North American Monsoon Precipitation Reconstructed from Tree-Ring Latewood Daniel Grifﬁn,1,2 Connie A

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

North American monsoon precipitation reconstructed from tree-ring latewood 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 of paleoclimatology [e.g., Asmerom et al., 2007; Barron et al., modern and paleoclimate research, but relatively little is 2012], but relatively little is known about pre-instrumental known about interannual- to decadal-scale monsoon monsoon variability at interannual to decadal timescales. moisture variability in the pre-instrumental era. This study Understanding the plausible range of monsoon variability is draws from a new network of subannual tree-ring latewood critical because model projections of monsoon response to width chronologies and presents a 470-year reconstruction anthropogenic greenhouse gas forcing remain unclear of monsoon (June–August) standardized precipitation for [Castro et al., 2012; Bukovsky and Mearns, 2012; Cook southwestern North America. Comparison with an and Seager, 2013]. The American Southwest has a rich independent reconstruction of cool-season (October–April) history of dendroclimatology, but the vast majority of tree-ring standardized precipitation indicates that southwestern reconstructions from the region reflect only cool-season or decadal droughts of the last five centuries were characterized annual-scale moisture variability. This is epitomized by the not only by cool-season precipitation deficits but also by North American Drought Atlas, which, for the Southwest, concurrent failure of the summer monsoon. Monsoon principally reflects the influence of cool-season precipitation drought events identified in the past were more severe and on annual water balance [St. George et al., 2010]. persistent than any of the instrumental era. The relationship [3] Annual tree-ring records do not reflect monsoon between winter and summer precipitation is weak, at best, moisture variability, but subannual chronologies created and not time stable. Years with opposing-sign seasonal from the summer-forming “latewood” component of tree precipitation anomalies, as noted by other studies, were rings can contain strong, monsoon-specific precipitation sig- anomalously frequent during the mid to late 20th century. nal [see Griffinetal., 2011, and references therein]. Citation: Griffin, D., C. A. Woodhouse, D. M. Meko, D. W. However, to date, relatively few latewood width (LW) width Stahle, H. L. Faulstich, C. Carrillo, R. Touchan, C. L. Castro, chronologies and monsoon reconstructions have been and S. W. Leavitt (2013), North American monsoon precipitation generated for the southwestern United States. Targeting a reconstructed from tree-ring latewood, Geophys. Res. Lett., 40, small area in southeastern Arizona, Meko and Baisan [2001] doi:10.1002/grl.50184. used a set of five Pseudotsuga menziesii (PSME) LW chronologies and a nonlinear binary recursive classification model to estimate the probability of dry monsoons for the period 1. Introduction 1791–1992. Stahle et al. [2009] conducted LW analysis of the [2] The North American monsoon is a fundamental climate multimillennial PSME record from El Malpais [Grissino- component in the Southwest that provides moisture relief and Mayer, 1996] and produced a July precipitation reconstruc- modulates ecosystem structure, wildfire, agricultural tion for northwestern New Mexico, which was interpreted productivity, public health, and water resources supply and as a record of onset and early monsoon precipitation. demand [Ray et al., 2007]. Interannual monsoon moisture Comparing the July estimate with a November–May precip- variability, which is pronounced over the southwestern United itation reconstruction, Stahle et al. [2009] found that years States, has been the focus of numerous studies using instru- with winter precipitation extremes tended to have an oppos- mental observations [e.g., Higgins and Shi,2000;Seager ing-sign July moisture anomaly. That phenomenon, also et al., 2009; Turrent and Cavazos,2009;Arias et al., 2012]. identified in instrumental studies, may be dynamically linked The North American monsoon is also a research focus for to a negative land-surface feedback [e.g., Gutzler, 2000; Zhu et al., 2005; Notaro and Zarrin, 2011] and atmospheric 1Laboratory of Tree-Ring Research, University of Arizona, Tucson, teleconnections to Pacific sea surface temperature variability Arizona, USA. [e.g., Castro et al., 2001]. 2School of Geography and Development, University of Arizona, [4] The present study offers several advancements on Tucson, Arizona, USA. 3Department of Geosciences, University of Arkansas, Fayetteville, prior research. It uses a new multispecies LW chronology Arkansas, USA. network to reconstruct monsoon (June–August) precipitation 4Department of Atmospheric Sciences, University of Arizona, Tucson, for a large region in the Southwest. Following the Stahle Arizona, USA. et al. [2009] analytic, the paleomonsoon record is compared Corresponding author: D. Griffin, Laboratory of Tree-Ring Research, with an October–April precipitation reconstruction developed University of Arizona, 1215 E. Lowell Street, Tucson, AZ 85721, USA. from chronologies of tree-ring earlywood width (EW). (dgriffi[email protected]) These reconstructions provide novel perspective on monsoon ©2013. American Geophysical Union. All Rights Reserved. paleoclimatology and reveal instability in the relationship 0094-8276/13/10.1002/grl.50184 between winter and summer precipitation through time.

1 GRIFFIN ET AL.: NA MONSOON PRECIPITATION RECONSTRUCTION

2. Methods and Results with the instrumental SPI variables were submitted as candidate predictors to forward stepwise multiple linear [5] This study focuses on monsoon region 2 (NAM2), as fi fi regression. Model skill was veri ed with standard methods. de ned by the North American Monsoon Experiment Periods of persistent drought and wetness were identified as Forecast Forum (Figure 1) [Gochis et al., 2009]. NAM2 is consecutive-year runs of negative or positive SPI and using a region of spatially coherent variability in monsoon fi ranked values in the time series smoothed with a 5-year precipitation that approximates regions identi ed by other cubic spline. The reconstructions were analyzed to determine studies. Interannual monsoon variability is strong over NAM2, when both seasons were wet (positive SPI) or dry (negative which includes critical waterways, major metropolitan areas, – SPI) or when one season was wet and the other was dry. and more than 550 km of the United States Mexico border. Cross-wavelet analysis [Grinsted et al., 2004] was used to NAM2 is a target for seasonal climate forecasts and has been summarize spectra and coherence of the reconstructions the focus of dynamically downscaled regional climate model through time. Details on the study area, data, methods, analyses, projections [Castro et al., 2012]. Monthly precipitation data and rationale are provided in the Supporting Information. are from a 0.5 gridded product developed by NOAA that [7] Seasonal SPI reconstruction models included PSME extends across North America and uses a terrain-sensitive and Pinus ponderosa predictor chronologies that were well algorithm to interpolate between available instrumental sta- – distributed across NAM2 (Figure 1; Tables S1 and S2 in tion records for the period 1895 2010. NAM2 grid point data Supporting Information). The assumptions of the regression were averaged to produce time series of monthly precipita- approach were met and verification statistics indicate that tion and correlation analysis was used to assess relationships – both models exhibit robust predictive skill (Table S3 in to the subannual tree-ring chronologies. October April (cool Supporting Information). Relationships between the instru- season) and June–August (monsoon) were selected as the mental and reconstructed variables were highly significant, seasons for reconstruction, and precipitation totals for linear, positive, and stable through time (Figure 2). The cool- these seasons were converted to standardized precipitation season estimate exhibits moderately superior regression indices (SPI). statistics, but the LW reconstruction does capture nearly half [6] Tree-ring data are from a new network of subannual of the variance in instrumental monsoon SPI. This is an chronologies developed using EW and LW measurements important and somewhat unexpected result given the spatial on collections from more than 50 sites in the southwestern heterogeneity of monsoon precipitation and its subordinate United States and Baja California (Figure 1). EW and LW role in annual water balance. indices were standardized with the protocol described by Griffinetal. [2011]. Chronologies more than 450 years long from NAM2 that exhibited significant (p<0.01) correlations

Figure 1. The North American monsoon domain, monsoon regions 1–8, and the new network of EW and LW chronologies. Figure 2. Scatterplots (a and b) and time-series graphs Colored triangles denote tree-ring predictor locations for the (c and d) of observed and reconstructed SPI for the cool region 2 SPI reconstructions. season and monsoon.

2 GRIFFIN ET AL.: NA MONSOON PRECIPITATION RECONSTRUCTION

[8] The monsoon and cool-season SPI reconstructions are Visual comparison indicates that cool-season SPI contains uncorrelated for the 1539–2008 common period (r=0.02; more middle- to low-frequency variability than monsoon p=0.67) and the 1896–2008 instrumental period (r=0.06; SPI and that the EW-based reconstruction tracks these p=0.53), although the monsoon and cool-season SPI instru- frequency components well. mental data exhibit weak negative correlation (r=-0.19; [10] The reconstructions offer an expanded perspective, re- p=0.04). Wavelet analysis indicates that both reconstructions vealing seasonal drought events more persistent and severe are dominated by high-frequency variability (Figure S5 in than those of the instrumental era (Figure 3). The period of Supporting Information). Low-frequency covariability be- most persistent summer drought was 1882–1905, when 19 tween the two seasonal reconstructions is visually evident of 24 years had negative monsoon SPI. In the 1890s and early (Figure 3a and b). Cross-wavelet squared coherence, analo- 1900s, this monsoon failure was complemented by severe and gous to correlation between the reconstructions as a function persistent cool-season drought. This dual-season drought of time and frequency, is variable and time unstable at periods event factors importantly into the socioenvironmental history less than 32 years (Figure S5 in Supporting Information). At of the region. Arrival of the Southern PacificRailroadinthe longer periods, the reconstructions generally exhibit in-phase early 1880s ushered an expansion of the Arizona cattle popula- coherence. Potential sources of the low-frequency reconstruction from ~40,000 to 1.5 million individuals [Sheridan, 1995]. tion coherence include long-term changes in tree physiology By the early 1890s, however, an estimated 50–75% of the herd related to root and crown mass [Fritts, 2001], standardization starved and perished from drought-induced rangeland failure of the ring-width time series, or true low-frequency coherence [Sayer, 1999]. Cattle overgrazing triggered landscape-scale between the seasonal climate regimes. Diagnosing the source vegetation change that remains evident today. Environmental (s) of this coherence is considered important but also beyond consequences of the 1890s drought were also severe in the brief scope of the present study. northern Mexico, as discussed by Seager et al. [2009]. Iron- ically, this decades-long drought was immediately followed by the well-known early 20th century pluvial, which in- 3. Seasonal Precipitation History for NAM2 cluded the highest frequency of dual-season wetness in the [9] Several persistent periods of anomalous SPI stand out entire reconstruction (Figure S4 in Supporting Information). in the instrumental records (Figure 2). Both seasons had [11] Other dual-season drought events are evident in the negative SPI near the turn of the 20th century and again in early 1820s and the 1770s. Yet more severe was the 17th the early 21st century. Precipitation was consistently above century “Puebloan Drought,” an event Parks et al. [2006] average in both seasons during the early 20th century implicate as one factor leading to the Pueblo Revolt of pluvial, as recently noted by Cook et al. [2011]. These 1680. In northwestern New Mexico, Stahle et al. [2009] persistent variations are tracked remarkably well by the found that this drought included below-average precipitation tree-ring reconstructions. Monsoon SPI was negative from during both the cool season and July. For NAM2, the period 1972 to 1982, the longest run of dry monsoons in the from 1666 to 1676 included a seven-year run of drier-than- instrumental record. This episode is not particularly well average monsoons and six years with below-average SPI in matched by the LW reconstruction, possibly because it both seasons. According to the smoothed record, this was contained several notably wet May and June months that the most extreme monsoon drought episode of the last 470 would have differentially benefitted summer tree growth. years. 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 plotted 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).

3 GRIFFIN ET AL.: NA MONSOON PRECIPITATION RECONSTRUCTION

[2009], indicates that monsoon failure during the 17th extreme (e.g., mid-17th century) than any during the instru- century Puebloan Drought was robust and widespread across mental era to date. With the exception of the ongoing early the Southwest. 21st century drought, the instrumental era lacks the persistent [12] The late 16th century “megadrought” is evident in dual-season drought episodes common to centuries past. The both the monsoon and cool-season SPI reconstructions for NAM2 seasonal precipitation relationship is weak at best NAM2 (Figure 3). This event, among the most severe and is not time stable. The mid to late 20th century experi- droughts of the last millennium, drove landscape-scale enced a relatively high frequency of years with opposing-sign ecosystem change across the Southwest [e.g., Swetnam and precipitation anomalies. These results indicate that the instru- Betancourt, 1998]. From 1566 to 1579, 11 of 14 monsoons mental era, especially the later half of the 20th century, may were drier than average. Monsoon SPI rebounded to average not be the ideal period for characterizing NAM2 monsoon or near average in the 1580s, but cool-season drought precipitation climatology, its relationship to the cool-season persisted through that decade. From 1566 to 1587, 21 of climate regime, and potential connections to the coupled 22 years were reconstructed to include at least one season ocean-atmosphere system. of below-average SPI and 10 of those years had negative SPI in both seasons. July dryness was previously noted in [15] Acknowledgments. This research was funded by NSF P2C2 northwestern New Mexico [Stahle et al., 2009], but this is award #0823090 and NOAA award #NA08OAR4310727. D. Griffin was fi supported by Environmental Protection Agency STAR award #FP917185. the rst time that persistent monsoon failure has been The authors thank R. Vose and R. Heim for access to the gridded climate connected with the megadrought in the NAM2 region. data, M. Losleben and numerous students for assistance in subannual tree- Several exceptionally dry monsoons in the late 1560s reveal ring chronology development, and two anonymous reviewers for comments that the megadrought may have first manifest as a warm- that improved the article. season phenomenon. This idea is lent some support by spatial analysis of the megadrought through time (Figure 6 References in Stahle et al. [2007]), which mapped the 1560s drought Arias, P. A., R. Fu, and K. C. Mo (2012), Decadal variation of rainfall center over northwestern Mexico, where the monsoon seasonality in the North American monsoon region and its potential provides more than 70% of annual precipitation. causes, J. Climate, 25, 4258–4274, doi:http://dx.doi.org/10.1175/JCLI- [13] These reconstructions offer a novel opportunity to D-11-00140.1 assess the relationship between cool-season and monsoon Asmerom, Y., V. Polyak, S. Burns, and J. Rassmussen (2007), Solar forcing of Holocene climate: New insights from a speleothem record, southwestern precipitation in NAM2. Years with opposing-sign SPI United States, Geology, 35,1–4, doi:10.1130/G22865A.1 anomalies, plotted in Figure 3c, appear to be randomly Barron, J. A., S. E. Metcalfe, and J. A. Addison (2012), Response of the distributed through time. There are brief periods when these North American monsoon to regional changes in ocean surface temperature, Paleoceanography, 27, PA3206, doi:10.1029/2011PA002235 events appear to congregate (e.g., 1960s and 1720s) and Bukovsky, M. S., and L. O. Mearns (2012), Projections of climate change others when they were rare (i.e., the decadal drought and for the North American monsoon from the NARCCAP regional climate wet events described above). A 30-year running count models, Paper presented at the 92nd American Meteorological Society illustrates some temporal variability and indicates that the Annual Meeting, New Orleans, LA. Castro, C. L., T. B. McKee, and R. A. Pielke Sr. (2001), The relationship of mid to late 20th century was characterized by a relatively the North American Monsoon to tropical and north Pacific sea surface high frequency of such events. These results are congruent temperatures as revealed by observational analyses, J. Climate, 14, with instrumental data analysis by Gutzler [2000] and by 4449–4472, doi: http://dx.doi.org/10.1175/1520-0442(2001)014<4449: “ TROTNA>2.0.CO;2. Zhu et al., who found that the linkage is strong from Castro, C. L., H. Chang, F. Domiguez, C. Carrillo, J Schemm, and H.H. 1965–1990 and weak otherwise” [2005]. Opposing-sign Juang (2012), Can a regional climate model improve the ability to fore- years were also relatively frequent in the mid-18th and early cast the North American Monsoon?, J. Climate, 25, 8212–8237, doi: http://dx.doi.org/10.1175/JCLI-D-11-00441.1. 17th centuries. In contrast, the period centered near the turn Cook, B. I., and R. Seager (2013), The response of the North American of the 20th century contained relatively few of these events. Monsoon to increased greenhouse gas forcing, J. Geophys. Res., Wavelet analysis reveals no time-stable coherence at doi:10.1002/jgrd.50111, in press. subdecadal to decadal timescales, indicating that the Cook, B. I., R. Seager, and R. L. Miller (2011), On the causes and dynamics of the early twentieth-century North American Pluvial, J. Climate, 24, NAM2 seasonal precipitation relationship is dominated by 5043–5060, doi:10.1175/2011JCLI4201.1. noise (Figure S3 in Supporting Information). Fritts, H. C. (2001), Tree Rings and Climate, 2nd ed., Blackburn Press, Caldwell, New Jersey, 567 pp. Gochis, D., J. Schemm, W. Shi, L. Long, W. Higgins, and A. Douglas 4. Summary (2009), A Forum for Evaluating Forecasts of the North American Monsoon, Eos Trans. AGU, 90(29), 249, doi:10.1029/2009EO290002. [14] This study provides a high-quality, precisely dated, Griffin, D, D. M. Meko, R. Touchan, S. W. Leavitt, and C. A. Woodhouse seasonally resolved geochronology for the NAM2 region. (2011), Latewood chronology development for summer-moisture reconstruction in the U.S. Southwest, Tree-Ring Res., 67,87–101, The reconstructions represent a plausible range of interannual- doi:10.3959/2011-4.1. to decadal-scale variability in multiseason precipitation for Grinsted, A., J. C. Moore, and S. Jevrejeva (2004), Application of the the past 470 years. These precipitation estimates are suitable cross wavelet transform and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11, 561–566, doi:10.5194/npg-11-561-2004. for comparison with paleoproxy, historical, modern, and Grissino-Mayer, H. D. (1996), A 2129-year reconstruction of precipitation projected climate data and should be useful for a more precise for northwestern New Mexico, USA, in Tree Rings, Environment, and interpretation of the social and environmental history in the Humanity, Radiocarbon, pp. 191–204, Department of Geosciences, University of Arizona, Tucson. American Southwest. Decadal drought events in this region Gutzler, D. S. (2000), Covariability of spring snowpack and summer rainfall over the past five centuries (e.g., 1570s, 1660s, 1770s, across the Southwest United States, J. Climate, 13, 4018–4027, doi:http:// 1820s, 1890s, and early 2000s) were characterized not only dx.doi.org/10.1175/1520-0442(2000)013<4018:COSSAS>2.0.CO;2. by cool-season precipitation deficits but also by consistent Higgins, R. W., and W. Shi (2000), Dominant factors responsible for interannual variability of the summer monsoon in the southwestern failure of the summer monsoon. Monsoon drought events in United States, J. Climate, 13, 759–776, doi:http://dx.doi.org/10.1175/ the past were more persistent (e.g., late 19th century) and 1520-0442(2000)013<0759:DFRFIV>2.0.CO;2.

4 GRIFFIN ET AL.: NA MONSOON PRECIPITATION RECONSTRUCTION

Meko, D. M., and C. H. Baisan (2001), Pilot study of latewood-width of con- Stahle, D. W., F. K. Fye, E. R. Cook, and R. D. Griffin (2007), Tree-ring ifers as an indicator of variability of summer rainfall in the North American reconstructed megadroughts over North America since A.D. 1300, Cli- monsoon region, Int. J. Climatol., 21,697–708, doi:10.1002/joc.646 matic Change, 83, 133–149, doi:10.1007/s10584-006-9171-x Notaro, M., and A. Zarrin (2011), Sensitivity of the North American Stahle, D. W., M. K. Cleaveland, H. Grissino-Mayer, R. D. Griffin, monsoon to antecedent Rocky Mountain snowpack, Geophys. Res. Lett., F. K. Fye, M. D. Therrell, D. J. Burnette, D. M. Meko, and J. Villanueva- 38, L17403, doi:10.1029/2011GL048803. Diaz (2009), Cool- and warm-season precipitation reconstructions over Parks J. A., J. S. Dean, and J. L. Betancourt (2006), Tree rings, drought, and western New Mexico, J. Climate, 22,3729–3750, doi:http://dx.doi.org/ the pueblo abandonment of south- central New Mexico in the 1670s, in: 10.1175/2008JCLI2752.1. Environmental Change and Human Adaptation in the Ancient American Swetnam, T. W., and J. L. Betancourt (1998), Mesoscale disturbance and Southwest, pp. 214–227, University of Utah Press, Salt Lake City, UT. ecological response to decadal climatic variability in the American Ray, A. J., G. M. Garfin, M. Wilder, M. Vasques-Leon, M. Lenart, and Southwest. J. Climate, 11(89), 3128–3147, doi:http://dx.doi.org/ A. C. Comrie (2007), Applications of monsoon research: opportunities 10.1175/1520-0442(1998)011<3128:MDAERT>2.0.CO;2. to inform decision making and reduce regional vulnerability, J. Climate, St. George, S., D. M. Meko, and E. R. Cook (2010), The seasonality of 20,1608–1627, doi:http://dx.doi.org/10.1175/JCLI4098.1. precipitation signals encoded within the North American Drought Atlas. Sayer, N. (1999), The cattle boom in southern Arizona: towards a critical Holocene, 20, 983–988, doi:10.1177/0959683610365937 political ecology, J. Southwest, 41, 239–271. Turrent, C., and T. Cavazos (2009), Role of the land-sea thermal contrast in Seager, R., M. Ting, M. Davis, M. Cane, N. Naik, J. Nakamura, C. Li, E. Cook, the interannual modulation of the North American Monsoon, Geophys. and D. W. Stahle (2009), Mexican drought: an observational modeling and Res. Lett., 36, L02808, doi:10.1029/2008GL036299. tree ring study of variability and climate change, Atmósfera, 22,1–31. Zhu, C., D. P. Lettenmaier, and T. Cavazos (2005), Role of antecedent land Sheridan, T.E. (1995), Arizona: A History, University of Arizona Press, surface conditions on North American monsoon rainfall variability, J. – Tucson, Arizona, 434 pp. Climate, 18, 3104 3121, doi:http://dx.doi.org/10.1175/JCLI3387.1.

5 1 Auxiliary Materials: Griffin et al. North American monsoon precipitation reconstructed

2 from tree-ring latewood.

3 1. Study Area

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

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

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

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

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

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

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

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

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

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

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

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

17 2. Tree-Ring Data

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

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

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

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

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

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

1 24 paleoclimatic history of the North American monsoon [see references in Griffin et al., 2011].

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

46 [2011].

2 47

48 3. Instrumental data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

69 significantly correlated (p<0.05) with monthly precipitation from October through May. Despite

3 70 that correlation, May is the driest month of the year, it falls outside of the cool season, and its

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

88 tree-ring and instrumental data.

90 4. Tree-ring reconstructions of seasonal SPI

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

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

4 93 predictors if they exhibited highly significant correlation (p < 0.01) with the reconstruction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

114

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

5 116 distributed across the NAM2 region (Figure 1). All predictor coeffecients were positive. The

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

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

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

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

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

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

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

124 season reconstruction captures more instrumental variance than does the monsoon

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

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

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

128

129 5. Analyses

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

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

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

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

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

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

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

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

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

6 139 values were ranked to identify the non-overlapping events of greatest magnitude.

140

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

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

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

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

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

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

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

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

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

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

151 coherence is generally evident (Figure S3d).

152

7 153 References Cited

154 Castro, C. L, H. Chang, F. Domiguez, C. Carrillo, J Schemm, and H.H. Juang (2012) Can a

155 regional climate model improve the ability to forecast the North American Monsoon?, J.

156 Climate, doi:http://dx.doi.org/10.1175/JCLI-D-11-00441.1, in press.

157

158 Comrie, A. C., and E. C. Glenn (1998), Principal components-based regionalization of

159 precipitation regimes across the southwest United States and northern Mexico, with an

160 application to monsoon precipitation variability, Climate Res., 10, 201–215.

161

162 Cook, E. R., D. M. Meko, D. W. Stahle, and M. K. Cleaveland (1999), Drought reconstructions

163 for the continental United States, Journal of Climate, 12, 1145–1162, doi:

164 http://dx.doi.org/10.1175/1520-0442(1999)012<1145:DRFTCU>2.0.CO;2.

165

166 Cook, E. R. (1985), A Time-Series Approach to Tree-Ring Standardization, Ph.D. Dissertation,

167 University of Arizona, Tucson, Arizona.

168

169 Cook, E. R., and L. A. Kariukstis (1990), Methods of Dendrochronology: Applications in the

170 Environmental Sciences, Klewer, Boston.

171

172 Cook, E. R., and K. Peters (1981), The smoothing spline: A new approach to standardizing forest

173 interior tree-ring width series for dendroclimatic studies, Tree-Ring Bulletin, 41, 45–53.

174

8 175 Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P.

176 A. Pasteris (2008), Physiographically-sensitive mapping of temperature and precipitation across

177 the conterminous United States. Int. J. Climatol., 28, 2031-2064, doi:10.1002/joc.1688.

178

179 Douglass, A.E. (1941) Crossdating in dendrochronology, Journal of Forestry, 39, 825-828.

180

181 Gochis, D., J. Schemm, W. Shi, L. Long, W. Higgins, and A. Douglas (2009), A Forum for

182 Evaluating Forecasts of the North American Monsoon, Eos Trans. AGU, 90(29), 249,

183 doi:10.1029/2009EO290002.

184

185 Fritts, H. C. (2001), Tree Rings and Climate, 2nd ed., Blackburn Press, Caldwell, New Jersey.

186

187 Griffin, D, D. M. Meko, R. Touchan, S. W. Leavitt, and C. A. Woodhouse (2011), Latewood

188 chronology development for summer-moisture reconstruction in the U.S. Southwest, Tree-Ring

189 Research, 67, 87–101, doi: 10.3959/2011-4.1.

190

191 Grinsted, A., J. C. Moore, and S. Jevrejeva (2004), Application of the cross wavelet transform

192 and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11, 561-566,

193 doi:10.5194/npg-11-561–2004.

194

195 Gutzler, D. S. (2004) An index of interannual precipitation variability in the core of the North

196 American Monsoon region, J. Climate, 17, 4473-4480, doi:http://dx.doi.org/10.1175/3226.1.

197

9 198 Holmes, R. L., (1983), Computer-assisted quality control in tree-ring dating and measurement.

199 Tree-Ring Bulletin, 44, 69–78.

200

201 Liebmann, B., I. Bladé, N. A. Bond, D. Gochis, D. Allured, and G. T. Bates (2008),

202 Characteristics of North American summertime rainfall with emphasis on the monsoon. J. Clim.,

203 21, 1277–1294, doi: 10.1175/2007JCLI1762.1

204

205 McKee, T. B., N. J. Doesken, and J. Kleist (1993), The relationship of drought frequency and

206 duration of time scales, Paper presented at the Eighth Conference on Applied Climatology,

207 American Meteorological Society, Jan17-23, 1993, Anaheim, CA., pp.179–186.

208

209 Meko, D. M., and C. H. Baisan (2001), Pilot study of latewood-width of conifers as an

210 indicator of variability of summer rainfall in the North American monsoon region,

211 Int. J. Climatol., 21, 697–708, doi:10.1002/joc.646

212

213 Meko, D. M., R. Touchan, and K. J. Anchukaitis (2011), Seascorr: a MATLAB program for

214 identifying the seasonal climate signal in annual tree-ring time series, Comput.Geosci., 37, 1234–

215 1241, doi: 10.1016/j.cageo.2011.01.013.

216

217 Michaelsen, J. (1987), Cross-validation in statistical climate forecast models, J. Clim. Appl.

218 Meteorol. 26, 1589−1600. doi:http://dx.doi.org/10.1175/1520-

219 0450(1987)026<1589:CVISCF>2.0.CO;2.

220

10 221 Mock , C. J. (1996), Climatic controls and spatial variations of precipitation in the western

222 United States. J. Climate, 9, 1111–1125, doi: http://dx.doi.org/10.1175/1520-

223 0442(1996)009<1111:CCASVO>2.0.CO;2.

224

225 NAME, North American Monsoon Experiment, (2013), website:

226 http://www.eol.ucar.edu/projects/name/documentation/NAME_Forecast_Forum.pdf, last

227 accessed 11 January, 2013.

228

229 Stahle, D. W., F. K. Fye, E. R. Cook, and R. D. Griffin (2007), Tree-ring reconstructed

230 megadroughts over North America since A.D. 1300, Climatic Change, 83,133–149,

231 doi:10.1007/s10584-006-9171-x

232

233 Stahle, D. W., M. K. Cleaveland, H. Grissino-Mayer, R. D. Griffin, F. K. Fye, M. D. Therrell, D.

234 J. Burnette, D. M. Meko, and J. Villanueva-Diaz (2009), Cool- and warm-season precipitation

235 reconstructions over western New Mexico, J. Climate, 22, 3729–3750,

236 doi:http://dx.doi.org/10.1175/2008JCLI2752.1.

237

238 Sheppard, P. R., A. C. Comrie, G. D. Packin, K. Angersbach, and M. K. Hughes, (2002), The

239 climate of the U.S. Southwest. Climate Research 21, 219–238.

240

241 Stokes, M.A. and T.L. Smiley, (1996), An Introduction to Tree-Ring Dating, 2nd ed., University

242 of Arizona Press, Tucson, Arizona.

243

11 244 St. George, S., D. M. Meko, and E. R. Cook (2010), The seasonality of precipitation signals

245 encoded within the North American Drought Atlas. The Holocene, 20, 983–988,

246 doi:10.1177/0959683610365937

247

248 Zhu, C., D. P. Lettenmaier, and T. Cavazos (2005), Role of antecedent land surface conditions

249 on North American monsoon rainfall variability, J. Climate, 18, 3104–3121, doi:

250 http://dx.doi.org/10.1175/JCLI3387.1.

251

12 252 Table S1.

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

Site Elev. Inner Outer

EWa LWb Code Site Name Species ITRDB Codec Lat. Long. (m) Date 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

255 aCool-season SPI candidate predictor based on correlation screening and chronology length

256 bMonsoon SPI candidate predictor based on correlation screening and chronology length

257 cInternational Tree-Ring Data Bank reference code (http://www.ncdc.noaa.gov/paleo/treering.html) 258

13 259 Table S2.

260 Calibration model equations.

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

14 263 Table S3.

264 Calibration and verification information.

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

15 267 Figure S1.

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

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

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

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

272 extreme values beyond that range.

273

274 Figure S2.

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

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

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

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

279

280 Figure S3.

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

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

283

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

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

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

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

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

289

16 290 Figure S5. Continuous wavelet power transform of reconstructed cool-season SPI (a) and

291 monsoon SPI (b), with significant (p<0.05) concentrations of power indicated by black contours.

292 Cross wavelet transform illustrates common power (c) and cross-wavelet coherence and phase

293 illustrates time-frequency co-variability of the reconstructions (d). Warm colors indicate strong

294 power or coherence. Arrows pointing to the right represent zero degree, in-phase coherence.

295 Arrows pointing to the left represent 180 degree, anti-phase coherence with cool-season SPI

296 leading monsoon SPI.