Western Himalayan snow cover and Indian monsoon rainfall: A re-examination with INSAT and NCEP=NCAR data

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1 Theor. Appl. Climatol. 74, 1 18 (2003) DOI /s z Indian Institute of Tropical Meteorology, Pune, India Western Himalayan snow cover and Indian monsoon rainfall: A re-examination with INSAT and NCEP=NCAR data R. H. Kripalani, A. Kulkarni, and S. S. Sabade With 12 Figures Received January 15, 2002; revised May 5, 2002; accepted June 23, 2002 Published online January 13, 2003 # Springer-Verlag 2003 Summary This study presents the monthly climatology and variability of the INSAT (Indian National Satellite) derived snow cover estimates over the western Himalayan region. The winter=spring snow estimates over the region are related to the subsequent summer monsoon rainfall over India. The NCEP=NCAR data are used to understand the physical mechanism of the snow-monsoon links. 15 years ( ) of recent data are utilized to investigate these features in the present global warming environment. Results reveal that the spring snow cover area has been declining and snow has been melting faster from winter to spring after Connections between snow cover estimates and Indian monsoon rainfall (IMR) show that spring snow cover area is negatively related with maximum during May, while snow melt during the February May period is positively related with subsequent IMR, implying that smaller snow cover area during May and faster snow melt from winter to spring is conducive for good monsoon activity over India. NCEP=NCAR data further shows that the heat low over northwest India and the monsoon circulation over the Indian subcontinent, in particular the cross-equatorial flow, during May are intensified (weakened) when the snow cover area during May is smaller (extensive) and snow melts faster (slower) during the February May period. The well-documented negative relationship between winter snow and summer rainfall seems to have altered recently and changed to a positive relationship. The changes observed in snow cover extent and snow depth due to global warming may be a possible cause for the weakening winter snow IMR relationship. 1. Introduction Over a century ago, Blanford (1884) suggested that the varying extent and thickness of the Himalayan snows have a prolonged influence on the climatic conditions over India. Blanford (1884) and Walker (1910) were apparently the first to correlate some measure of snow extent with Indian monsoon rainfall (IMR). The snow accumulation in the Himalayas up to the end of May was in fact the first predictor discovered and used for forecasting the summer monsoon rain in India. High snow accumulation during winter= spring was unfavourable for the following IMR. Subsequently the relationship became unsteady and further progress in predicting the monsoon by ground-based snow cover data was limited owing to difficulties in assessing snow coverage due to the complex terrain over the Himalayas and its inaccessibility. With the advent of space-based satellite technology, snow cover estimates have been available since mid 1960s. Numerous studies have examined the relationship between satellite-derived snow cover estimates over Eurasia and the Himalayas and various facets of the monsoon variability over India (e.g. Hahn and Shukla, 1976;

2 2 R. H. Kripalani et al. Dey and Bhanu Kumar, 1983; Dickson, 1984; Dey et al., 1985; Dey and Kathuria, 1986; Parthasarathy and Pant, 1987; Bhanu Kumar, 1988; Verma, 1990; Kripalani et al., 1996; Rao et al., 1996; Bamzai and Shukla, 1999 etc). All these studies showed that the correlation was negative, implying that extensive (limited) Eurasian= Himalayan snow cover in winter=spring was followed by deficient (excess) IMR, thus confirming Blanford s hypothesis. Based on the results of these studies, the India Meteorological Department have used snow cover over Eurasia during December and over the Himalayas during January through March as two of the predictors in the operational long-range forecasting of monsoon rainfall over India (Gowariker et al., 1989, 1991). Several groups have reproduced the observed relationships by means of numerical experiments based on General Circulation Models (e.g. Vernekar et al., 1995; Bamzai and Marx, 2000). A review on the influence of the Himalayas and snow cover on the weather and climate over India can be found in Sikka (1999) and a treatise on the Himalayan Environment in Dash and Bahadur (1999). Recent studies have added some new facets not only to the ENSO (El Ni~no Southern Oscillation)- monsoon but also to the snow-monsoon teleconnections. There are localized regions, in particular western Eurasia, where snow variations are negatively related with IMR (Kripalani et al., 1996; Kripalani and Kulkarni, 1999; Bamzai and Shukla, 1999). Besides the well documented negative relationship, snow variations over eastern Eurasia are positively related with IMR (Kripalani and Kulkarni, 1999). Further studies have also shown a weakening ENSO-monsoon relationship after the 1990s (Kripalani and Kulkarni, 1997; Krishna Kumar et al., 1999). The Indian monsoon has not only delinked with the Pacific but appears to have delinked with the Eurasian continent as well (Kripalani et al., 2001, 2002). While one school of thought attributes the ENSO-monsoon weakening to global warming (Krishna Kumar et al., 1999; Ashrit et al., 2001), another view does not support global warming as a possible cause for the recent changes (Kripalani et al., 2001, 2002). In the light of the present global warming scenario, this paper proposes to re-examine the Himalayan snow-monsoon links by using 15 years ( ) of recent INSAT (Indian National Satellite)-derived snow cover estimates over the western Himalayas. Further, to understand the physical mechanism of the observed snow-monsoon links, NCEP=NCAR (National Center for Environmental Prediction=National Center for Atmospheric Research) Reanalyses data has been used. 2. Data The Western Himalayas extend from the northern Indian states of Jammu and Kashmir through Himachal Pradesh, Hills of Uttar Pradesh to the western boundary of Nepal (Fig. 1). This region receives significant precipitation in the form of snow during the winter months due to the movement of mid-latitude westerly synoptic systems commonly known as Western Disturbances. Fig. 1. Map showing the western Himalayan region

3 Western Himalayan snow cover and Indian monsoon rainfall 3 (i) Since the INSAT derived snow estimates are probably being used for the first time information on this data product is required. The basic meteorological payload on INSAT is the Very High Resolution Radiometer which measures the radiance in two narrow spectral windows, viz mm in the visible channel and mm in the thermal infrared band. The spatial resolution is 2 km for the visible channel and 8 km for the infrared channel. The aerial snow cover estimates over the western Himalaya (74 80 E, N) in square kilometers derived from the INSAT satellite imagery are computed carefully using the Meteorological Data Analysis (MDA) System (METDAS) software by experienced meteorologists at the India Meteorological Department (IMD) New Delhi. This software package was supplied by M=S MDA, Canada for the current INSAT II Meteorological Data Processing System of IMD. The imagery of several days is examined visually and whenever the absence of clouds is observed by the meteorologists, that imagery is selected for computation. Though the computed areas have so far not been validated against NOAA=NESDIS (National Oceanic and Atmospheric Administration= National Environmental Satellite Data and Information Services) observations, considering the above, the data can be considered highly reliable. The data provided by IMD covers the period January 1986 through March For each month, daily estimates are available for 0 to 4 days. Monthly average estimates for each month are computed depending on the number of days data are available. Data used in this study is for the period January 1986 through December 2000 (15 years). For the months January to May data for February 1993 and May 1999 were not available. (ii) Gridded 2.5 by 2.5 latitude=longitude mean sea level (MSL) pressure and 850 hpa wind vectors for the month of May over the Indian domain have been extracted from the NCEP=NCAR Reanalyses data set (Kalnay et al., 1996). (iii) The Indian monsoon rainfall (IMR: June through September) series has been downloaded from the website of the Indian Institute of Tropical Meteorology ( The time series of IMR has been generated by areaweighting the rainfall at 306 well distributed raingauges across the whole country. Thus IMR represents rainfall for the country and the monsoon season as a whole and can be considered as a measure of the intensity of monsoon over the Indian region. 3. Annual cycle and variability The average snow cover area for each month, depicting the annual cycle is shown in Fig. 2. Over the course of the annual cycle the snow cover area changes from about km 2 in October to about km 2 in February and thereafter to about km 2 in June. The annual cycle is characterized by an increase from autumn to winter and thereafter a gradual decrease from winter to spring=summer. Thus the climatic snow cover reaches its maximum accumulation by February. During March to July the accumulation is negligible as the seasonal snow starts melting. Fig. 2. Monthly mean snow cover area depicting the annual cycle (S ¼ September, O ¼ October and so on)

4 4 R. H. Kripalani et al. Table 1. Measures of variability (MON month; S September and so on; NYR number of years; STD standard deviation; MAX maximum snow cover area; MIN minimum snow cover area) MON S O N D J F M A M J J A NYR STD MAX MIN Fig. 3. Year-to-year variations of standardized snow cover areas January through May and snow melt during the February May period depicting the interannual variability in snow estimates

5 Western Himalayan snow cover and Indian monsoon rainfall 5 The slower snow melt speed is probably due to its high elevation and snow is maintained even until the beginning of the summer period. The variability (standard deviation) and the maximum and minimum snow cover areas for each month are shown in Table 1. The variability is least during the winter period and more preceding and following the winter period. The range can be inferred from the maximum and minimum values. During winter snow is always present, hence variability is least while during the accumulation process and the snow-melting season the year-to-year variations are high. The monthly standardized (standardized to standard deviation) values of snow cover area for the months of January through May and the snow melt area during the February May period are shown in Fig. 3, depicting interannual variability. An interesting point here is that the standardized snow cover values are on the negative side from 1993 onwards in particular during the months of April and May. The bottom panel in Fig. 3 shows the snow melt values from February through May period. Snow cover areas for February 1993 and May 1999 are not available, hence snow melt areas for these two years could not be computed. After 1993, whatever data is available shows snow melt on the positive side. This probably suggests that snow-melt area has been increasing since This reduction of snow cover area during the spring period and increase in the snow-melt area from winter to spring in the 1990s may be due to global warming (Jones, 1994). The 1990s has been the warmest decade (IPCC, 2001). Pant et al. (1999) analyzed long-term temperature data over the western Himalayas and found an increasing trend in all the seasons except the monsoon season. This increasing trend in temperature may have resulted in less (more) snow cover (snow melt) area. Water from the melting snow is a major source to rivers in North India. Thus melting of snow can result in outburst of floods before the commencement of the monsoon season (in mid June) over North India near the foothills of the Himalayas due to faster snow melt. 4. Relationship with IMR Due to its high albedo, high emissivity, poor conductivity and low melting point snow acts as a strong cooling agent. This could induce simultaneous and delayed effects on the climate system and modify the atmospheric circulation. To examine the impact of winter=spring snow cover area on the summer monsoon rainfall over India, lag correlation coefficients (CCs) between IMR and monthly times series of snow cover are computed from November till May preceding the monsoon period (Fig. 4 upper panel). Figure 4 reveals that the winter-time (November through February) snow cover area shows positive relationship, while the spring (March through May) shows negative relationship. The positive relationship weakens from the autumn to the winter period. However the negative relationship amplifies from the winter to the spring period with maximum for the month of May ( 0.5; significant at 5% level). This implies that extensive (limited) snow cover area during May is unfavourable (favourable) for the subsequent monsoon rainfall over India. In the above analysis it is observed that the winter-time snow cover IMR relationship has changed sign. Using observed Soviet snow depth data Kripalani et al. (2001, 2002) have noted that relationship of January snow depth over western Eurasia with IMR has also changed sign and is positively related after 1990s. Thus it appears that the IMR has been delinked with winter snow cover estimates, but the spring snow IMR relationship is still maintained. The winter northern hemisphere=eurasian snow cover extent has been decreasing due to the global warming (IPCC, 2001). However the snow depth over Eurasia has been increasing in recent times (Ye, 2000; Kripalani et al., 2002). This increase in snow depth is likely to be associated with increasing precipitation related to the warming in surface air temperature (Ye, 2000; IPCC, 2001). Thus the changes in snow estimates due to global warming may be a possible cause for the delinking of IMR with winter snow, but this needs a separate investigation. To further examine whether the aerial coverage of snow or the snow melt from winter to spring is better related with IMR, snow melt areas have been computed by subtracting May snow cover area from the snow cover areas for the months of January through April. Time series of January May, February May, March May, April May are correlated with subsequent IMR

6 6 R. H. Kripalani et al. Fig. 4. Lag correlation coefficients of IMR with monthly snow cover (SC) areas (upper panel) and with snow melt (SM) areas (lower panel) (Jan May: snow area melted during the January May period and so on). Values exceeding in magnitude the dashed line are significant at 95% level (Fig. 4 lower panel). Figure clearly shows that the maximum relationship is for the snow melt during the February through May period (CC ¼ 0.6, significant at 1% level). Thus snow melt area seems to be better related with IMR than snow cover area. More (less) snow melt area signifies faster (slower) snow melt process. Hence faster snow melt during the February May period is conducive for favourable monsoon activity over India. Since the data period is small (15) inferences based on correlation coefficients may not be convincing, hence to support the analysis scatter plots are plotted between standardized May snow cover area, February May snow melt area and IMR. Figure 5 (top panel) shows the scatter plot between May snow cover area and IMR. The negative relationship between these two variables implies that the points should fall in Quadrant II and IV. Figure 5 (top panel) shows that 10 points out of 14 (missing value for 1999 shown on the zero line) do fall in Quadrant II and IV. The positive relationship between February May snow melt area and IMR suggests that the points should fall in Quadrant I and III. Figure 5 (central panel) shows that 9 points out of 13 (missing values for 1993 and 1999 shown on zero line) fall in Quadrant I and III. Finally the negative relationship (CC ¼ 0.77) between May snow cover area and February May snow melt area suggests that the points should lie in Quadrant II and IV. Here also 9 out of 13 points fall in these quadrants. Hence the scatter plots reveal the relationship better. The larger snow cover area and slower snow melt may increase surface albedo. This higher albedo may further be indirectly responsible for a weak summer monsoon activity. Generally higher albedo lowers atmospheric temperature and increases sea level pressure, thereby weakening monsoon circulation. On the other

7 Western Himalayan snow cover and Indian monsoon rainfall 7 Fig. 5. Scatter plots of standardized values of May snow cover area and IMR (upper panel), February May snow melt area and IMR (central panel) and May snow cover and February May snow melt area (lower panel) hand lower snow cover or faster snow melt decreases surface albedo and increases atmospheric temperature (sensible heat) which may lead to a strong monsoon (Dey and Bhanu Kumar, 1982). 5. Application in forecasting To explore the possible utility of snow cover= snow melt data over the western Himalayas for forecasting IMR, linear regression equation of the form y ¼ a þ bx is developed for the same period , where y ¼ IMR, x ¼ snow cover area during May or snow melt area during February May period. Based on these two equations IMR is estimated for the period. The interannual variations in IMR are reasonably well depicted by both the equations. Both the observed and estimated IMR exhibits considerable variability till

8 8 R. H. Kripalani et al. Fig. 6. Year-to-year variations of observed IMR and estimated through snow cover and snow melt areas 1995, thereafter the variability appears damped (Fig. 6). Such damping of IMR variability in recent times has been noted earlier (Wang et al., 2001; Kripalani et al., 2002). The correlation coefficients between the observed and regressed values estimated through May snow cover area (February May snow melt area) is 0.48 (0.59). 6. Physical mechanism for snow-monsoon links The land-sea temperature contrast is the basic forcing mechanism of the Indian summer monsoon. Shukla (1987) suggested that an excessive snowfall during the previous winter and spring season can delay the build up of the monsoonal temperature gradient because part of the solar energy will be reflected and part will be utilized for melting the snow or for evaporating the soil moisture. A relatively small amount of energy will be left for warming the surface and hence the atmosphere. Thus snow cover tends to create a net radiation deficit. This will weaken the heat low over northern India and Persia resulting in weak southwesterly winds over the monsoon region and may result in a significant decrease in rainfall. Bansod and Singh (1995) have noted that the May pressure over the heat low region shows distinct relationship with seasonal rainfall during the first half of the monsoon season. Thus the lingering of deep snow and greater aerial extent of snow cover in winter=spring could be an important factor for the slower and smaller build-up of the summer season continental heat sources and subsequent monsoon strength. This is supported by the negative (positive) relationship of snow cover (snow melt) area with IMR seen in Section 4. Several groups have simulated the above features through numerical experiments using General Circulation Models (GCM). Using a COLA (Centre for Ocean Land Atmosphere) GCM Vernekar et al. (1995) observed that excessive snow cover was associated with a weak monsoon characterized by higher sea level pressure, weak Somali Jet and weaker lower tropospheric westerlies. Hardly any observational=empirical study has explicitly quantified the impact of snow on monsoon circulation through data analysis. Hence to investigate whether the snow estimates affect the heat low over north India and the crossequatorial and southwesterly winds over the Indian region NCEP=NCAR Reanalyses MSL pressure and the lower tropospheric 850 hpa wind vector data sets have been used. To start with, years have been identified where the May snow cover and the February May snow melt have been the most extensive (positive) and

9 Western Himalayan snow cover and Indian monsoon rainfall 9 the least (negative) in area. Three sets of years for each category could be identified during the period as follows: Negative snow cover: 1990; 1995; 1997 ðaþ Positive snow cover: 1987; 1989; 1992 ðbþ Negative snow melt: 1986; 1987; 1989 ðcþ Positive snow melt: 1988; 1990; 1995 ðdþ These can be inferred from the bottom two panels of Fig. 3. To examine whether the categories A and B for snow cover areas and categories C and D for snow melt areas are appropriate, within group variability is compared with between group variability by the F-ratio (F-ratio ¼ between group variance=within group variance). For May snow cover areas F ¼ 14.8 which is larger than the critical value (significant F value at 5% level is 7.71), indicating that between group variability (A and B) is more than within group variability (A or B) and implying that the categorization of groups A and B is correct. Similarly the F-ratio for the C and D groups is (much larger than the critical value of 7.71) suggesting again that between group variability (C and D) is more than within group variability (C or D) implying that the categories C and D for snow melt areas are also correct. Thus the intracategory variability is smaller than the intercategory variability for May snow cover area as well as for February May snow melt areas. Before the composites for May MSL pressure based on the above categories are presented, the mean and variability (standard deviation) for the MSL field is presented in Fig. 7. One of the important features of the monsoon circulation is the establishment of the monsoon trough along the Gangetic plains from northwest India to the southeast. Figure 7 (upper panel) shows that the minimum pressure ( 1002 hpa) is over the northern parts of the country from northwest India (i.e. the heat low area) till the east-coast of India. The variability (Fig. 7 lower panel) is maximum ( 1.6 hpa) over this region. Based on the above sets of years, composites for the month of May for the MSL pressure are computed. To see how the aerial extent of snow and snow melt areas affect the heat low over northwest India differences of the above composites are presented in Fig. 8 (A minus B above panel; C minus D bottom panel). The negative values throughout India indicate that the monsoon circulation has intensified (Fig. 8, above panel). The strength of the negative values is numerically maximum over northwest India ( 1 hpa) suggesting that the heat low has intensified when the aerial extent of snow cover is least. The differences of the above composites are tested by a two-tailed Student s t-test, no significance is noted over the Indian region. However the positive values over northwest India (Fig. 8, bottom panel) further reveal that the heat low has weakened when the snow melt area is least i.e. snow melts slowly from winter to spring. The fact that the intensity of heat low changes by 1 hpa for snow cover area and by 2 hpa (greater than the standard deviation of 1.6 hpa over this region) for the snow melt area further suggests that the snow melt dominates more than the snow cover area. However these composites show significant difference over the Indian ocean sector only, southwest of India (shaded in grey). Vernekar et al. (1995) also noted that the difference in May MSL pressure with heavy snow and light snow experiments to be about 0.5 to 1.0 hpa over the Indian region. Thus this analysis confirms that the heat low over northwest India intensifies when the aerial extent of snow is least and when the snow melts faster from winter to spring. Another feature of the monsoon circulation is that the lower tropospheric easterly flow over the Indian Ocean associated with the Mascerene High crosses the equator along the Somali Jet and then turns eastward to cross the Arabian Sea and the Indian peninsula as southwesterly flow. The mean May 850 hpa wind vector pattern (Fig. 9 upper panel) illustrates the cross-equatorial flow, while the variability (standard deviation) pattern (Fig. 9 lower panel) shows that the magnitude of maximum variability is over the Indian ocean lying between 10 Sto10 N. To examine this aspect similar differences (A minus B; C minus D) for the 850 hpa wind vectors are shown in Fig. 10. The flow pattern shows easterly wind over the southern Indian Ocean, the Somali Jet, southwesterly flow over the Arabian Sea, westerly flow over the southern Indian peninsula and southwesterly flow over the Bay of Bengal (Fig. 10, top panel). This signifies that the monsoon circulation over the Indian monsoon region

10 10 R. H. Kripalani et al. Fig. 7. Climatology (Actual pressure ¼ contour value þ 1000 in hpa) and variability (standard deviation in hpa) for the May MSL pressure field based on period intensifies when the western Himalayan snow cover is least. The anomalous northerlies over the Bay of Bengal, anomalous easterlies over the southern part of the Indian peninsula and anomalous northeasterlies=northerlies over the region of the cross-equatorial flow (Fig. 10, bottom panel), suggests that the monsoon circulation has weakened when snow melts slowly from winter to spring. These results are consistent with model simulated results of Vernekar et al. (1995). The areas of significant differences are shaded in Fig. 10. The main areas of significance for snow cover (Fig. 10 upper panel) are near the

11 Western Himalayan snow cover and Indian monsoon rainfall 11 Fig. 8. Regional distribution of May sea level pressure difference in hpa for the composites of negative minus positive snow cover area (top panel) and snow melt area (bottom panel). Light (dark) grey shading illustrates the significance of the difference at 95 (99)% level determined from the two-sided Student s t-test Mascarene High (80 E 100 E, south of 10 S), southeast Bay of Bengal (80 E 90 E, equator- 10 N) and southern Indian ocean (60 E 70 E, 5 15 S). While for snow melt the main areas of significance are near the cross-equatorial flow (50 60 E, equator 10 S) and southwest Bay of Bengal ( E, equator 10 S). In the above analysis the influence of snow cover and snow melt has been treated separately. It would be worthwhile to examine the

12 12 R. H. Kripalani et al. Fig. 9. Climatology and variability (standard deviation) for the 850 hpa wind vectors in m=s based on period. Contours in the lower panel shows the magnitude of variability in m=s

13 Western Himalayan snow cover and Indian monsoon rainfall 13 Fig. 10. Same as Fig. 8 but for the 850 hpa wind vectors in m=s

14 14 R. H. Kripalani et al. Fig. 11. MSL pressure anomalies in hpa from climatology for selected years having May snow cover area on the negative side and Feb May snow melt area on the positive side [left panels: SC ( )SM(þ )] and May snow cover area on the positive side and Feb May snow melt area on the negative side [right panels: SC ( þ )SM( )]

15 Western Himalayan snow cover and Indian monsoon rainfall 15 simultaneous effect of snow cover and snow melt on the May MSL pressure field and the 850 hpa wind field. During the analysis period there are 5 years (1988, 1990, 1994, 1995, 1997) where the snow cover has been on the negative side and snow Fig. 12. Same as Fig. 11 but for 850 hpa wind vector anomaly in m=s

16 16 R. H. Kripalani et al. melt on the positive side, while there are 4 years (1986, 1987, 1989, 1991) where the snow cover is on the positive side and snow melt on the negative side (refer Fig. 3). Anomalies from climatology (based on period) for 3 selected years of each of the above set are shown in Fig. 11 for MSL pressure and Fig. 12 for 850 hpa winds. Figure 11 (left panels: snow cover negative and snow melt positive) shows that the pressures are below normal over the Indian region while Fig. 11 (right panels: snow cover positive and snow melt negative) shows above normal MSL pressures except for the year 1986 where negative departures of reduced magnitude are seen. In general it can be inferred that a smaller snow cover area and faster snow melt (larger snow cover area and slower snow melt) intensifies (weakens) the heat low over northwest India. A similar analysis with 850 hpa wind vector reveals that the Somali jet is strengthened (Fig. 12 left panels) when the snow cover area is small and snow melts faster, except for the year On the other hand the Somali jet is weakened (Fig. 12 right panels) where the snow cover area is larger and snow melts slower. In summary a smaller snow cover area in spring and faster snow melt from winter to spring is favourable for good monsoon activity over India. 7. Conclusions The purpose of this study was to re-examine the snow-monsoon connections by using the recent 15 years ( ) of INSAT-derived snow cover estimates over western Himalayas. The physical mechanism of these connections were investigated using the NCEP=NCAR Reanalyses data. Though only 15 years of INSAT data were available with the authors, this study has provided some useful information. The main conclusions of the study are (i) Spring snow cover area has been declining and snow has been melting faster from winter to spring after The 1990s has been the warmest decade, hence the recent changes in snow estimates over the western Himalayas may be due to global warming. This may lead to floods in the regions near the foothills of the Himalayas even before the commencement of the monsoon. (ii) The May (February May) snow cover (snow melt) area is negatively (positively) related with subsequent summer monsoon rainfall over India, implying that a smaller snow cover area and faster snow melt is conducive for good monsoon activity over India. (iii) The impact of snow melt on monsoon rainfall and circulation appears to be stronger than that of snow cover. (iv) NCEP=NCAR data reveal that the heat low over northwest India and the lower tropospheric monsoon circulation over the Indian region, in particular the cross-equatorial flow during May, are intensified when snow cover area during May is least and snow melt is faster during the February May period. (v) The well-documented negative winter snowsummer monsoon relationship seems to have been broken in recent times and changed to a positive relationship. These may be related with changes in snow cover extent= depth due to global warming. However the negative spring Himalayan snow-summer monsoon relationship is still maintained in the present global warming environment. Operational IMD long range forecasts of IMR have used December Eurasian snow cover and January through March snow accumulation over the Himalayas as predictors. Based on this study snow cover area during May and snow melt area during the February May period over the western Himalayan region could serve as better predictors. Thus Himalayan snow still forms an important component for IMR forecasting. In view of this, snow data over the Himalayas should be collected on a regular basis. There should be a coordinated effort in utilizing different technologies to collect data over the highly rugged, inaccessible and inhospitable terrain of the Himalayas. Different data sets are being collected by different agencies for different purpose. At National Remote Sensing Agency (NRSA) expertise has been developed for utilizing satellite data for mapping and monitoring snow covered area on a operational basis in a few Himalayan regions. Much needs to be done in using remote sensing for monitoring snow cover over the Himalayas (Dash and Bahadur, 1999).

17 Western Himalayan snow cover and Indian monsoon rainfall 17 Acknowledgements Thanks are due to Dr. G. B. Pant, Director and Dr. S. S. Singh, Deputy Director, Indian Institute of Tropical Meteorology for all the facilities provided and to Dr. M. Rajeevan, India Meteorological Department (IMD) for fruitful discussions. Thanks are also due to IMD, New Delhi for providing the INSAT-derived snow cover estimates. The comments= suggestions of the anonymous reviewers has improved the manuscript. This study was supported by a grant No. ES=48=ICRP=006=99 from the Department of Science and Technology, Govt. of India, New Delhi under a project ENSO Snow-Monsoon Interactions: Understanding and predicting Monsoon Variability. References Ashrit RG, Rupa Kumar K, Krishna Kumar K (2001) ENSO- Monsoon relationships in a greenhouse warming scenario. Geophys Res Lett 28: Bamzai AS, Shukla J (1999) Relation between Eurasian snow cover, snow depth and the Indian summer monsoon: An observational study. 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18 18 R. H. Kripalani et al.: Western Himalayan snow cover and Indian monsoon rainfall Vernekar AD, Zhou J, Shukla J (1995) The effect of Eurasian snow cover on the Indian monsoon. J Climate 8: Walker GT (1910) On the meteorological evidence for supposed changes of climate in India. Mem Indian Meteor 21: 1 21 Wang B, Wu R, Lau K-M (2001) Interannual variability of the Asian summer monsoon: contrasts between the Indian and the Western North Pacific East Asian monsoon. J Clim 14: Ye H (2000) Decadal variability of Russian winter snow accumulations and its associations with Atlantic sea surface temperature anomalies. Int J Climatol 20: Authors address: R. H. Kripalani ( krip@tropmet. res.in), Ashwini Kulkarni and S. S. Sabade, Forecasting Research Division, Indian Institute of Tropical Meteorology, NCL PO, Pashan, Pune , India.