S. K. Dash Æ G. P. Singh Æ M. S. Shekhar

A. D. Vernekar

Response of the Indian summer monsoon circulation and rainfallto seasonal snow depth anomaly over Eurasia

Received: 20 March 2003 / Accepted: 24 March 2004 / Published online: 19 November 2004� Springer-Verlag 2004

Abstract Several observational and modeling studiesindicate that the Indian summer monsoon rainfall(ISMR) is inversely related to the Eurasian snow extentand depth. The other two important surface boundaryconditions which influence the ISMR are the Pacific seasurface temperature (SST) to a large extent and the In-dian Ocean SST to some extent. In the present study,observed Soviet snow depth data and Indian rainfalldata for the period 1951–1994 have been statisticallyanalyzed and results show that 57% of heavy snowevents and 24% of light snow events over west Eurasiaare followed by deficient and excess ISMR respectively.Out of all the extreme monsoon years, care has beentaken to identify those when Eurasian snow was themost dominant surface forcing to influence ISMR.During the years of high(low) Eurasian snow amounts inspring/winter followed by deficient(excess) ISMR,atmospheric fields such as temperature, wind, geopo-tential height, velocity potential and stream functionbased on NCEP/NCAR reanalyses have been examinedin detail to study the influence of Eurasian snow on themidlatitude circulation regime and hence on the mon-soon circulation. Results show that because of the westEurasian snow anomalies, the midlatitude circulations inwinter through spring show significant changes in theupper and lower level wind, geopotential height, velocitypotential and stream function fields. Such changes in thelarge-scale circulation pattern may be interpreted asprecursors to weak/strong monsoon circulation anddeficient/excess ISMR. The upper level velocity potentialdifference fields between the high and low snow years

indicate that with the advent of spring, the winteranomalous convergence over the Indian region gradu-ally becomes weaker and gives way to anomalousdivergence that persists through the summer monsoonseason. Also the upper level anomalous divergencecentre shifts from over the Northern Hemisphere andequator to the Southern Hemisphere over the IndianOcean and Australia.

1 Introduction

Studies by Hahn and Shukla (1976), Dickson (1984) andSankar-Rao et al. (1996) based on observed data exhibitan inverse relationship between the strength of theIndian summer monsoon and the extent of Eurasiansnow cover in the preceding season. Kripalani et al.(1996) using snow depth data from Nimbus-7 satelliteshowed that two regions over Eurasia had negativecorrelation with the monsoon rainfall; one region to thenortheast of Moscow and the other between Mongoliaand Siberia. Further studies by Kripalani and Kulkarni(1999) based on Historical Soviet Daily Snow Depthversion I (HSDSD-I) data for the period 1881–1985inferred that winter-time snow depth over western Eur-asia surrounding Moscow shows a significant negativerelationship with subsequent monsoon rain. In contrast,that over eastern Eurasia in central Siberia has a sig-nificant positive relationship with monsoon rainfall.They conjectured the existence of a mid-latitude longwave pattern with an anomalous ridge (trough) overAsia during the winter prior to a strong (weak) mon-soon. For better understanding of the snow monsoonrelationship and for easy comparison with all the pre-vious studies, Bamzai and Shukla (1999) correlatedDecember, January, February and March mean snowcover anomalies for four regions with the subsequentIndian summer monsoon rainfall (ISMR). Their fourregions of study were (1) west Eurasia (40�N–60�N,

S. K. Dash (&) Æ G. P. Singh Æ M. S. ShekharCentre for Atmospheric Sciences,IIT Delhi Hauz Khas, New Delhi, IndiaE-mail: skdash@cas.iitd.ernet.in

A. D. VernekarDepartment of Meteorology,University of Maryland College Park, Maryland, USA

Climate Dynamics (2005) 24: 1–10DOI 10.1007/s00382-004-0448-3

10�W–30�E), (2) the whole of Eurasia (20�N–90�N, 0�–190�E), (3) southern Eurasia (20�N–50�N, 0�–190�E)and (4) the Himalayas (30�N–45�N, 60�E–105�E). Theyused satellite-derived snow cover data for 22 yearsspanning the period 1973 to 1994. Bamzai and Shukla(1999) found that ISMR had the highest correlation of –0.63 with the western Eurasia snow cover compared to –0.34 with the snow over the whole of Eurasia. The winterand spring snow cover of southern Eurasia and theHimalayas have high interannual variability but arepoorly correlated with the subsequent monsoon rainfall.

Results of sensitivity studies with general circulationmodels (GCMs) such as those of Barnett et al. (1989)and Vernekar et al. (1995) also confirm that when large,spatially coherent, positive snow anomalies are put inplace over Eurasia in winter/spring, the monsoon cir-culation in the following summer is weaker than aver-age. Using several simulations of the European Centerfor Medium range Weather Forecasts (ECMWF) model,Ferranti and Molteni (1999) have concluded that theinter-annual variability of Eurasian snow-depth in earlyspring is influenced by the boundary forcing arising fromsea surface temperature (SST) anomalies over the trop-ical eastern Pacific during previous winter. Thisboundary forcing modifies the zonal wind distributionover Asia. The results of their summer integration,where the climatological SSTs were used as prescribedocean boundary conditions, indicate that Eurasian snowdepth also influences the seasonal mean monsoon inde-pendently of El Nino-Southern Oscillation (ENSO).

Corti et al. (2000) performed 12 ensemble runs of theECMWF atmospheric GCM and concluded thatthe snow monsoon relationship is just an artifact of theinfluence of El-Nino anomalies on both the winter andthe summer circulation. They interpreted the link be-tween SST and Eurasian snow as the result of changes inthe northern extra-tropical circulation caused by tropi-cal SST forcing, which in turn modify the intensity anddistribution of precipitation over Eurasia. However,Corti et al. (2000) did not rule out an active role of thesnow anomaly in causing the persistence of the Eurasianwind anomaly. They also, cited the 1994 monsoon case(Soman and Slingo 1997) indicating that the snow-re-lated wind anomaly over Eurasia persisted from winterto summer independently of the Pacific SST anomaly.The relationship between the SST anomaly over thePacific and Eurasian snow-depth anomaly needs furtherinvestigation. Therefore, lag correlations between sea-sonal snow-depth anomaly over west and east Eurasiaand two regions of Pacific ocean namely Nino-3 (5�N–5�S, 150�W–90�W) and Nino-3.4 (5�N–5�S, 170�E–120�W) for the period 1951–1994 are also computed inthis study.

Almost all the observational studies of snow-mon-soon teleconnections are based on the relationshipbetween snow and the monsoon rainfall only. Despiteseveral studies in the past, the mechanism of the winterand spring signals in the circulation pattern are notclearly understood at present. How the earlier snow

fall is linked to the large-scale monsoon circulation isnot clear. To understand the physical mechanismresponsible for the snow-monsoon connection, it isnecessary to understand the related changes in mon-soon circulation pattern which may be linked to themid-latitude circulation. Sankar-Rao et al. (1996) usingdata from National Environmental Satellite Data andInformation Service (NESDIS) of the National Oce-anic and Atmospheric Administration (NOAA) for theperiod 1967 to 1992 concluded that following thewinters of more snow, stationary perturbations withhigher pressures over central Asia north of India areproduced in the lower atmosphere and the followingAsian summer monsoon is weaker. Simultaneously, inthe upper-atmosphere, lower anomalous pressure oc-curs during summer which weakens the upper levelmonsoon high. The anomalous upper troposphericlow-pressure system covers a large area extending fromthe middle latitudes in Asia to India.

A detailed study on the relationship between thenumber of days with snow of varying depths overdifferent parts of Eurasia with ISMR was carried outby Dash et al. (2004). They examined the evolution ofmidlatitude circulation features such as temperature,wind and velocity potential in response to the numberof days of snow of varying depths over Eurasia inwinter/spring. In another study (Dash et al. 2003),analysis of the characteristics of atmospheric circula-tion during two contrasting years of high(low) winter/spring snow depth followed by deficient (excess)monsoon rain over India has also been conducted inorder to identify the signal of excess or deficientmonsoon rainfall a season in advance. These twostudies show that there is a complete phase reversal inthe dipole structure of the upper tropospheric velocitypotential anomaly between the extreme cases ofhigh(low) Eurasian snow amounts followed by defi-cient(excess) ISMR.

In the present study, based on Soviet snow data andrainfall data of the India Meteorological Department(IMD) during the period 1951–1994, two years havebeen identified with low Eurasian snow amounts fol-lowed by excess ISMR and three years have been iden-tified with high snow falls followed by deficient ISMR.Careful examination has shown that in these five years,the SST anomalies of Pacific and Indian Ocean are notsignificant. The difference fields of temperature, wind,geopotential, stream function and velocity potentialbetween the two sets of extreme cases have been exam-ined in detail with a view to study the relationship be-tween the Eurasian snow, midlatitude circulation andthe Indian summer monsoon circulation features.

Section 2 describes the details of the data used. Sec-tion 3 gives the statistical relationship between snowdepth anomaly, SST anomaly and ISMR. The selectionof the years of high and low snow has been given inSect. 4. Section 5 examines the composite differences inthe circulation characteristics in extreme years andSect .6 describes the summary and conclusions.

2 Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth

2 Data sources

ISMR data of Parthasarathy et al. (1995) for June toSeptember during the period 1881–1994 have been usedin this study to classify excess, normal and deficient rainyears. National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysed (Kalnay et al. 1996) fields of tem-perature, wind, velocity potential, stream function andgeopotential at upper and lower atmospheric levels forthe period 1948–1994 have also been used to examine theatmospheric circulation patterns. The monthly meansnow depth data over Eurasia have been obtained fromthe HSDSD version II (HSDSD-II) data set of NationalSnow and Ice Data Center (NSIDC 1999) on CD-ROM.This Soviet data set provides long-term climatologicalsnow depths for 1881 to 1995 and updates and replacesthe original HSDSD-I data set that was previouslyavailable from NSIDC. The HSDSD data were ex-tracted from the Soviet meteorological archive, whichcontains daily data from World Meteorological Orga-nization (WMO) stations over that region.

HSDSD include the daily snow depth and daily stateof snow cover (percentage of surrounding area that iscovered by snow). Products derived at NSIDC and in-cluded in the CD-ROM are the daily, monthly, seasonaland climatological summaries. In this study daily datasummaries are used to compute the monthly mean snowdepth for all the 284 WMO defined stations. These sta-tions are located in the mid-latitudes, mostly inhabitedarea of Eurasia. The geographical distribution of thestations lies between 35�N and 72�N latitude lines andbetween 20�E and 180�E meridians, while the elevationof the stations varies from –15 m to 2100 m. In addition,the monthly mean snow depth data are categorizedaccording to the number of days where snow depth wasgreater than 5 cm, 10 cm and 50 cm. The HSDSDproduct has been updated from 1881 (for the earliestoperational stations) through 1995 using improved dataquality control and sophisticated computer software. Itmay be noted that where slight differences between theposition of data provided and the WMO station positionexisted, the latter was used. In this study, the monthlymean values of SST over Nino-3 (5�N–5�S, 150�W–90�W) and Nino-3.4 (5�N–5�S, 170�E–120�W) for theperiod 1951–1994 have been obtained from the ClimateDiagnostic Bulletin of the Climate Prediction Center,USA.

3 Statistical relationships between ISMR and Eurasiansnow depth anomaly

The HSDSD-II data set used in this study does notcontain uninterrupted data for all the 284 WMO sta-tions. The stations situated to the north of 65�N are notconsidered in this analysis because their number isinsignificant and also much data are missing. It may be

noted that no attempt has been made to generate themissing data either by interpolation or by any othertechnique. For a statistical meaningful relationship, 70stations have been selected over which monthly meansnow depth data are available uninterrupted for a con-siderable length of time. These 70 selected stations havebeen grouped into two zones, west (25�E–70�E, 35�N–65�N) and east (70�E–140�E, 35�N–65�N) where con-sistent data are available. The lag correlations betweenseasonal snow depth anomaly for western and easternEurasia and ISMR for the period 1951–1994 are com-puted in order to examine their statistical relationship.

Table 1 presents the correlation coefficients betweenwinter/spring snow depth anomalies over western andeastern Eurasia and ISMR for the entire study periodspanning 1951–1994 and also for smaller durations suchas 1951–1969, 1961–1979, 1971–1989 and 1971–1994.The respective significance levels are also given inbrackets in Table 1. Results show that the mean snowdepth anomalies over west/east Eurasia in December,January and February (DJF) have an inverse/directsignificant relationship with the following ISMR. Thisrelationship persists through March, April and May(MAM) at 5% significance level during 1971–1989 bothin west and east Eurasia and at 1% significance for 1971to 1994 period only in west Eurasia.

Figure 1a shows that DJF snow depth anomaliesover west Eurasia lying between 25�E and 70�E havenegative correlations with the following ISMR. On theother hand winter snow depth anomalies over eastEurasia between 70�E and 140�E have positive correla-tions with the subsequent ISMR. The central whitepatch over west Eurasia in Fig. 1a signifies the lack ofdata there. This result confirms the relationship of thetwo coherent regions identified by Kripalani and Kulk-arni (1999) with ISMR based on HSDSD-I. It is veryimportant to note that the strong and significant nega-tive correlation between the west Eurasia snow depthanomalies and subsequent ISMR after 1970 is sustainedthrough winter to the end of spring as indicated duringthe periods 1971–1989 and 1971–1994 in Table 1. Dur-ing 1971–1989, the winter correlation coefficient of –0.52falls a little to –0.42 in spring. Similarly during 1971–1994, the winter correlation coefficient of –0.56 becomes–0.49 in the following spring.

Table 1 Correlation coefficients between DJF and MAM seasonalsnow depth anomalies over west and east Eurasia and ISMR fordifferent periods. Numbers in brackets indicate the levels of sig-nificance in percentage

Periods West Eurasia East Eurasia

DJF MAM DJF MAM

1951–1994 –0.44 (0.1) –0.22 0.49(0.1) 0.241951–1969 –0.41(5) –0.07 0.44(5) 0.251961–1979 –0.58 (0.1) –0.30 0.51(1) 0.181971–1989 –0.52 (1) –0.42(5) 0.74(0.1) 0.40(5)1971–1994 –0.56 (0.1) –0.49(1) 0.69(0.1) 0.18

Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth 3

Empirical orthogonal functions (EOFs) ofwinter snowdepth anomaly for the period 1951–1994 have also beencomputed for the whole of Eurasia and its western andeastern regions separately. It is found that EOF-1 over thewhole of Eurasia, and western and eastern Eurasia ex-plains about 20%, 29% and 27% of variability respec-tively. Figure 1b shows EOF-1 for the whole of Eurasia.Our study using HSDSD-II yields stronger variabilitythan the corresponding value obtained for the whole ofEurasia byKripalani andKulkarni (1999) usingHSDSD-I. They found 15.8% and 15.5% variability in the firstcomponent during January andNovember over thewholeof Eurasia. Improvements in the results obtained in thepresent study may be ascribed to better quality of snowdepth data in HSDSD-II than in HSDSD-I.

4 Identification of high and low snow depth years

Using the monthly snow depth data for the years 1951–1994, DJF mean values for each year in west Eurasia are

computed along with the mean of the series and thestandard deviations. The snow depth is expressed as astandardized snow-depth anomaly by dividing thevaraition of each year from its normal value by thestandard deviation. The standardized snow-depthanomaly thus calculated over west Eurasia for the per-iod 1951–1994 is shown by solid line in Fig.2. The yearswith snow-depth anomaly between ±0.5 standarddeviations are considered as normal snow years. Simi-larly, the years with snow-depth anomaly equal to orabove +0.5 standard deviation are counted as highsnow years and those with equal to or less than –0.5standard deviation snow depth anomaly are identified aslow snow years. Based on this criterion, it is found that1951, 1952, 1954, 1955, 1960, 1961, 1962, 1963, 1964,1971, 1972, 1973, 1975, 1977, 1980, 1983 and 1988 arelow snow years and 1957, 1966, 1968, 1974, 1976, 1979,1981, 1982, 1985, 1986, 1987, 1989, 1993 and 1994(Table 2), are high snow years. The rest of the years inthe period 1951 to 1994 had a normal snow-depthanomaly. Thus, there are 14 high snow depth years and17 low snow years over west Eurasia in DJF. On theother hand, when the classification is made based on thelimits of ±1 standard deviation, there are only sevenhigh snow years and ten low snow depth years over westEurasia in DJF. In order to increase the sample size, theclassification based on ±0.5 standard deviation isadopted here.

The ISMR anomaly for each year has also beencomputed and plotted using the dotted line in Fig. 2.Following the criterion of IMD, the years having ISMRanomaly more than or equal to +1 standard deviation(above 90% of its long-term average value) are termedas excess monsoon years and those less than or equal to–1 standard deviation are considered deficient monsoonyears. The years with an ISMR anomaly between –1 and+1 standard deviations are classified as normal mon-soon years. Based on this criterion 1956, 1959, 1961,1970, 1975, 1983, 1988 and 1994 are excess monsoonyears and 1951, 1965, 1966, 1968, 1972, 1974, 1979,

Fig. 1 a Correlation coefficients between DJF Eurasian snow depthanomalies with the following ISMR and b EOF-1 of standardizedsnow depth anomalies in DJF during 1951–1994

Fig. 2a Standardized DJF snow depth anomalies over westernEurasia and ISMR anomalies during 1951–1994

4 Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth

1982, 1985, 1986 and 1987 are deficient monsoon years.Figure 2 confirms the well-established inverse relation-ship between the DJF Eurasian snow-depth anomalyand ISMR deviation. Table 2 also shows that out of 14high snow events over west Eurasia, 8 (57%) events arefollowed by deficient ISMR, 5(36%) events are associ-ated with normal ISMR and only one (7%) event isfollowed by excess ISMR. Similarly, out of 17 low snowevents over west Eurasia in the winter season, themajority of events i.e., 11 (65%) are associated withnormal ISMR, four (24%) and two (12%) events arefollowed by excess and deficient ISMR respectively.Thus, about 57% of high snow events over west Eurasiaare followed by deficient monsoon rains whereas 24% oflow snow events are associated with excess monsoonrains. Out of all the years of high and low snow depthswhich are related to below normal and above normalmonsoon years respectively, we have extreme cases in (1)1961, 1975, 1983 and 1988 when low winter snow wasfollowed by excess monsoon rain and (2) 1966, 1968,

1974, 1979, 1982, 1985, 1986 and 1987 when high snowwas followed by deficient monsoon rain.

It may be noted that the major boundary forcingsaffecting the Indian monsoon (Krishnan and Mujumdar1999) are the Pacific and southern Indian ocean SST andEurasian snow. Out of the eight high snow years, 1982/83 and 1986/87 are identified as El-Nino years while1974 and 1985 are La-Nina years. In order to choosecases not influenced by significant SST anomalies in thePacific, we have selected only three years 1966, 1968 and1979 as high snow cases followed by deficient ISMR.Similarly, out of four low snow years, 1983 and 1988were influenced by El-Nino and La-Nina respectively.Therefore, only two years i.e., 1961 and 1975 are selectedas low Eurasian snow followed by excess ISMR. With aview to confirm whether these cases of high (low) Eur-asian snow followed by deficient (excess) ISMR are freefrom the influence of SST, a composite of SST differencefields between high and low snow years has been com-puted. It can be seen that between the years of high andlow Eurasian snow, the SST difference over large partsof the Pacific is less than 1�C and that over the southIndian Ocean is about 0.5�C. Normally ENSO years areidentified with SST difference >1�C over the equatorialPacific. Further, the correlations between the tropicalSST anomaly in DJF and ISMR are computed for theperiod 1966–1994 and the values are depicted in Fig. 3.It is seen that correlation coefficients over large parts ofthe Pacific and Indian oceans are insignificant apartfrom some weak signals over west Pacific and Indianoceans. The low magnitudes of SST anomaly and thecorresponding correlation coefficients in Fig. 3 points tothe fact that the major forcing which might have con-tributed to the difference in monsoon circulation duringthe high and low snow years is the difference in the westEurasian snow.

Table 3 presents the correlation coefficients alongwith their significance levels between winter/spring snowdepth anomaly over west and east Eurasia and winterSST anomaly over Nino-3 and Nino-3.4 regions for theperiod 1951 to 1994. It shows that DJF and MAM snowdepth anomalies over east Eurasia have significant andpositive correlations with the SST anomalies of Nino-3

Table 2 Occurrence of high and low snow depth over west Eurasiaduring DJF in the period 1951–1994 and their association withdeficient (D), normal (N) and excess (E) Indian summer monsoonrainfall

High snowyears (14)

Characteristicof ISMR

Low snowyears (17)

Characteristicof ISMR

1957 N 1951 D1966 D 1952 N1968 D 1954 N1974 D 1955 N1976 N 1960 N1979 D 1961 E1981 N 1962 N1982 D 1963 N1985 D 1964 N1986 D 1971 N1987 D 1972 D1989 N 1973 N1993 N 1975 E1994 E 1977 N

1980 N1983 E1988 E

Fig. 3a Correlation coefficientsbetween DJF sea surfacetemperature anomalies and thefollowing ISMR anomaliesduring 1966–1994

Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth 5

and Nino3.4 sectors in DJF. West Eurasia does notshow any significant relationship with the SST anomalyof either of the Nino regions, thus indicating that thewest Eurasia snow anomaly may be used as a predictorfor ISMR, independent of the Pacific SST anomaly.

5 Difference in circulation characteristics between yearsof more and less snow

Figure 4a, 4b shows the composite mean of the abovementioned three years that had high snow for winter andspring respectively. Similarly, Fig. 4c, 4d depicts thesnow differences between the composites of three highsnow years and two low snow years for DJF and MAMrespectively. From these it can be seen that high snow aswell as snow anomalies over Eurasia persisted to someextent through DJF upto the end of MAM. However,the persistence of snow from winter to spring is seen tobe more prominent over west Eurasia. As mentioned inSect. 3, the inverse relationship of snow depth anomaly

over west Eurasia with ISMR is also strong into April.Further, Fig. 4c exhibits a dipole structure with positiveand negative anomalies oriented along the zonal direc-tion. It is important to note that the dipole structure inthe snow depth anomaly persists in spring (Fig. 4d),though to a lesser extent. Also it is interesting that inFig. 1a, b, similar dipole characteristics are depicted inthe correlation coefficients and EOF-1 respectively.Figure 1a, b is based on 44 years of data whereasFig. 4c, d shows the composite difference of three highsnow and two low snow years respectively. Nevertheless,the qualitative agreement in the dipole structures mayindicate a reasonable reliability of results in spite of thefact that a small sample size of only five years is used forthe extreme snow events followed by extreme ISMR.

Bamzai and Shukla (1999) emphasized that the in-verse snow-monsoon relationship holds especially inthose years when snow is anomalously high or low forboth the winter as well as the consecutive spring season.If there is heavy snow cover in winter it is likely that itwill affect the snow cover in spring. Heavy snow cover inmidwinter usually does not easily melt because of low-level solar insolation. If there is already snow on theground, the precipitation in February, March and inApril is likely to be in the form of snow (because of thesurface temperature close to 0�C) rather than rain. Sucha process could explain the results of Bamzai and Shukla(1999). Hence, to examine the evolution of the meanmonsoon fields a season in advance in response to thesnow depth over west Eurasia, we have selected the twogroups of extreme snow and ISMR years. The differencein the circulation characteristics in high snow (1966,1968 and 1979) and low snow (1961 and 1975) years are

Table 3 Correlation coefficients between winter/spring east Eur-asian snow depth and winter SST anomalies over Nino-3 and Nino3.4 for the period 1951–1994

Eurasian snowanomaly

Nino-3 SST anomaly(5�N–5�S,150�W–90�W)

Nino-3.4 SSTanomaly (5�N-5�S,170�E–120�W)

East Eurasia in DJF 0.39 (5) 0.36 (5)East Eurasia in MAM 0.45 (2) 0.40 (5)West Eurasia in DJF –0.20 –0.15West Eurasia in MAM 0.02 0.05

Fig. 4a Composite mean snowdepths (cm) for a DJF andb MAM and snow depthdifferences (cm) between highand low snow years in c DJFand d MAM

6 Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth

studied in detail by analyzing the composite differencefields of temperature, wind, geopotential height, streamfunction and velocity potential in DJF, MAM and themean of June, July, August and September (JJAS) sep-arately. The NCEP/NCAR monthly mean reanalyses atupper and lower levels are used for such study.

Examination of temperature distribution in DJFshows that, in general, the whole of Eurasia north of45�N is cooler in high snow winters compared to lowsnow winters. At 850 hPa, the west Eurasia is cooler byabout 5�C in high snow years whereas the east Eurasia iscooler by 4�C. In west Eurasia, the atmosphere is cooleras far as 500 hPa level by a magnitude of 2�C. In thespring season, both west and east Eurasia remain coolerby about 2�C due to high snow and the cooling is foundto spread southwards to the Caspian Sea. Such a coolingof the atmosphere can be ascribed to the difference insnow depth shown in Fig. 4c, d. The cooling as far as theCaspian Sea by about 2�C might have played significantrole in the weak monsoon circulation and deficient rainin the high snow years compared with low snow years. Itis also found that even in JJAS, the Tibetan area iscooler by about 2�C in the years of high snow overEurasia.

The composite seasonal wind difference field at850 hPa is shown in Figs. 5a–c for the winter, spring andmonsoon seasons respectively. The circulation patternsshown are consistent with the corresponding seasonaltemperature difference fields. Figure 5a indicates thatthe western part of west Eurasia is dominated by ananomalous cyclonic circulation. The westerly anomaliesto the west of Caspian Sea are much stronger duringhigh snow years than in the low snow years. In spring(Fig. 5b), there is an anomalous anticyclonic circulationover west Eurasia and anomalous cyclonic circulationover east Eurasia. Such anomalous circulations are wellorganized over the midlatitude belt 40�N to 70�N. Alsoin MAM, the westerlies to the west of Caspian sea areseen to be stronger in high snow years than in low snowyears. In Fig. 5c, there is indication of an anomalousanticyclonic circulation over the north Arabian sea andwest India centred at about 20�N and 75�E which con-tributes to the anomalous easterlies over the ArabianSea during JJAS. This circulation feature can be tracedback to similar wind anomalies but less organized inspring as shown in Fig. 5b. Most of India affected by theanomalous anticyclonic circulation during monsoonseason at the lower level (Fig. 5c) results in a weakmonsoon circulation in high snow compared to lowsnow years. From the examination of the upper level(200 hPa) wind difference fields in JJAS, a well-devel-oped anomalous anticyclonic circulation is found oversoutheast Asia in high snow years compared to lowsnow years. The anomalous winds at the upper levelcorrespond to weaker easterlies in the deficient monsoonyears in response to high snow compared to the excessrain years. Closer investigation shows that during theyear of high snow, the easterlies started weakening inspring to give rise to weak monsoon upper level easter-

lies during JJAS. Also in DJF, anomalous westerlies arefound over the South China Sea which persist throughMAM in to JJAS. Such anomalous westerlies duringhigh snow years arrest the north northwest movement ofthe Tibetan anticyclone from its winter position over theequator to its summer position over Tibet. Krishnan andMujumdar (1999) have shown the prominent southwardincursion of midlatitude westerlies over northwest Indiaduring May at 200 hPa. Such anomalous features sup-press the development of upper tropospheric anticy-clones and hence the monsoonal circulation over India.Earlier, Joseph (1978) and Joseph et al. (1981) had alsonoted similar anomalous features which lead to weakmonsoons over India.

The composite geo-potential height difference fieldsin DJF, MAM and JJAS are shown in Fig. 6a–6c at the

Fig. 5a Composite wind differences (m/s) between high and lowsnow years at 850 hPa level a mean of December, January andFebruary b mean of March, April and May and c mean of June,July, August and September

Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth 7

lower troposphere and in Fig. 7a–7c at the upper tro-posphere. The negative difference fields over the westernEurasia in Figs. 6(a) and (6b) can be attributed to thehigher snow in Fig. 4c. From Fig. 6a–6c it is seen thatthe monsoon heat low over central south Asia is weaker(positive values) in high snow years compared to lowsnow years. This positive geo-potential difference fieldindicates an adverse pressure gradient anomaly in winterwhich persists through spring up to the monsoon season.The geo-potential difference field at 200 hPa in Fig. 7a–7c indicates a weaker upper level high (negative values)over west Eurasia and east Asia in DJF, MAM andJJAS. Such lower anomalous pressure at the upper levelcovering the entire region from the middle latitudes inAsia to India was reported by Sankar-Rao et al. (1996).

The corresponding upper level stream function differ-ence field has also been examined. Based on this analysisit may be inferred that weakening of the upper levelcirculation over India in the high snow years started inDJF and persisted through MAM into JJAS. Rajeevan(1993) examined the composite mean circulation thermalfields at 200 hPa from April to June and found a cylonic(anticyclonic) anomalous circulation with cold (warm)temperature observed over central Asia near the CaspianSea during the April months of drought(flood) years.The cold cyclonic anomalous circulation adverselyaffects the monsoon activity due to excess Eurasiansnow cover and the large-scale intrusion of dry westerliesinto Indian region.

Fig. 6a–c Same as in Fig. 5 except for the geopotential heightdifferences (m) at 850 hPa

Fig. 7a–c Same as in Fig. 6 except for 200 hPa

8 Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth

The upper level seasonal tropospheric velocity po-tential difference fields in winter, spring and JJAS areshown in Fig. 8a—8c. It is well known that there is astrong upper level divergent center (Krishnamurti 1972)associated with the Asian monsoon. The positive dif-ference fields over the Indian subcontinent and thenegative difference fields to the east in Fig. 8c indicatethat the upper level divergence center was weaker overIndia in high snow years compared to low snow. Thisdivergent circulation changed in such a way that theintensity of easterlies over the monsoon regions ofsouthern Asia and Africa was weak in high snow years.In their study Chen and van Loon (1987) have inferredthat the anomalous divergent circulation during years of

weak tropical easterly jet reduces the generation ofkinetic energy on the upstream side of the jet and thedestruction of kinetic energy on the downstream side ofthe jet. When comparing the dipole structures inFig. 8a–8c, two important facts may be noticed in theevolution of divergence and convergence centers fromwinter to spring and to summer monsoon seasons. Theanomalous convergence center in winter over India(Fig. 8a) gradually becomes weaker as spring comes andgives way to anomalous divergence which persiststhrough JJAS (Fig. 8c). Also the anomalous divergencecenter (Fig. 8b) shifts from over the Northern Hemi-sphere and equator (Fig. 8a) to the Southern Hemi-sphere over the Indian Ocean and Australia (Fig. 8c).Analysis of velocity potential difference fields both at theupper and lower levels yields that the upper leveldivergence/convergence centers have their correspond-ing convergence/divergence centers in the lower level.

6 Summary and conclusions

The HSDSD-II data set has snow depth for a longperiod from 1881–1995. This snow data set availablefrom NSIDC is the improved version of the previouslyavailable HSDSD-I, with the help of updated improveddata quality control and sophisticated computer soft-ware. In this study, HSDSD-II data set has been used toexamine the empirical relationship between the anoma-lies in winter/spring snow depth over west (25�E–70�E,35�N–65�N) and east (70�E–140�E, 35�N–65�N) Eurasiaand ISMR. Statistical studies indicate that for the westEurasia, the antecedent DJF snow depth anomaly dur-ing the period 1951–1994 has a correlation of –0.44 withthe subsequent ISMR (at 0.1% significance level). Sim-ilarly, for east Eurasia, the correlation is 0.49 at the samesignificance level. The opposite relationship of west andeast Eurasia snow depth with the subsequent ISMR maybe attributed to the difference in the characteristics ofinter-annual variations of snow over these two regions.Analysis of long-term data shows that lag correlationsare significant from winter to spring season only overwest Eurasia in the recent past from 1971 to 1994.Observations show that high snow depth events inwinter over west Eurasia are associated with deficientISMR in 57% of cases; most of them being droughtyears. However, low snow events are associated withnormal and excess ISMR in 65% and 24% of casesrespectively. In the 44 years studied, only one case ofhigh snow and two cases of low snow depth are followedby excess and deficient ISMR respectively. Also signifi-cant correlations had been found between antecedentwinter/spring snow depth anomalies only over eastEurasia and SST anomalies over Nino-3 and Nino-3.4regions. Based on these results, it may be inferred thatindependent of the SST anomalies, the winter/springtime west Eurasian snow depth anomalies may affectISMR.

Fig. 8a–c Same as in Fig. 5 except for the velocity potentialdifferences (m2/s) at 200 hPa

Dash et al.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth 9

The evolution of the seasonalmean (June, July,Augustand September) monsoon circulation features from themidlatitude circulation in winter through the spring sea-son has been examined in detail using the NCEP/NCARreanalyzed data for the period 1948–1994. The charac-teristics of atmospheric circulation during contrastingyears of more and less snow depths in winter/spring fol-lowed by deficient and excess monsoon rain over Indiahave been studied, to identify the signal of excess ordeficient monsoon rainfall, a season in advance. Whileselecting such extreme cases of the snow-monsoon rela-tionships, we also ensured that in these years there was nosignificant influence of Pacific and Indian Ocean SSTanomalies. Based on these selection criterion, it is clearthat the existing data period has only two years, 1961 and1975, when low Eurasian snow was followed by excessISMR. Similarly, only in the three years 1966, 1968 and1979, was there a high Eurasian snow depth followed bydeficient ISMR. Analysis of wind fields shows that duringthe years of high snow, the easterlies start weakening inspring to give rise to weak monsoon upper level easterliesduring JJAS. The anomalous westerlies over the SouthChina Sea in DJF persist throughMAM in to JJAS. Suchwesterlies during high snow years arrest the north north-westmovement of theTibetanAnticyclone from itswinterposition over the equator to its summer position overTibet. The geopotential height field at the lower tropo-sphere shows that the heat low of monsoon system overcentral southAsia is weaker (positive values) in high snowyears compared to low snow years. Also the upper levelgeopotential difference field, indicating weaker upper le-vel high (negative values) over west Eurasia and east Asiain DJF and MAM, becomes prominent in JJAS. Twoimportant facts are noticed in the evolution of anomalous(high minus low snow depth years) divergence and con-vergence centers from winter through spring in to thesummer monsoon season. The winter anomalous con-vergence center over India gradually becomes weaker asspring comes and gives way to anomalous divergencewhich persists in JJAS. Also the anomalous divergencecenter shifts from over the Northern Hemisphere andequator to the Southern Hemisphere over the IndianOcean and Australia.

Acknowledgements Some of the results presented in the paper areobtained from the projects sponsored by the National ScienceFoundation, USA and the Department of Science and Technology,Government of India. The monthly snow depth data for the period1881 to 1995 have been obtained from the NSIDC on CD-ROM.The atmospheric fields for the period 1948 to 1994 were obtainedfrom the NCEP/NCAR reanalyzed data CDs. Indian SummerMonsoon Rainfall data have been taken from Parthasarathy et al.(1995) based on observed rainfall data of the India MeteorologicalDepartment.

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