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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 21: 1607–1621 (2001) DOI: 10.1002/joc.695

LAGGED TELECONNECTIONS BETWEEN SNOW DEPTH IN NORTHERN EURASIA, RAINFALL IN SOUTHEAST ASIA AND SEA-SURFACE TEMPERATURES OVER THE TROPICAL PACIFIC OCEAN a

HENGCHUN YEa,* and ZHENHAO BAOb Department of Geography and Urban Analysis, California State Uni6ersity, Los Angeles, CA, USA b Department of Physics, Uni6ersity of Toronto, Toronto, Canada Recei6ed 4 January 2001 Re6ised 25 May 2001 Accepted 25 May 2001

ABSTRACT This study shows that above-(below-)normal winter snow depth over European Russia and corresponding below-(above-)normal snow depth over central Siberia is associated with reduced (increased) summer monsoon rainfall over southern and western India and eastern Pakistan, and above-(below-)normal sea-surface temperatures (SSTs) over the eastern and central tropical Pacific Ocean during the following winters. The connection is slightly stronger when snow depth over European Russia is above normal than below normal. These results are derived from an examination of 60 years (1936–1995) of winter snow depth data and SSTs, and 45 years (1951 – 1995) of summer precipitation records. The results of this study suggest that winter snow depth over the western rather than the eastern portion of Eurasia is critical to Southeast Asian summer monsoon rainfall and eastern tropical Pacific SSTs during the following seasons. Copyright © 2001 Royal Meteorological Society. KEY WORDS: monsoon;

northern Eurasia; sea-surface temperature; snow; Southeast Asia

1. INTRODUCTION Teleconnections between Eurasian winter snow and Southeast Asian summer monsoon rainfall provide a valuable tool for summer precipitation prediction in Southeast Asia, where the local economy is highly dependent on the monsoon. This linkage is due to the critical importance of snow to spring heating over land areas and hence the onset date and strength of the summer monsoon. Abnormally high snow extent/volume over Eurasia (i) reduces solar radiation absorption due to snow’s high albedo, (ii) consumes more solar energy for snow melting and (iii) increases soil moisture after melting, which also increases heat consumption for evaporation. These factors contribute to reduced heating over land which delays the build up of the critical temperature differences between land areas and nearby oceans necessary for monsoon circulation. Modelling studies have confirmed this inverse relationship of Eurasian winter/spring snow and Southeast Asian summer monsoon precipitation in general. They have also found that snow volume has a larger impact on monsoon rainfall than spatial snow coverage (e.g. Barnett et al., 1988, 1989; Douville and Royer, 1996; Dong and Valdes, 1998). This suggests that the hydrological changes caused by melting snow play a more significant role than the albedo effect in altering the surface and atmospheric energy budgets. * Correspondence to: Department of Geography and Urban Analysis, California State University, Los Angeles, CA 90032-8222, USA.

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Observational studies have shown fairly consistent results with those from modelling simulations. However, most studies used snow area coverage rather than snow volume (or depth) which renders the statistical significance of the association debatable (Dey and Kumar, 1983; Ropelewski et al., 1984; Kumar, 1988). Of course, problems associated with the quality and length of snow cover data as well as with the regional variability of both snow and precipitation are also to blame. Furthermore, the impact of snow on monsoon rainfall is regionally dependent and using an area average to represent either snow or precipitation condition may not be the best way to assess these relationships (Parthasarathy and Yang, 1995; Douville and Royer, 1996; Kripalani and Kulkarni, 1999). For example, rainfall has different temporal variability over different regions of India and China, therefore its relationship to snow may not be uniform over the entire region (Yang and Xu, 1994; Parthasarathy and Yang, 1995). Recent studies by Bamzai and Shukla (1999) and Kripalani and Kulkarni (1999) suggest that only snow depth and snow cover in western Eurasia rather than all of Eurasia is associated with area-averaged Indian monsoon rainfall. This implies that the correspondence of summer monsoon to snow depth is dependent on where anomalous snowfall occurs. The pattern of sea-surface temperature (SST) anomalies over the eastern tropical Pacific known as El Nin˜ o is also found to be associated with abnormal Asian summer monsoon rainfall (Rasmusson and Carpenter, 1983; Elliott and Angell, 1987; Khandekar, 1991) and Eurasian snowfall (Barnett et al., 1989). A dominant feature of these connections is the lags in the sequence of Eurasian snow, monsoon, and El Nin˜ o with one or two seasonal lags between each. Studies by Yang (1996) and Yang and Lau (1998) suggest that El Nin˜ o plays a more significant role in Indian monsoon rainfall anomalies than Eurasian winter snow cover and related soil moisture. Yasunari and Seki (1992) proposed that the interactions between Asian summer monsoon and El Nin˜ o are strongly modified by the North Atlantic Oscillation (NAO) which stochastically amplifies or dampens the biannual nature of the monsoon and the coupled tropical atmosphere/ocean system. In summary, it seems that (i) snow volume (or depth) has a more significant connection to summer monsoon rainfall than snow coverage; (ii) the teleconnections between winter snow and summer rainfall are geographically dependent; and (iii) abnormal SSTs over the eastern tropical Pacific interact with Eurasian snowfall and monsoon rainfall through extratropical atmospheric circulation. Considering the large spatial variability of both Eurasian snow depth (Ye et al., 1998; Ye, 2000) and Southeast Asian monsoon rainfall (e.g. Lau and Li, 1984; Parthasarathy and Yang, 1995), it is necessary to assess the geographical correspondence of teleconnections between these two phenomena. The purpose of this study is a comprehensive examination of the geographical patterns of the teleconnections between winter snow depth over northern Eurasia and summer precipitation over Southeast Asia and SST anomalies associated with these teleconnections. These quantified regional connections will provide a valuable basis for theoretical and modelling studies to search for key physical mechanisms of this snow– monsoon teleconnection.

2. DATA Snow depth data are from the Historical Soviet Snow Depth— Version 2.0 available from the National Snow and Ice Data Center. The monthly snow depth is derived from daily averaged values from three measuring rods surrounding each station. The reading from each measuring rod is rounded to 1 cm and snow depth of less than 0.5 cm is considered as zero. Based on the high temporal continuity of winter snow depth at stations where the monthly mean snow depth is 10 cm or above (Ross Brown, personal communication), the monthly mean snow depth is averaged from at least eight daily values within the month. For regions where the mean monthly snow depth is below 10 cm, the monthly snow depth is averaged from more than 15 days. Otherwise, the month is considered to have a missing value. The missing month is then interpolated from the 2 months before and after if they are available. Winter seasonal snow depth is the average of the three monthly snow depths of January, February and March. If 1 or more months (out of 3) have missing values, a winter snow depth value is not calculated. Balancing Copyright © 2001 Royal Meteorological Society

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Figure 1. Snow station distribution and number of missing winters during 1936– 1995

station density and record length, 137 stations, each of which has no more than three missing winters during the 60-year study period (1936 – 1995), have been selected for this study (Figure 1). Mean winter snow depth and standard deviation are displayed in Figure 2. The highest snow depths of about 70 and 60 cm occur over northern central Siberia and the northern Ural Mountains, respectively. Low snow depth of less than 10 cm is found over southern regions of the study area (Figure 2(a)). The standard deviation ranges from 2 to 16 cm over the western portion of the study area and the largest standard deviation of about 20 cm occurs over the eastern coastal region of Siberia (Figure 2(b)). Asian precipitation data are from Dai et al.’s (1997) monthly gridded precipitation departure (from the mean of 1951–1979) available from NASA’s GISS lab. This dataset has a resolution of 2.5° latitude by 2.5° longitude. Grid points located between 10°– 40°N, 70°– 120°E covering most of Southeast Asia are selected. Due to many missing grid values in early years, a reasonably good grid point coverage over Southeast Asia is only found during the time period between 1951 and 1995 (a total of 45 years) and hence is used in this study (Figure 3). The summer precipitation departure is the average of the monthly departures of June, July and August.

Figure 2. Mean winter snow depth in cm (a) and standard deviations (b) during 1936– 1995 Copyright © 2001 Royal Meteorological Society

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Figure 3. Grid point distribution for summer precipitation. Grid points marked ‘1’ indicate no missing values for the time period of 1951– 1995 and are used in this study

SST data are from the gridded monthly Global Sea-Ice and SST dataset (GISST, version 2.3b) compiled and quality-controlled by the Hadley Centre for Climate Prediction and Research, UK Meteorological Office. This dataset has a resolution of 1° latitude by 1° longitude and covers 1871 to the present (Folland and Parker, 1995; Parker et al., 1995). Four different reconstruction methods were used to construct this SST dataset for the four different time periods of 1871 – 1902, 1903–1948, 1949–1982 and 1982 to the present as summarized by Hurrell and Trenberth (1999). The summer and winter seasonal SSTs are derived from the monthly mean of June/July/August and December/January/February, respectively. 3. METHODS First, rotated principal components analysis (RPCA) is used to define major snow depth variation patterns over northern central Eurasia (Richman, 1986). Since there is a general increasing snow depth trend over much of the study region (Ye et al., 1998; Ye, in press), stations with a statistically significant trend (at 95%) are detrended before interpolation into grid points. The trend in each station time series is removed by developing a least-square regression trend line and subtracting the projected regression value from the snow depth for each year. Then each station time series is standardized with a zero mean and standard deviation of one. Finally, interpolation using Shepard’s local-search interpolation on a spherical surface (Willmott et al., 1985) is applied to the standardized and detrended station data. RPCA is applied to the gridded snow depth field with and without using a weighting coefficient adjusted for different surface areas of grids separately. Little difference is found between the two results. Thus, results of RPCA with no weighting coefficient are used in this study. Second, the resulting time series of each rotated principal component (snow-RPC) is correlated to the precipitation grid points to produce cross-correlation maps. The areas where statistically significant correlation occurs are delineated using these maps. The auto-correlation in the time series that would reduce the independent sample size of the correlation is considered when defining statistically significant correlations. The independent sample size of the correlation between each snow-RPC and precipitation grid point is estimated by dividing sample size (n: the record length) by the ‘effective time of independent sample or decorrelation time’, t, n−1 k t= 1+2 % 1 − zx (k)zy (k) n k=1


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where zx (k) and zy (k) are the covariance (at lag k) of the time series of the grid point precipitation and snow-RPC, respectively (Livezey and Chen, 1983; von Storch and Zwiers, 1999). The first order of auto-correlation approximation derived from the Yule–Walker equation is used for both fields. The statistically significant correlation coefficient (at a 95% level) is based on the derived independent sample size (or ‘effective sample size’) for each grid point instead of n. In addition, the field significance of the correlation is examined by adopting Chen (1982) and Livezey and Chen’s (1983) Monte Carlo simulation. One thousand random noise time series are generated and correlated to time series of precipitation at grid points to estimate the rejection rate of the statistically significant correlation areas (in percentage). The rejection rate is the critical value of area percentage that must be exceeded in order to have field significance at a 95% confidence level. Finally, composite map analyses of summer precipitation departures corresponding to the five highest and five lowest values of snow-RPCs (about 10% of summer precipitation sample size) of the previous winters are performed. Also, the composite maps of SST departures in the winters following the six highest and six lowest values of snow-RPCs are produced (about 10% of SST sample size). A t-test is applied to each of these composite maps to delineate areas where statistically significant differences in precipitation and SSTs between extreme conditions are present. These composite maps reveal any asymmetric teleconnections for positive and negative phases among three fields (Hoerling et al., 1997).

4. RESULTS The first five principal components (PCs) from PCA are retained for Varimax rotation based on a statistically significant break between the 5th and 6th eigenvalues using North et al.’s (1982) formulation. These five PCs explain about 41% of the total original snow depth variance. The spatial patterns (EOFs) of the resulting final five rotated PCs (snow-RPCs) are shown in Figure 4. The first EOF (EOF1) describes winter snow depth variation over the southwest portions of the former Soviet Union, an area around the Caspian Sea and west of China (Figure 4(a)). The second EOF (EOF2) emphasizes snow depth variation over eastern Siberia (Figure 4(b)). The third EOF (EOF3) represents an opposite snow depth variation over European Russia and central Siberia (Figure 4(c)). The fourth EOF (EOF4) describes snow depth variation over the northern Ural Mountains and the fifth EOF (EOF5) describes snow depth variations over an area northeast of the Caspian Sea and a few other scattered regions (Figure 4(d) and (f)). Each of the five snow-RPCs is correlated to summer monthly and total precipitation at grid points over Southeast Asia. Snow-RPC1 has positive correlations to June precipitation over central eastern China (Figure 5(a1)) and negative correlations to July precipitation in central China (Figure 5(a2)) and is correlated to summer total precipitation over both regions (Figure 5(a4)). Only the correlation to July precipitation passes the field significance test at a 95% confidence level (6.7% significant correlation area that equals the rejection rate of 6.7% for July precipitation). Snow-RPC2 does not have any significant correlations to summer precipitation (Figure 5(b)). Snow-RPC3 is significantly correlated to summer precipitation over India and eastern Pakistan (Figure 5(c)). Significant correlation starts in southern India in July and spreads over most of India and eastern Pakistan in August. Only August and summer precipitation pass the field significance test in which the significant correlation areas are larger than the rejection rates of 7.1% (August) and 8.1% (summer), respectively. This suggests that below-(above-)normal snow depth over European Russia and above-(below-)normal snow depth over central Siberia is associated with increased (decreased) August precipitation and thus summer precipitation over southern and western India and eastern Pakistan. Snow-RPC4 has some significant correlations to summer precipitation scattered over all of Southeast Asia (Figure 5(d)). Only the June precipitation correlation marginally passes the field significance test. This suggests that snow depth over the northern Ural Mountains may have some influence on June precipitation over Southeast Asia. However, the correlation pattern does not show any geographical concentration Copyright © 2001 Royal Meteorological Society

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Figure 4. The five EOFs of winter snow depth produced by RPCA represented by correlation coefficients between the corresponding snow-RPC and snow depth. Shaded areas are statistically significant at a 99% confidence level

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Figure 5. Correlation coefficients between five snow-RPCs (a)– (f) and the June, July, August, and total summer precipitation (1)–(4). Shaded areas are statistically significant at a 95% confidence level after auto-correlation in the time series has been allowed for. Positive areas are shown in dark shades and negative areas in light shades



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Figure 5 (Continued)

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Figure 6. Composite maps of summer precipitation departures corresponding to the five highest (a) and five lowest (b) snow-RPC3s and the differences between the five highest and five lowest composites (c). The shaded areas are statistically significant at a 95% confidence level based on a one-tailed t-test



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Table I. Years corresponding to the five highest and five lowest values of snow-RPC3 during 1951–1995 and the six highest and six lowest values of snow-RPC3 during 1936–1995 Five highest Five lowest Six highest Six lowest

1961 1957 1936 1942

1973 1966 1937 1957

1975 1968 1961 1966

1983 1979 1975 1968

1990 1987 1983 1979

1990 1987

(Figure 5(d1)); this branch of correlation will not be further investigated. Snow-RPC5 has little significant correlation to summer precipitation (Figure 5(e)). The composite maps of summer precipitation departures corresponding to the five highest and five lowest previous winter snow-RPC3 values are shown in Figure 6. The years used in these composites are listed in Table I. Following the high (positive) snow-RPC3 winters, the Indian summer precipitation has positive departures of 10– 20 cm while following the low (negative) snow-RPC3 winters, negative Indian summer precipitation departures of similar magnitude are found (Figure 6(b)). The statistically significant area of

Figure 7. The same as Figure 6 except for snow depth departures of the highest six and lowest six snow-RPC3 winters (out of 60 years instead of 45 years for precipitation) Copyright © 2001 Royal Meteorological Society

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Figure 8. Correlation coefficients between snow-RPC3 and SSTs of the (a) same winter, (b) following summer, (c) following winter. Shaded areas are statistically significant at a 95% confidence level after auto-correlation in the time series has been allowed for. Positive correlations are shown in dark shading and negative ones in light shading

summer precipitation departure is larger, corresponding to the low snow-RPC3 winters. This suggests that above-normal snow depth over European Russia has a more significant influence on reduced Indian summer monsoon rainfall. The magnitude of summer precipitation differences associated with the high and low snow-RPC3 winters ranges from 10 to 40 cm (Figure 6(c)). The composite of snow depth departures of the six highest and six lowest snow-RPC3 winters are shown in Figure 7. During high snow-RPC3 winters, snow depth is 5 –10 cm below normal over European Russia and 10 cm above normal in central Siberia (Figure 7(a)). During low snow-RPC3 winters, snow depth is 10 cm above normal over European Russia and 5 –10 cm below normal over central Siberia (Figure 7(b)). The magnitude of snow departures is about 10 –20 cm over both regions (Figure 7(c)). The correlation coefficients between snow-RPC3 and SSTs are shown in Figure 8. Snow-RPC3 is primarily related to SSTs over the Atlantic Ocean during the same winter (Figure 8(a)). The summer SSTs over the central tropical Pacific have negative correlations with the previous winter’s snow-RPC3 (Figure 8(b)). When Copyright © 2001 Royal Meteorological Society

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correlation is calculated between snow-RPC3 and next winter’s SSTs, a large negative correlation area appears over the central and eastern tropical Pacific Ocean coinciding with the El Nin˜ o region. Also, SSTs over the Indian Ocean and the China Sea show negative correlations as well (Figure 8(c)). The composite maps of SST departures corresponding to the six highest and six lowest snow-RPC3 values of the previous winters and the SST differences between these high and low composite maps are shown in Figure 9. Negative SST departures ranging from 0.4 to 0.6°C are found over small areas of the eastern and central tropical Pacific, western North Pacific, northern North Atlantic and positive SST departures are found over the western North Atlantic after high snow-RPC3 winters (Figure 9(a)). Positive SST departures of 0.4– 0.8°C are found over the central tropical and eastern equatorial Pacific, northwestern Pacific, tropical Atlantic and the Indian Ocean after low snow-RPC3 winters (Figure 9(b)). Larger SST departures and more extensive significant departure areas in the central tropical Pacific corresponding to low-RPC3 winters are in evidence during winters following low snow-RPC3s. This is consistent with more significant negative Indian summer precipitation departures corresponding to low snow-RPC3 winters. The difference in SSTs corresponding to the six highest and six lowest previous winter’s snow-RPC3 is 0.5–1.0°C over the central and eastern tropical Pacific and the Atlantic (Figure 9(c)).

Figure 9. The same as Figure 7, except for SSTs of the following winters. Shaded areas are significant at a 95% confidence level using a two-tailed t-test Copyright © 2001 Royal Meteorological Society

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The SST composite maps suggest that eastern tropical SSTs are below (above) normal during winters 1 year after abnormally high (low) snow depth over European Russia and low (high) snow depth over central Siberia. The abnormally high snow depth over European Russia has a larger influence on Indian summer monsoon rainfall and the eastern tropical Pacific Ocean than low snow depth winters.

5. CONCLUSIONS AND DISCUSSIONS This study uses snow depth records over northern Eurasia to reveal regional teleconnection patterns between winter snow depth and Southeast Asian summer precipitation and Pacific SSTs. Winter snow depth over an area east of the Caspian Sea and west of China is associated with July precipitation over central China in an area between the Huang and Yangtze rivers. The winter snow depth anomalies over European Russia and central Siberia are inversely connected to summer precipitation over southern and western India and eastern Pakistan. The connection is mostly established during August and the connection is stronger when European Russian snow depth is above normal rather than below normal. These teleconnections are duplicable using Diaz’s 5° latitude by 5° longitude gridded precipitation data during 1964 –1993 (not shown). The results suggest that an inverse relationship between winter snow and summer Asian monsoon is highly regionally dependent. There is a critical snow depth area that is negatively associated with Indian monsoon rainfall over European Russia. However, a positive relationship occurs between snow depth over central Siberia and monsoon rainfall over India. The snow depth anomaly of 5–10 cm over these regions corresponds to about 10– 20 cm of summer rainfall anomaly over western India and eastern Pakistan. The variation of snow depth over eastern Eurasia does not seem to have a significant influence on summer monsoon rainfall over Southeast Asia. This positive relationship between central Siberian snow depth and Indian summer rainfall is probably related to the decreased permafrost depth over central Siberia due to snow’s insulation effect (Clark M, Tingjun Zhang, personal communication). The snow’s insulation effect may more than offset the snow’s hydrological effects on soil moisture and surface air temperature. Winter SSTs over the eastern and central tropical Pacific are associated with the previous winter’s snow depth variation over European Russia and central Siberia. Specifically, the above-(below-)normal snow depth over European Russia and below-(above-)normal snow depth over central Siberia are followed by positive (negative) SST anomalies over the central and eastern tropical Pacific Ocean. Thus, the above- and below-normal snow depth over European Russia and central Siberia are connected to reduced summer monsoon rainfall over southern and western India and eastern Pakistan as well as above-normal SSTs over the western and central tropical Pacific during the following winter. The connection to tropical Pacific SSTs is consistent with findings by Elliott and Angell’s (1987) observational study and Barnett et al.’s (1989) model simulation that Indian monsoon rainfall is negatively correlated with eastern tropical Pacific SSTs one to two seasons later. The correlation coefficient between snow-RPC3 and the following winter’s Southern Oscillation Index (SOI) is 0.35 (p = 0.006) for the study time period from 1936 to 1995. The SOI used is the standardized sea level pressure difference between Tahiti and Darwin available from the National Centers for Environmental Prediction (NCEP)’s Climate Prediction Center (Chelliah, 1990). These results are consistent with the theory that snow-induced monsoon perturbations may be one of the multiple triggers that can initiate an El Nin˜ o – Southern Oscillation (ENSO) cycle. It is also important to keep in mind that the monsoon –El Nin˜ o relationship is non-stationary (Kumar et al., 1999). It was suggested that the relationship has weakened since the 1980s and one explanation was that increasing mid latitude continental temperatures have favoured strong monsoons (Kumar et al., 1999). Another possibility may be related to decadal variations of the NAO which is found to be a significant mechanism affecting Asian monsoon and El Nin˜ o relationships (Yasunari and Seki, 1992). The snow depth variation pattern of snow-RPC3 is, in significant part, associated with the North Atlantic Oscillation (NAO) linked with Atlantic SST anomalies similar to the pattern revealed in Figure 8(a) (e.g. Lau and Nath, 1990; Wallace et al., 1990; Ye, in press). In positive NAO phases, cyclones track northward, bringing positive temperature and negative precipitation anomalies over northern Europe and thus Copyright © 2001 Royal Meteorological Society

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below-normal snow depth over European Russia (Ye, in press). Opposite conditions occur during negative NAO extremes (Hurrell, 1995; Rogers, 1997; Serreze et al., 1997; Clark et al., 1999; Ye, in press). The persistently stronger NAO in recent decades favours below-normal snow depth over European Russia that in turn increases summer Indian monsoon rainfall. The evidence suggests that connections involve a feedback process of atmospheric circulation, northern Eurasian snow depth, SSTs and summer monsoon rainfall over India. This study provides observational evidence of the connections between winter snow depth over northwestern Eurasia, summer monsoon over Southeast Asia, and the following winter’s eastern tropical SST anomaly. Further investigation by modelling processes is necessary to shed light on these connections. ACKNOWLEDGEMENTS

The authors would like to thank data providers: NSIDC, the Hadley Centre for Climate Prediction and Research, UK Meteorological Office, and NASA’s GISS Lab. The authors highly appreciate Martyn Clark and Allan Frei; the two reviewers’ valuable comments improved this manuscript significantly. The authors also thank Argyl B. Houser for editing the final version of the manuscript. This project is partially supported by the NSF Geography and Regional Science Program and Climate Dynamics Program. REFERENCES Bamzai AS, Shukla J. 1999. Relation between Eurasian snow cover, snow depth, and the Indian summer monsoon: An observational study. Journal of Climate 12: 3117–3132. Barnett TP, Dumenil L, Schlese U, Roeckner E. 1988. The effect of Eurasian snow cover on global climate. Science 239: 504 – 507. Barnett TP, Dumenil L, Schlese U, Roeckner E, Latif M. 1989. The effect of Eurasian snow cover on regional and global climate variations. Journal of Atmospheric Science 46: 661 –685. Chelliah M. 1990. The global climate for June–August 1989: A season of near normal conditions in the tropical Pacific. Journal of Climate 3: 138 – 160. Chen WY. 1982. Fluctuations in Northern Hemisphere 700 mb height field associated with the Southern Oscillation. Monthly Weather Re6iew 110: 808 – 823. Clark MP, Serreze MC, Robinson DA. 1999. Atmospheric controls on Eurasian snow extent. International Journal of Climatology 19: 27 – 40. Dai A, Fung IY, Genio AD. 1997. Surface observed global land precipitation variations during 1900– 88. Journal of Climate 10: 2943 – 2962. Dey B, Kumar OSUB. 1983. Himalayan winter snow cover area and summer monsoon rainfall over India. Journal of Geophysical Research 88: 5471 –5474. Dong B, Valdes PJ. 1998. Modeling the Asian summer monsoon rainfall and Eurasian winter/spring snow mass. Quarterly Journal of Royal Meteorology Society 124: 2567– 2596. Douville H, Royer J-F. 1996. Sensitivity of the Asian summer monsoon to an anomalous Eurasian snow cover within the Me´ te´ o-France GCM. Climate Dynamics 12: 449 –466. Elliott WP, Angell JK. 1987. The relation between Indian monsoon rainfall, the Southern Oscillation, and hemispheric air and sea temperature: 1884–1984. Journal of Climate and Applied Meteorology 26: 943 – 948. Folland CK, Parker DE. 1995. Correction of instrumental biases in historical sea surface temperature data. Quarterly Journal of Royal Meteorological Society 121: 319 –367. Hoerling MP, Kumar A, Zhong M. 1997. El Nin˜ o, La Nin˜ a, and the nonlinearity of their teleconnections. Journal of Climate 10: 1769 – 1786. Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science 269: 676 – 679. Hurrell JW, Trenberth KE. 1999. Global sea surface temperature analyses: Multiple problems and their implications for climate analysis, modeling, and reanalysis. Bulletin of American Meteorology Society 80: 2661 – 2678. Khandekar ML. 1991. Eurasian snow cover, Indian monsoon and El Nin˜ o/Southern Oscillation—a synthesis. Atmosphere-Ocean 29: 636 – 647. Kripalani RH, Kulkarni A. 1999. Climatology and variability of historical Soviet snow depth data: Some new perspectives in snow-Indian monsoon teleconnections. Climate Dynamics 15: 475 – 489. Kumar OSRUB. 1988. Eurasian snow cover and seasonal forecast of Indian summer monsoon rainfall. Hydrology Sciences 33: 515 – 525. Kumar KK, Rajagopalan B, Cane MA. 1999. On the weakening relationship between the Indian monsoon and ENSO. Science 284: 2156 – 2159. Lau K, Li M. 1984. The monsoon of east Asia and its global associations— a survey. Bulletin of American Meteorological Society 65: 114 – 125. Lau N-C, Nath MJ. 1990. A general circulation model study of the atmospheric response to extratropical SST anomalies observed in 1950 – 79. Journal of Climate 3: 965 –989. Livezey RE, Chen WY. 1983. Statistical field significance and its determination by Monte Carlo techniques. Monthly Weather Re6iew 111: 46 – 59. North GR, Thomas LB, Cahalan RF. 1982. Sampling error in the estimation of empirical orthogonal functions. Monthly Weather Re6iew 110: 699 – 706. Copyright © 2001 Royal Meteorological Society

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