1 Ministry of Earth Sciences, Lodi Road, New Delhi India Meteorological Department, Lodi Road, New Delhi

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1 Trends in Extreme Temperature Events over India during A. K. JASWAL, AJIT TYAGI 1 and S. C. BHAN 2 India Meteorological Department, Shivajinagar, Pune Ministry of Earth Sciences, Lodi Road, New Delhi India Meteorological Department, Lodi Road, New Delhi jaswal4@gmail.com; ajit.tyagi@gmail.com; scbhan@yahoo.com ABSTRACT In this study we aim to explore changes in monthly extremes of summer and winter temperatures in India in last forty years by analyzing daily maximum (D max ) and minimum (D min ) temperature data. The extreme events (number of days) in this study are defined in two ways, one in relative sense by working out percentile threshold of common long-period data ( ) of each station and another by an absolute value criterion. The three relative/absolute hot events named are hot days (HD) (D max > 90 th percentile; D max > 35 o C respectively), very hot days (VHD) (D max > 95 th percentile; D max > 40 o C respectively) and extremely hot days (EHD) (D max > 98 th percentile; D max > 43 o C respectively), while the three relative/absolute cold events named are cold nights (CN) (D min > th percentile; D min < o C respectively), very cold nights (VCN) (D min < 5 th percentile; D min < 5 o C respectively) and extremely cold nights (ECN) (D min < 2 nd percentile; D min < 2 o C respectively). We have used linear regression to assess trends in extreme events of hot days and cold nights for each station and a trend is termed significant if the t-test for the estimate of the slope is found significant at 95% level. Based on 227 surface meteorological stations data for , the analysis show that summer months HD are significantly increasing at many stations in west, central and south and winter months CN nights are significantly decreasing at many stations in north and extreme south India, both in relative and absolute sense. Also, there is significant increase in summer months HD and decrease in winter months CN at coastal stations of India. Absolute value based EHD are significantly increasing in central India in April-June and decreasing in Indo-Gangetic plains in May-June. Absolute value based ECN are significantly decreasing at stations in Himalayan region in February indicating warming trends in night temperature. Key Words: Temperature Extremes, Trend, Hot days, Cold nights 1

2 1. Introduction Intergovernmental Panel on Climate Change (IPCC) in their recent report has stated that effects of climate change will lead to more extreme temperatures and more hot days than cold days (IPCC, 13). It has now become increasingly evident that one of the major impacts of global warming on the earth s surface will be in the form of increasing frequency and intensity of extreme weather events. According to IPCC (07), the global mean surface temperatures have increased by 0.76±0.18 o C over the last 0 years ( ). A great number of research papers focusing on extreme temperature on global, regional and national scales have been written by many researchers worldwide (e.g. Zhai and Pan, 03; Griffiths et al., 05; Klein Tank et al., 06 etc.). The percentile-based temperature indices have been used to analyze changes in temperature extremes for various parts of the world (e.g. Peterson et al., 02; Vincent et al., 05; Zhang et al., 05; Klein Tank et al., 06; Alexander et al., 06 etc.). Tebaldi et al. (06) have shown that the twenty-first century would bring global changes in temperature extremes consistent with a warming climate. Regional studies of extreme temperature variations for India are very few. Kothawale (05) studied the temperature extremes in India for the period and noted that the number of hot days is maximum over central part of India and minimum along the west coast of India during the summer season. Rao et al. (05) have reported that 80% stations in Peninsular India, 40% stations in Northern India showed increasing trend in the days with critical extreme maximum temperature while about 80% of the stations in Northern India showed increasing trend in the extremes in night temperatures during Kothawale et al. () have found widespread increasing trend in the frequency of occurrence of hot days and hot nights and widespread decreasing trend in those of cold days and cold nights in pre-monsoon season. Revadekar et al. (12) have found widespread warming with increase in intensity and frequency of hot events and decrease in frequency of cold events in India. The main purpose of this study is to analyze monthly temperature extremes over India during summer and winter seasons using daily temperature data for Data and methodology 2.1 Methodology Available literature shows that there are usually two criteria to define high temperature days (DeGaetano and Allen, 02; Zhai and Pan, 03). One is relative using temperature percentiles and the other is absolute temperature values. However, temperature percentile indices have now been used widely in many regional studies world over. Considering the serious effects on human health caused by high temperatures, we have used both criteria in defining extreme temperature events (number of days) in this study. In relative sense, we have worked out extreme temperature events on the basis of percentile threshold of long-period data ( ) of each station by comparing daily maximum temperature (D max ) and daily minimum temperature (D min ) of the station. On the basis of percentiles and absolute values, the three relative/absolute hot events are hot days (HD) (D max > 90 th percentile; D max > 35 o C respectively), very hot days (VHD) (D max > 95 th percentile; D max > 40 o C respectively) and extremely hot days (EHD) (D max > 98 th percentile; D max > 43 o C respectively), while the three relative/absolute cold events are cold nights (CN) (D min > th percentile; D min < o C respectively), very cold nights (VCN) (D min < 5 th percentile; D min < 5 o C respectively) and extremely cold nights (ECN) (D min < 2 nd percentile; D min < 2 o C respectively). The percentile and absolute value based data series of HD, VHD, EHD and CN, VCN, ECN are prepared for all 227 stations for summer and winter months respectively for the period which are further analyzed. 2.2 Data used The data used in this study are the daily surface maximum temperatures and minimum temperature obtained from the database of India Meteorological Department (IMD), National 2

3 Data Centre (NDC) located at Pune, where all climatological data of India are processed, quality checked and archived. Since daily values of maximum temperature are available from 1969 only, the period of study is restricted to Initially we have selected more than 0 surface meteorological stations from IMD s network, which are having long data series of daily maximum and minimum temperatures. First we checked the availability of daily maximum temperature values for each year and stations with too many continuous missing values are dropped from the selected data. Finally, a dataset belonging to 227 meteorological stations covering most of the climate types found in the country is selected for monthly extreme temperature analysis in India. The geographical coverage of stations utilized in this study is shown in Figure 1. As the high and low temperature events that can cause large-scale discomfort to human being occur mainly in summer and winter seasons, we have conducted our study for hot days in March, April, May and June months and cold nights in December, January and February months only. Therefore for each station, data series of relative and absolute HD, VHD and EHD for March, April, May and June months and CN, VCN and ECN for December, January and February months are prepared for the period of In order to investigate trends in monthly number of relative/absolute HD, VHD, EHD, CN, VCN and ECN, a linear regression is fitted to each time series by using a least square regression for each station. Two-sided Student s t-tests are used to check the statistical significance levels at 95% level of significance. Statistics of numbers of stations showing positive or negative trends in relative/absolute HD, VHD, EHD, CN, VCN and ECN is given in Tables 1-2. The spatial patterns of linear trends are shown in Figures Results and Discussion 3.1 Number of hot days trends in summer months March The trend results for the extreme temperature indices of 227 stations in this study indicate overall increase in HD, VHD and EHD over India during The numbers of stations showing positive trends in HD, VHD and EHD are 149, 9 and 166 respectively out of which almost 40% are statistically significant at 95% level (Table 1a). The percentile based HD, VHD and EHD trends are coherently significantly positive over extreme north, west, south and northeast India as shown in Figures 2 [(a), (c) and (e)]. The trends in percentile based HD, VHD and EHD are significantly negative over Indo-Gangetic plains and east India. Out of total 227 stations under study, the numbers of stations having absolute value based HD, VHD and EHD trends in March are 9, 136 and 29 respectively (Table 1b). Stations having absolute value based positive trends in HD, VHD and EHD for the month of March are 136, 96 and respectively. Absolute value based HD and VHD are also having similar trends as percentiles based. Stations having absolute EHD are very few as not many stations report D max > 43 o C in the month of March as shown in Figure 2 (f) April The trend analysis of both percentile as well as absolute value based data series of April month indicates general increase in HD, VHD and EHD over India during Out of 227 stations analyzed in this study, numbers of stations showing positive trends in HD, VHD and EHD are 5, 146 and 145 respectively out of which almost 35% are statistically significant at 95% level (Table 1a). The percentile based HD, VHD and EHD trends are coherently significantly positive over almost entire country except east where many stations are having negative trends as shown in Figures 3 [(a), (c) and (e)]. The trends in percentile based HD, VHD and EHD are significantly positive over coastal stations particularly on the west coast of India where trend values are higher at many stations. Out of total 227 stations under study, the numbers of stations having absolute value based HD, VHD and EHD trends in April are 2, 166 and 127 respectively (Table 1b). Stations having absolute value based positive trends in HD, VHD and EHD for the month of April are 118, 3

4 2 and 82 respectively. Patterns of absolute value based HD and VHD trends for April are similar to percentiles based trends except over south India where many stations are having opposite trends (negative) as compared to percentile based trends [Figures 3 (b) and (d)] May In the month of May, trend analysis of both percentile as well as absolute value based data series indicates overall increase in HD, VHD and EHD over India during except over Indo Gangetic plains. The numbers of stations showing positive trends in HD, VHD and EHD are 0, 149 and 149 respectively out of which almost 35% are statistically significant at 95% level (Table 1a). The percentile based HD, VHD and EHD trends are coherently significantly positive over almost entire country except Indo Gangetic plains where many stations are having significant negative trends as shown in Figures 4 [(a), (c) and (e)]. The trends in percentile based HD, VHD and EHD are significantly positive over coastal stations particularly on the west coast of India where trend values are higher at many stations. Out of total 227 stations under study, the numbers of stations having absolute value based HD, VHD and EHD trends in May are 2, 171 and 145 respectively (Table 1b). Stations having absolute value based positive trends in HD, VHD and EHD for the month of May are 133, 95 and 91 respectively. The patterns of absolute value based HD, VHD and EHD trends are similar to the percentiles based trends except over south India where many stations are having opposite trends (negative) as compared to the percentiles based trends as shown in Figures 4 (b), (d) and (f) June The trend analysis of both percentile as well as absolute value based data series of HD, VHD and EHD in the month of June indicates overall increase over many regions during except over Indo Gangetic plains and a few pockets along east coast of India. The numbers of stations showing positive trends in percentile based HD, VHD and EHD are 171, 175 and 181 respectively out of which almost % are statistically significant at 95% level (Table 1a). The percentile based HD, VHD and EHD trends are coherently significantly positive over almost entire country except over some pockets along east coast and over Indo Gangetic plains where many stations are having significant negative trends as shown in Figures 5 [(a), (c) and (e)]. The trends in percentile based HD, VHD and EHD are significantly positive over west coast of India where trend values are also higher at many stations. Out of total 227 stations under study, the numbers of stations having absolute value based HD, VHD and EHD trends in June are 2, 161 and 1 respectively (Table 1b). Stations having positive trends in absolute value based HD, VHD and EHD for the month of June are 123, 3 and 95 respectively. The patterns of absolute value based HD, VHD and EHD trends are similar to the percentiles based trends except over central India where many stations are having opposite trends (negative) in absolute value based HD as compared to the percentiles based as shown in Figure 5 (b). 3.2 Number of cold nights trends in winter months December The trend results for the extreme temperature indices of 227 stations in this study indicate overall decrease in CN, VCN and ECN over India during suggesting warming in night temperature. The numbers of stations showing negative trends in percentile based CN, VCN and ECN are 4, 1 and 5 respectively out of which almost 40% are statistically significant at 95% level (Table 3a). The percentile based CN, VCN and ECN trends are coherently significantly negative over the country as shown in Figures 6 [(a), (c) and (e)]. The magnitude of negative trends in percentile based CN, VCN and ECN are higher over the coastal stations suggesting large scale warming of night temperatures in December. Out of total 227 stations under study, the numbers of stations having absolute value based CN, VCN and ECN trends in December are 163, 9 and 56 respectively (Table 3b). Stations 4

5 having absolute value based negative trends in CN, VCN and ECN for the month of December are 116, 71 and 34 respectively. Spatial patterns of absolute value based CN and VCN trends [Figures 6 (b) and (d)] are similar to percentiles based trends [Figures 6 (a) and (c)]. Though stations having absolute value based ECN trends are very few in December (many stations do not report D min < 2 o C), the spatial patterns indicate warming in night temperature over north and northwest India as shown in Figure 6 (f) January The results of trend analysis of CN, VCN and ECN data series of January are mixed. There are pockets of warming and cooling in night temperature over India during Out of 227 stations, numbers of stations showing negative trends in percentile based CN, VCN and ECN are 147, 132 and 124 respectively out of which almost 40% are statistically significant at 95% level (Table 3a). The spatial patterns of percentile based CN, VCN and ECN trends show pockets of significantly negative as well as pockets of significantly positive trends over the country as shown in Figures 7 [(a), (c) and (e)]. The magnitude of negative trends in percentile based CN, VCN and ECN are higher over the coastal stations as well as in south India suggesting strong warming of night temperatures in January. Out of 227 stations under study, the numbers of stations having absolute value based CN, VCN and ECN trends in January are 166, 116 and 80 respectively (Table 3b). Stations having absolute value based negative trends in CN, VCN and ECN for the month of January are 6, 73 and 40 respectively. The geographical patterns of absolute value based CN, VCN and ECN trends are similar to percentiles based trends February The trend analysis of extreme temperature indices of 227 stations for the month of February indicates overall decrease in CN, VCN and ECN over north India and increase/decrease in south India during the study period The numbers of stations showing negative trends in percentile based CN, VCN and ECN are 163, 9 and 148 respectively out of which almost 45% are statistically significant at 95% level (Table 3a). The spatial patterns of percentile based CN, VCN and ECN trends are coherently significantly negative over north India and positive over south India as shown in Figures 8 [(a), (c) and (e)]. The magnitude of negative trends in percentile based CN, VCN and ECN are higher over north India suggesting large scale warming of night temperatures in February which agrees with the earlier reported study by Jaswal (). Out of total 227 stations under study, the numbers of stations having absolute value based CN, VCN and ECN trends in February are 7, 1 and 61 respectively (Table 3b). Stations having absolute value based negative trends in CN, VCN and ECN for February are 1, 82 and 45 respectively. Spatial patterns of absolute value based CN and VCN trends are similar to percentiles based trends. This study is undertaken in the backdrop of recent IPCC report stating effects of climate change will lead to more extreme temperatures and more hot days than cold days (IPCC, 13). Unusually hot or cold temperatures can result in prolonged extreme weather events like summer heat waves or winter cold spells. Heat waves can lead to illness and death, particularly among older adults, the very young and other vulnerable groups. The impacts of most extremes are typically felt at a local or regional scale and so regional studies of climate extremes are important for assessing potential climate impacts. The great economic and social impacts of extreme events support increasing attention to studies on such issues (Ryoo et al., 04). Therefore, in this study we have examined the statistics and behavior of monthly extreme climate events based on percentile as well as absolute value methods over India for summer and winter months for the period of The data analysis shows overall widespread increase in HD, VHD and EHD for March over India during but more coherently over west and south India. In the month of April, positive trends in HD, VHD and EHD are well spread over the country but more over north, 5

6 west, central India and also along the west coast. Our results are in line with earlier studies by Kothawale (05), Jaswal (), Kothawale et al. () and Revadekar et al. (12). The geographical patterns of HD, VHD and EHD trends are positive almost all over the country in May and June except over the Indo Gangetic plains where the trends are negative. The data analysis of winter months CN, VCN and ECN for December and January shows general negative trends over the country during suggesting increase in night temperature. The geographical distribution of CN, VCN and ECN trends for February indicates more coherent significantly negative trends in northern half of the country while the trends in southern half are mixed. Possible reasons for having such patterns might be due to global warming and/or urbanization which need further investigation. Numbers of stations showing positive trends in HD, VHD and EHD are above 65% in most of the summer months both on relative and absolute basis. The highest number of stations with positive trends is for EHD in June with 80% stations on relative basis and 73% stations on absolute basis suggesting strong daytime warming over the country which may be due to late onset of monsoon over the country. Similarly, numbers of stations showing negative trends in CN, VCN and ECN are above 65% in most of winter months both on relative and absolute basis. February month is having highest numbers of stations with negative trends for CN on relative basis (72%) and for VCN on absolute basis (81%) suggesting strong warming in night temperatures which is consistent with the study by Jaswal (). The positive trends in summer months hot days and negative trends and winter months cold nights over India during the period of study corroborates well with the projections given by IPCC (07) and potential future assessment in changes in climate extremes given by Tebaldi et al. (06). 4. Conclusions An attempt has been made in the present study to analyse observed summer and winter month s temperature extremes over Indian during Following are the conclusions of this study: a. On all India scale, hot, very hot and extremely hot days are increasing in all summer months suggesting hot days have become more common now. b. Geographical distribution of trends suggests significant increase in hot days in North India in April and South India in March. Also hot, very hot and extremely hot days are significantly increasing over west coast of India in all summer months. c. There is significant decrease in hot, very hot and extremely hot days over Indo Gangetic plains in the month of May. d. Stations having extremely hot days are the highest in June when 80% of the total number of stations are having positive trends which may be due to erratic behaviour of onset of monsoon over India. e. All India trend analysis of cold, very cold and extremely cold nights suggests significant decreasing trend in December, mixed trend in January while in February, there is significant decreasing trend in North India and mixed trend in South India. Acknowledgements Authors are thankful to the reviewers for suggesting constructive suggestions that helped to improve the manuscript. 6

7 References Alexander L, Zhang X, Peterson TC, et al., 06. Global observed changes in daily climate extremes of temperature and precipitation. Jou. of Geophy. Res. 111: D059 D059, DOI:.29/05JD DeGaetano, A. T., and R. J. Allen, 02: Trends in twentieth-century temperature extremes across the United States, Jou. of Clim., (22), Griffiths GM, Chambers LE, Haylock MR, et al., 05. Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific region. Int. Jou. of Clim. : 11 13, DOI:.02/joc IPCC, 07, Climate change 07, The physical science basis, contribution of working group I to the fourth assessment report of the IPCC, Cambridge University Press, Cambridge, United Kingdom. IPCC, 13. Climate Change 13 - The Physical Science Basis. Intergovernmental Panel on Climate Change. Jaswal, AK.. Recent winter warming over India spatial and temporal characteristics of monthly maximum and minimum temperature trends for January to March. Mausam, 61, 2, Klein Tank AMG, Peterson TC, Quadir DA, et al., 06. Changes in daily temperature and precipitation extremes in Central and South Asia. Jou. of Geophy. Res. 111: D165, DOI:, 29/05JD Kothawale DR. 05. Surface and upper air temperature variability over India and its influence on Indian monsoon rainfall; Ph.D Thesis, University of Pune, pp Kothawale DR, Revadekar JV and Rupa Kumar K.. Recent trends in pre-monsoon daily temperature extremes over India. J. Earth Syst. Sci. 119, No. 1, pp Peterson TC, Taylor MA, Demeritte R, et al., 02. Recent changes in climate extremes in the Caribbean region. Jou. of Geophy. Res. 7(D21): 4601, DOI:.29/02JD0021. Rao GSP, Murthy MK and Joshi UR. 05. Climate change over India as revealed by critical extreme temperature analysis. Mausam 56, 3, Revadekar JV, Kothawale DR, Patwardhan SK, Pant GB and Rupakumar K. 12. About the observed and future changes in temperature extremes over India. Nat Hazards 60 (3). pp Tebaldi C, Hayhoe K, Arblater JM and Meehl GA. 06. Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events. Clim. Change, 79, , doi:.07/s Vincent LA, Peterson TC, Barros VR, et al., 05. Observed trends in indices of daily temperature extremes in South America Jou. of Clim. 18: Zhai P, Pan X. 03. Trends in temperature extremes during in China. Geophy. Res. Lett. : 1913, DOI:.29/03GL Zhang X, Aguilar E, Sensoy S, et al., 05. Trends in middle east climate extremes indices during Jou. of Geophy. Res. 1(D22): 4, DOI:.29/05JD

8 Figure 1: Geographical location of 227 surface meteorological stations used in the study. 8

9 a) 35 March Max Temp> 90th Percentile b) 35 March Max Temp> 35 deg. C -3.0 to to to to to to.0.0 to.0 c) 35 March Max Temp> 95th Percentile d) 35 March Max Temp> 40 deg. C 3.0 to to 6.5 e) 35 March Max Temp> 98th Percentile f) 35 March Max Temp> 43 deg. C -1.0 to to 5.0 Figure 2: Spatial distribution of trends in frequencies of daily maximum temperature above 90 th, 95 th and 98 th percentile and daily maximum temperature above 35 o C, 40 o C and 43 o C in the month of March during the period

10 a) 35 April Max Temp> 90th Percentile b) 35 April Max Temp> 35 deg. C -2.0 to to to to to to to.0.0 to.0 c) 35 April Max Temp> 95th Percentile d) 35 April Max Temp> 40 deg. C 1.5 to to to to to.0.0 to.0 e) 35 April Max Temp> 98th Percentile f) 35 April Max Temp> 43 deg. C 0.5 to to to to.0 Figure 3: Spatial distribution of trends in frequencies of daily maximum temperature above 90 th, 95 th and 98 th percentile and daily maximum temperature above 35 o C, 40 o C and 43 o C in the month of April during the period

11 a) 35 May Max Temp> 90th Percentile b) 35 May Max Temp> 35 deg. C -2.2 to to to to to to.0.0 to.0 c) 35 May Max Temp> 95th Percentile d) 35 May Max Temp> 40 deg. C 3.0 to to to to.0.0 to.0 e) 35 May Max Temp> 98th Percentile f) 35 May Max Temp> 43 deg. C 1.5 to to.0 Figure 4: Spatial distribution of trends in frequencies of daily maximum temperature above 90 th, 95 th and 98 th percentile and daily maximum temperature above 35 o C, 40 o C and 43 o C in the month of May during the period

12 a) 35 June Max Temp> 90th Percentile b) 35 June Max Temp> 35 deg. C -2.0 to to to to to.0 c) 35 June Max Temp> 95th Percentile d) 35 June Max Temp> 40 deg. C -1.2 to to 8.0 e) 35 June Max Temp> 98th Percentile f) 35 June Max Temp> 43 deg. C 1.5 to to 8.0 Figure 5: Spatial distribution of trends in frequencies of daily maximum temperature above 90 th, 95 th and 98 th percentile and daily maximum temperature above 35 o C, 40 o C and 43 o C in the month of June during the period

13 a) 35 December Min Temp< th Percentile b) 35 December Min Temp< deg. C -3.5 to to to to to to to.0 c) 35 December Min Temp< 5th Percentile d) 35 December Min Temp< 5 deg. C -3.0 to to to to to to.0.0 to.0.0 to 21.0 e) 35 December Min Temp< 2nd Percentile f) 35 December Min Temp< 2 deg. C -8.0 to to.0 Figure 6: Spatial distribution of trends in frequencies of daily minimum temperature below th, 5 th and 2 nd percentile and daily minimum temperature below o C, 5 o C and 2 o C in the month of December during the period

14 a) 35 January Min Temp< th Percentile b) 35 January Min Temp< deg. C -3.0 to to to to to 8.0 c) 35 January Min Temp< 5th Percentile d) 35 January Min Temp< 5 deg. C -3.0 to to to to to.0.0 to 12.0 e) 35 January Min Temp< 2nd Percentile f) 35 January Min Temp< 2 deg. C -2.5 to to to to.0.0 to.0 Figure 7: Spatial distribution of trends in frequencies of daily minimum temperature below th, 5 th and 2 nd percentile and daily minimum temperature below o C, 5 o C and 2 o C in the month of January during the period

15 a) 35 February Min Temp< th Percentile b) 35 February Min Temp< deg. C -2.0 to to to to to 8.0 c) 35 February Min Temp< 5th Percentile d) 35 February Min Temp< 5 deg. C -1.4 to to to 8.0 e) 35 February Min Temp< 2nd Percentile f) 35 February Min Temp< 2 deg. C -1.0 to to to -5.0 Figure 8: Spatial distribution of trends in frequencies of daily minimum temperature below th, 5 th and 2 nd percentile and daily minimum temperature below o C, 5 o C and 2 o C in the month of February during the period

16 Table 1: Numbers of stations having positive or negative trends in monthly hot days (HD), very hot days (VHD) and extremely hot days (EHD) based on percentiles and absolute methods for March to June over India during a) Numbers of stations based on percentiles March April May June HD VHD EHD HD VHD EHD HD VHD EHD HD VHD EHD Positive Positive significantly Negative Negative significantly b) Numbers of stations based on absolute values criterion March April May June HD VHD EHD HD VHD EHD HD VHD EHD HD VHD EHD Positive Positive significantly Negative Negative significantly

17 Table 2: Numbers of stations having positive or negative trends in monthly cold nights (CN), very cold nights (VCN) and extremely cold nights (ECN) based on percentiles and absolute methods for December, January and February over India during a) Numbers of stations based on percentiles December January February CN VCN ECN CN VCN ECN CN VCN ECN Positive Positive significantly Negative Negative significantly b) Numbers of stations based on absolute values criterion December January February CN VCN ECN CN VCN ECN CN VCN ECN Positive Positive significantly Negative Negative significantly