis Paper Title: The Influence of Atmospheric Rivers on Extreme Precipitation in the Continental United States

Major Professor: Dr. Justin Schoof

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