CLIMATE RESEARCHClim Res

Vol. 33: 215–227, 2007 Published April 20

1. INTRODUCTION

An important area of recent research is the predic-tion of climate change in the Pacific Northwest(PNW). Several investigators have examined thepotential impacts of greenhouse gas induced climatechange on the natural resources and ecosystems ofthe PNW (Hamlet & Lettenmaier 1999, Miles et al.2000, Mote et al. 2003). Modeling results of 8 GeneralCirculation Models (GCMs) show that while tempera-ture in the PNW is expected to increase, trendsregarding winter and summer season precipitationvary widely up or down depending on the GCM(Mote et al. 2003), with great uncertainty in the pre-diction of precipitation in each season. Regional cli-mate modeling studies (Payne et al. 2004, amongothers) show similar results. In general, the modelingresults suggest a continuation of trends observed inthe PNW during the 20th century (Mote 2003). One

drawback in developing regional climate change pre-dictions from climate models is the poor resolution ofthe models (IPCC 2001). Due to this and the climaticcomplexity of the PNW, it is important to understandthe sub-regional variability of spatial patterns andtemporal trends.

The PNW is typically defined as the states of Oregon,Washington, and Idaho, as well as the coastal parts ofBritish Columbia and southeastern Alaska. However,in this study, we define the PNW as Oregon, Washing-ton, and Idaho in their entirety, western Montana,northern California, and northern Nevada. Although itis outside the traditional boundaries of the PNW, north-ern Nevada is included in this study to demonstratethe similar precipitation regimes between adjacentphysiographic regions (e.g. the Great Basin). More-over, the climate divisions of northern Nevada are geo-graphically similar to those of southeastern Oregonand southern Idaho.

© Inter-Research 2007 · www.int-res.com*Email: james.a.miller@asu.edu

Regionalization and trends in winter precipitationin the northwestern USA

James A. Miller1,*, Gregory B. Goodrich2

1Department of Geography, Arizona State University, PO Box 870104, Tempe, Arizona 85287, USA2Department of Geography and Geology, Western Kentucky University, Rm. 31066, 1906 College Heights Boulevard,

Bowling Green, Kentucky 42101, USA

ABSTRACT: Recent modeling studies have predicted that while temperature is expected to increaseover the next few decades in the Pacific Northwest (PNW), model predictions of winter season pre-cipitation are highly variable. Implications of potential climate change were explored by regionaliz-ing PNW climate. Using rotated Principal Components Analysis (PCA), sub-regions within the PNWwere developed for winter season precipitation from 1895–2003. Analyses include synoptic discus-sions based on trends of the time series of the component scores as well as an examination of the sen-sitivity to the El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) for eachsub-region. Four sub-regions during the winter season were created, based on temporal variability ofprecipitation. While previous studies suggest that there is regional coherence in PNW precipitation,analysis of the time series of the component scores for each sub-region suggests otherwise. Severalsub-regions show widely diverging trends in winter season precipitation over the past 30 yr, a periodthat also coincides with significant warming across the PNW. Differences in the sign and strength ofcorrelations between ENSO and the PDO with winter season precipitation occur between the sub-regions, which further suggests a lack of coherence in the PNW.

KEY WORDS: Pacific Northwest · Sub-regional variability · Principal Components Analysis · ENSO ·PDO · Precipitation · PCA · Teleconnection

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The complex terrain of the PNW drives much of theintra-regional climatology (Fig. 1). Areas west of theCascades have a maritime climate with abundant win-ter precipitation and relatively dry summers. In thisregion, as much as 80% of the annual precipitationoccurs between October and March. Areas east of theCascades also have a winter wet and summer dryseason, but absolute differences between winter andsummer precipitation are generally smaller. In the leeof the Cascades, winter season (October–March) pre-cipitation accounts for 50% to 70% of the annual total,considerably less than in the windward locations. Infact, cold season precipitation accounts for less thanhalf of the annual precipitation in parts of southeasternIdaho and western Montana. However, elevation is animportant control on the seasonality of precipitationeast of the Cascades. For instance, some lowland loca-tions have a trinomial distribution in precipitation. Thisdemonstrates the intra-regional variability of precipi-tation regimes.

The hydrology of the PNW is greatly dependent onwinter season precipitation. Tree growth is well corre-lated with the amount of winter season precipitation(Peterson & Peterson 2001, Mote et al. 2003, Knapp etal. 2004), while the spring and summer streamflow that

results from the melting snowpack is important forhydropower, irrigation and drinking water (Mote et al.2003). There is also evidence that antecedent precipi-tation anomalies are correlated with anomalous sum-mer fire activity, though this relationship varies region-ally and temporally (Westerling et al. 2003, Westerlinget al. 2006). However, there is considerable uncertaintyabout the relationship between summer fire activityand antecedent precipitation (Keeley 2004).

Two teleconnections drive much of the long-termprecipitation variability in the PNW. Annual variabilityis largely controlled by the El Niño/Southern Oscilla-tion (ENSO), which describes variability in the oceanand atmosphere of the tropical Pacific on a 3 to 7 yrcycle. La Niña is associated with wet and cold wintersin the PNW, while El Niño is associated with dry andwarm winters (Redmond & Koch 1991). While ENSOrepresents variability within the tropical ocean andatmosphere, the Pacific Decadal Oscillation (PDO) rep-resents variability within the extratropical ocean andatmosphere. The PDO characterizes low frequencychanges in sea surface temperatures (SST) in thePacific Ocean with a period of roughly 50 yr (Mantua etal. 1997). The warm (cold) phase of the PDO has asimilar climatic effect on the PNW as El Niño (La Niña).

The longer term PDO may have theability to ‘modulate’ the strength of theENSO climate signal on winter pre-cipitation, as suggested by Gershunov& Barnett (1998), McCabe & Dettinger(1999,2002),Goodrich (2004),and others.The PDO is also linked to decadaldrought patterns in the western UnitedStates (Hidalgo 2004, McCabe et al.2004).

While the physical mechanismsbehind ENSO have been well docu-mented, there is still considerableuncertainty regarding the dynamics ofthe PDO as well as the complexity ofits relationship to ENSO (Gutzler et al.2002, Brown & Comrie 2004). Miller& Schneider (2000) outline severaloptions for multidecadal variability inthe north Pacific Ocean, which rangefrom stochastic surface wind stressto tropical–extratropical interactions.Others have suggested that the PDOrepresents the ENSO signal plus rednoise (Gedalof et al. 2002, Newman etal. 2003). More recently, Schneider &Cornuelle (2005) suggested that thePDO is a function of the superpositionof SST fluctuations emanating fromdynamical modes such as ENSO and

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Fig. 1. Relief map of the Northwest USA with the 36 climate divisions used in the analysis numbered and outlined in black

Montana

Idaho

Utah

Nevada

California

Oregon

Washington

Miller & Goodrich: Pacific Northwest precipitation trends

the Kuroshio-Oyashio Extension, among others. Re-gardless of the relationship between the PDO andENSO, the usefulness of ENSO as a seasonal predic-tive tool does depend on the phase of the PDO. Onegoal of this study was to determine the extent of sub-regional variability between the impact of ENSO andthe PDO on winter season precipitation in the PNW.

Principal Components Analysis (PCA) has oftenbeen used to investigate climate regionalization usingeither drought indices or precipitation data (Mitchell &Blier 1997, Comrie & Glenn 1998, Cook et al. 1999,Englehart & Douglas 2002, Keyantash & Dracup 2004,McCabe et al. 2004). The studies use PCA to under-stand the underlying spatial structure of the climatedata. The component scores from the PCA can thenbe used to examine temporal trends within the givensub-regions.

This study consisted of 4 parts. (1) PCA was used todevelop climatic sub-regions within the PNW based onregions of similar variance for winter season pre-cipitation. (2) Synoptic climate analysis of each of theprincipal component (PC) regions was performed todemonstrate the physical efficacy of the resulting spa-tial patterns. (3) Time series of the component scoresfrom the PCA for each sub-region were created toexamine long-term trends. It has been suggestedthat the various sub-regions of the PNW, while experi-encing significantly different absolute precipitationamounts, have a regional coherence in temporal vari-ability (Mote et al. 2003). We test this assertion with adifferent methodological approach. The effects ofENSO and the PDO on sub-regional precipitation wereexamined to establish the great variability in tele-connection sensitivity within the greater PNW.

2. DATA AND METHODS

2.1. Data

2.1.1. Precipitation

This study considers the winter season in the PNW tooccur from October through March. Climate divisiondata used in this study were obtained online from theNOAA National Climatic Data Center (NCDC) (avail-able at www.cdc.noaa.gov/Timeseries), and therewere no missing data. Monthly October–March pre-cipitation was summed for all the climate divisions ofWashington, Oregon and Idaho, the northern 3 climatedivisions of California, the northern 2 climate divisionsof Nevada, and the 2 westernmost climate divisions ofMontana for the years 1895–2003.

Each climate division represents a simple un-weighted average from all representative stations

within that division (Guttman & Quayle 1996). Whiledata from 1931 to the present were generated usingthe above method, from 1895–1930 a regression tech-nique was used based on available United StatesDepartment of Agriculture (USDA) statewide aver-ages, which has reduced the variance for these years(Guttman & Quayle 1996). Because some researchers(Keim et al. 2003) have argued that the quality of thepre-1931 climate division data may introduce spuriouslong-term trends, all analyses were repeated using thesubset of years 1931–2003 and only minor differencesin sub-regions were found. Moreover, the componenttime series scores using the shorter period of recordwere basically unchanged from the PCA based on thecomplete dataset. Inhomogeneity issues regardingtime-varying distributions of station data were re-conciled by Gutzler et al. (2002).

While performing PCA on non-normal data hasbeen shown to be acceptable in certain cases (Rum-mel 1970), the precipitation data were neverthelessanalyzed for normality. Using standardized coef-ficients of skewness and kurtosis, we found that only1 of 36 climate divisions had a significant deviationfrom normality (based on a 95% confidence level).A square root transformation eliminated the signifi-cant deviation from normality. However, the resultsof the PCA were not substantially different whetherraw or transformed data were used, as found inComrie & Glenn (1998).

2.1.2. Teleconnections

SST data from the Niño 3.4 region were used to rep-resent ENSO. Niño 3.4 is an area bounded latitudinally5° N and S of the equator and longitudinally between120° and 170° W. A common method to determinevarious ENSO events is outlined in Trenberth (1997),and states that when the 5 mo moving average of Niño3.4 anomalies exceeds +0.4 (–0.4) for 6 consecutivemonths, an El Niño (La Niña) is said to occur. Thestrongest lagged relationship between Niño 3.4 SSTsand winter precipitation in the western United Statesoccurs from September to November (Harshburger etal. 2002). Therefore, ENSO events are classified byusing the September–November averaged Niño 3.4anomalies preceding the winter period. The ENSO val-ues were split into 3 nearly equal groups with thewarmest third classified as El Niño, the middle third as‘neutral’ and the coldest third as La Niña. This clas-sification produces a set of ENSO events that is similarto those obtained by using the method outlined inTrenberth (1997). The dataset used in this study isthe Kaplan extended Niño 3.4 dataset (Kaplan et al.1998), obtained online from the International Research

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Institute for Climate Prediction (IRI) data library(http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO34/).

The PDO characterizes low frequency changes inSSTs in the Pacific Ocean with a period of roughly50 yr. The PDO index is the leading principal compo-nent or eigenvector of the mean monthly SSTs in thePacific Ocean north of 20° N latitude (Mantua et al.1997). Positive values of the index refer to above nor-mal SSTs along the west coast of North America andalong the equator and below normal SSTs in the cen-tral and western North Pacific around 45° N latitude.Negative values of the index refer to the oppositedistribution of SSTs in these same areas. Accordingto Mantua et al. (1997) there have only been 3 com-plete phases of the PDO since 1925 (warm:1925–1946 and 1977–1998, cold: 1947–1976). Morerecently, Mantua & Hare (2002) established that thePDO was in the cold phase from 1890–1924. Whilesome have speculated that the PDO may have shiftedto a cold phase beginning in 1999 (Hare & Mantua2000, Schwing & Moore 2000), most scientists agreethat this may not be known for several years (Man-tua & Hare 2002, Chavez et al. 2003). In most of ourPDO analyses, we categorized the PDO as either a‘warm’ or a ‘cold’ event according to the yearly defi-nitions noted above from Mantua et al. (1997). Forthe correlation analyses only, antecedent PDO valueswere used in a similar manner to the antecedentENSO values described above. Certain analyseswere truncated to 1998 because of the uncertainty ofthe current PDO phase after the strong 1997–98El Niño event. The PDO dataset used in this studywas obtained online from the Joint Institute for theStudy of Atmosphere and Oceans (JISAO) at theUniversity of Washington (http://jisao.washington.edu/pdo/PDO.latest).

2.1.3. NCEP reanalysis data

To demonstrate that the regional precipitation pat-terns revealed by the PCA analysis are physically real-istic, we examined synoptic atmospheric circulationpatterns associated with wet and dry conditions ineach PC region using 700 hPa height data producedunder the auspices of the NCEP Reanalysis Project(Kistler et al. 2001). The height data were downloadedfrom the Climate Diagnostics Center (CDC) (www.cdc.noaa.gov/) and cover the period 1949–2003. Weselected the monthly 700 hPa field, because this levelis close to the average crest height in the westernUnited States and is generally considered the mostappropriate for weather analysis in this region(McCabe & Legates 1995).

2.2. Methods

2.2.1. PCA

One goal of this study was to use PCA to identifygroups of climate divisions in the PNW with similarvariance structures that can be explained by synopticmechanisms. Often, PCA is done on individual stationdata to determine climate regionalization (Woodhouse& Kay 1990, White et al. 1991, Baeriswyl & Rebetez1997, Comrie & Glenn 1998, Romero et al. 1999). How-ever, the use of climate division data in PCA is alsocommonly employed (e.g. Mitchell & Blier 1997,Chang & Smith 2001, Gutzler 2004, Keyantash &Dracup 2004, McCabe et al. 2004). Karl & Koscielny(1982) demonstrated that performing PCA on non-gridded data can lead to distorted loading patterns, butdue to our prior knowledge of PNW climate, and thefact that this analysis groups climate divisions insteadof creating contoured loading maps, problems withusing non-gridded data should be avoided.

Because each climate division represents the un-weighted monthly average of all representative sta-tions within that division, our PCA placed climatedivisions into several homogeneous groups. Like Com-rie & Glenn (1998), we used the correlation matrix, asopposed to the covariance matrix, to allow climate divi-sions under similar synoptic conditions to be groupedtogether. The assumption was made that all represen-tative stations within each climate division share asimilar temporal pattern of precipitation, even if totalprecipitation values are different. To test this, repre-sentative stations with long-term precipitation recordsfrom each climate division were selected and a PCAwas conducted on this subset of stations. The sub-regions created by the PCA were comparable regard-less of whether climate divisions or individual stationswere used, and the resultant time series of the compo-nent scores were also similar.

After the initial PCA, 2 popular rotation methodswere used to determine group membership of the cli-mate divisions; orthogonal varimax and oblique (DirectOblimin) with an obliquity parameter γ = 0. Both rota-tions have been used in climate regionalization stud-ies, with oblique rotation, which allows for slight corre-lation between vector swarms, often favored for itsmore realistic loading patterns and relationship withsynoptic patterns (White et al. 1991, Comrie & Glenn1998). However, we found little difference betweenthe 2 rotation methods and ultimately preferred theorthogonal solution, because it tends to preserve maxi-mum loading of individual components (Cook et al.1999, Frei & Robinson 1999, McCabe et al. 2004).Finally, the Scree test (Cattell 1966) and eigenvalueseparation test (North et al. 1982) were used to deter-

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mine the number of factors to retain from the PCAanalysis. The maximum loading was used to determinegroup membership for each climate division.

2.2.2. Synoptic climatology and PC regions

Using National Center for Environmental Protection(NCEP) reanalysis atmospheric height data for 1949–2003, correlation maps demonstrating the relationshipbetween PC seasonal precipitation and the concurrent700 hPa field were created. To show the physicalrepresentativeness of the climate regions, seasonalprecipitation amounts from individual stations withineach PC region were also correlated to 700 hPa heightsfor winter season precipitation. Klein & Bloom (1987)showed that the assumption of a linear relationshipbetween seasonal height fields andprecipitation is valid for diagnosingcirculation patterns associated withanomalous conditions.

2.2.3. PC region time series analysis

Time series of the component scoreswere created to examine periods ofanomalous precipitation during the20th century for each of the PCA gen-erated sub-regions for winter seasonprecipitation. Each of the time serieswas smoothed with an 11 yr low-pass Gaussian filter to show decadaltrends; other wavelengths (7 to 9 yr)were examined with little differencenoted.

2.2.4. Teleconnection analysis

We performed a correlation analysisbetween the time series of the compo-nent scores and the ENSO and PDOindices to illustrate the variable natureof winter season teleconnectivity inthe PNW sub-regions. Initially, weexamined the effects of ENSO and PDOseparately. Next, we analyzed theinfluence of PDO phase on ENSO in themanner of Gershunov & Barnett (1998),Gutzler et al. (2002), and others. Fi-nally, we examined how the relation-ship between precipitation and ex-treme values of ENSO and PDO foreach of the sub-regions varies.

3. RESULTS AND ANALYSES

3.1. Regions

The orthogonal rotation produced 4 eigenvectorsthat met our selection criteria. Table 1 shows the vari-ance explained for both the unrotated and varimaxrotated eigenvectors. More than 83% of the variancein winter season precipitation is explained by the 4eigenvectors.

As illustrated in Fig. 2, Region PC 1 (green) containsclimate divisions that are considered the ‘classic’Pacific Northwest spatial pattern comprising coastalOregon and Washington, as well as the western valleysof both states. In addition, the eastern highlands ofWashington and the northern tip of Idaho are includedin Region 1. Overall, the wettest areas of the PNW are

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Initial eigenvalues Rotation sums of squared loadingsPC Eigenvalue % of s2 Cumulative Eigenvalue % of s2 Cumulative

1 21.4 59.4 59.4 10.0 27.9 27.92 5.6 15.5 74.9 8.1 22.6 50.53 1.9 5.2 80.1 6.6 18.3 68.84 1.4 4.0 84.1 5.5 15.3 84.1

Table 1. Unrotated and varimax rotated percent of variance (s2) explained for the4 dominant principal components of winter season (October–March) precipitation

Fig. 2. Rotated varimax loadings of climate divisions for the 4 dominant princi-pal components (PC 1–4 principal components) of October–March precipitation

using the maximum loading rule

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contained within PC 1 with more than 1000 mm of pre-cipitation averaged between October and March inhalf of the divisions representing this sub-region.

The sub-region identified by Region PC 2 (yellow)includes northern California, northwestern Nevada,southern Oregon, and central Washington. We weresurprised by the spatial pattern in Region 2, mostnotably the common classification of central Washing-ton and northern California. Due to this unexpectedresult, we carefully examined precipitation totals fromDivisions CA 1 and WA 3, 5, 7 & 8 for the 108 winters inthe study. While there is a statistically significant rela-tionship (R2 = 0.32) between winter season precipita-tion in WA 3 and WA 7, the correlation between CA 1and WA 7 (R2 = 0.55) precipitation is more robust(Fig. 3). In fact, each of the 3 CA climate divisions ismore significantly correlated to both WA 7 and WA 8precipitation than either of these 2 WA divisions is cor-related to any of the western Washington climate divi-

sions. We also examined the correlation between rep-resentative stations located in each climate divisionand obtained similar results. In section 3.4 we presentsynoptic climate analyses that explain the statisticalrelationship between northern California and centralWashington winter season precipitation.

Region PC 3 (brown) contains the valleys of theSnake River (Divisions ID 5, 7 & 9) and northern GreatBasin (Divisions NV 2 & ID 6). Compared to the otherPC regions, precipitation amount in Region PC 3 ismore spatially homogenous. For instance, the wettestparts of the region, MT 2 and ID 10, receive approxi-mately 400 mm of precipitation per year, while the dri-est parts receive about 275 mm. Furthermore, precipi-tation is generally more evenly distributed throughoutthe year in Region PC 3 than in the other sub-regions.

Region PC 4 (blue) contains a complex array of climatedivisions including the southern highlands of Oregon,eastern Oregon, central Idaho, and western Montana. As

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Fig. 3. Relationship between 1896–2003 winter season precipitation of climate divisions for: (a,c) northern California (CA 1) andcentral Washington (WA 7 & WA 8); (b,d) relationship between Puget Sound (WA 3) and central Washington (WA 7 & WA 8)

Miller & Goodrich: Pacific Northwest precipitation trends

shown in Fig. 1, Region PC 4 contains several sig-nificant mountain ranges exceeding 3000 m in elevation.Therefore, we classify Region PC 4 as the mountainousinland PNW given the topographic similarity betweenthe climate divisions composing this sub-region.

3.2. Correlation analysis

The regionalization produced by the varimax rotationrevealed physically realistic spatial patterns of seasonalprecipitation. However, in some instances (e.g. RegionPC 2) the regionalization was counterintuitive. In thissection, we analyze the synoptic controls on precipita-tion in each of the 4 sub-regions. This analysis willdemonstrate that each sub-region is physically realisticgiven the unique synoptic controls on winter seasonprecipitation in each of the derived regions.

Fig. 4 illustrates the correlations between the Octo-ber–March 700 hPa height field and precipitation foreach of the 4 PC regions. In Region PC 1, the ‘classic’

PNW pattern, wet conditions are associated withanomalous cyclonic circulation centered over south-eastern Alaska. This atmospheric pattern, which isspatially similar to the positive phase of the East PacificOscillation found by Barnston & Livezey (1987), pro-motes west to southwesterly flow into coastal Oregonand Washington. The strength of the Aleutian Low isalso an important control on precipitation in RegionPC 1. In addition, PC 1 precipitation is positively corre-lated to 700 hPa heights off the coast of Baja California,a characteristic unique to PC 1. Fig. 4 demonstratesthat during dry winters in PC 1, southern Californiaexperiences anomalous cyclonic flow, which capturesthe well-documented inverse correlation betweenwinter precipitation in southern California and thenorthwestern United States (Redmond & Koch 1991).

Region PC 2 is the most spatially complex of ourderived sub-regions containing climate divisions innorthern California and central Washington that areseparated by almost 1000 km. We contend there is alogical explanation for the counterintuitive relation-

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Fig. 4. Correlations between winter (October–March) mean 700 hPa height anomalies and winter season precipitation in (a) PC 1;(b) PC 2; (c) PC 3; (d) PC 4. Shaded areas represent each PC region. Positive correlations are indicated by solid isolines and

negative correlations are indicated by dashed isolines: the isoline interval is 0.1. The period of record is 1949–2003

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ship between northern California and central Wash-ington winter precipitation. Wet winters in PC 2 occurin concert with anomalous cyclonic circulation cen-tered off the coast of North America on the California-Oregon border. This leads to enhanced southerly flowthroughout the entire PC 2 region. In contrast, duringwet winters in PC 1, central Washington is influencedby strong cross-barrier flow over the Cascades, leadingto relatively dry conditions. However, central Wash-ington experiences moisture advection from the southduring anomalously wet winters in PC 2. An increasein southerly flow reduces the desiccating effects ofcross-barrier flow in central Washington. The correla-tion isolines indicate that anomalous southeasterlyflow occurs in western Washington in association withwet winters in Region PC 2. This may lead to weakcross-barrier flow over the Cascades into the valleys ofwestern Washington, promoting relatively dry condi-tions west of the mountains. Notably, precipitation inRegion PC 2 is unrelated to the strength and positionof the Aleutian Low, a unique feature among thesub-regions.

Unlike Regions PC 1 or 2, the center of maximumcorrelation in PC 3 occurs directly over the region. Wetwinters in the Snake River Valley and northeasternreaches of the Great Basin (PC 3) are associated withanomalous cyclonic circulation over the westernUnited States, a pattern that is spatially similar to thenegative phase of the Pacific North American pattern(PNA) presented in Barnston & Livezey (1987). In con-trast, anomalous anticyclonic circulation over the west-ern third of the United States tends to produce dryconditions. In general, a weakening of the AleutianLow resulting in an eastern expansion of above normal700 hPa heights over the north Pacific leads to abovenormal precipitation.

Unique among the 4 sub-regions, winter season pre-cipitation in PC 4 is linked to 700 hPa heights in thecentral and south Pacific. Anomalously wet winters inthe sub-region are generally associated with belownormal 700 hPa heights near Hawaii and over much ofthe North American continent. This correlation patternalso closely approximates the negative PNA pattern.While the center of maximum correlation is locatedwest of each of the first 3 sub-regions, this feature islocated northeast of PC 4. This suggests that anom-alous cyclonic circulation to the lee of the Rocky Moun-tains is a primary characteristic of wet winters in themountainous regions of the inland PNW.

We also examined correlation maps obtained byusing precipitation data from representative meteoro-logical stations in each climate division. In each case,the correlation patterns we found for representativestations in the derived regions closely matched theresults in Fig. 4.

3.3. Time series analysis

Although the PNW is a relatively small geographicarea, there are surprising sub-regional decadal scaletrends in precipitation. Time series of the componentscores were constructed for the 4 winter season eigen-vectors (Fig. 5) to determine long-term trends inprecipitation. An 11 yr Gaussian low-pass filter wasapplied to the time series data.

Fig. 5 reveals that 3 of the PC regions have experi-enced marked trends (p < 0.05) in winter seasonprecipitation, as revealed by linear trend analysis.Only the second eigenvector describing the northernCalifornia, southern Oregon, and central Washingtonregion shows no long-term trend. The coastal Oregonand Washington region described by PC 1 entered ananomalously wet period around 1990 with some of thewettest winters in the 108 yr period occurring since thistime. Conversely, the driest period in the instrumentalrecord characterizes the period since 1980 in PC 4, thenorthern Rocky Mountains and northern Idaho region.Interestingly, the exact opposite trend was found inPC 3, the Snake River Plain region of southern Idaho,where winter precipitation has been consistentlyabove normal since 1970. The trend toward extremewet (dry) winters within the southern (northern) partof Idaho has not yet been documented in the literature.

3.4. Teleconnection analysis

When the PNW is viewed as a coherent region, it isgenerally assumed that winter season precipitation isnegatively correlated with ENSO (Redmond & Koch,1991) and the PDO (Mantua et al. 1997). In other words,dry (wet) winters are associated with El Niño (La Niña)and the warm (cold) phase of the PDO. Table 2 shows

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PC 1 PC 2 PC 3 PC 4

Correlation coefficient (r)Niño 3.4 –0.25 0.23 –0.22 –0.40PDO –0.31 0.11 0.04 –0.29

Mean PC time series scoreEl Niño –0.42 0.22 –0.22 –0.40La Niña 0.25 –0.25 0.31 0.57p 0.01 0.07 0.01 0.01

Warm PDO –0.13 –0.08 0.16 –0.25Cold PDO 0.03 0.07 –0.09 0.26p 0.40 0.46 0.21 0.01

Table 2. Correlation of winter season principal component(PC) time series to atmospheric teleconnections and the meanwinter season PC time series score for atmospheric telecon-nection phases with significance values from t-test. Bold

values are considered significant (p < 0.05)

Miller & Goodrich: Pacific Northwest precipitation trends

that when time series of the 4 winter season PC regioncomponent scores are correlated with ENSO and thePDO, there is significant sub-regional variability. PC 2,which is located in northern California and the rain-shadow regions of Oregon and Washington, is posi-tively correlated (El Niño – wet) with Niño 3.4 SSTanomalies (p < 0.05) while the other 3 PC regions havethe expected El Niño – dry relationship. Two regions(PC 2 and PC 3) show a weak positive correlation to thePDO but these results are not statistically significant.

Table 2 also represents a comparison of the meancomponent scores for each PC region for the variousphases of ENSO and the PDO. The reciprocal rela-tionship between El Niño and La Niña and winterseason precipitation is statistically significant (p < 0.05)for 3 of 4 PC regions. PC 2 is the only one of the 4 PCregions in the PNW to have an El Niño – wet re-lationship, of which the physical mechanisms arealluded to in the correlation analysis presented inSection 3.2. It is also worth noting that while notshown, the neutral phase of ENSO has a mean com-ponent score near 0.0 in nearly all PC regions and

represents the fulcrum of the seesaw relationship. Thisdistinction becomes important when the influence ofPDO phase on ENSO is considered later in this section.The warm and cold phases of the PDO have a similarrelationship with winter season precipitation, but areonly significant in PC 4, which is centered over thenorthern Rockies. When the influence of PDO phase onthe relationship between El Niño, La Niña and neutralENSO conditions and winter season precipitation isconsidered (Fig. 6), years of El Niño (La Niña) tend tobe drier (wetter) than normal during the warm (cold)phase of the PDO. There is some visual evidencefor PDO modulation during years of neutral ENSO asfound in the Southwest USA by Goodrich (2004). Yearsof neutral ENSO during the warm (cold) PDO areslightly wetter (drier) than normal, although thesedifferences are in most cases not statistically sig-nificant. A difference of means test for each of the36 climate divisions comparing El Niño/warm PDO toEl Niño/cold PDO shows that 8 of the 36 climatedivisions were significantly different (p < 0.05). Most ofthese 8 climate divisions are located in the PC 4 region.

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Fig. 5. Principal component (PC) score time series for each of the 4 regions. An 11 yr Gaussian low-pass filter and linear best-fitline was applied to the time series. The slope and R2 of the linear fit for each PC region is significant at p < 0.05 for all but PC 2

Clim Res 33: 215–227, 2007224

Fig. 6. Winter season precipitation relative to normal for ENSO/PDO combinations in the Pacific Northwest of the USA

Miller & Goodrich: Pacific Northwest precipitation trends

A similar test of La Niña years split by PDO phaseyielded comparable but less significant results.

Table 3 is similar to Table 2 except that instead ofshowing the separate impacts of ENSO and PDO onmean component scores for the 4 PC regions, the mod-ulation effect of PDO on ENSO is shown. The differ-ence in mean PC score when years of El Niño are splitinto subsets of warm and cold PDO is significant (p <0.05) in PC 4. Similar differences in mean PC scoresemerge when years of neutral ENSO are split by PDOphase in PC 3 (p < 0.05). The variety of relationshipsbetween the PC regions and the 6 ENSO/PDO combi-nations for precipitation suggests that teleconnectiverelationships within the PNW are quite variable andmore complex than a simple El Niño – dry (La Niña –wet) relationship. ANOVA on the mean PC componentscores also shows that all 4 PC regions (except PC 2)demonstrate a significant difference (p < 0.05) betweenthe ENSO/PDO combinations.

Finally, we ranked the Niño 3.4 SSTs and PDO indexto determine the 15 highest and lowest values.Because winter season precipitation is so importanthydrologically to the PNW, we were interested in howthe sensitivity of precipitation to extreme ENSO andPDO events varies intra-regionally. Table 4 shows themean component scores from each PC for the extremevalues of El Niño and La Niña, as well as the highindex values of the warm and cold PDO. These resultscan be compared with those of Table 2, which repre-sents all years of the PDO and ENSO phases. Telecon-nection sensitivity can best be understood here bywhether or not the spread of the mean scores for agiven teleconnection phase increases or decreases. Forexample, PC 4 has a spread of 0.97 (–0.40 for El Niño to0.57 for La Niña) from Table 2 and a spread of 1.61(–0.67 for El Niño to 0.94 for La Niña) from Table 4.Three of the 4 PC regions (all except PC 1) show anincreased spread (more sensitivity) during high indexENSO values. In sum, as ENSO SST anomaliesincrease, the impact on precipitation becomes moreanomalous in these regions. Moreover, sensitivity tothe PDO increases dramatically for PC Regions 1 and 4.

PC 1, which demonstrates little relationship to PDOwhen all years are considered, exhibits a highly signif-icant difference of means (p

Clim Res 33: 215–227, 2007

PC 1. This region, which consists primarily of Washing-ton and northern Oregon from the windward slopes ofthe Cascades to the Pacific Ocean, displayed expectedENSO and PDO relationships (El Niño – dry and warmPDO – dry) and had synoptic controls that are spatiallysimilar to that of the East Pacific Oscillation (EPO). Itcomes as no surprise that the consecutive rainfallrecords set in January 2006 in this sub-region co-incided with anomalously high values of the EPO.While the climate of PC 1 is often said to represent theentire region, the results in this study suggest that 3other climatically distinct sub-regions exist in thePNW. Northern California as well as the leeward sec-tions of Washington and Oregon comprise PC 2, whichhas an opposite teleconnective relationship to ENSOand PDO (El Niño – wet and warm PDO – wet) com-pared to any other sub-regions and has synoptic con-trols spatially similar to that of the Pacific North Amer-ican pattern. The final 2 sub-regions (PC 3 and PC 4)consist of the Rocky Mountains from northern Nevadato Idaho and western Montana and are split roughlyalong the Snake River plain. Although these final 2regions are teleconnectively similar to winter PC 1,they are both synoptically more similar to winter PC 2.

In fact, the 2 Rocky Mountain sub-regions showwidely diverging temporal trends in winter seasonprecipitation over the past 30 yr, a timeframe that co-incides with increased warming across the PNW.While climate regionalizations that use rotated PCAare always open to interpretation, we subjected thesub-regions to a number of tests that included compar-ison of individual stations in the sub-regions, examina-tion of time series of PC component scores, computa-tion of synoptic correlations with upper air data, andteleconnection sensitivity. We feel that these resultssuggest that the assumption of coherency in the cli-mate signal of the Pacific Northwest is not valid andthat further climate modeling of climate change in theregion must take these differences into account.

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Editorial responsibility: Robert Davis,Charlottesville, Virginia, USA

Submitted: April 3, 2006; Accepted: December 5, 2006Proofs received from author(s): March 9, 2007