A Wintertime Arctic Oscillation Signature on Early-Season Indian Ocean Monsoon Intensity

A Wintertime Arctic Oscillation Signature on Early-Season Indian OceanMonsoon Intensity

WOLFGANG BUERMANN*

Berkeley Atmospheric Sciences Center, and Department of Earth and Planetary Sciences, University of California, Berkeley,Berkeley, California

BENJAMIN LINTNER*

Berkeley Atmospheric Sciences Center, and Department of Geography, University of California, Berkeley, Berkeley, California

CÉLINE BONFILS

Berkeley Atmospheric Sciences Center, and Department of Earth and Planetary Sciences, University of California, Berkeley,Berkeley, California

(Manuscript received 5 November 2003, in final form 15 July 2004)

ABSTRACT

The Indian Ocean monsoon (IOM) exhibits considerable year-to-year variations that have previouslybeen attributed to a number of forcing mechanisms including the El Niño–Southern Oscillation (ENSO)and Eurasian snow cover anomalies. In this study, spatial data of Eurasian spring land surface temperaturesare analyzed as well as proxies for soil moisture, summer IOM precipitation, and summer IOM 850-mbzonal winds for the 1979–99 period to isolate correlated modes of variability. The results indicate theexistence of a prominent mode that appears to be related to the boreal winter Arctic Oscillation (AO); thismode projects strongly on the June precipitation and 850-mb zonal wind fields in the vicinity of the IOMregion. Its projection on spatial fields of temperature and proxies for soil moisture shows springtime surfacewarming and drying in the region to the north and west of the Indian subcontinent and cooling over thehigher Eurasian latitudes during years of anomalously intense June monsoon rainfall. Such surface signa-tures are consistent with the negative phase winter AO. It is hypothesized that the preconditioning of thespring season surface characteristics may be associated with an AO-induced quasi-stationary troposphericcirculation anomaly: the impact of this anomaly is to displace the mid-Eastern jet poleward during AO-negative phases, resulting in anomalous surface heating and drying that persist into the later spring seasonand finally affect the rainfall over the IOM region in June.

1. Introduction

Much of the earth’s population lives in climatesdominated by monsoons—seasonally reversing circula-tions typically characterized by alternating wet and dryseasons. The basic economic livelihood of those inhab-iting regions influenced by monsoons often depends onthe amount of rainfall accompanying the wet-seasonphase (Webster et al. 1998). In fact, the term monsoon

has become synonymous with this wet-season rainfall(Trenberth et al. 2000). Monsoon failure can lead todrought, famine, and shortages of water. On the otherhand, abnormally intense monsoon precipitation mayinduce flooding, crop loss, and disease epidemics.Given the socioeconomic impact of monsoons, there isa great need to understand the underlying dynamics ofmonsoon regions (Webster et al. 1998). Moreover, froma public policy perspective, reliable predictions of mon-soon intensity before wet-season onset can facilitate theimplementation of proactive policies designed to miti-gate the negative impacts of adverse monsoon condi-tions.

Over the past century, much scientific effort has beendevoted to the study of monsoon systems, especially thesouth Asian monsoon system and the portion of this

* These authors contributed equally to this work.

Corresponding author address: Dr. Wolfgang Buermann, De-partment of Earth and Planetary Sciences, University of Califor-nia, Berkeley, McCone Hall, Berkeley, CA 94720.E-mail: [email protected]

1 JULY 2005 B U E R M A N N E T A L . 2247

© 2005 American Meteorological Society

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monsoon that influences the Indian subcontinent, here-after referred to as the “Indian Ocean monsoon”(IOM). A large body of work has focused on under-standing the interannual variations of monsoon inten-sity. The role of the El Niño–Southern Oscillation(ENSO) as a modulator of year-to-year fluctuations ofmonsoon intensity has received considerable attention.During the late nineteenth and early twentieth centu-ries, Sir Gilbert Walker observed a phase relationshipbetween monsoon rainfall and the Southern Oscillation(Walker 1924; Walker and Bliss 1932). Walker noted atendency for below-normal monsoon rainfall to occurwith negative phases of the Southern Oscillation index(i.e., El Niño events). Later studies have supported theconnection between weak monsoons and warm centralPacific SSTs (Angell 1981; Rasmusson and Carpenter1983; Ropelewski and Halpert 1987; Shukla 1987). Ithas been argued that anomalous subsidence over theIndonesian archipelago and south Asian regions asso-ciated with a reduction in the strength of the Walkercirculation during El Niño events may inhibit monsoondevelopment (Webster and Yang 1992). Significantly,though, the relationship between ENSO and the mon-soon is not stationary and has been rather weak sincethe mid- to late 1970s (Torrence and Webster 1999).

The apparent breakdown of the phase relationshipbetween monsoon intensity and ENSO over the lastseveral decades highlights the need to consider forcingmechanisms beyond ENSO. One potential source ofmonsoon forcing is the state of the Eurasian land sur-face (Hahn and Shukla 1976; Dickson 1984). The landsurface–monsoon interaction follows from the underly-ing dynamics of the IOM system, in particular the dif-ferential heating of the continental and oceanic surface(Ramage 1971). Changes in the land surface albedo orthermal inertia (or both) can alter the differential land–sea contrast. Through a snow–albedo feedback, for ex-ample, more extensive snow cover leads to a higheralbedo and greater reflection of shortwave radiation atthe surface, thereby slowing the rate of land surfaceheating. Deeper snow accumulation may also inhibitland surface heating via snow–hydrology feedbacks: theexcess snowpack requires energy input for melting,thereby reducing net heating, and the groundwater gen-erated by snowmelt may further limit surface heatingby evaporative cooling (Yasunari et al. 1991).

Although a land surface–monsoon connection wasfirst suggested over 100 yr ago by Blanford (1884),there is still much that is uncertain about the nature ofthe coupling. Hahn and Shukla (1976) found a strongconnection between the extent of Eurasian snow coverand monsoon intensity. Later, Dickson (1984) and Bar-nett (1984, 1985) argued that greater snowfall and snow

accumulation precedes drier-than-normal summertimemonsoon conditions. Modeling studies have providedsome support for these ideas (Barnett et al. 1989; Ver-nekar et al. 1995; Meehl 1994; Douville and Royer1996). Meehl (1994), for example, describes a series ofGCM simulations from various model frameworks thatall show stronger monsoon conditions associated with agreater land–sea thermal contrast, lower land surfacepressure, and less snow cover. Meehl (1994) also sug-gests a positive feedback between soil moisture and pre-cipitation, with positive soil moisture anomalies provid-ing a moisture source for subsequent monsoon rainfall,although the occurrence of positive soil moistureanomalies in the months preceding monsoon onset mayrepresent (as previously noted) a negative feedback(Yasunari et al. 1991).

Not all studies, however, have reached similar con-clusions concerning the coupling of the land surface andmonsoons or the mechanisms involved. Bamzai andShukla (1999), for example, suggest that the location ofthe most sizeable monsoon-related snow cover anoma-lies in regions remote to the monsoon region (e.g., west-ern Eurasia) points to low-frequency changes in plan-etary-scale circulations as a source of monsoon variabil-ity. It is possible that such remote snow cover anomaliesare not directly responsible for modulating monsoonintensity via the snow–albedo or snow–hydrology feed-backs. Rather, they may represent a passive response tocirculation changes (like ENSO), or they actively in-duce circulation changes that force monsoon intensityvariations. Bamzai and Shukla (1999) also emphasizethe importance of soil moisture anomalies as the sourceof interseasonal memory. Subsequent studies, includingShinoda (2001) and Robock et al. (2003), find only alimited role for soil moisture anomalies.

The present study aims to shed some light on the landsurface–monsoon connection. In exploring this connec-tion, special emphasis is placed upon understanding therole of the boreal winter AO, the leading mode of in-terannual variability in the Northern Hemisphere (NH)extratropics, as a source of surface variability. We beginby presenting an overview of IOM variability as repre-sented by a discrete, area-averaged precipitation rateindex [the “large-area monsoon” index (LAMI), or itsvariant LAMI*]. While the LAMI is characterized byconsiderable variability on both interannual and in-traseasonal time scales, the June LAMI exhibits a sig-nificant relationship to the boreal winter Arctic Oscil-lation. Using simple correlation analysis, the June pre-cipitation index is observed to project similar spatialpatterns onto the late winter/early spring surface tem-perature and soil moisture proxy fields as the borealwinter AO.

2248 J O U R N A L O F C L I M A T E VOLUME 18

We next describe the results of a technique—canonical correlation analysis (CCA)—that isolatescoupled patterns of variability between two fields. TheCCA framework represents a valuable tool for under-standing how the development of anomalous, early-season (June) IOM region precipitation variability maybe related to the land surface conditions over Eurasiaduring the preceding late winter and spring periods.Two different combinations of meteorological fields areanalyzed: late winter/early spring temperatures andJune precipitation, and late winter/early spring tem-peratures and June zonal winds at 850mb. In both cases,CCA isolates a coupled ENSO mode and a coupled AO(or mixed AO–ENSO) mode. The latter mode supportsthe relationship between the boreal winter AO andJune LAMI. It is speculated that the AO inducesspringtime surface warming and drying of the land sur-face as well as warming of the ocean surface in thevicinity of south Asia, possibly via an anomalous tro-pospheric stationary wave pattern. These surface con-ditions are associated with an anomalous southwesterlyflow in the Arabian Sea in early summer (June) thatenhances airmass convergence and cross-equatorialmoisture transport into the monsoon region.

2. Data and methods

a. Data

The principal fields examined in this study are pre-cipitation, 850-mb horizontal wind, land surface tem-perature, and the Palmer Drought Severity Index(PDSI). To further diagnose the results, we also utilized500-mb zonal wind and snow cover data. The analysis isrestricted to the 21-yr period from 1979 to 1999 becauseof the existing overlap of all datasets used. Two pre-cipitation datasets, the Climate Prediction Center(CPC) Merged Analysis of Precipitation (CMAP; Xieand Arkin 1997) and National Centers for Environmen-tal Prediction–National Center for Atmospheric Re-search (NCEP–NCAR) reanalysis (Kalnay et al. 1996)products, are utilized, although particular emphasis isplaced on the CMAP dataset. Both the CMAP andNCEP–NCAR reanalysis precipitation fields consist ofgridded global monthly means of precipitation rate (inunits of millimeters per day) with resolutions of 2.5° �2.5°. The CMAP data are obtained observationallyfrom both rain gauge and satellite measurements andhave been supplemented by numerical model outputs.NCEP–NCAR reanalysis precipitation rates, on theother hand, are diagnostically determined and dependon the reanalysis model convective parameterization.The 850- and 500-mb wind data are also obtained fromthe NCEP–NCAR reanalysis and have a resolution of

2.5° � 2.5°. While interpolated to a uniform spatial gridby the reanalysis procedure, the variability in the windfield is largely observation driven. The monthly meantemperature data utilized in this study are from Hansenet al. (1999). These gridded, land-only temperaturedata have a resolution of 2° � 2° and are obtained fromstation measurements, typically in sparsely populatedareas or towns.

To express variability in soil moisture, we utilized aCMAP-based cumulative rainfall index in the form ofthe standardized precipitation index (SPI; McKee et al.1993) and the 2.5° � 2.5° PDSI record from Dai et al.(2004). The SPI provides a standardized measure ofanomalies on atmospheric moisture supply over a speci-fied time period leading up to and including the monthof interest. For this study, a 4-month SPI [SPI(4)] iscomputed for May to capture the variability of accu-mulated precipitation over the late winter/spring period(i.e., February through May). The PDSI is computedthrough a two-layer bucket-type model that incorpo-rates prior and present precipitation, surface air tem-perature, and information on local field water-holdingcapacity (Dai et al. 2004). Hence, the PDSI considersboth cumulative atmospheric moisture supply and de-mand via surface temperature, in contrast to the SPIthat is based on precipitation alone. Recently, variabil-ity in the PDSI was shown to be comparable to ob-served soil moisture over many regions including thosethat are relevant for the present study (e.g., China,Mongolia, and the former USSR; Dai et al. 2004). Thesatellite-derived snow cover data used stem from theNational Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Informa-tion Service (NOAA/NESDIS). This snow coverdataset is the 2° � 2° gridded Northern Hemispheredigitized version, indicating absence/presence of snowat weekly resolution.

Comparisons are made between the various meteo-rological fields and indices of ENSO and the AO. Themeasure of ENSO employed here is the Niño-3 index(Trenberth 1997). This index represents an area aver-age of sea surface temperature anomalies over the re-gion 5°S–5°N, 150°–90°W (Reynolds and Smith 1994).Positive (negative) anomalies of Niño-3 correspond toEl Niño (La Niña) conditions. The AO index repre-sents the leading principal component of an empiricalorthogonal function (EOF) decomposition of NH ex-tratropical surface pressure anomalies (Thompson andWallace 1998).

b. Methods

Two analysis techniques are applied in this study.First, simple regression/correlation analysis is applied


between measures of monsoon intensity and continu-ous temperature fields as well as those that representsoil moisture variations in NH winter and spring beforethe monsoon. Such an approach is typical of monsoonstudies in which monsoon intensity is defined in termsof a discrete index over a specified region. For the pur-poses of this study, we construct an index of monsoonintensity, which we refer to as LAMI, that consists ofthe area-averaged precipitation rate anomaly for theregion spanning 10°–20°N, 60°–100°E. LAMI, as well asa more zonally elongated index spanning 60°–120°E(denoted LAMI*) is constructed to reflect the large-scale variability of the IOM. While LAMI (or LAMI*)is somewhat arbitrarily defined here, it will be shown insection 4 that its interannual behavior is consistent withan “objectively” determined mode of large-scale pre-cipitation variability derived from a more sophisticatedtechnique. Also, it should be noted that, in contrast tosome more common measures of monsoon intensity,such as the All-India Rainfall Index (AIRI; Parthasar-athy et al. 1995), LAMI includes both land and oceanprecipitation variability; nevertheless, AIRI andLAMI/LAMI* are significantly positively correlatedwith one another.

In addition to simple regression/correlation analysis,we applied CCA to isolate coupled modes of spatiotem-poral variability in two fields (Barnett and Preisendor-fer 1987). CCA seeks to determine the features in onefield that are maximally correlated with the features ofthe other field using an algorithm analogous to EOFanalysis. Similar to EOF, the CCA modes must satisfyan orthogonality constraint, although (in contrast toEOF) orthogonality is required only in the time do-main. Consequently, the canonical factor time series(CFTs) of a specified CCA mode is orthogonal to theCFTs of all other modes, while the spatial componentsof the modes are, in general, nonorthogonal.

To highlight a possible (land) surface–IOM connec-tion, CCA analysis is performed on standardized, area-weighted spring Eurasian/North African land surfacetemperature anomalies (0°–90°N, 20°W–180°) and stan-dardized area-weighted June precipitation as well asJune 850-mbar zonal wind anomalies over the IOMspatial domain, respectively. The IOM spatial domain isdefined as the regions between 10°–50°N, 30°–130°Eand 10°S–10°N, 30°–95°E. Indonesia is excluded fromthe analysis to avoid strong ENSO variability in theprecipitation fields (Dai and Wigley 2000). Even withthe ad hoc exclusion of Indonesia, however, consider-able ENSO variability remains.

The first step in CCA involves an EOF decomposi-tion of each input field to limit the amount of noisepresent in the data. Such “EOF screening” is applied to

remove between 30%–50% of the total variance fromeach field. Since the objective of this analysis is an un-derstanding of the propagation of a late winter/earlyspring signal into late spring/early summer, the tem-perature EOF is constructed to carry the individualmonths of February, March, April, and May as an ad-ditional spatial dimension. This technique, referred toas a “sliding EOF” analysis, can ascertain patterns witha persistent temporal signature but with spatial patternsthat may vary intraseasonally. Motivation for the use ofthis form of sliding EOF is provided by the results ofthe simple correlation analysis (see section 3d).

A subset of the first six EOFs from each input field isthen subjected to the CCA algorithm. The CCA outputconsists of two weighting matrices, one for the tempera-ture EOFs and one for the precipitation (or u850mb)EOFs that are used to reconstruct the CFTs from theoriginal EOF time series. It also produces an eigen-value matrix representing squared correlations of theCFTs of each field (Table 1). In both the temperature–precipitation and temperature–u850mb CCA analyses,these eigenvalues (Table 2) suggest that the first twofactor correlations are very high (r � 0.78). The robust-ness of these canonical factors (CFs) was assessed bysystematically varying the number of EOFs retainedprior to CCA. This test suggests that the first two CFsare also fairly robust, though it must be noted that if thenumber of retained EOFs becomes too small (�5),CCA fails to separate the first two CFs. In contrast,higher-order but less correlated CFs exhibit sensitivityto the effects of EOF truncation and also, in some in-stances, appear to isolate more localized modes ofcoupled variability that explain little of the total vari-ance in the respective fields.

3. Regression/correlation analysis

a. Indian Ocean monsoon climatology

An overview of the climatology of the south Asiansummer monsoon circulation is presented in Fig. 1. Thesouth Asian summer monsoon system consists of mul-tiple components, including the Indian Ocean, eastAsian, and western Pacific monsoons (Webster andYang 1992). Our principal focus in this study is theportion of the monsoon that directly influences the In-dian subcontinent (the IOM). The principal features ofthe IOM include centers of heavy precipitation rates(�10 mm day�1) over the equatorial central IndianOcean, the western Ghats/eastern Arabian Sea, the Bayof Bengal, and southeast Asia (Ramage 1971). In addi-tion, the wind field at 850 mb is characterized by ahorseshoe-shaped pattern, with easterly flow near theequator and westerly flow across south Asia. A jet-like


flow—the Findlater jet—exists along the western mar-gin of the Indian Ocean basin and connects the near-equatorial easterlies and Arabian Sea/Bay of Bengalwesterlies (Findlater 1969).

b. LAMI interannual and intraseasonal variations

Time series of the deseasonalized LAMI, calculatedfrom both the CMAP and NCEP–NCAR reanalysisdatasets, are presented in Fig. 2. Both the seasonallyaveraged [June–August (JJA)] and individual monthlytime series are illustrated. Several important features

are evident in these time series. First, the time seriescomputed from both datasets exhibit qualitatively simi-lar interannual variations: Pearson correlation mo-ments of 0.67, 0.72, 0.60, and 0.66 are noted for corre-lations of the June, July, August, and JJA-averagedCMAP and NCEP–NCAR LAMI, respectively. Thisagreement suggests that the NCEP–NCAR precipita-tion field over the large-area monsoon index regionreflects much of the observed precipitation variability;in other words, the reanalysis precipitation variations inthis region are driven, to a large degree, by observa-tions rather than the reanalysis model framework. The

TABLE 2. Correlations of LAMI and LAMI* with selected AO indices. Months are denoted by their first letter. Values in italics aresignificant at the 95% level (r � 0.43; two-tailed Student’s t test).

CMAP NCEP

LAMI LAMI* LAMI LAMI*

AO J J A JJA J J A JJA J J A JJA J J A JJA

J �0.35 0.21 0.01 0.02 �0.34 0.27 0.18 0.11 �0.40 0.06 �0.03 �0.27 �0.44 0.15 0.21 �0.07F �0.46 0.11 �0.06 �0.12 �0.43 0.18 �0.08 �0.10 �0.58 0.07 0.02 �0.34 �0.59 0.10 0.06 �0.31M �0.18 0.08 0.24 0.06 �0.26 0.09 0.00 �0.05 �0.37 �0.10 0.14 �0.23 �0.37 �0.04 0.22 �0.13A �0.19 �0.33 0.11 �0.30 0.00 �0.22 �0.11 �0.20 �0.14 �0.34 0.43 �0.03 0.00 �0.24 0.35 0.11M 0.08 �0.11 �0.19 �0.11 0.22 �0.11 �0.07 �0.01 �0.04 �0.37 0.14 �0.17 �0.03 �0.45 0.19 �0.16J 0.29 0.10 �0.12 0.16 0.26 0.21 �0.12 0.24 0.18 0.32 �0.31 0.13 0.10 0.36 �0.29 0.08JFM �0.44 0.18 0.08 �0.01 �0.45 0.24 0.05 �0.01 �0.59 0.01 0.05 �0.37 �0.61 0.09 0.22 �0.22FMA �0.38 �0.00 0.13 �0.12 �0.35 0.07 �0.07 �0.14 �0.54 �0.12 0.21 �0.31 �0.50 �0.04 0.25 �0.20MAM �0.16 �0.10 0.14 �0.11 �0.09 �0.06 �0.06 �0.11 �0.31 �0.31 0.29 �0.22 �0.25 �0.26 0.34 �0.10AMJ 0.07 �0.22 �0.10 �0.17 0.26 �0.10 �0.17 �0.01 �0.02 �0.27 0.20 �0.05 0.03 �0.23 0.19 0.02

TABLE 1. Summary of CCA decompositions.

CCA of FMAM temperature and Jun precipitation

Explained variance

Factor Eigenvalue* Temperature Precipitation Mode interpretation

1 0.73 7.3% 8.0% DJF ENSO2 0.61 12.7% 8.6% FMA AO and JJA ENSO3 0.40 8.4% 12.6%4 0.09 10.4% 8.5%5 0.03 8.4% 9.8%6 �0.01 10.0% 9.2%

Total variance 57.2% 56.7%

CCA of FMAM temperature and Jun u850mb

Explained variance

Factor Eigenvalue* Temperature U850mb Mode interpretation

1 0.73 7.8% 12.3% DJF and JJA ENSO2 0.66 11.6% 12.7% FMA AO3 0.42 9.5% 7.7%4 0.27 10.6% 8.8%5 0.12 10.3% 15.6%6 �0.01 7.4% 8.6%

Total variance 57.2% 65.7%

* Eigenvalues represent the squared correlations between the respective CFTs.


consistency of the NCEP–NCAR and CMAP resultssuggest that it may be possible to use the reanalysis datato extend the present study to earlier time periods. Suchan extension may prove useful since the coupling of themonsoon and other components of the climate system,such as ENSO, appear to be nonstationary over longer(e.g., interdecadal) time scales (Kumar et al. 1999).

Moreover, the time series of individual months areobserved to show relatively little agreement with oneanother. That is, the seasonal persistence of area-averaged rainfall anomalies during a given year is weak.The lack of intraseasonal persistence may point to dif-ferent forcing mechanisms of the early and late mon-soon season variability. Alternatively, intraseasonalvariability may be strongly influenced by high-frequency synoptic-scale weather events that are essen-tially unrelated to the relatively low frequency forcingmechanisms (Palmer 1994; Becker et al. 2001). Regard-less of the source of intraseasonal variability, the inter-pretation of relationships between the monsoon and itsforcing mechanisms may be sensitive to the data prepa-ration, for example, whether seasonally averaged or in-dividual monthly data are utilized.

c. Association between the wintertime AO and LAMI

Recently, the AO has received widespread attentionbecause of its dominant influence on the NH winter and

spring climate (Thompson and Wallace 1998). This pre-dominantly zonally symmetric hemispheric mode ofvariability is characterized by a seesaw of atmosphericmass between the NH polar regions and the midlati-tudes. The AO positive phase is associated with lower-than-normal pressure over the polar regions andhigher-than-normal pressure at �45°N latitude,thereby inducing a poleward displacement of oceanicstorm tracks and favoring zonal advection of relativelywarm and wet air deep into continental interiors(Thompson and Wallace 2000).

Table 2 summarizes Pearson correlation momentsbetween the AO index and the LAMI (and LAMI*)calculated from the CMAP and the NCEP–NCAR re-analysis products. A boreal winter (especially JFM)AO signal is evident in the June indices derived fromboth datasets. The phase relationship is such that nega-tive AO events are associated with enhanced June pre-cipitation over the LAMI region. The wintertime AOsignal, however, does not appear to extend beyondJune into the later monsoon season: correlations ofwintertime AO and July and August, as well as JJAseasonal mean precipitation, respectively, are negli-gible. This result suggests that the wintertime AO in-fluences only the early-season evolution of the IOM.Although only the winter AO–June LAMI/LAMI* cor-relations are consistently significant with different

FIG. 1. Summertime (Jun–Aug) IOM climatology for 1979–99. Vectors are horizontal 850-mb wind (u, �)components, and filled contours are precipitation rates. The LAMI and LAMI* regions are also highlighted.


Fig 1 live 4/C

datasets and index representations, the April and MayAO indices show some evidence (albeit generallyweak) of a negative relationship to precipitationvariability during July. Furthermore, the sign of theApril/May AO–August LAMI/LAMI* correlations forthe NCEP precipitation dataset tend to be of oppositesign.

d. The roles of temperature/soil moisture inconnecting the wintertime AO and June LAMI

The existence of a robust boreal winter AO–earlyseason monsoon connection suggests that indices of theAO and monsoon variability should exhibit similarfootprints in the large-scale fields (such as temperatureand proxies for soil moisture) during the late winter/

spring period. To explore the characteristics of thesefootprints, covariances of the monthly February–May(FMAM) temperature and correlations of the MaySPI(4) and PDSI fields computed with respect to theinverse wintertime AO index and the June LAMI(based on CMAP) are shown in Fig. 3.

A classic AO surface signature is obtained when theFMAM temperature fields are regressed upon the win-tertime AO index (Fig. 3a), namely AO-negative phasecooling over Eurasian high latitudes and warming overNorth Africa and Eurasian low latitudes (Thompsonand Wallace 1998). The February and March AO-negative phase warming over the North Africa/easternMediterranean/Red Sea region has been previouslynoted and has been attributed to anomalous warm airadvection from the southwest (Thompson and Wallace

FIG. 2. Time series of the deseasonalized LAMI for both CMAP (red) and NCEP (blue) precipitation data.The LAMI is shown for individual months and for the JJA seasonal mean.


Fig 2 live 4/C

FIG. 3a. Covariance maps for NH land surface temperature anomalies based upon the standardized JFM AO index for the period1979–99: the regression patterns between the inverse JFM AO index and (i) Feb, (ii) Mar, (iii) Apr, and (iv) May temperatureanomalies. Land areas not contoured indicate missing data. Thick black contour line indicates regions that are significant at the 90%level (r � 0.37; two-tailed Student’s t test).


Fig 3a live 4/C

2000). The April warming, for which direct near-surfaceAO-induced advection effects are not clearly evident,occurs to the east of the February (and March) warm-ing. It is possible that this warming occurs in response

to changes in the near-surface energy balance inducedby anomalous snow cover/soil moisture anomalies orchanges in circulation. In May, the wintertime negativeAO phase-warming signal over the land surface is quite

FIG. 3b. Same as in Fig. 3a, but the standardized Jun LAMI is used instead of the inverse JFM AO index.


Fig 3b live 4/C


Fig 3c live 4/C

weak. However, regression of the winter AO indexonto the May SST fields reveals the presence of a sig-nificant warming signal over the northern portion of theArabian Sea (not shown).

The projection of the June LAMI onto the FMAMtemperature fields show a similar meridional structureas in the wintertime AO case, with cool temperatures tothe north and warm temperatures to the south in yearsof anomalously wet June IOM conditions (Fig. 3b).There is, however, some mismatch in the location of thecenters of strongest covariance between Figs. 3a and 3b:in February and March, for example, the maximum co-variance between June LAMI and temperature occursover western Eurasia, whereas the winter AO/temperature covariance pattern extends farther east-ward. For April, the intensity and spatial extent of thewarming signal over the southern Eurasian latitudes isconsiderably smaller in the June LAMI/temperaturecovariances. Also, in contrast to the winter AO/temper-ature covariance pattern, a more pronounced (thoughstill weak) wet LAMI/warm temperature anomaly isobserved over the Indian subcontinent in May.

The spatial features of May water surplus/deficit cor-relations with respect to each of the inverse wintertimeAO and the June LAMI also broadly resemble oneanother in their meridional structure (Fig. 3c). NegativeAO phases and anomalously wet June conditions in themonsoon region are associated with a tendency towardwetter conditions throughout the Eurasian midlatitudesand drier conditions over Eurasian low latitudes, in-cluding the Persian Gulf region, the Indian subconti-nent, and parts of southern China. As with the tem-perature covariances (Figs. 3a,b), the spatial features ofthe SPI(4) and PDSI correlation maps are more pro-nounced in the vicinity of the respective centers of ac-tion of the LAMI and the AO (e.g., the June LAMIcorrelations show a more prominent May water deficitover the Persian Gulf–Indian subcontinent region). Itshould be noted that effects of snowmelt and frozensoils on soil moisture are not considered in the Palmermodel (Dai et al. 2004), which renders the PDSI duringthe month of May over high latitudes and high eleva-tions of the midlatitudes less reliable.

4. Canonical correlation analysis

In section 3, a JFM AO signature in the June IOMprecipitation field is discussed. Furthermore, the win-

tertime AO and the June LAMI are observed to projectsimilar spatial patterns onto the late winter/early springsurface temperature and soil moisture proxy fields. Inthe present section, CCA is employed with the objec-tive of further exploring how the variability of the earlysummer monsoon may be related to boreal winter AOvariability. As noted in section 2b, one of the powerfuladvantages of CCA relative to simple correlation analy-sis is its ability to isolate the coupling between twofields without specification of discrete indices.

a. FMAM temperatures and June precipitation

The results of the CCA on Eurasian/North AfricanFMAM temperature and IOM June precipitation(CMAP) anomalies suggest the existence of two robustcoupled modes (Table 1). The use of the June-onlyprecipitation field follows from the simple correlationanalysis, which suggests that the boreal winter AO sig-nal in the precipitation field is quite weak in July andAugust (Table 1). Inclusion of the entire June–Augustperiod is, in fact, found to weaken the AO-like coupledpattern described below, although qualitatively similarleading-order modes (especially ENSO) are still ob-tained.

1) DJF ENSO MODE

The leading CCA mode is ENSO-like both tempo-rally and spatially. A good correlation is observed be-tween the CFT (r � 0.50; first CFT of temperature) ofthis mode and the December–February (DJF) Niño-3index [Fig. 4a(i)]. The patterns of El Niño phase warm-ing over the land regions of southeast Asia and south-ern India (in April) are consistent with the analysis ofBuermann et al. (2003) using seasonally averaged fields[Fig. 4a(ii)–(v)]. A tongue of enhanced June precipita-tion oriented along a northwest–southeast (NW–SE)axis from the Arabian Sea into the central IndianOcean is observed during positive phases of this mode[Fig. 4a(vi)]. These precipitation anomalies suggest thatEl Niño phase forcing maintains a more southerly po-sition of the ITCZ, thereby inducing anomalously largeprecipitation rates at latitudes closer to the equator inJune and possibly delaying the onset of monsoon con-ditions at higher latitudes until later in the year. Indeed,El Niño events are associated with anomalous warmingof the entire tropical troposphere relative to adjacentlatitudes farther poleward. This meridional pattern of

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FIG. 3c. Correlation maps of Eurasian May SPI(4) and May PDSI associated with the JFM AO index and the Jun LAMI for theperiod 1979–99. (i), (ii) May SPI(4) and May PDSI correlations with respect to the inverse JFM AO index; (iii), (iv) May SPI(4) andMay PDSI correlations with respect to the Jun LAMI. Land areas not contoured indicate missing data.


FIG. 4a. (i) Normalized time series of the first canonical factor of FMAM Eurasian/North African land surface temperature (blacksolid) and Jun precipitation (black dashed) anomalies over the IOM spatial domain for 1979–99. The standardized DJF average Niño-3index time series is overplotted (red dashed–dotted). The corresponding regression patterns for (ii) Feb, (iii) Mar, (iv) Apr, and (v) Maytemperature and (vi) Jun precipitation anomalies based upon the standardized first CFT are also illustrated. Thick black contour lineindicates regions that are significant at the 90% level.


Fig 4a live 4/C

FIG. 4b. Same as in Fig. 4a, but for the second canonical factor. (i) The inverse standardized Feb–Apr average AO (blue dotted), thestandardized Jun LAMI* (red dashed–dotted), and the standardized inverse JJA average Niño-3 (green dotted) time series are alsoshown.


Fig 4b live 4/C

warming creates an anomalous temperature gradientfavoring an anomalous equatorward displacement ofintense convection.

2) MIXED FMA AO/JJA ENSO MODE

Although the second mode appears to contain anadmixture of contemporaneous (i.e., JJA) ENSO vari-ability (r � 0.47; second CFT of temperature), its spa-tiotemporal features are suggestive of AO-like variabil-ity. As can be seen in Fig. 4b(i), there is good corre-spondence between the interannual variations of thesecond CFT and the inverse February–April (FMA)AO; the correlation is r � 0.67 (second CFT of tem-perature). Spatially, the meridional structure of warm-ing at lower latitudes and cooling at higher latitudesassociated with the second mode resembles the patternof surface warming associated with AO negative phases[Fig. 4b(ii)–(v) and Fig. 3a]. Also, in contrast to theleading CCA mode, the temperature anomalies of thesecond mode extend farther to the north and west ofEurasia and exhibit more stability in their temporalevolution. Additionally, there is some suggestion of aneastward “propagation” of temperature anomaliesfrom the North Africa/Mediterranean Sea/Red Sea sec-tor in February to the Persian Gulf/Indian subcontinentsector in April.

This second CFT is also observed to correlate wellwith both the June LAMI (r � 0.54; second CFT ofprecipitation) and June LAMI* (r � 0.69; second CFTof precipitation), respectively. The spatial distributionof the precipitation anomalies associated with this sec-ond mode reveals why its correlation with the LAMI isstrong [Fig. 4b(vi)]: uniformly wetter conditions are evi-dent over the area used to define LAMI and LAMI*,namely, the latitude band from �10°–20°N and longi-tude band from the Arabian Sea into southeast Asia(contrast this to the more dipole-like nature of precipi-tation anomalies associated with the ENSO mode).Anomalously dry conditions are noted over the easternportion of the Mediterranean as well as the near-equatorial central Indian Ocean. Repeating the CCAwith the NCEP–NCAR reanalysis precipitation fieldsyields qualitatively similar results, although some dif-ferences in the spatial pattern of the precipitation CFsare evident, especially over oceanic regions. Diver-gence between the CMAP and NCEP–NCAR reanaly-sis results may be attributed to differences in the ob-served and reanalysis climatologies. Given the overallagreement, however, it should be possible to use thereanalysis to extend the analysis backwards in time. Inthis way, issues such as the stationarity of AO–monsoon coupling may be addressed.

3) HIGHER-ORDER CCA MODES

Although none of the higher-order modes are foundto be robust with respect to EOF truncation effects, theCCA does provide some weak evidence for the influ-ence of an “independent” April AO influence on themonsoon region precipitation field. In contrast to theboreal winter AO mode, the center of the April AOsignal appears to lie in the east Asian monsoon region.Such a result is especially intriguing since Gong and Ho(2003) have recently noted an inverse relationship be-tween the May AO and the east Asian monsoon asso-ciated with an anomalous poleward displacement of thewestern Pacific jet. It remains to be seen, however, whatconnection (if any) the weak April AO mode identifiedhere may have to the results of Gong and Ho (2003).

b. FMAM temperatures and June u850mb

Although an AO-like second mode is evident in theFMAM temperature–June precipitation CCA, a borealsummer ENSO signal is shown to contaminate themode. One source of this mixing may be related to apossible influence of the polarity of ENSO on the struc-ture of the AO (Quadrelli and Wallace 2002). Anotherreason may be the brevity of the datasets: the shortduration of the temperature and precipitation time se-ries may not be sufficient to unmix the AO and ENSOsignals. The noisiness and the more local characteristicsof the spatial scales of precipitation fields may also con-tribute. In this section, the results of a temperature–850-mb zonal wind CCA are described. In contrast tothe temperature–precipitation analysis, the temperature–zonal wind CCA yields better separated ENSO and AOmodes.

1) ENSO MODE

Like the FMAM temperature–June precipitationCCA, the leading FMAM temperature–June u850mb

CCA mode is ENSO-dominated. In contrast to the firsttemperature–precipitation mode, however, the leadingtemperature–wind CCA mode is correlated with boththe boreal winter and summer ENSO variability: cor-relations of the DJF and JJA Niño-3 with the recon-structed temperature CFT [Fig. 5a(i)] are r � 0.64 and0.60, respectively. The FMAM temperature signal[Figs. 5a(ii)–(v)] is largely confined to the eastern por-tion of the Eurasian continent and is especially strongin southeast Asia. The June zonal wind signature ischaracterized by a meridional dipole over the westernIndian Ocean [Fig. 5a(vi)]: anomalous easterlies to thenorth—present during El Niño conditions—are associ-ated with anomalously dry June conditions over thenortheastern Indian subcontinent (not shown).


FIG. 5a. (i) Same as in Fig. 4a(i), except for Jun u850mb anomalies (black dashed). (ii)–(vi) Same as in Fig. 4a(ii)–(vi), except for (vi)Jun u850mb anomalies. The standardized DJF (red dashed–dotted) and JJA (green dashed–dotted) average Niño-3 index time series areoverplotted.


Fig 5a live 4/C

FIG. 5b. Same as in Fig. 5a, but for the second canonical factor. (i) The inverse standardized Feb–Apr average AO (blue dotted) andthe standardized Jun LAMI (red dashed–dotted) time series are also shown.


Fig 5b live 4/C

2) FMA AO MODE

Because both boreal winter and summer ENSO vari-ability is captured in the leading temperature–windCCA mode, the second mode is more purely AO-likethan its counterpart from the temperature–precipita-tion CCA [Fig. 5b(i)]. The second CFTs projectstrongly on both the FMA AO (r � 0.70; second CFTof temperature) and the LAMI/LAMI* (r � 0.69/r �0.64; second CFT of u850mb), respectively. In Figs.5b(ii)–(v), a pattern of late winter/early spring tempera-ture anomalies consistent with a wintertime AO influ-ence is evident (see also Fig. 3a). Analogous to theleading mode, the second mode reconstructed windfield consists of a meridional dipole, although the cen-ter of the dipole is shifted slightly to the north com-pared to the ENSO mode [Fig. 5b(vi)]. As a conse-quence of this displacement of the wind field pattern,there is a more zonally oriented response in the pre-cipitation field across the entire IOM region (see be-low).

A closer inspection of the anomalous June 850-mbwind field associated with the FMA AO mode suggestsa possible source of the enhanced monsoon region pre-cipitation during negative-AO/wet June years (Fig. 6).In particular, an anomalous southwesterly flow is evi-

dent in the western and central Indian Ocean basin.This anomalous flow field, which is located to the eastof the climatological lower-troposphere monsoon jet, ispart of an anomalous low pressure center located overthe Northern Arabian Sea—some associated easterlyflow over south Asia and northeasterly flow over theArabian peninsula can also be discerned. It is specu-lated that this anomalous flow pattern may provideanomalous moisture transport into the IOM region,which follows from the work of Dube et al. (1990) andCadet and Reverdin (1981). The latter study, based onobservations, suggests that 70% of the climatologicalwater vapor transport to the Indian subcontinent by theboreal summer monsoon originates to the south of theequator. Thus, the anomalous flow establishes anotherpathway (in addition to the climatological Findlater jet)for Southern Hemisphere water vapor to reach the mon-soon region. Once advected into the climatological west-erly flow over south Asia, the anomalous moisture canfeed “downstream” portions of the monsoon system.

c. Consistency of CCA and simple correlationanalysis

Comparison of Figs. 3, 4, and 5 suggests favorablequalitative agreement between the CCA of tempera-

FIG. 6. Regression maps for Jun precipitation (filled contours) and Jun 850-mb horizontal wind (vectors) anomalies based upon thesecond standardized CFT (u850mb) for 1979–99. The second CFT (u850mb) used stems from the temperature–u850mb CCA analysis. Thickblack contour line indicates regions that are significant at the 90% level.


Fig 6 live 4/C

ture and precipitation/u850mb and simple correlationanalysis. That is, the AO-like mode of coupled tem-perature–precipitation (or temperature–u850mb) vari-ability isolated by the CCA technique largely resemblesboth the forward projection of wintertime AO variabil-ity onto the temperature field of subsequent monthsand the backward projection of June LAMI variabilityonto the temperature field of preceding months. Itshould be emphasized, however, that the CCA tech-nique produces the coupled patterns without the choiceof specific indices. In that sense, the CCA modes rep-resent more objectively determined coupled modes.

5. Potential mechanisms of wintertime AO–JuneLAMI coupling

The simple correlation and canonical correlationanalyses described in sections 3 and 4 highlight charac-teristic patterns of variability that are suggestive of aboreal winter AO influence on the June monsoon in-tensity in the vicinity of south Asia. It is hypothesizedthat anomalous surface conditions associated with theoccurrence of negative AO phases induce circulationanomalies that are favorable for intense early monsoonseason development. What remains to be determined ishow the influence of the winter AO is propagated andmaintained throughout the spring season. With a char-acteristic time scale of 10 days, the AO lacks a longatmospheric memory (Thompson and Wallace 2001).However, Thompson and Wallace (2001) also observedthat the AO exhibits a tendency to prevail more fre-quently in a particular phase over longer periods (i.e.,at seasonal time scales), for reasons that are not yetfully understood. Furthermore, Buermann et al. (2003)report that NH spring land surface temperatures arebetter correlated with the preceding winter AO indexthan with the (contemporaneous) spring index, suggest-ing that some form of persistence at longer time scalesis operational.

There are several pathways through which the winterAO may be connected to the early summer IOM. Forexample, snow cover (or snow depth) and/or soil mois-ture variations generated by the AO may impact thesurface energy balance and thereby impact monsoononset and intensity. SST anomalies represent anotherpossible mechanism for linking AO and monsoon vari-ability: because of the large heat capacity of the upperocean, SST anomalies induced in one season may per-sist through subsequent seasons. Finally, low-frequencymodulation of atmospheric circulation patterns, such asstorm-track displacements, may serve to couple thewinter AO and the summer monsoon. It is likely thatsome or all of these mechanisms act together to pro-

duce the observed AO forcing of early-season monsoonvariability. For example, an anomalous storm-track dis-placement may alter regional snow cover patterns,thereby affecting soil moisture distributions and thesurface energy budget.

From the analyses presented in sections 3 and 4, wetJune conditions in the IOM region are observed to oc-cur with warmer and to some extent drier conditions tothe northwest of India during the preceding springmonths. The temperature signal is especially pro-nounced over the south Asian land surface in April butless so in May; however, a stronger May temperaturesignal is evident over the northern portion of the Ara-bian Sea. The warming of the SST field over the Ara-bian Sea may be especially significant for the develop-ment of the intense rainfall in the LAMI region. Flatauet al. (2004) suggested that the delayed onset (and weakintensity) of the IOM in June of 2002 may have resultedfrom anomalous cooling of nearby ocean regions duringMay. Using a numerical model, Flatau et al. (2004)simulated a stronger monsoon by increasing the heatcontent of the upper layers of the Bay of Bengal.

We have considered how the presence of anomalousspring surface conditions may influence monsoon inten-sity in the LAMI region. But how do these surfaceconditions come about? More specifically, how mightthe late-spring surface state be forced by winter AOvariability? As has been noted before, anomalous ad-vection of warm air has been invoked to explain thewarming in the mid-East region during February andMarch. However, we find little evidence for a directlinkage between anomalous surface advection and thelater (April and May) warming that occurs farther tothe east. We suggest that the connection between theApril and May surface conditions and the winter AOmay be mediated, at least in part, by middle- and upper-tropospheric planetary wave anomalies and the effectof these anomalies on cloudiness and surface heating.To illustrate this, covariances of FMAM NCEP-derived500-mb zonal wind anomalies, computed with respectto the second temperature CFT (from the temperature–u850mb CCA analysis), are presented in Fig. 7. The pat-tern of zonal wind anomalies is associated with a quasi-stationary wave train in 500-mb geopotential heightanomalies oriented along a northwest–southeast axiswith poles of the same sign over western Europe andthe northern Indian Ocean and a pole of opposite signover the mid-East (not shown). This meridionally ori-ented wave train, as well as a zonally oriented wavetrain spanning Eurasia, appear to be associated withwintertime AO variability. Chang et al. (2001) notedsimilar large-scale wave patterns that they suggestmight be related to changes in IOM intensity. It is pos-


FIG. 7. Regression maps for (a) Feb, (b) Mar, (c) Apr, and (d) May 500-mb zonal wind anomalies based upon the second standardizedCFT (temperature), derived from the temperature–u850mb CCA analysis, for 1979–99. Thick black contour line indicates regions thatare significant at the 90% level. The 15 m s�1 monthly climatological contour line (thick gray) is also indicated.


Fig 7 live 4/C

sible that anomalous surface conditions induced by thewinter AO, such as changes in snow cover, may forcesuch wave trains, in a manner analogous to the mecha-nism suggested by Cohen and Entekhabi (1999).

The pattern of springtime zonal wind anomalies pre-ceding wet June conditions tends to decelerate (accel-erate) the equatorward (poleward) side of the climato-logical spring jet over North Africa and the mid-East;consequently, the storm track is displaced to the northin the spring season prior to an anomalously intenseearly monsoon. By contrast, the zonal-mean subtropicaljet is shifted anomalously equatorward during negativeAO/wet June phases. In the context of the evolution ofthe surface anomalies during spring, a northward dis-placement of the storm track over the Middle East isassociated with reduced spring cloudiness over NorthAfrica, the Arabian peninsula, the northern IndianOcean, and adjacent land regions of south Asia (notshown). Negative cloud cover anomalies are consistentwith enhanced insolation at the surface, resulting inspringtime warming of the land and ocean surfaces (anddrying of the land surface) in years of anomalously in-tense June monsoon conditions.

The role of snow cover anomalies in forcing monsoonvariations and the connections of these anomalies tothe boreal winter AO also warrants special consider-ation. Regression of the second temperature CFT fromthe temperature–u850mb CCA analysis (i.e., the FMAAO mode) onto the FMAM snow cover fields reflectsan occurrence of somewhat less extensive local snowcover over the Himalayan/Tibetan plateau region butmore extensive remote snow cover at higher latitudesprior to wet June conditions (Fig. 8). The spatial pat-tern of local and remote snow cover anomalies isbroadly consistent with the action of the negative AOphase, which induces cooling at higher latitudes andwarming at lower latitudes. However, while the tempo-ral behavior of the remote snow cover anomalies is con-sistent with boreal winter AO forcing, the local snowcover anomalies appear to be less strongly related toboreal winter AO variability. Instead, these anomaliesappear to be more strongly tied to DJF ENSO variabil-ity (not shown).

Differing sources of and roles for local and remotesnow cover anomalies associated with monsoon inten-sity variations in the LAMI region may contribute tothe heterogeneity of the correlation map. Indeed, snowcover anomalies in the Himalaya/Tibetan Plateau re-gion may influence the monsoon directly via a snow–albedo feedback: more snow cover leads to greater re-flection of solar radiation at the surface and reducedheating. The upstream snow cover anomalies in west-ern Eurasia, on the other hand, may influence the mon-

soon indirectly via a snow–circulation feedback, as sug-gested by Cohen and Entekhabi (1999). In this way, theremote snow cover anomalies may be significant in es-tablishing or maintaining the quasi-stationary circula-tion pattern that fosters the development of the southAsian land surface temperature/northern Indian OceanSST anomalies described above.

6. Conclusions

The Indian Ocean monsoon (IOM) exhibits consid-erable year-to-year variations that have previouslybeen attributed to a number of forcing mechanisms in-cluding ENSO and Eurasian snow cover anomalies. Inthis paper, we have described the relationship betweenthe boreal winter AO and early-season IOM precipita-tion over the period 1979–99. Correlation analysis ofthe AO and an index of area-averaged IOM precipita-tion—the Large Area Monsoon Index (LAMI) or itsvariant LAMI*—documents a statistically significantinverse relationship between the JFM AO and JuneLAMI/LAMI*. Confirmation of a JFM AO–JuneLAMI/LAMI* connection is found in the linear regres-sions of these indices with temperature fields and thosethat represent soil moisture variations in the period“bridging” boreal winter and early summer: forward(backward) projections of the JFM AO (June LAMI/LAMI*) onto the NH February–May temperature andMay SPI and PDSI fields are observed to be in quali-tative agreement with one another. In particular, theseprojections show springtime surface warming and dry-ing in the region to the north and west of the Indiansubcontinent during years of anomalously intense Junemonsoon rainfall/negative winter AO phases.

To highlight the coupling of the monthly February–May North African/Eurasian land surface temperatureand June IOM precipitation fields, CCA has been per-formed. Two significant modes of coupled tempera-ture–precipitation variability are identified: a leadingENSO mode and a next-to-leading AO (or mixed AO–ENSO) mode. A separate CCA analysis, using Febru-ary–May temperature anomalies and June IOM zonal850-mb wind fields, more clearly decouples the ENSOand the AO signals. As in the simple correlation analy-sis, intense June LAMI region rainfall is preceded byspringtime warming and drying to the north and west ofthe Indian subcontinent. The “preconditioning” of thespringtime land and ocean regions in the vicinity of themonsoon region appears to be associated with an AO-induced quasi-stationary tropospheric circulationanomaly: the impact of this anomaly is to displace themid-Eastern jet poleward during AO-negative phases,resulting in anomalous surface heating and drying.


FIG. 8. Correlation maps for (a) Feb, (b) Mar, (c) Apr, and (d) May snow cover anomalies based upon the second CFT (temperature),derived from the temperature–u850mb CCA analysis, for 1979–99. Regions of permanent snow cover (light gray) and no snow cover(dark gray) are also contoured.


Fig 8 live 4/C

These surface conditions are associated with an anoma-lous southwesterly flow to the east of the lower-troposphere monsoon jet that enhances the airmassconvergence (and moisture transport) into the mon-soon region.

Acknowledgments. We thank Inez Y. Fung and JohnChiang for their helpful comments and suggestions.This research was funded by the NASA EOS-IDSGrant NAG5-9514 (PI: Fung).

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A Wintertime Arctic Oscillation Signature on Early-Season Indian Ocean Monsoon Intensity

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