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Atmospheric River-Induced Precipitation and Snowpack 1
during the Western United States Cold Season 2
3
Hisham Eldardiry, Asif Mahmood, Xiaodong Chen, Faisal Hossain and Bart Nijssen 4
Department of Civil and Environmental Engineering, University of Washington, 5
Seattle 6
7
Dennis P Lettenmaier 8
Department of Geography, University of California, Los Angeles 9
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Corresponding Author: Faisal Hossain 14
Department of Civil and Environmental Engineering 15
More 201, University of Washington, 16
Seattle, WA 98195 17
Email: [email protected] 18
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Abstract 19
Atmospheric Rivers (ARs) are narrow elongated corridors embedded in the warm sector 20
of extratropical cyclones. We characterize land surface hydrologic conditions 21
associated with ARs intersecting the coast of the Western US from Southern California 22
to the Canadian border during the cold season (November through March) of water 23
years 1949-2015. Landfalling ARs resulted in higher precipitation amounts throughout 24
the domain than did non-AR storms. ARs contributed the most extreme precipitation 25
events during January and February. Daily snow water equivalent (SWE) changes 26
during ARs indicate that higher positive net snow accumulation conditions accompany 27
ARs in December, January, and February. We also assess the historical impact of AR 28
storm duration and precipitation frequency by considering the precipitation depth 29
estimated during a 72-hour window bounding the AR landfall date. More extreme 30
precipitation amounts are produced by ARs in the South Cascades and Sierra Nevada 31
range compared with ARs with landfall farther north. Most AR extreme precipitation 32
events are produced during warm AR dates, especially towards the southern end of our 33
domain. Analysis of ARs during dry and wet years reveals that ARs during wet years 34
are more frequent, and produce heavier precipitation and snow accumulation as 35
compared with dry years. 36
Keywords: Atmospheric Rivers, Western United States, Extreme Precipitation, Snow 37
Water Equivalent. 38
3
1. Introduction 39
Atmospheric Rivers (ARs) are narrow elongated corridors with horizontal water 40
vapor transport embedded in the warm sector of extratropical cyclones (Browning and 41
Pardoe 1973; Bao et al. 2006; Neiman et al. 2008; Ralph and Dettinger 2011; Zhu and 42
Newell 1998). When an AR event makes landfall, the transport of large amounts of 43
water vapor can lead to heavy precipitation and sometimes flooding, especially if the 44
moisture-laden air is forced to rise rapidly over a mountain barrier (Dettinger et al. 45
2011). For instance, in January 2017, an AR event in northern California produced 46
powerful storms and resulted in extensive flooding, triggered power outages, and 47
mudslides (Rosen 2017). Such heavy storms bring challenges to water managers 48
operating dams whose operating purposes include flood risk reduction. Thus, better 49
management strategies for water resources in the western US are needed to cope with 50
the expected increase in AR-induced precipitation as the climate continues to change 51
(Hagos et al. 2016; Dettinger 2011). 52
While it is widely known that most heavy precipitation events across the coastal 53
Western US are driven by ARs (e.g., Leung and Qian 2009; Dettinger et al. 2011; Ralph 54
and Dettinger 2011; Warner et al. 2012), multi-decadal characteristics of AR induced 55
precipitation and snowpack changes have not yet been fully documented. We provide 56
here a comprehensive analysis of landfalling AR dates that generated heavy rainfall in 57
the coastal Western US over the period 1949-2015. We also evaluate changes in snow 58
water equivalent (SWE) associated with AR events over the same period. Snowmelt 59
can exacerbate the direct effects of extreme precipitation, and is a particularly important 60
consideration given recently observed trends toward earlier snowmelt across the region 61
(Cayan et al. 2001; Regonda et al. 2005; Trujillo and Molotch 2014). 62
4
Several studies have highlighted the importance of ARs and their connection 63
with heavy precipitation and flooding across the Western US (Ralph et al. 2006; 64
Neiman et al. 2008; Neiman et al. 2011). For instance, Ralph et al. (2006) used 65
observations from an 8-year series of field observations combined with satellite 66
observations in Northern California to explore the possible role of ARs in generating 67
precipitation that has led to flood events in the Russian River basin. They concluded 68
that AR conditions caused heavy rainfall as a result of orographically enhanced 69
precipitation for all major flooding events. Recently, Young et al. (2017) examined the 70
relationship between flood events and ARs in California for water years 2005-2014 71
using a catalog of landfalling ARs produced by Rutz et al. (2013). Their results 72
indicated that most of the high impact events related to floods and debris flows were 73
associated with cold season ARs with landfall in Northern California. Nonetheless, the 74
relationship among land surface hydro-meteorological variables, including 75
precipitation, snowpack changes, and associated surface temperature during AR events 76
has received relatively little attention in past studies. Here, we extend the analysis of 77
Guan et al. (2010) who considered the relationship between SWE change and surface 78
temperature for AR events across the Sierra Nevada range to include the entire coastal 79
Western US from Northern California to the Canadian border. 80
The questions that motivate our study are: 1) How do precipitation and 81
snowpack changes induced by ARs vary across the cold season and how do the 82
hydrologic signatures of ARs differ along a latitudinal transect through the Pacific 83
maritime region of the western US? and 2) what is the impact of AR events associated 84
with warm conditions on produced precipitation amounts, as well as SWE changes? To 85
address these questions, we retrieved hydrometeorological outputs from dynamically 86
downscaled atmospheric reanalyses (NCEP-NCAR) using the Advanced Research 87
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Weather Research and Forecast (AR-WRF) model over the period 1949-2015. We 88
incorporate in our analysis the relationships among frequently observed 89
hydrometeorological variables, including precipitation, surface temperature, and SWE. 90
Our characterizations are intended to lay the background for subsequent 91
investigation into the role of AR events and associated hydrologic controls on 92
catastrophic flooding. Addressing such questions has implications for managing water 93
resources and mitigating AR-related flood and drought events in the Western US. 94
Griffin and Anchukaitis (2014) and Swain et al. (2014) for example discussed ARs as 95
relevant to the severe California drought conditions in 2012-2014). The remainder of 96
the paper is organized as follows: we summarize the data sources and methods applied 97
in section 2, with results and discussion in section 3, and concluding remarks are in 98
section 4. 99
2. Data Sources and Methods 100
2.1 Atmospheric River (AR) Catalog 101
Following the criteria defined by Ralph et al. (2004), we characterize ARs as 102
having integrated water vapor (IWV) concentrations of more than 2 cm between 1000 103
hPa and 300 hPa and wind speeds of greater than 12.5 meters per second in the lowest 104
2 km above the surface. The typical AR is no more than 400– 500 kilometers wide, and 105
extends for thousands of km of trajectory (Zhu and Newell 1998). Methods that have 106
been used to identify ARs are generally based on thresholding Integrated Water Vapor 107
(IWV) or integrated water vapor transport (IVT), tracking spatial features in a moisture-108
related field, and setting specific criteria for AR length, orientation and intensity (Ralph 109
et al. 2004; Neiman et al. 2008; Ralph et al. 2013; Nayak et al. 2014; Guan and Waliser 110
2015). Here, we identified ARs using the IVT-based catalog produced by Guan and 111
Waliser (2015) using the National Centers for Environmental Prediction/National 112
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Center for Atmospheric Research (NCEP/NCAR) reanalysis for the 67-year period of 113
water years 1949-2015. 114
We compared the ARs in the Guan and Waliser (2015) catalog with the AR 115
record published in Neiman et al. (2008) (hereinafter referred to as the Neiman catalog) 116
which is based on IWV observed by the Special Sensor Microwave Imager/Sounder 117
(SSM/I) satellites for the period of overlapping water years 1998-2005. The Neiman 118
catalog using SSM/I generally provides the most reliable identification of ARs and has 119
been evaluated in many AR studies (Ralph et al. 2006; Guan et al. 2010; Ralph and 120
Dettinger 2012). We determined the percentage of agreement between the two catalogs 121
for two sets of AR dates: 1) North Coastal AR dates (NAR) where the North Coast is 122
defined as the Washington, Oregon, and British Columbia coasts (from latitudes 41.0°–123
52.5°N), and 2) South Coastal AR dates (SAR) where the South Coast is defined as the 124
California coast between latitudes 32.5°–41.0°N (Figure 1). Table 1 shows the 125
percentage of agreement between the Neiman and Guan-Waliser dates for NAR and 126
SAR dates during the common detection period (1998-2005). We computed the 127
agreement percentage as the ratio between number of ARs identified in the two catalogs 128
for NAR, SAR, and the total AR dates, i.e., summing NAR and SAR dates (Table 1). 129
We allowed an offset of ±1 and ±2 days to account for differences in timing between 130
the two algorithms. The agreement percentage for NAR dates was about 98% when 131
allowing a ±1 day offset. When comparing the total number (union) of AR dates (i.e., 132
NAR+SAR), the Guan-Waliser catalog captures 90% of the Neiman AR dates with ±2 133
days of offset. Based on the relatively high degree of coincidence of AR dates in the 134
overlap period, and the fact that the Guan-Waliser catalog spans many more years 135
(hence better supports statistical analyses of the type we performed here) we adopted 136
the Guan-Waliser catalog as the basis for our study. 137
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We characterized precipitation and SWE changes separately for ARs in 2.5 138
degree latitudinal bands at their intersections with the U.S. Pacific Coast from just north 139
of the Canadian border south to 30° latitude. The use of 2.5 degree latitudinal bands 140
matches the spatial scale provided by the Guan-Waliser AR catalog (2.5 x 2.5 degrees). 141
We focus our analysis on events during the cold season from November through March 142
when ARs are associated with water vapor fluxes sufficient to produce extreme 143
precipitation (Neiman et al. 2008; Lamjiri et al. 2017; Young et al. 2017). Figure 2a 144
shows the monthly distribution of cold season landfalling ARs (4315 dates) during 145
water years (1949-2015). As shown in Figure 2a, a larger number of AR dates are 146
identified in northern latitudes, e.g., at 45° and 50° degree latitudes, compared to 147
southern latitudes with January receiving the largest number of dates (1206) followed 148
by December (1073). Some of these ARs produced little or no precipitation and only 149
about 10% of the ARs (449) contribute to the upper 10th percentile precipitation as 150
depicted in Figure 2b. 151
2.2 WRF Downscaling 152
For each AR landfalling date, we retrieved the precipitation and relevant 153
hydrometeorological variables produced through the dynamic downscaling (via WRF) 154
of the NCEP/NCAR atmospheric reanalysis (Kalnay et al., 1996) which is available at 155
a 6-hourly temporal resolution and about 2 degrees latitude-longitude spatial resolution 156
(Kalnay et al. 1996; Kistler et al. 2001). Our approach for dynamic downscaling is 157
essentially identical to that used by Chen and Hossain (2016) who relied on the same 158
NCEP/NCAR reanalysis products as initial and boundary conditions for reconstructing 159
historical extreme storms between 1948 and 1979. 160
We performed our WRF simulations over a single domain, i.e., without nesting. 161
Our domain covered the Western US with a grid resolution of 15 km (Figure 1). The 162
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WRF physics options we selected included the Morrison double-moment scheme 163
(option 10) for microphysics, and Kain-Fritsch (option 1) for the cumulus physics. This 164
set of parameterization schemes was also selected by Chen et al. (2017) and Chen and 165
Hossain (2016) as it best reproduced observed precipitation over the Pacific Northwest 166
and California. We used the Noah-MP (Version 1.6) land surface model with Monin-167
Obukhov (option 1) for the surface layer drag coefficient calculation, CLASS option 168
for ground surface albedo, and Jordan (1991) model (option 1) for precipitation 169
partitioning between snow and rain. These parameters, which affect the model’s 170
accumulation and ablation of SWE, are recommended by Niu et al (2011) and Yang et 171
al (2011). We ran the simulations at a time step of 90 seconds with output variables 172
archived hourly for the months of November through March from 1949 through 2015 173
(67 years). For the purpose of our analysis, we archived the following hourly variables: 174
precipitation, SWE, and instantaneous temperature at 2m height. 175
We opted to base our analysis on WRF output rather than data sets derived 176
directly from observations (e.g., gridded precipitation and temperature fields) in the 177
interest of avoiding issues associated with variability in gauge density. Sparse 178
precipitation (and temperature) gauge coverage is especially problematic at high 179
elevations, where most of the precipitation occurs in our study domain. 180
We compared the daily SWE from WRF simulations with the SWE output from 181
the Livneh et al (2013) dataset. The Livneh et al (2013; hereafter L2013) data were 182
generated using the Variable Infiltration Capacity (VIC) hydrologic model, version 183
4.1.2 (Liang et al., 1994) forced with a dataset of gridded precipitation, and temperature 184
adjusted for orographic variations using a climatology from the Precipitation-elevations 185
Regressions on Independent Slopes Model (PRISM) (Daly et al., 1994). Lundquist et 186
al (2015) showed that daily SWE from the L2013 dataset performed reasonably well 187
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when compared with daily snow pillow measurements across the Sierra Nevada, 188
California. Figure 3 shows scatterplots for the April 1 SWE as simulated by the WRF 189
and VIC models for three mountainous subregions within our domain shown in Figure 190
1: 1) North Cascades; 2) South Cascades; and 3) Sierra Nevada. We picked these 191
subregions because they all have substantial orographic relief, and the mountain divides 192
are relatively close to the coast. We delineated their boundaries based on the 193
Commission for Environmental Cooperation Ecological Regions of North America, 194
Level III (McMahon et al. 2001; Omernik 2004). WRF and L2013 agree well for the 195
Sierra Nevada. However, WRF produces slightly lower values in the North and South 196
Cascades regions. This higher daily SWE from VIC model has also been reported by 197
Livneh et al (2013) and Maurer et al (2002, who produced a predecessor data set to 198
L2013) when compared to snow observations over some mountainous regions of the 199
Western US including the Cascades. Based on these comparisons and the previous 200
referenced work, we conclude that the WRF simulations perform plausibly well relative 201
to independent model- and observation-based SWE estimates. 202
2.3 Analysis Approach 203
We characterized precipitation and snowpack during dates of AR landfalls as 204
follows: 205
1) We aggregated the hourly precipitation and SWE change from WRF to daily 206
amounts on the calendar date of each AR. We performed these aggregations for 207
each month considering only the AR landfall dates. We then calculated the AR 208
daily precipitation (or SWE change) for each WRF grid cell by considering the 209
maximum daily estimate during a 72-hour window bounding the AR landfall 210
date (i.e., one day before and after the AR date). We defined high impact AR 211
dates as corresponding to ARs with upper 10th percentile daily precipitation (or 212
10
SWE change) for each month of the cold season (November through March). 213
We used the average of the upper 10th percentile precipitation (or SWE) as a 214
way to represent AR dates with large precipitation and/or SWE changes. In 215
addition, we assessed the severity of AR-induced precipitation by quantifying 216
the fraction of AR dates among the upper 10th percentile of daily precipitation 217
(considering all dates and not only AR dates) for each latitudinal band. In 218
analyzing the SWE changes for each grid cell, we treated positive and negative 219
changes separately to indicate the snow accumulation and snowmelt associated 220
with each AR date. 221
2) We evaluated the impact of AR storm duration and frequency on precipitation 222
depth over the three regions (Figure 1) using 2.5° degree latitudinal bands 223
within which the AR landfalling latitude was located. To address the frequency 224
of AR-induced precipitation, we constructed a precipitation series of AR dates 225
for each WRF grid by selecting the AR dates with maximum precipitation in 226
the cold season of each year (sample size =67 out of 4315 total AR dates). We 227
calculated precipitation amounts for each AR date for different durations using 228
hourly precipitation accumulations from WRF outputs. The durations ranged 229
from one hour (duration of the maximum precipitation in the AR event), to three 230
days enveloping the AR event date, i.e., one day before and one day after the 231
nominal AR date (the dates of AR landfall were identified as described in 232
Section 2.1). We also calculated the precipitation associated with an AR event 233
as the maximum precipitation for n consecutive hours out of 72 (3-day window), 234
where n is the duration of the event (and n = 1, 2, 3, 6, 12, 24, 48, 72 hours). We 235
then fitted the precipitation series for each grid to the Generalized Extreme 236
Value distribution (GEV) using the L-moment method of (Hosking and Wallis 237
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1997). The GEV distribution has been widely applied for predicting hydrologic 238
and meteorological extremes (e.g., Fowler and Kilsby, 2003; Overeem et al., 239
2009; and Eldardiry et al., 2015). We estimated the frequency of each event by 240
converting the cumulative probability of each event into return periods ( =241
), where T is the return period and P is the cumulative probability of an AR 242
event. 243
3) We partitioned ARs into dry vs wet years. To do so, we ranked the accumulated 244
winter (November through March) precipitation over our study period, and 245
defined dry years as being in the lower 10th percentile, and wet years in the 246
upper 10th percentile. We then calculated the AR-induced daily precipitation 247
and SWE changes for each month of the dry and wet years. 248
4) Finally, we analyzed the changes in precipitation depth and SWE change with 249
surface temperature. We performed this analysis by considering the warm 250
events as being associated with the AR dates with upper 10th percentile daily 251
precipitation (or SWE change). We defined warm events as the 20% of AR dates 252
with the highest surface temperature. 253
We present our results for snowpack characterization at a regional scale for the 254
three mountainous subregions shown in Figure 1: 1) North Cascades; 2) South 255
Cascades; and 3) Sierra Nevada. In addition, to account for the elevation effects on 256
snowpack, we used elevation bands with 500 m vertical intervals using the ETOPO1 257
elevation dataset constructed at 1 arc-minute resolution (Amante and Eakins 2009). 258
3. Results 259
3.1 Precipitation 260
Figure 4 shows the average values of the upper 10th percentile of daily 261
precipitation produced by all ARs. The precipitation associated with the AR events 262
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covers most of California to (and slightly beyond) the Sierra Nevada crest and 263
Washington and Oregon about to the Cascade crest. The maximum daily precipitation 264
from these events occurs mostly in December and January in the Sierra Nevada region 265
with many grid cells there having events that average over 160 mm; event averages 266
tend to decline northward (e.g., to an average of 84 mm for the North Cascades), 267
although the number of AR events increases at northern latitudes (Figure 2) with an 268
anomaly for the band centered at 47.5 N latitude, which may be associated with the rain 269
shadow from the Olympic Mountains. 270
Figure 5 shows the fraction of upper 10th percentile precipitation produced by 271
AR events in each latitude band. There is a general gradient with higher extreme (upper 272
10th percentile) AR fractions occurring towards the southern part of our domain, with a 273
local maximum at latitude 37.5°, which covers most of the central part of the Sierra 274
Nevada region. At all southern landfalling latitudes (42.5° and south), January and 275
February have the most extreme AR precipitation (i.e., higher fraction of upper 10th 276
percentile events that are associated with ARs). AR-related extreme precipitation along 277
the Sierra Nevada was also explored by Young et al. (2017) who found that 2-day 278
precipitation associated with ARs was generally ranked in the upper 5% of all such 279
events (AR and non-AR) during water years 2004-2014. 280
We used Depth Duration Frequency (DDF) analysis for the three regions shown 281
in Figure 1 to account for the impact of AR storm duration on precipitation depth and 282
frequency. At each grid cell, we used a parametric approach in which we estimated 283
quantiles from fitted GEV distributions using the L-moment method (Hosking and 284
Wallis, 1997). Figure 6 shows the resulting precipitation depths and frequencies at 1- 285
and 3-day durations for the three regions. Higher precipitation amounts are produced 286
by ARs falling at southern latitudes, i.e., Sierra Nevada region across a range of 287
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durations (only 1-day and 3-day durations are shown in Figure 6) and return periods. 288
During AR dates along the Sierra Nevada, larger relative changes in precipitation 289
amounts occur between low and high return periods, e.g., 2-year vs 100-year (Figure 290
6a) especially at the areas bounding the AR landfalling latitude 37.5°. The larger 291
precipitation relative changes (e.g., ratio of the 100- to 2-year events) are associated 292
with more highly skewed GEV distributions, where the maxima are around 37.5 and 293
40.0 N latitude. For instance, the 3-day AR-induced precipitation along the Sierra 294
Nevada region for the 2-year and 100-year return periods ranges from 176 mm to 512 295
mm (representing 109 % and 318 % of the mean maximum precipitation), respectively 296
at the AR landfalling latitude 37.5°, whereas the precipitation depth for the same return 297
periods ranges from 100 mm to 290 mm (representing 74 % and 215 % of the mean 298
maximum precipitation) at the AR landfalling latitude 50° of the North cascades. 299
3.2 SWE Change 300
We classified the AR dates into ARs producing snow accumulation or snowmelt 301
by separating the daily SWE changes into positive or negative values for each AR date. 302
We calculated the AR-induced SWE change by elevation bands (500 m intervals). We 303
ignored elevation bands less than 500 m and greater than 2500 m because no significant 304
SWE changes occurred at these elevations for any of the three regions. We also 305
considered only ARs that produced SWE changes in the upper 10th percentile of all AR-306
related positive and negative SWE changes. Figure 7 shows daily positive and negative 307
SWE changes which reflect the variations in snowfall and snowmelt respectively. The 308
upper 10th percentile ARs dominantly result in snow accumulations across all regions 309
compared to snowmelt (negative SWE changes). We also observed increases in SWE 310
for more intense ARs representing the upper 5th percentile (figure not shown). For all 311
AR landfalling latitudes and across elevation bands, the highest positive SWE changes 312
14
occur in January. The Sierra Nevada region had the highest positive SWE changes at 313
AR landfalling latitude 40° at different elevation bands compared to the North Cascades 314
and South Cascades regions. For instance, snow accumulation occurring during cold 315
season AR dates is greatest in January in the Sierra Nevada region at the AR landfalling 316
latitude 40° and elevation band 2000-2500 m with an average of 67 mm. Along the 317
Sierra Nevada range, higher snowfall is produced by ARs falling at the northern-most 318
latitudinal band (40°) compared to southern-most band (35°). A similar result was 319
reported by Huning et al., (in review) who concluded higher magnitudes of AR 320
cumulative snowfall across northwestern Sierra Nevada during the 1985-2015 winter 321
season. The North Cascades and South Cascades experience higher snowfall in January 322
(compared to other winter months) at the elevation bands (1000-1500) and (1500-2000) 323
with an average of 40 mm and 30 mm respectively. Such positive SWE changes 324
associated with ARs were also observed by Neiman et al. (2008) who concluded that 325
ARs predominantly increase SWE in the autumn and winter along both the northern 326
and southern coast regions of the western US. 327
In addition to snow accumulation, AR dates resulted in snowmelt (i.e., AR-328
related negative SWE changes) during the cold season months across all three regions. 329
The largest AR-related snowmelt (-16 mm) is in December at the AR landfalling 330
latitude (50°) and averaged over the northern cascades at elevation band (1000-1500). 331
To further explore the impact of ARs on snowmelt, we investigated some of the largest 332
floods in our record that are associated with AR events (Table 2). For each flooding 333
event, we calculate the exceedance probability of the AR daily snowmelt from the upper 334
10th percentile daily negative SWE changes produced during AR and non-AR dates. 335
As indicated in Table 2, the ARs in the all three regions resulted in snowmelt amounts 336
that had exceedance probabilities (with one exception) of less than 5%. Hence, at least 337
15
some of the ARs that brought copious amounts of rainfall with subsequent major 338
flooding were associated with extreme snowmelt Similar results were also reported for 339
the Sierra Nevada region by Guan et al. (2016) who found that 50% of the ROS 340
occurrences between the water years (1998-2014) are associated with landfalling ARs. 341
3.3 Dry vs Wet Years 342
We evaluated AR-related precipitation and SWE change during dry vs wet 343
years, where we defined dry years as having lower 10th percentile accumulated 344
precipitation, and wet years as having upper 90th percentile precipitation during the 345
winter months. Table 3 shows the six years in our record in each region that are defined 346
as dry and wet years. As Table 3 indicates, the number of AR dates in the three regions 347
in wet years is larger than the number of AR dates during dry years. For instance, 594 348
AR dates were recorded along the northern cascades during the wet years compared to 349
322 AR dates during the dry years. The largest number of AR dates was in the wet year 350
1983 along the Sierra Nevada region with 120 AR dates. 351
For each region, we evaluated the precipitation and SWE change at the elevation 352
band associated with the highest AR-related SWE change (as identified in section 3.2). 353
Accordingly, we used the elevation bands (1000-1500), (1500-2000), and ((2000-2500) 354
to represent the North Cascades, South Cascades, and Sierra Nevada region 355
respectively. It is worth noting that the same elevation bands, selected for SWE change, 356
resulted also in the maximum AR induced precipitation for each region (Figure not 357
shown). Figures 8, 9, and 10 show the range of precipitation amounts during dry and 358
wet years for the North Cascades, South Cascades, and Sierra Nevada regions 359
respectively. The box plot for each month represents the range of precipitation or SWE 360
change depth produced by AR dates identified during dry or wet years. Comparing the 361
three regions during wet years, we notice higher precipitation amounts in the Sierra 362
16
Nevada region, which decrease moving north. The maximum interquartile range of 363
precipitation amounts produced by AR dates is in January (170 AR dates during 6 wet 364
years) at the AR landfalling latitude 40° with 113 mm. 365
In addition to larger number of AR dates in wet years, it is also clear that wet 366
year AR dates produce heavier precipitation compared to dry years during the cold 367
season months. For instance, the median AR daily precipitation at the AR landfalling 368
latitude (50°) for January is 40 mm in wet years compared to median precipitation of 369
only 13 mm during January of dry years. Such differences are attributable to differences 370
in both the number of AR events and AR precipitation intensities between wet and dry 371
years. The differences in the intensity of AR precipitation between dry and wet years 372
agree in general with the results of Dettinger (2016) (see his Figure 5) who showed that 373
the wettest 5% of precipitation events (not segregated into AR-vs non-AR, however the 374
largest events are disproportionately associated with ARs) in northern California 375
account for a higher fraction of total (water year) precipitation in wet years than in dry 376
years. 377
The differences in AR induced precipitation between dry and wet years become 378
much smaller in February and March. In particular, for the North Cascades dry year 379
ARs produce precipitation with very comparable values to wet years (Figures 9 and 380
10). For example, the median AR daily precipitation produced in March at AR 381
landfalling latitude (50°) over the North Cascades is 22.9 mm during wet years 382
compared to 21.2 mm during dry years. Furthermore, when comparing between 383
northern and southern ARs during dry years, Figures 8, 9, and 10 indicate that southern 384
ARs falling on Sierra Nevada produce much less intense precipitation with a median 385
precipitation less than 5 mm in all winter months. Such differences in the intensity of 386
AR precipitation between dry and wet years agree with the results of Dettinger (2016) 387
17
who showed that ARs drives the wettest 5% events in northern California with higher 388
precipitation amounts compared to drier events. 389
Similar patterns are also present for positive SWE changes in the three regions 390
where higher snow accumulation is observed in the wintertime months (December to 391
March) during wet years (Figures 11,12, and 13). The highest snow accumulation is 392
produced by the ARs at landfalling latitude (40°) where the median positive SWE 393
change is about 44.5 mm in January with interquartile range of 96 mm. The differences 394
between wet and dry years in the magnitudes of positive SWE changes are smaller for 395
the North and South Cascades (Figures 12 and 13). In addition, the AR events occurring 396
in the North Cascades in March during dry years resulted in higher snow accumulation 397
compared to wet years. For example, the median of positive SWE changes during 398
March for ARs with landfall at latitude 50° during dry years is 16 mm (interquartile 399
range=20 mm) compared to 10 mm (interquartile range=6 mm) during wet years. The 400
negative SWE changes during dry and wet years are comparable although some parts 401
of wet year winters have higher snowmelt, e.g., ARs in November at landfalling latitude 402
42.5 over the South Cascades and January at landfalling latitude 37.5 over the Sierra 403
Nevada region. Earlier snowmelt (November to January) during ARs in wet years 404
occurs in the North and South cascades regions and could also explain the existence of 405
rain-on-snow conditions during AR dates. 406
Under the right conditions, AR events can serve as the sources of major storms 407
that end droughts on the western US or “drought busters” as coined by Dettinger (2013). 408
The AR role in this context is evident by the comparison of wet vs dry years over three 409
mountainous subregions in the western United States, where some dry years are 410
followed by wet years. For example, the (1976-1977) drought years in the Sierra 411
18
Nevada were followed by a very wet 1978 with 118 AR dates (compared to only 60 412
and 27 AR dates in 1976 and 1977, respectively). 413
3.4 Warm AR Events 414
We investigated the effects of surface temperatures associated with AR events 415
on the daily precipitation depth and SWE change by considering the largest warm AR 416
events (20% of AR dates with the highest surface temperature). Figure 14 shows the 417
precipitation at different landfalling latitudes for each of the three regions during these 418
warm events. We also compare the precipitation produced during these warm events 419
(the red box plots in Figure 14) with that produced by all upper 10th percentile AR daily 420
precipitation (the blue box plots in Figure 14). For all the three regions, warm AR events 421
are associated with the most extreme precipitation as indicated by Figure 14 where the 422
warm event precipitation lies above the median of all the upper 10th percentile daily 423
precipitation. The warm AR events resulted in higher precipitation along the Sierra 424
Nevada region compared to the North and South Cascades regions which is attributed 425
to warmer and more water vapor contained in south-coast ARs as concluded by Neiman 426
et al. (2008). For instance, the largest precipitation depth is produced along the 427
latitudinal band (40°) in the Sierra Nevada region with a median depth of 131 mm 428
compared to 89 mm at AR landfalling latitude (42.5°) (the largest median precipitation 429
over the North and South Cascades). The high precipitation amounts produced by warm 430
AR events, especially those falling at southern latitudinal bands, is consistent with 431
Lueng and Qian (2009) who concluded large AR-induced precipitation and warmer 432
temperature over most of the mountain ranges in western US. 433
Unsurprisingly, warm AR events have lower median SWE accumulation 434
compared to the upper 10th percentile AR daily positive SWE changes (Figure 15). The 435
largest median positive SWE change occurs for ARs with landfalling latitude (40°) with 436
19
127 mm. The average surface temperature for the warm landfalling ARs at this latitude 437
is 1.1 °C which is the lowest average temperature during the warm AR dates with snow 438
accumulation. The patterns are similar for snowmelt rates where smaller negative SWE 439
changes are associated with all of the AR landfalling latitudes during warm events 440
compared to all upper 10th percentile AR daily negative SWE changes. For example, 441
although the highest AR-related negative SWE changes occur along the northern 442
cascades region at latitude (50°), the largest warm AR events (with average 443
temperature=7.2 °C) resulted in lower snowmelt (median -29 mm and 75th percentile -444
33 mm) compared to those produced by the upper 10th percentile AR daily negative 445
SWE change (median -38 mm). 446
4. Conclusions 447
We utilized hydrometeorological outputs retrieved from dynamically 448
downscaled atmospheric reanalyses (NCEP/NCAR) using the WRF mesoscale 449
numerical weather prediction model to characterize precipitation amounts and 450
snowpack changes during atmospheric river dates over the period 1949-2015. We focus 451
on three regions of the coastal western U.S.: North Cascades, South Cascades, and 452
Sierra Nevada. We find that: 453
1) Landfalling ARs in the southern part of our domain (Sierra Nevada region) 454
resulted in higher precipitation amounts than in the two northern regions, 455
although more AR events occur on average in the northern regions. At all 456
southern landfalling latitudes (less than 42.5°), more extreme events occur 457
in January and February than earlier in the winter. 458
2) The highest positive SWE change induced by ARs in all regions occur in 459
January, with the highest snow accumulations occurring in the Sierra 460
Nevada region. AR-related negative SWE changes generally occur during 461
20
the cold months. Some of the most extreme snowmelt conditions 462
accompanied major flooding events. High AR-related snowmelt explains 463
the early snowmelt and the potential existence of rain-on-snow (ROS) 464
conditions associated with AR dates. 465
3) ARs play an important role as drought buster as evident by the comparison 466
of the statistics of ARs in wet vs dry years across the three regions. In the 467
Sierra Nevada region, ARs during wet years are not only greater in number 468
than in dry years, but they also produce heavier precipitation and snow 469
accumulation per event compared to dry years. In contrast, precipitation 470
amounts during AR events are very comparable in wet and dry years in the 471
two northern regions, the main difference is that the number of AR dates is 472
smaller during dry years. 473
4) Warm AR events result in more extreme precipitation events among the 474
upper 10th percentile AR daily precipitation than cold AR events, especially 475
in the Sierra Nevada region. Conversely, Lower SWE accumulation and 476
snowmelt are produced by warm AR events in the northern and southern 477
regions when compared to all upper 10th percentile AR daily SWE changes. 478
479
The implications of AR characterization presented in this study are clearly 480
evident in water management, flood and drought risk assessment, and operational 481
weather forecasting. For instance, better management strategies of water resources in 482
western US are recommended to cope with the expected increase in AR induced 483
precipitation with changing climate. In addition, future efforts will be essential to 484
investigate the impacts of ARs precipitation on modeling the water budget of western 485
US basins and how reservoir operations can be altered based on AR forecasting. 486
21
Furthermore, highlighting the spatial differences in areas impacted by AR dates can be 487
a key input for the future development of atmospheric river observatory (ARO) 488
systems. Such systems are currently being developed to detect and monitor the 489
atmospheric forcing that leads to heavy precipitation along the coastal range and inland 490
mountains (Neiman et al. 2009; White et al. 2009). 491
Acknowledgement 492
This study was performed under support from the Strategic Environmental Research 493
and Development Program (SERDP) – Project #RC-2513 granted to the University of 494
California, Los Angeles, and subcontract to the University of Washington. The first 495
author was also supported by NASA grant NNX16AC63G to the University of 496
Washington. The NCEP/NCAR AR catalog used in this paper can be obtained from 497
https://ucla.box.com/ARcatalog. The WRF downscaled reanalysis data is available on 498
the Dropbox folder: https://www.dropbox.com/home/Public/SERDP/Northwest 499
22
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Tables 726
Table 1: Percentage agreement between Neiman AR catalog and NCEP/NCAR catalog 727
during cold seasons of eight water year overlap period (1998-2005). 728
NARa
% Agreement
SARb
% Agreement
Total AR
(NAR+SAR)
% Agreement
±0 Day 84 66 73
±1 Day 98 80 87
±2 Day 98 86 90
aNAR = Northern AR region, north to 41° latitude . 729
bSAR = Southern AR region, south to 41° latitude. 730
731
Table 2: Exceedance probability of negative SWE changes (snowmelt) during AR 732
flooding events. 733
Region Latitudinal Band Flooding event date
Average Negative
SWE Change
Exceedance Probability
North Cascades
50 DEC 1-3, 2007 -21.5 11.5 47.5 -22.0 5.4
South Cascades
47.5 NOV 17-19,
1996
-31.1 3.0 45 -32.0 4.5
42.5 -30.3 3.6
Sierra Nevada 40
DEC 26, 1996 to JAN 3, 1997
-29.4 4.4 37.5 -29.1 5.4 35 -53.3 0.2
734
735
33
Table 3: Dry and wet years identified for the three mountainous regions based on total 736
surface precipitation during the 67 year study period (1949-2015). The number in 737
brackets indicates the total number of AR dates identified in the cold season during dry 738
and wet years. 739
Dry Years Wet Years
North
Cascades
South
Cascades
Sierra
Nevada
North
Cascades
South
Cascades
Sierra
Nevada
1970 (88) 1962 (74) 1964 (53) 1953 (100) 1950 (70) 1952 (76)
1977 (27) 1977 (27) 1972 (69) 1954 (95) 1954 (95) 1969 (92)
1979 (61) 1992 (72) 1976 (60) 1967 (100) 1956 (96) 1974 (102)
1985 (47) 1993 (76) 1977 (27) 1974 (102) 1974 (102) 1983 (120)
1993 (76) 2001 (23) 1990 (37) 1999 (96) 1997 (102) 1986 (87)
2001 (23) 2005 (71) 2014 (67) 2007 (101) 1999 (96) 1995 (113)
740
34
Figure Captions 741
Figure (1) WRF model domain covering western United States (solid black box) and 742
the three study regions delineated using the Commission for Environmental 743
Cooperation Ecological regions of North America, Level III: 1) North Cascades; 2) 744
South Cascades; and 3) Sierra-Nevada. The red line represents the inland boundary of 745
the coastal domain considered in our analysis. 746
Figure (2) a) Number of all AR dates for each month during the wintertime season 747
(November to March) using the NCEP/NCAR AR catalog. b) Same as (a) but when 748
only considering the AR dates that resulted in upper 10th percentile precipitation. 749
Figure (3) Scatter plots for April 1st SWE as simulated by WRF Noah-MP and VIC 750
models for 67-year record (1949-2015) and averaged over three mountainous regions: 751
North Cascades, South Cascades, and Sierra Nevada. 752
Figure (4) The average of upper 10th percentile daily precipitation over the western 753
coastal domain during the winter (November to March) ARs for water years 1949-754
2015 (Precipitation amounts less than 14 mm are displayed in white). 755
Figure (5) Fraction of AR dates with the upper 10th percentile of daily precipitation 756
for each latitudinal band by winter month. 757
Figure (6) Left panel: 1-day AR precipitation depths and frequencies by AR 758
landfalling latitude. Right panel: same as left panel but for 3-day totals. 759
Figure (7) Average of upper 10th percentile positive and negative changes in daily 760
Snow Water Equivalent (SWE) during the upper 10th percentile of daily precipitation 761
on AR dates by winter month. Daily SWE changes are calculated at the AR 762
landfalling latitudes crossing the domain as shown in Figure 1 for different elevation 763
bands. 764
35
Figure (8) Daily precipitation associated with AR dates in North at elevation band 765
(1000-1500 m) and for different AR landfalling latitudes during dry and wet years 766
(For each month, the left and right boxplot represents the precipitation during dry and 767
wet years respectively). On each box, the central mark indicates the median depth, 768
and the bottom and top edges of the box indicate the 25th and 75th percentiles (or 769
interquartile range), respectively. The whiskers of the box extend to the most extreme 770
data points not considered outliers. 771
Figure (9) Same as Figure (8) but for the South Cascades at elevation band (1500-772
2000 m). 773
Figure (10) Same as Figure (8) but for the Sierra Nevada at elevation band (2000-774
2500 m). 775
Figure (11) Daily SWE change induced by AR dates in North Cascades at elevation 776
band (1000-1500 m) and for different AR landfalling latitudes during dry and wet 777
years (For each month, the left and right boxplot represent the precipitation during dry 778
and wet years respectively). 779
Figure (12) Same as Figure (11) but for the South Cascades at elevation band (1500-780
2000 m). 781
Figure (13) Same as Figure (11) but for the Sierra Nevada at elevation band (2000-2500 782
m). 783
Figure (14) Boxplot of the upper 10th percentile daily precipitation considering all AR 784
dates and only warm events for each mountainous region at different AR landfalling 785
latitudes. 786
Figure (15) Boxplot of the upper 10th percentile daily positive and negative SWE 787
change considering all AR dates and only warm events for each mountainous region at 788
different AR landfalling latitudes. 789
36
Figures 790
791
Figure (1) WRF model domain covering western United States (solid black box) and 792
the three study regions delineated using the Commission for Environmental 793
Cooperation Ecological regions of North America, Level III: 1) North Cascades; 2) 794
South Cascades; and 3) Sierra-Nevada. The red line represents the inland boundary of 795
the coastal domain considered in our analysis. 796
797
37
798
Figure (2) a) Number of all AR dates for each month during the wintertime season 799
(November to March) using the NCEP/NCAR AR catalog. b) Same as (a) but when 800
only considering the AR dates that resulted in upper 10th percentile precipitation. 801
802
Figure (3) Scatter plots for April 1st SWE as simulated by WRF Noah-MP and VIC 803
models for 67-year record (1949-2015) and averaged over three mountainous regions: 804
North Cascades, South Cascades, and Sierra Nevada. 805
38
806
Figure (4) The average of upper 10th percentile daily precipitation over the western 807
coastal domain during the winter (November to March) ARs for water years 1949-808
2015 (Precipitation amounts less than 14 mm are displayed in white). 809
39
810
Figure (5) Fraction of AR dates with the upper 10th percentile of daily precipitation 811
for each latitudinal band by winter month. 812
813
Figure (6) Left panel: 1-day AR precipitation depths and frequencies by AR 814
landfalling latitude. Right panel: same as left panel but for 3-day totals. 815
40
816
Figure (7) Average of upper 10th percentile positive and negative changes in daily 817
Snow Water Equivalent (SWE) during the upper 10th percentile of daily precipitation 818
on AR dates by winter month. Daily SWE changes are calculated at the AR 819
landfalling latitudes crossing the domain as shown in Figure 1 for different elevation 820
bands. 821
822
41
823
824
Figure (8) Daily precipitation associated with AR dates in North at elevation band 825
(1000-1500 m) and for different AR landfalling latitudes during dry and wet years 826
(For each month, the left and right boxplot represents the precipitation during dry and 827
wet years respectively). On each box, the central mark indicates the median depth, 828
and the bottom and top edges of the box indicate the 25th and 75th percentiles (or 829
interquartile range), respectively. The whiskers of the box extend to the most extreme 830
data points not considered outliers. 831
42
832
Figure (9) Same as Figure (8) but for the South Cascades at elevation band (1500-833
2000 m). 834
43
835
Figure (10) Same as Figure (8) but for the Sierra Nevada at elevation band (2000-836
2500 m). 837
838
44
839
840
Figure (11) Daily SWE change induced by AR dates in North Cascades at elevation 841
band (1000-1500 m) and for different AR landfalling latitudes during dry and wet 842
years (For each month, the left and right boxplot represent the precipitation during dry 843
and wet years respectively). 844
45
845
Figure (12) Same as Figure (11) but for the South Cascades at elevation band (1500-846
2000 m). 847
46
848
Figure (13) Same as Figure (11) but for the Sierra Nevada at elevation band (2000-849
2500 m). 850
851
47
852
853
Figure (14) Boxplot of the upper 10th percentile daily precipitation considering all 854
AR dates and only warm events for each mountainous region at different AR 855
landfalling latitudes. 856
48
857
Figure (15) Boxplot of the upper 10th percentile daily positive and negative SWE 858
change considering all AR dates and only warm events for each mountainous region 859
at different AR landfalling latitudes. 860
