North American monsoon precipitation reconstructed from ... › wordpressua.uark.edu › ... · studies. Interannual monsoon variability is strong over NAM2, which includes critical

North American monsoon precipitation reconstructed from tree-ringlatewood

Daniel Griffin,1,2 Connie A. Woodhouse,1,2 David M. Meko,1 David W. Stahle,3

Holly L. Faulstich,1,2 Carlos Carrillo,4 Ramzi Touchan,1 Christopher L. Castro,4

and Steven W. Leavitt1

Received 30 November 2012; revised 18 January 2013; accepted 21 January 2013.

[1] The North American monsoon is a major focus ofmodern and paleoclimate research, but relatively little isknown about interannual- to decadal-scale monsoonmoisture variability in the pre-instrumental era. This studydraws from a new network of subannual tree-ring latewoodwidth chronologies and presents a 470-year reconstructionof monsoon (June–August) standardized precipitation forsouthwestern North America. Comparison with anindependent reconstruction of cool-season (October–April)standardized precipitation indicates that southwesterndecadal droughts of the last five centuries were characterizednot only by cool-season precipitation deficits but also byconcurrent failure of the summer monsoon. Monsoondrought events identified in the past were more severe andpersistent than any of the instrumental era. The relationshipbetween winter and summer precipitation is weak, at best,and not time stable. Years with opposing-sign seasonalprecipitation anomalies, as noted by other studies, wereanomalously frequent during the mid to late 20th century.Citation: Griffin, D., C. A. Woodhouse, D. M. Meko, D. W.Stahle, H. L. Faulstich, C. Carrillo, R. Touchan, C. L. Castro,and S. W. Leavitt (2013), North American monsoon precipitationreconstructed from tree-ring latewood, Geophys. Res. Lett., 40,doi:10.1002/grl.50184.

1. Introduction

[2] The North American monsoon is a fundamental climatecomponent in the Southwest that provides moisture relief andmodulates ecosystem structure, wildfire, agriculturalproductivity, public health, and water resources supply anddemand [Ray et al., 2007]. Interannual monsoon moisturevariability, which is pronounced over the southwestern UnitedStates, has been the focus of numerous studies using instru-mental observations [e.g., Higgins and Shi, 2000; Seageret al., 2009; Turrent and Cavazos, 2009; Arias et al., 2012].The North American monsoon is also a research focus for

paleoclimatology [e.g., Asmerom et al., 2007; Barron et al.,2012], but relatively little is known about pre-instrumentalmonsoon variability at interannual to decadal timescales.Understanding the plausible range of monsoon variability iscritical because model projections of monsoon response toanthropogenic greenhouse gas forcing remain unclear[Castro et al., 2012; Bukovsky and Mearns, 2012; Cookand Seager, 2013]. The American Southwest has a richhistory of dendroclimatology, but the vast majority of tree-ringreconstructions from the region reflect only cool-season orannual-scale moisture variability. This is epitomized by theNorth American Drought Atlas, which, for the Southwest,principally reflects the influence of cool-season precipitationon annual water balance [St. George et al., 2010].[3] Annual tree-ring records do not reflect monsoon

moisture variability, but subannual chronologies createdfrom the summer-forming “latewood” component of treerings can contain strong, monsoon-specific precipitation sig-nal [see Griffin et al., 2011, and references therein].However, to date, relatively few latewood width (LW) widthchronologies and monsoon reconstructions have beengenerated for the southwestern United States. Targeting asmall area in southeastern Arizona, Meko and Baisan [2001]used a set of five Pseudotsuga menziesii (PSME) LW chronol-ogies and a nonlinear binary recursive classification model toestimate the probability of dry monsoons for the period1791–1992. Stahle et al. [2009] conducted LW analysis of themultimillennial PSME record from El Malpais [Grissino-Mayer, 1996] and produced a July precipitation reconstruc-tion for northwestern New Mexico, which was interpretedas a record of onset and early monsoon precipitation.Comparing the July estimate with a November–May precip-itation reconstruction, Stahle et al. [2009] found that yearswith winter precipitation extremes tended to have an oppos-ing-sign July moisture anomaly. That phenomenon, alsoidentified in instrumental studies, may be dynamically linkedto a negative land-surface feedback [e.g., Gutzler, 2000; Zhuet al., 2005; Notaro and Zarrin, 2011] and atmosphericteleconnections to Pacific sea surface temperature variability[e.g., Castro et al., 2001].[4] The present study offers several advancements on

prior research. It uses a new multispecies LW chronologynetwork to reconstruct monsoon (June–August) precipitationfor a large region in the Southwest. Following the Stahleet al. [2009] analytic, the paleomonsoon record is comparedwith an October–April precipitation reconstruction developedfrom chronologies of tree-ring earlywood width (EW).These reconstructions provide novel perspective on monsoonpaleoclimatology and reveal instability in the relationshipbetween winter and summer precipitation through time.

1Laboratory of Tree-Ring Research, University of Arizona, Tucson,Arizona, USA.

2School of Geography and Development, University of Arizona,Tucson, Arizona, USA.

3Department of Geosciences, University of Arkansas, Fayetteville,Arkansas, USA.

4Department of Atmospheric Sciences, University of Arizona, Tucson,Arizona, USA.

Corresponding author: D. Griffin, Laboratory of Tree-Ring Research,University of Arizona, 1215 E. Lowell Street, Tucson, AZ 85721, USA.([email protected])

©2013. American Geophysical Union. All Rights Reserved.0094-8276/13/10.1002/grl.50184

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GEOPHYSICAL RESEARCH LETTERS, VOL. 40, 1–5, doi:10.1002/grl.50184, 2013

2. Methods and Results

[5] This study focuses on monsoon region 2 (NAM2), asdefined by the North American Monsoon ExperimentForecast Forum (Figure 1) [Gochis et al., 2009]. NAM2 isa region of spatially coherent variability in monsoonprecipitation that approximates regions identified by otherstudies. Interannual monsoon variability is strong over NAM2,which includes critical waterways, major metropolitan areas,and more than 550 km of the United States–Mexico border.NAM2 is a target for seasonal climate forecasts and has beenthe focus of dynamically downscaled regional climate modelprojections [Castro et al., 2012]. Monthly precipitation dataare from a 0.5� gridded product developed by NOAA thatextends across North America and uses a terrain-sensitivealgorithm to interpolate between available instrumental sta-tion records for the period 1895–2010. NAM2 grid point datawere averaged to produce time series of monthly precipita-tion and correlation analysis was used to assess relationshipsto the subannual tree-ring chronologies. October–April (coolseason) and June–August (monsoon) were selected as theseasons for reconstruction, and precipitation totals forthese seasons were converted to standardized precipitationindices (SPI).[6] Tree-ring data are from a new network of subannual

chronologies developed using EW and LW measurementson collections from more than 50 sites in the southwesternUnited States and Baja California (Figure 1). EW and LWindices were standardized with the protocol described byGriffin et al. [2011]. Chronologies more than 450 years longfrom NAM2 that exhibited significant (p

[8] The monsoon and cool-season SPI reconstructions areuncorrelated for the 1539–2008 common period (r=0.02;p=0.67) and the 1896–2008 instrumental period (r=0.06;p=0.53), although the monsoon and cool-season SPI instru-mental data exhibit weak negative correlation (r=-0.19;p=0.04). Wavelet analysis indicates that both reconstructionsare dominated by high-frequency variability (Figure S5 inSupporting Information). Low-frequency covariability be-tween the two seasonal reconstructions is visually evident(Figure 3a and b). Cross-wavelet squared coherence, analo-gous to correlation between the reconstructions as a functionof time and frequency, is variable and time unstable at periodsless than 32 years (Figure S5 in Supporting Information). Atlonger periods, the reconstructions generally exhibit in-phasecoherence. Potential sources of the low-frequency reconstruc-tion coherence include long-term changes in tree physiologyrelated to root and crown mass [Fritts, 2001], standardizationof the ring-width time series, or true low-frequency coherencebetween the seasonal climate regimes. Diagnosing the source(s) of this coherence is considered important but also beyondthe brief scope of the present study.

3. Seasonal Precipitation History for NAM2

[9] Several persistent periods of anomalous SPI stand outin the instrumental records (Figure 2). Both seasons hadnegative SPI near the turn of the 20th century and again inthe early 21st century. Precipitation was consistently aboveaverage in both seasons during the early 20th centurypluvial, as recently noted by Cook et al. [2011]. Thesepersistent variations are tracked remarkably well by thetree-ring reconstructions. Monsoon SPI was negative from1972 to 1982, the longest run of dry monsoons in theinstrumental record. This episode is not particularly wellmatched by the LW reconstruction, possibly because itcontained several notably wet May and June months thatwould have differentially benefitted summer tree growth.

Visual comparison indicates that cool-season SPI containsmore middle- to low-frequency variability than monsoonSPI and that the EW-based reconstruction tracks thesefrequency components well.[10] The reconstructions offer an expanded perspective, re-

vealing seasonal drought events more persistent and severethan those of the instrumental era (Figure 3). The period ofmost persistent summer drought was 1882–1905, when 19of 24 years had negative monsoon SPI. In the 1890s and early1900s, this monsoon failure was complemented by severe andpersistent cool-season drought. This dual-season droughtevent factors importantly into the socioenvironmental historyof the region. Arrival of the Southern Pacific Railroad in theearly 1880s ushered an expansion of the Arizona cattle popula-tion from ~40,000 to 1.5 million individuals [Sheridan, 1995].By the early 1890s, however, an estimated 50–75% of the herdstarved and perished from drought-induced rangeland failure[Sayer, 1999]. Cattle overgrazing triggered landscape-scalevegetation change that remains evident today. Environmentalconsequences of the 1890s drought were also severe innorthern Mexico, as discussed by Seager et al. [2009]. Iron-ically, this decades-long drought was immediately followedby the well-known early 20th century pluvial, which in-cluded the highest frequency of dual-season wetness in theentire reconstruction (Figure S4 in Supporting Information).[11] Other dual-season drought events are evident in the

early 1820s and the 1770s. Yet more severe was the 17thcentury “Puebloan Drought,” an event Parks et al. [2006]implicate as one factor leading to the Pueblo Revolt of1680. In northwestern New Mexico, Stahle et al. [2009]found that this drought included below-average precipitationduring both the cool season and July. For NAM2, the periodfrom 1666 to 1676 included a seven-year run of drier-than-average monsoons and six years with below-average SPI inboth seasons. According to the smoothed record, this wasthe most extreme monsoon drought episode of the last 470years. This result, coupled with the finding by Stahle et al.

Figure 3. Time-series graphs of reconstructed SPI for the cool season (a) and monsoon (b) with a 5-year cubic spline plot-ted in black. Vertical gray bars denote years with opposing-sign SPI anomalies and the black line represents a centered 30-year running count of these events (c).

GRIFFIN ET AL.: NA MONSOON PRECIPITATION RECONSTRUCTION

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[2009], indicates that monsoon failure during the 17thcentury Puebloan Drought was robust and widespread acrossthe Southwest.[12] The late 16th century “megadrought” is evident in

both the monsoon and cool-season SPI reconstructions forNAM2 (Figure 3). This event, among the most severedroughts of the last millennium, drove landscape-scaleecosystem change across the Southwest [e.g., Swetnam andBetancourt, 1998]. From 1566 to 1579, 11 of 14 monsoonswere drier than average. Monsoon SPI rebounded to averageor near average in the 1580s, but cool-season droughtpersisted through that decade. From 1566 to 1587, 21 of22 years were reconstructed to include at least one seasonof below-average SPI and 10 of those years had negativeSPI in both seasons. July dryness was previously noted innorthwestern New Mexico [Stahle et al., 2009], but this isthe first time that persistent monsoon failure has beenconnected with the megadrought in the NAM2 region.Several exceptionally dry monsoons in the late 1560s revealthat the megadrought may have first manifest as a warm-season phenomenon. This idea is lent some support byspatial analysis of the megadrought through time (Figure 6in Stahle et al. [2007]), which mapped the 1560s droughtcenter over northwestern Mexico, where the monsoonprovides more than 70% of annual precipitation.[13] These reconstructions offer a novel opportunity to

assess the relationship between cool-season and monsoonprecipitation in NAM2. Years with opposing-sign SPIanomalies, plotted in Figure 3c, appear to be randomlydistributed through time. There are brief periods when theseevents appear to congregate (e.g., 1960s and 1720s) andothers when they were rare (i.e., the decadal drought andwet events described above). A 30-year running countillustrates some temporal variability and indicates that themid to late 20th century was characterized by a relativelyhigh frequency of such events. These results are congruentwith instrumental data analysis by Gutzler [2000] and byZhu et al., who found that the “linkage is strong from1965–1990 and weak otherwise” [2005]. Opposing-signyears were also relatively frequent in the mid-18th and early17th centuries. In contrast, the period centered near the turnof the 20th century contained relatively few of these events.Wavelet analysis reveals no time-stable coherence atsubdecadal to decadal timescales, indicating that theNAM2 seasonal precipitation relationship is dominated bynoise (Figure S3 in Supporting Information).

4. Summary

[14] This study provides a high-quality, precisely dated,seasonally resolved geochronology for the NAM2 region.The reconstructions represent a plausible range of interannual-to decadal-scale variability in multiseason precipitation forthe past 470 years. These precipitation estimates are suitablefor comparison with paleoproxy, historical, modern, andprojected climate data and should be useful for a more preciseinterpretation of the social and environmental history in theAmerican Southwest. Decadal drought events in this regionover the past five centuries (e.g., 1570s, 1660s, 1770s,1820s, 1890s, and early 2000s) were characterized not onlyby cool-season precipitation deficits but also by consistentfailure of the summer monsoon. Monsoon drought events inthe past were more persistent (e.g., late 19th century) and

extreme (e.g., mid-17th century) than any during the instru-mental era to date. With the exception of the ongoing early21st century drought, the instrumental era lacks the persistentdual-season drought episodes common to centuries past. TheNAM2 seasonal precipitation relationship is weak at bestand is not time stable. The mid to late 20th century experi-enced a relatively high frequency of years with opposing-signprecipitation anomalies. These results indicate that the instru-mental era, especially the later half of the 20th century, maynot be the ideal period for characterizing NAM2 monsoonprecipitation climatology, its relationship to the cool-seasonclimate regime, and potential connections to the coupledocean-atmosphere system.

[15] Acknowledgments. This research was funded by NSF P2C2award #0823090 and NOAA award #NA08OAR4310727. D. Griffin wassupported by Environmental Protection Agency STAR award #FP917185.The authors thank R. Vose and R. Heim for access to the gridded climatedata, M. Losleben and numerous students for assistance in subannual tree-ring chronology development, and two anonymous reviewers for commentsthat improved the article.

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Auxiliary Materials: Griffin et al. North American monsoon precipitation reconstructed 1

from tree-ring latewood. 2

1. Study Area 3

[1] This study is focused on North American monsoon region 2 (NAM2), one of eight regions 4

identified using EOF analysis of instrumental station data by researchers of the North American 5

Monsoon Experiment [Gochis, 2009; NAME, 2013]. NAM2 is similar in domain to monsoon 6

regions identified in other studies, including Comrie and Glenn [1998], Gutzler [2004], and Zhu 7

et al. [2005], suggesting this regional delineation is relatively robust. NAM2 extends from 30 to 8

35.25 degrees north and from 107.75 to 113.25 degrees west (Figure 1). The region is 9

physiographically diverse, ranging from the continental divide west to the low deserts beyond 10

Phoenix, and from the complex ecosystems of the Arizona-Sonora border region north to the 11

heavily forested Mogollon Rim and the southern Colorado Plateau. NAM2 contains many 12

environmentally and socially significant waterways, including the Bavispe, Gila, Magdalena, 13

Salt, Santa Cruz, San Pedro, Sonora, and Verde rivers. NAM2 population centers include the 14

Phoenix-Tucson metropolitan corridor, Flagstaff, Prescott, Nogales, and Silver City. 15

16

2. Tree-Ring Data 17

[2] Tree-ring data in this study come from a new systematic network of “earlywood” width 18

(EW) and “latewood” width (LW) chronologies developed for the southwestern U.S. and Baja 19

California, Mexico (Figure 1). Annual growth rings of many southwestern conifer trees exhibit a 20

clear anatomical transition from the earlywood produced in spring to the denser latewood 21

produced in summer. The width of these sub-annual components varies independently, and 22

several studies have demonstrated that latewood width can be used as a proxy for evaluating the 23

2

paleoclimatic history of the North American monsoon [see references in Griffin et al., 2011]. 24

Field sampling from 2009–2011 was used to update and augment archived collections of 25

Pseudotsuga menzieszii (PSME) and Pinus ponderosa (PIPO) tree ring samples from 52 sites 26

across the southwestern U.S. and Baja California (Figure 1). Twenty-six sites fall within the 27

NAM2 region. Field sampling explicitly targeted young to middle aged trees (100–250 years) to 28

produce age-stratified chronologies, a strategy recommended by prior research [Meko and 29

Baisan 2001; Stahle et al., 2009] to maximize monsoon precipitation signal. The new samples 30

were prepared and cross-dated using classical methods [Douglass, 1941; Stokes and Smiley, 31

1996]. For the first time on the new or archived samples, EW and LW were measured to the 32

nearest 0.001mm. Calendar-year dating was verified using COFECHA [Holmes, 1983]. 33

34

[3] Numerical tree-ring chronologies were computed with standard methods [Cook and 35

Kariukstis 1990]. Sub-annual EW and LW measurement time series were detrended using a 36

cubic smoothing spline [Cook and Peters, 1981] with a frequency response of 0.5 at a 37

wavelength of 100 years. The tree-ring index time series exhibited low-order persistence 38

theorized to be of biological origin [Fritts, 2001]. Pooled autocorrelation was modeled and 39

removed to produce so-called “residual” indices [Cook, 1985]. LW  indices  were  “adjusted”  to  40

remove the interannual EW dependence by computing the residual of LW index regressed onto 41

EW index. Isolating the variability unique to LW can enhance monsoon precipitation signal in 42

the final chronologies [Meko and Baisan, 2001]. Site-level EW and adjusted LW chronologies 43

were computed as the Tukey robust bi-weight mean index for each year [Cook, 1985]. Further 44

detail on the strategies and methods of chronology development were provided by Griffin et al. 45

[2011]. 46

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3. Instrumental data 48

[4] Instrumental precipitation data comes from a 0.5{degree sign} gridded climate product 49

developed by Russ Vose and Richard Heim of the U.S. National Oceanic and Atmospheric 50

Administration (NOAA). Similar to the Parameter Regression on Independent Slopes Model 51

(PRISM) [Daly et al., 2008], the NOAA data were developed for North America using a terrain-52

sensitive algorithm to interpolate between all available monthly station records for the period 53

1895–2010. Additional detail is provided by Castro et al. [2012]. Monthly precipitation was 54

averaged for grid points within NAM2. Figure S1 uses box plots to illustrate the distribution of 55

monthly precipitation values for the 1896–2008 period common with the tree-ring data. The 56

region experiences a bi-modal precipitation distribution [e.g., Sheppard et al., 2002]. May is the 57

driest month and represents the climatological transition from the cool season to the summer 58

monsoon. In NAM2, the monsoon is strongest from July through September. On average, 59

precipitation during those months comprises 39% of the 335.95 mm annual total for NAM2. 60

However, NAM2 occupies a transition zone for monsoon onset and the southeastern part of the 61

region experiences onset in late June [see Liebmann et al., 2008], as is more common in the core 62

monsoon region over northwestern Mexico. On average, precipitation during the monsoon 63

months of June through September contributes 49% of the annual total for NAM2. 64

65

[5] Exploratory analysis used the Seascorr software [Meko et al., 2011] to produce Pearson 66

coefficients for NAM2 EW and LW data correlated with monthly precipitation (Figure S2) and 67

seasonal precipitation (not shown) for the period 1896–2008. Most of the EW chronologies were 68

significantly correlated (p

4

that correlation, May is the driest month of the year, it falls outside of the cool season, and its 70

inclusion in seasonal precipitation totals did not improve the calibration with the EW 71

chronologies. Consequently, October through April was selected as the period for cool-season 72

precipitation calibration. Most of the adjusted LW chronologies were significantly correlated 73

with July precipitation and several were also significantly correlated with June and August 74

precipitation. These results are congruent with findings by Fritts [2001], who continuously 75

observed Pinus ponderosa growth for several years in southern Arizona and found sensitivity to 76

monsoon moisture variations. The results are also in line with more recent findings that the LW 77

chronologies are most sensitive to moisture variability in the early- to mid-monsoon season with 78

some sites remaining sensitive through the month of August [Meko and Baisan, 2001; Stahle et 79

al., 2009; Griffin et al., 2011]. No LW chronologies were significantly correlated with 80

September precipitation in the current year, suggesting that radial tree growth is completed 81

before or during that month. As such June through August was selected as the period for 82

monsoon precipitation calibration. On average, June through August precipitation represents 83

80% of that from the classic monsoon months of June through September. Precipitation totals 84

were converted to Standardized Precipitation Indices (SPI) [Mckee et al., 1993] for the 7-month 85

period ending in April (cool season) and 3-month period ending in August (monsoon), with the α  86

and  β parameters of the gamma distribution computed for the 1896–2008 period common to the 87

tree-ring and instrumental data. 88

89

4. Tree-ring reconstructions of seasonal SPI 90

[5] EW chronologies were used to reconstruct cool-season SPI and adjusted LW chronologies 91

were used to reconstruct monsoon SPI. Chronologies were only considered as candidate 92

5

predictors if they exhibited highly significant correlation (p < 0.01) with the reconstruction 93

targets over the full common period (1896–2008) and its split halves (1896–1952; 1953–2008). 94

Forward stepwise multiple linear regression was used to develop the SPI reconstructions, and 95

predictors were only allowed to step in when their coefficients were significantly different than 96

zero (p < 0.05). Exploratory regression analysis involved four categories of predictors: 1) site-97

level chronologies over 350 years long (a majority of NAM2 chronologies); 2) site-level 98

chronologies over 450 years long (i.e. those covering the 1559–1582 megadrought event [Stahle 99

et al., 2007]); 3) principal-component scores of the 350+ year-long chronologies; 4) principal-100

component scores of the 450+ year long chronologies. Regression model skill was assessed with 101

standard validation methods, including the sign test [Fritts, 2001], leave-one-out cross validation 102

[Michaelsen, 1987], and the reduction of error statistic [Cook et al., 1999]. Verification results 103

indicated that the site-level predictors and PC-score predictors produced very similar regression 104

results. The site-level chronologies produced marginally higher coeffecients of determination 105

and improved results in the sign test (frequency of cases when the instrumental and reconstructed 106

indices were of the same sign relative to the zero mean). The monsoon estimates based on 350-107

year chronologies captured more instrumental variance than those with the 450-year 108

chronologies (adjusted R2 = 0.53 and 0.45, respectively). To maximize length and simplify 109

presentation, the reconstructions using site-level chronologies over 450 years long were 110

presented and analyzed. Seven EW chronologies and four LW passed the correlation and length 111

screening criteria and were submitted as candidate predictors in forward stepwise linear 112

regression (Table S1). Information on final SPI calibration models are presented in Table S2. 113

114

[6] Both reconstructions used three predictors representing PSME and PIPO chronologies well 115

6

distributed across the NAM2 region (Figure 1). All predictor coeffecients were positive. The 116

leading predictors in the reconstructions were PSME EW and LW chronologies from the same 117

site (FSM) in the Santa Rita Mountains near Tucson. EW and LW chronologies from a PIPO site 118

near Flagstaff (WMP) also entered as predictors in each reconstruction. Time series and scatter 119

plots illustrate the relationship between observed and reconstructed SPI (Figure 2). All 120

reconstruction predictors, predictands, and residuals met regression assumptions of normality 121

and zero-autocorrelation (Table S3). The calibration and verification statistics indicated that 122

reconstructions have significant predictive skill, even by southwestern standards. The cool-123

season reconstruction captures more instrumental variance than does the monsoon 124

reconstruction, but this is not surprising because summer precipitation exhibits greater spatial 125

heterogeneity than winter precipitation [Mock, 1996] and cool-season precipitation dominates 126

annual water balance and tree growth in the region [St. George et al., 2010]. 127

128

5. Analyses 129

[7] Runs analysis was used to identify reconstructed events with consecutive years above (wet) 130

or below (dry) the zero mean. The cool-season reconstruction contains wet and dry events more 131

persistent than those identified in the instrumental record while the monsoon instrumental record 132

contains a wet event and a dry event more persistent than any in the reconstruction. Plotting 133

these events through time highlights periods of persistent drought and wetness in the 134

reconstructions (Figure S1). Runs analysis is sensitive to values that fall near the zero mean, such 135

that one year of slightly above average precipitation causes a break in the drought run (e.g., late 136

19th century in Fig. S1b). To highlight the most persistent SPI anomalies, five-year cubic 137

smoothing splines were fit and plotted on the long reconstructions (Figure 3). Five-year spline 138

7

values were ranked to identify the non-overlapping events of greatest magnitude. 139

140

[8] The reconstructions were analyzed to determine when both seasons were wet, dry, or when 141

one season was wet and the other was dry (Figure S2a). Results indicate periods of time when 142

both seasons were consistently wet (e.g., early 20th century) or dry (e.g., 1570s). Running 30-143

year counts were calculated for each case (Figure S2b) and for cases when the seasonal moisture 144

anomalies were of opposing sign (Figure S2c). To evaluate the reconstructions in the time-145

frequency domain, they were submitted to cross-wavelet analysis [Grinstead et al., 2004]. 146

Results reveal limited time-restricted concentrations of spectral power at various wavelengths for 147

both the cool-season and monsoon SPI reconstructions (Figure S3ab) and some concentrations 148

significant and common to both (Figure S3c). Coherence in the reconstructions is limited and 149

highly time restricted at periods less than about 32 years while at longer periods, in-phase 150

coherence is generally evident (Figure S3d). 151

152

8

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251

13

Table S1. 252

Metadata for tree-ring chronologies considered as candidate predictors for SPI reconstructions 253

EWa LWb

Site

Code Site Name Species ITRDB Codec Lat. Long.

Elev.

(m)

Inner

Date

Outer

Date

Yes No BFM Black Mountain Fir PSME NM565 33.35 -108.26 2489 1306 2008

Yes No BPM Black Mountain Pine PIPO NM566 33.35 -108.26 2489 1494 2008

Yes Yes FSM Florida Saddle PSME AZ504 31.72 -110.84 2385 1493 2008

Yes Yes SCM Santa Catalina High PSME AZ501 32.44 -110.78 2746 1539 2009

Yes No WKM Wahl Knoll PSME n/a 33.99 -109.38 2969 1460 2008

Yes Yes WMP Walnut Canyon Pine PIPO AZ049, AZ547 35.17 -111.51 1995 1531 2010

Yes Yes WPU Webb Peak PSME AZ558, AZ559 32.71 -109.92 3014 1466 2008

254 aCool-season SPI candidate predictor based on correlation screening and chronology length 255 bMonsoon SPI candidate predictor based on correlation screening and chronology length 256 cInternational Tree-Ring Data Bank reference code (http://www.ncdc.noaa.gov/paleo/treering.html) 257

258

14

Table S2. 259

Calibration model equations. 260 Model Equation

Cool-season SPI yhat = -2.700 + (1.393*FSM_EW) + (0.674*BFM_EW) + (0.673*WMP_EW)

Monsoon SPI yhat = -3.275 + (1.487*FSM_LW) + (0.986*WMP_LW) + (0.904*SCM_LW)

261

262

15

Table S3. 263

Calibration and verification information. 264

Cool-Season SPI Monsoon SPI

Potential predictors 7 4

Predictors in final equation 3 3

Calibration Period 1896–2008 1896–2008

n (years) 113 113

R2 0.616 0.462

adjusted R2 0.606 0.468

RE 0.589 0.42

Standard Error of Estimate 0.635 0.75

Root Mean Squared Error of

Cross-Validation 0.645 0.77

Regression model F-value 58.36 31.24

F-value probability < 0.00000 < 0.00000

Durbin Watson 1.88 1.96

Portmanteau Q 10.69 8.89

Portmateaau Q probability 0.38 0.54

Sign Test Hits 94/113 84/113

265

266

16

Figure S1. 267

Box and whisker plots illustrate the distribution of NAM2 monthly precipitation for the 1896–268

2008 period, centered on the water year of October through September. Boxes capture the inter-269

quartile range and the horizontal black line represents the median value. Upper and lower 270

whiskers capture values +/- 1.5 times the 75th and 25th percentiles, respectively. Dots represent 271

extreme values beyond that range. 272

273

Figure S2. 274

Dot plots illustrate Pearson coefficients for the 26 NAM2 chronologies correlated with monthly 275

precipitation from prior June through current October for the period 1896–2008. Earlywood 276

chronology correlations are shown in A and latewood chronology correlations are shown in B. 277

The horizontal black line represents the 0.05 non-exceedance probability threshold. 278

279

Figure S3. 280

Consecutive year events of positive (blue) and negative (red) SPI for the cool-season SPI (a) and 281

monsoon SPI (b). Notable consecutive year events are annotated. 282

283

Figure S4. Bar plots illustrate the distribution of cases (a) when both seasons were wet (blue), 284

when the cool season was wet and the monsoon was dry (green), vice-versa (orange), and when 285

both seasons were dry (red). Time series illustrate running 30-year counts of each case (b). Time 286

series illustrate running 30-year counts of cases when the seasonal moisture anomalies were of 287

opposing sign (c; wet-dry or dry-wet; black). 288

289

17

Figure S5. Continuous wavelet power transform of reconstructed cool-season SPI (a) and 290

monsoon SPI (b), with significant (p

griffin_grl_monsoongriffinGRL_revised_compiled_mid