Changes in the Frequency of Different Categories of ... - AMS Journals

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Changes in the Frequency of Different Categories of Temperature Extremes in India S. K. DASH AND ASHU MAMGAIN Centre for Atmospheric Sciences, IIT Delhi, Hauz Khas, New Delhi, India (Manuscript received 3 November 2010, in final form 3 May 2011) ABSTRACT In the context of climate change and its impact on sectors like agriculture and health, it is important to examine the changes in the characteristics of temperature extremes of different intensities and duration. In this study, an India Meteorological Department gridded temperature dataset is used to examine the changes in the frequency of occurrence of extreme temperatures over India and its seven homogeneous regions during the period 1969–2005. Results indicate a significant decrease in the frequency of occurrence of cold nights in the winter months in India and in its homogeneous regions in the north except in the western Himalaya. Southern regions show a drastic decrease in the frequency of cold nights relative to the period 1969–75. A significant increasing trend in the number of warm days in summer is noticed only in the interior peninsula. In the entire country and on the east coast and west coast, the maximum number of warm days in summer has been noticed only during the last decade, 1996–2005. Further, in the whole country the maximum number of intense warm days and nights in summer has been observed in the last decade. A significant increase in the number of cold days in winter is observed in the north-central and northeast regions. Changes in the frequency of warm and cold exceedances indicate maximum warming in the west coast as compared with all other regions. In sum, such spatial and temporal changes in the characteristics of all categories of temperature extremes broadly suggest warming trends in large parts of India.

1. Introduction The rise in the global mean surface air temperature and the increase in the number of warmer years during the past two decades have been investigated by a number of researchers (e.g., Jones and Briffa 1992; De et al. 2005). The Intergovernmental Panel on Climate Change (IPCC) in its Fourth Assessment Report (AR4; Meehl et al. 2007) has also projected more frequent and intense weather events in the twenty-first century with high confidence levels. Extreme-temperature events can be categorized under different types depending on their intensity and duration. Heat/cold-wave conditions with large intensity spanning over a number of days usually get noticed because of their widespread impacts on society in terms of loss of life. There are also other categories of warm/cold exceedances that may affect sectors such as health and agriculture, however. Abrupt and frequent temperature changes may give rise to more vectors and

Corresponding author address: Ashu Mamgain, Centre for Atmospheric Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India. E-mail: [email protected] DOI: 10.1175/2011JAMC2687.1 Ó 2011 American Meteorological Society

diseases. Working hours may also be reduced because of heat stress (Kjellstrom et al. 2009). Small changes in temperature may adversely affect the growth of crops and hence agricultural products to a large extent (Attri and Rathore 2003; Peng et al. 2004). Hence, it is essential to categorize the changes in day and night temperatures depending on their intensity and duration and then to critically examine those characteristics at regional as well as national levels. Note that the India Meteorological Department (IMD), on the basis of local climatic conditions, has calculated threshold values as given in Table 1 to declare heat/coldwave conditions on an operational basis. These criteria have been followed by several scientists for examining extreme-temperature events in India. The related studies based on meteorological measurements in India include those of Raghavan (1966, 1967), De and Mukhopadhyay (1998), Pai et al. (2004), and De et al. (2004, 2005). Pai et al. (2004) in their study have reported a significant increase in the frequency, persistence, and spatial coverage of extreme-temperature events in the decade 1991–2000 when compared with the two earlier decades of 1971–90. The effect of urbanization in 15 cities in India during the

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TABLE 1. Definitions of heat/cold-wave conditions used by the IMD for their operational purposes (from http://www.imd.gov.in/). Heat-wave conditions

Cold-wave conditions

Heat wave condition is considered when max temperature (Tmax) of a station in the plains reaches at least 408C; in other regions it is 308C Normal Tmax Departure of Tmax from normal

The actual min temperature (Tmin) of a station should be reduced to windchill effective min temperature (WCTn); cold-wave condition is considered when WCTn # 108C Normal Tmin Departure of Tmin from normal

58–68C: heat wave $78C: severe heat wave .408C 48–58C: heat wave $68C: severe heat wave When actual Tmax remains $458C, heat-wave condition is declared irrespective of normal Tmax

From 258 to 268C: cold wave 278C or less: severe cold wave ,108C From 248 to 258C: cold wave 268C or less: severe cold wave When WCTn is #08C, cold-wave condition is declared irrespective of normal Tmin of the station

#408C

second half of the last century was examined by Prakasa Rao et al. (2004) in terms of changes in the respective meteorological parameters. They concluded that the frequency of occurrence of summertime maximum temperature of more than 358C has a decreasing trend in northern India and an increasing trend in southern India. They also inferred that wintertime minimum temperature of less than 108C has an increasing trend in northern Indian cities. Their results are not statistically significant for all of the cities considered, however. Dash et al. (2007) and Dash and Hunt (2007), on the basis of observational data, have examined changes in the characteristics of surface air temperatures during the last century over seven homogeneous regions in India. Dash et al. (2007) identified a relatively large increase in the daily maximum temperature on the west coast as compared with other homogeneous regions. They have further highlighted the heat-wave conditions that occurred at seven stations on the east coast of India during the period from 19 May to 10 June 2003. Their study has identified four stations on the east coast of India at which the maximum temperatures crossed their respective 100-yr maximum values by about 18C or so. Similar unusually severe heat waves occurred in a large part of India in the second half of May 1998 and affected millions of people. Maximum numbers of heat-wave conditions were reported between 1980 and 1998. These occurrences of heat-wave conditions were comparatively higher than those in the previous decade of 1979–88 (De and Mukhopadhyay 1998). Cold-wave conditions observed in the hilly regions in the north of India and adjoining plains are usually influenced by the weather systems called the western disturbances. These systems are transient winter disturbances in the midlatitude westerlies that often have weak frontal characteristics. De et al. (2005), on the basis of observations from various sources, have inferred that the occurrence of cold-wave conditions in the last century was at a maximum in the Jammu and Kashmir regions followed by Rajasthan and Uttar Pradesh. Results of Pai et al. (2004) further show that cold-wave conditions

$108C

were most often experienced in west Madhya Pradesh in the decade 1971–80, in Jammu and Kashmir in 1981–90, and in Punjab in 1991–2000. Given the importance of different categories of extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) has defined some temperature and precipitation indices for uniform scientific analysis across the world. These indices have been used in different global and regional studies such as those of Manton et al. (2001), Frich et al. (2002), Yan et al. (2002), Klein Tank and Ko¨nnen (2003), Klein Tank et al. (2006), Aguilar et al. (2005), Vincent and Mekis (2006), Zhang et al. (2005), Alexander et al. (2006), New et al. (2006), and Sterl et al. (2008). Alexander et al. (2006) in their global analysis found that around 70% of total land areas sampled across the world have shown significant increase in the annual occurrence of warm nights and significant decrease in the annual occurrence of cold nights for the period 1951–2003. A study by Yan et al. (2002) on temperature exceedances in Europe and China shows a decrease in warm extremes before the late nineteenth century and an increase since 1960, particularly during summer. On the other hand, a gradual decrease in cold extremes in winter persisted during the twentieth century. Meehl et al. (2009), using daily observations over the United States, showed more of an increase in recordhigh maximum temperatures than in record-low minimum temperatures since the late 1970s. Further, they noticed greater mean warming of about 30% in the model simulations as compared with the observations by the early twenty-first century. The model simulations under the IPCC Special Report on Emissions Scenarios ‘‘A1B’’ emission scenario indicate that, as warming continues later in the twenty-first century, the ratio of record highs to record lows will continue to increase. In addition to these results, ensemble simulations from the ECHAM5–Max Planck Institute Ocean Model (MPI-OM) in response to A1B emission scenario were performed in the Essence project. Sterl et al. (2008) examined these simulations and projected that

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global mean surface warming of 3.5 K by the end of the twenty-first century and extreme annual maximum temperatures will be more severe in densely populated areas like India and the Middle East. In the recent past, Kothawale et al. (2010b) studied changes in temperature indices in India during the premonsoon months over 121 different meteorological stations for the period 1970– 2005. They found a significant increase in the numbers of hot days on the east coast, west coast, and interior peninsula and a decrease in the numbers of cold days over the western Himalaya and west coast. In their study, hot nights showed significant increasing trends on the east coast and west coast, and in the northwest. For India as a whole, hot days and nights showed increasing trends and cold days and nights showed decreasing trends. Their study is based on temperature exceedances during March–May. In India, warm weather conditions are also noticed in June and July over several regions. In this study we defined summer and winter seasons based on a threshold approach over all of India and its seven temperature homogeneous regions and categorized temperature exceedances as moderate and severe. Our study is not confined to physical months; it is based on actual warm/cold periods across all of the months. Another salient point in this study is that there is emphasis on the changes in the characteristics of long and short spells of temperature extremes, which are as important as the intensity of extremes. We have used an IMD gridded daily temperature dataset that is homogeneous in space and time. Further, we have followed the guidelines suggested by ETCCDI to characterize the extreme categories of surface air temperatures over the period 1969–2005. Section 2 describes the data source and the method used in this study to identify extreme-temperature events. Annual and seasonal variations in temperature are discussed in section 3. Numbers of warm and cold exceedances and spells and their regional changes are discussed in section 4. Important results are summarized in section 5.

2. Identifying extreme-temperature events on the basis of IMD gridded temperature data In the recent past, IMD has developed a high-resolution daily gridded temperature dataset at 18 3 18 resolution over the Indian land area. Srivastava et al. (2008) in their study used measurements at 395 quality-controlled stations and interpolated the station data into grids with the modified version of Shepard’s angular distance–weighting algorithm (Shepard 1968). This interpolation method was also adopted in previous studies by Piper and Stewart (1996), New et al. (2000), Kiktev et al. (2003), and Caesar et al. (2006) for preparing gridded temperature

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FIG. 1. Seven temperature homogeneous regions of India (source: www.tropmet.res.in). Northern India as mentioned in the text consists of the regions WH, NW, NC, and NE, whereas WC, IP, and EC belong to southern India.

datasets. The time series of actual station data and the interpolated grid data for the period of 1969–2005 were cross validated by calculating the root-meansquare errors. These errors associated with the interpolation scheme used in preparing gridded data over the plains were found to be 0.58C at the maximum. The errors were relatively large in the Jammu Kashmir and Uttarkhand hilly regions, however, with sparse sources of observational data there. These gridded temperature values were also compared with the monthlymean temperatures at 0.58 3 0.58 resolution prepared by Willmott and Matsuura (1995) of the University of Delaware (online at www.cdc.noaa.gov). Temporal correlations of more than 0.8 were found between the two datasets over parts of the country over the period of 1969–99. The Indian Institute of Tropical Meteorology at Pune (online at www.tropmet.res.in) has divided the whole of India into seven homogeneous temperature regions (Fig. 1) on the basis of spatial and temporal variations of surface air temperatures across the country. These regions are named as the east coast (EC), interior peninsula (IP), north central (NC), northeast (NE), northwest (NW), western Himalaya (WH), and west coast (WC). Of these seven regions, WH, NW, NC, and NE, located in the north of the country, are termed as northern India in this paper. In a similar way, WC, IP, and EC, located in the south of the country, are termed as southern India.

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TABLE 2. Definitions of extreme-temperature indices used in this study on the basis of gridded data. Temperature indices

Names

TX90p, TX95p, TX99p

Warm days

TN90p, TN95p, TN99p

Warm nights

TX10p, TX05p, TX01p

Cold days

TN10p, TN05p, TN01p

Cold nights

WSDI

Warm-spell duration index

CSDI

Cold-spell duration index

ETCCDI has defined a core set of 27 extremes indices for temperature and precipitation, of which 16 are temperature related. The list of the temperature indices calculated in this study is given in Table 2. From the perspective of climate change, the changes in the frequency of moderate warm days and nights (TX90p and TN90p) and moderate cold days and nights (TX10p and TN10p) are equally important. We also considered more intense cases above the 95th and 99th percentiles for warm exceedances and below the 5th and 1st percentiles for cold exceedances. In addition to the intensity of temperature extremes, warm and cold spells are also examined in this study. Temperature extremes are usually identified relative to the average weather conditions of the region of study. Extreme value theory (Fisher and Tippett 1928), a branch a statistics, is used in identifying the extremes of probability distribution of quantitative data and risk analysis of unusual events. The generalized extreme value (GEV) distribution is used to determine thresholds to model temperature extremes from the tail of the distribution. The GEV distribution encompasses the three standard extreme value distributions such as Frechet, Weibull, and Gumbel and is suitable for estimating extreme value percentile thresholds (Klein Tank et al. 2009). The generalized Pareto distribution can also be used to fit data in the case of exceedances over a high threshold (Smith 2003). While analyzing the dataset we have noticed multimodal frequency distribution in the daily temperature values for the entire period of study, 1969–2005. As per Clifton et al. (2011), the conventional use of extreme value theory is not appropriate for multimodal distribution. Because there is not a single mode to calculate distance from the mean to define the threshold limits, the annual, seasonal, and diurnal cycles need to be separated. On the basis of seasonality, it is logical and also factually correct to look for the window of summer months for warm exceedances and similarly the

Definitions Count of days on which max temperature TX . 90th, 95th, and 99th percentile, respectively Count of days on which min temperature TN . 90th, 95th, and 99th percentile, respectively Count of days on which max temperature TX , 10th, 5th, and 1st percentile, respectively Count of days on which min temperature TN , 10th, 5th, and 1st percentile, respectively Count of events on which max temperature TX . 90th percentile for at least six days continuously Count of events on which min temperature TN , 10th percentile for at least six nights continuously

window of winter months for cold exceedances. India being a vast country with a large geographical extent in all four standard directions, the exact lengths of summer and winter seasons vary from region to region. Hence, while identifying warm and cold extremes across all of the homogeneous regions we have not followed the usual method of analyzing data pertaining to fixed summer and winter months. Instead, we have adapted the following two steps. First the annual data series for each region are used to separate those above the 75th percentile and those below the 25th percentile. Then the extents of summer and winter periods for that region are identified by analyzing threshold values of temperatures above the 75th percentile and below the 25th percentile, respectively. For the phases or timings of the recorded maximum and minimum temperatures, it may be kept in mind that IMD observations are made at synoptic hours with 3-h intervals and hence that the minimum and maximum temperatures may have occurred close to 0530 Indian standard time (IST) (0000 UTC) and 1430 IST (0900 UTC), respectively. Note here that the significance testing of the probability density function has been done using Kolmogorov– Smirnov statistics, which is one of the tests followed for goodness of fit (Lilliefors 1967). In our study, we have used the GEV distribution, which fits to the data at 5% significance level for all regions, with two exceptions. The fit is not significant in the western Himalaya for maximum temperature in summer and minimum temperature in winter. Also, in the interior peninsula, minimum temperature in summer does not fit significantly to the GEV distribution. It would not qualitatively change the interannual variation of temperature extremes, although it might slightly affect the threshold values. The extreme indices given in Table 2 are further analyzed for interannual and interdecadal variations in terms of frequencies and spells. For detection of trends, we have used the Mann–Kendall rank-based test (Kendall 1975). This

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nonparametric test does not require any assumption regarding the normality of data. It is the most popular method for trend detection in climatological and hydrological time series. The method of identifying extreme-temperature events has further been cross validated with the actual heat/cold-wave events declared by IMD at some selected stations as per operational usage (Table 1). Indian weather reports published in the quarterly research journal Mausam provides the station names with the lowest and highest temperature records. Maximum numbers of extreme-temperature cases are available at Churu in Rajasthan and Amritsar in Punjab. One more station, Bareilly in the north-central region, is also selected because long-term daily maximum and minimum temperatures are available there. Using IMD gridded temperature data at three grid points close to these three stations, extreme indices such as warm days and cold nights are identified as per the definitions given in Table 2. Such temperature exceedances at the grid points close to Churu and Amritsar stations are found to agree with heat/cold-wave conditions declared by IMD and published in Mausam during the same verification years. For verification of events at Bareilly, it is found that the heatwave conditions as per the IMD definition (Table 1) are close to the 95th-percentile category (Table 2) of the warm days in summer during the period of 1969–2000. In a similar way, cold-wave conditions of IMD are near the 5th-percentile category of the cold nights in winter.

3. Observed changes in annual and seasonal temperatures In this section, we discuss the changes in annual and seasonal mean temperatures in India and in its seven homogeneous regions before examining the changes in the warm and cold exceedances. IPCC AR4 states that the diurnal temperature range across the world is likely to decrease because of a relatively greater rise in minimum temperature than in maximum temperature. In the recent past, long-term variations and trends in surface air temperature in India have been investigated by a number of scientists, including Dash et al. (2007), Dash and Hunt (2007), and Kothawale et al. (2010a). These studies suggest an increase in mean annual temperature at the surface during the twentieth century, with unprecedented warming in the last decade. During the last 30–40 years, the increase in surface minimum temperature is found to be more rapid than that in the maximum temperature. In particular during winter and premonsoon, the increasing trends are highly significant over large areas of the country (Kothawale et al. 2010a). Seasonal asymmetry in the temperature trends has also been

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emphasized in these papers. Studies by Mearns et al. (1984), Katz and Brown (1992), Colombo et al. (1999), and Meehl et al. (2000, 2007) indicate that relatively small changes in the mean values could result in considerable changes in the severity of the extremes. Figure 2 shows anomalies in the maximum (TX), mean (T), and minimum (TN) temperatures averaged over India as well as in its seven homogeneous regions during 1969–2005. Note that the annual and seasonal temperature anomalies are calculated with respect to the mean values over the study period. For any homogeneous region the annual mean of each year is calculated from the daily weighted average values of that region. These 37 annual values are then deducted from their respective climate values to get the anomalies for all years. In a similar way the seasonal anomalies are calculated using seasonal mean values of each year. The temperature trends in some regions are not significant at the 5% level but are significant at the 10% level. IPCC AR4 has categorized 5% significance as ‘‘extremely likely’’ and 10% significance as ‘‘very likely.’’ Although the trends with 10% significance level are minimally significant, these are documented because of their large societal importance. They are also important for future reference as longer gridded data records evolve through time. Significant increasing trends in all temperature categories are observed in India as a whole and also in the northwest, west coast, and east coast. The TX in the interior peninsula and TN in the north-central region and western Himalaya show significant increasing trends. Based on these results one may infer that there has been significant increase in TX in the southern Indian regions as compared with northern India. The northwest region also shows considerable increase in TX, however. Of all seven regions in India, maximum increasing trend in TN is noticed in the northwest region. Parthasarthy et al. (1990) and Kothawale and Rupa Kumar (2002) have suggested a positive relation of surface and upper-air temperature variations in the premonsoon season with the subsequent summer monsoon rainfall. Goswami et al. (2006) have reported a significant increase in the frequency and magnitude of extreme rain events and a decrease in moderate events over central India. Dash et al. (2009) have shown significant increase in short and dry rain spells and decrease in long rain spells. Thus, there is no direct and clear-cut relationship between changes in temperature and rainfall. Some of these changes may be attributed to aerosol loading (Ramanathan et al. 2007) or the remote influence of El Nin˜o–Southern Oscillation (ENSO). Kothawale and Rupa Kumar (2002) and recently Kothawale et al. (2010a) have discussed links between the ENSO phases and temperature anomalies across India.

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FIG. 2. Annual anomalies of TX, T, and TN (as defined in the text) from the 1969–2005 mean value over the whole of India and its seven homogeneous regions are shown with bars. The smooth curves are obtained using a five-point binomial filter. Increasing (decreasing) trends at 5% and 10% significant levels are marked by the m and n (. and ,) symbols, respectively.

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FIG. 3. Seasonal anomalies of TX from the 1969–2005 mean value over the whole of India and its seven homogeneous regions are shown with bars. Curves and symbols are as in Fig. 2.

It is well known that during June–September the southwestern summer monsoon plays a dominant role in India and that during October–November the northeastern winter monsoon has influence on the weather of the country in general. The four main seasons in India can be stated as the premonsoon from March to May (MAM), summer monsoon from June to September (JJAS), postmonsoon from October to November (ON), and winter from December to February (DJF). A number of warm-weather events, including the most severe ones such as heat-wave conditions, occur over most parts of the country during May and June. Some

temperature extremes are also recorded during April and July, however. Figure 3 shows a significant increasing trend in TX in all seasons in the west coast region only. The regions in southern India show significant increasing trends in TX during the winter months. In the case of the entire country, TX shows a significant increasing trend during the monsoon months. A decreasing trend in TX is noticed in the northeastern region during the premonsoon season. Figure 4 depicts the seasonal anomalies in the minimum temperatures. All India, the northwest, and the northeast show significant increasing trends in the

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FIG. 4. As in Fig. 3, but for TN.

TN category in all seasons. In winter, regions in northern India and also the west coast show significant increasing trends in TN. During the monsoon season in the west coast, east coast, and in all regions in northern India except the north-central region there is an increase in TN. For all India, both TX and TN categories of temperature show significant increasing trends during the summer monsoon months. The variations in the temperature trends discussed above can be linked to several factors. In principle, variations in temperature are related to the rainfall. Also, growing urbanization, an increase in greenhouse gas emissions, and aerosol loading are supposed to affect the distribution of heat sources over land and ocean. In

turn, the atmospheric circulation patterns can be affected. Increases in greenhouse gases and aerosol loading have opposing effects, and the changes in daytime and nighttime temperatures depend on their concentrations and locations. Padma Kumari et al. (2007) observed a decrease in solar radiation over 12 different stations in India in the period of 1981–2004. Despite a decrease in solar radiation due to anthropogenic aerosol emissions, they report an increase in the maximum and minimum surface temperatures. Thus, the relationship between changes in temperature on one hand and greenhouse gas emissions and aerosols on the other is very complicated, and it is very difficult to establish any direct relationship between the two factors.

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4. Warm and cold exceedances in summer and winter seasons The interannual and interdecadal variations in different categories of warm/cold exceedances are discussed in this section. Further continuous warm and cold episodes of 1–2 days and 3–5 days are examined. For episodes of more than 5 continuous days, warm- and cold-spell duration index (WSDI and CSDI) are identified. Figure 5 shows the frequency of warm days and nights in summer over all India as well as in its seven homogeneous regions. One important observation from this figure is that in the whole of India the frequencies of strongest warm events TX99p and TN99p are at a maximum in the last decade from 1996 to 2005 as compared with earlier decades. An increasing trend in TX95p in the interior peninsula is significant at 10% level. Frequencies of warm nights in the summer months show a significant increase in the northwest and northeast regions. On the west coast and in the interior peninsula, there is a significant increase in the number of strongest category of warm nights TN99p. Frequency of cold days and nights in winter is shown in Fig. 6. Northern regions in India such as the northcentral and northeast regions show significant increase in the number of cold days that could lead to cooling in daytime temperature. Significant cooling in TX in winter in these northern regions is not observed, however. The increase in the number of cold days over the two northern regions could be attributed to possible aerosol contributions over the Indo-Gangetic plains (Menon et al. 2002; Ramanathan et al. 2007; Flanner et al. 2007; Badarinath et al. 2007). During the period of persistence fog in winter months, the presence of black carbon aerosols and gaseous pollutants in the atmosphere—in particular, in the urban area in north India—may significantly affect the amount of sunlight reaching the ground and hence lead to a decrease in bright sunshine hours. Jenamani (2007) noticed an increase in average fog hours per day in Delhi in January since 1989. Further, Ramanathan et al. (2007) explained that atmospheric brown clouds, a mixture of aerosols, may contribute to atmospheric solar heating and surface cooling. Unlike in the northern regions, daytime warming in winter is noticed on the east coast and west coast because of a significant decrease in the number of TX05p and TX10p categories of temperature extremes. A decrease in the number of TX05p is also noticed in the western Himalaya. The number of cold nights shows a decreasing trend in all regions in northern India, except in the western Himalaya. Southern regions also show a decrease in the number of cold nights, except in the interior peninsula, where the decreasing trend is not significant.

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Figure 7 shows decadal variations in the warm and cold exceedances. In the whole of India a significant decadal increase is noticed in the TX99p category of warm days. In all categories of temperature on the east coast and west coast and also in the country as a whole, maximum numbers of warm days are observed in the decade 1996–2005 as shown in Fig. 7a. Earlier, we noticed a significant increasing trend in the time series of frequency of warm days in the interior peninsula. It indicates relatively more warm days during the last decade in the southern regions in India in comparison with the northern regions. Figure 7b shows an increase in warm nights during the last decade in the northeast, northwest, and west coast, the increase being significant in the latter two regions. Decadal variations in cold days in winter in Fig. 7c show a significant increase in the north-central and northeast regions, and this trend is also reflected in the all-India picture. This may lead to daytime cooling, which could be associated with an aerosol increase over northern India as discussed earlier. The western Himalaya, west coast, and east coast regions show significant decrease in the number of cold days in decadal time scale, however. This change is similar to the yearly variation in these regions. Figure 7d depicts a significant decrease in the number of cold nights in the northwest, north central, and northeast. All India and regions in the south also show a decrease in cold nights in the recent decades as compared with the earlier period of 1969–75. These trends are not significant, however. Significant decreasing trends in the number of cold nights, especially in the northern regions (except the western Himalaya) are noticed, whereas warm days in summer are at a maximum in the last decade in the southern regions. The number of warm nights in summer in the northern regions such as the northwest and northeast shows large enhancement in the last decade. Such warming is contrary to the increase in the number of cold days in winter in the north-central and northeast regions. Asymmetric changes in the number of cold days and nights in winter are observed in the north-central, northeast, and northwest regions. Figures 8a–d depict the changes in the percentage of WSDI and CSDI and those in the number of short spells of 3–5 and 1–2 continuous warm days in summer and cold nights in winter. Because WSDI and CSDI measured per year are small in number, we have presented their interdecadal variations in the last four decades. Percentage of WSDI in the west coast and interior peninsula has drastically increased in the last decade 1996– 2005. There is no significant decreasing trend in CSDI in Fig. 8c except that in the west coast there is decrease at 10% significance level. A Student’s t test does not show a strong significant trend in these indices, probably because

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FIG. 5. Frequency of occurrence of (a) warm days and (b) warm nights in summer per year in the period 1969–2005 over the whole of India as well as its seven homogeneous regions are shown with bars. Curves and symbols are as in Fig. 2.

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FIG. 6. As in Fig. 5, but for (a) cold days and (b) cold nights in winter per year.

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FIG. 7. Decadal variations in the percentage of (a) warm days and (b) warm nights in summer and (c) cold days and (d) cold nights in winter in the period 1969–2005 over the whole of India as well as its seven homogeneous regions. Increasing (decreasing) trends at 5% and 10% significant levels are marked by the symbols m and n (. and ,), respectively.

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FIG. 8. Decadal variations in the percentage of (a) WSDI (spells of more than 5 continuous warm days), (b) short spells of 1–2 and 3–5 continuous warm days in summer, (c) CSDI (spells of more than 5 continuous cold nights), and (d) short spells of 1–2 and 3–5 continuous cold nights in winter in the period 1969–2005 over the whole of India as well as its seven homogeneous regions. Increasing (decreasing) trends at 5% and 10% significant levels are marked by the symbols m and n (. and ,), respectively.

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FIG. 9. Decadal variations in the temperature PDFs for all of India in the years from 1969 to 2005 during (a) summer days, (b) summer nights, (c) winter days, and (d) winter nights. The smoothed curves represent the fit of the GEV distribution.

of small sample size and large variability. Nevertheless, there is a large decrease in CSDI in recent decades with respect to the period 1969–75 in all regions except in the western Himalaya where no long cold spells are seen. Figure 8b depicts a significant increase in the short warm spells of 1–2 continuous days in summer in the northcentral region. The northeast and west coast show a decrease in short warm spells. Figure 8d shows significant decreasing trends in the number of short cold spells in the northern regions except in the western Himalaya. All India and the west coast also indicate a decrease in short cold spells during winter nights. Decreasing trends in the occurrence of cold spells in these regions are also reflected in the decrease in the occurrence of cold nights and increase in the annual minimum temperatures. The changes in the frequency of occurrence of warm and cold exceedances in different homogeneous regions may be associated with the year-to-year variations in the seasonal temperatures in these regions.

The decadal variations in the temperature probability density functions (PDFs) during summer days, summer nights, winter days, and winter nights are shown in Fig. 9. A shift in the mean of PDF from left to right indicates warming. Minimum change is noticed in the PDF of summer days, whereas there is maximum change in winter nights. The area under the right tail of the summer-days PDF indicates an increase in the number of warmer days as well as the severity of extremes. The area under the left tail of the winter-nights PDF indicates a drastic decrease in the occurrence of cold exceedances in the last decade with respect to the period 1969–75. This shows the asymmetry in the changes in the frequency of occurrence of day and night warm/cold events discussed earlier. The comparative study of significant trends in the annual and seasonal temperatures with those in the frequency of occurrence of warm and cold exceedances is summarized in Tables 3 and 4, respectively. Results indicate that there

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TABLE 3. Summary of trends in annual and seasonal mean of maximum and minimum temperatures with trends in different categories of warm days and nights in summer. Increasing (decreasing) trends at 5% and 10% significant levels are marked by the m and n (. and ,) symbols, respectively; DL is decadal, and SS denotes short spells. Mean of max temperature

Categories of warm days

Mean of min temperature

Categories of warm nights

Regions

Annual

TX90p

Annual

MAM

JJAS

TN90p

TN95p

TN99p

AI WH NW NC NE WC IP EC

m

m n m m m m

m

m n m

m, DLn

m

m

m

m

MAM

JJAS

TX95p TX99p

m

DLm

m

m n n

, n

m

SSm SS, WSDIn, SS, WSDIn

DLn

n m

m m

n, DLn n

n n

are variations in temperature extremes from region to region. Such regional variations can be explained to some extent based on the following geographical reality. India is a vast country with inhomogeneity in land surface features spread over large geographical extent. Hence there is inhomogeneity in the surface temperature distribution too.

5. Summary and conclusions Several studies in the past have examined and reported on temperature extremes in India using station data that are not located uniformly in space. Not much work has been done on changes in the temperature extremes of different intensities and durations. In this study, an IMD gridded dataset spanning 37 years over the period 1969–2005 is examined in detail to identify changes in the number of warm days and nights; cold days and nights; and WSDI, CSDI, and short warm/cold spells of 3–5 and 1–2 days/nights for the entire country as well as for its seven different homogeneous regions. Temperature extremes have a profound impact on several sectors of the society such as availability of power and water, agricultural production, and tourism. More intense and

n

frequent temperature extremes directly or indirectly give rise to several weather-related diseases. The most important result of this study is the significant decreasing trends in the frequency and spells of cold nights for the period 1969–2005 in the country as a whole and in all the regions in the north except the western Himalaya. In this region the number of cold days shows a decreasing trend. Southern regions in India show a drastic decrease in the frequency of cold nights with respect to the period 1969–75. Of the three categories of winter cold nights, the decreasing trends in the moderate ones such as TN10p and TN05p are statistically significant at the 5% level. Relatively more warm days in the summers of the last decade 1996–2005 are noticed in southern regions as compared with in the northern regions. In this last decade, relatively more warm days of all categories have also been observed over all India, the east coast, and the west coast. A significant increasing trend in the warm days of intense category TX95p has been noticed only in the interior peninsula. The warm-spell duration index also shows a significant increase during the last four decades over the interior peninsula and the west coast. An increase in the frequency of warm nights in summer

TABLE 4. Summary of trends in annual and seasonal mean of maximum and minimum temperatures with trends in different categories of cold days and nights in winter. Increasing (decreasing) trends at 5% and 10% significant levels are marked by the m and n (. and ,) symbols, respectively. Mean of max temperature Regions

Annual

AI WH NW NC NE WC IP EC

m

ON

DJF

Categories of cold days

Mean of min temperature

Categories of cold nights

Annual

ON

DJF

TN10p

m

m n m m m m

., SS,

.

m n m m m m

.

n

TX10p

TX05p

DLn DL.

DLn ,, DL.

m m, DLm .

m

.

TX01p

m

m n n

m

m n m

m, DLn

m n

TN05p TN01p .

., DL., SS, ., DL. ,, SS, ,, DL. ,, DL,, SS. ., CSDI,, SS. . ,

, . . , .

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DASH AND MAMGAIN

is found in two northern regions—the northwest and northeast—and in the two southern regions—the west coast and interior peninsula. Frequency of cold days in winter has increased significantly over the two northern regions, north central and northeast. A large decrease in the cold-spell duration index with respect to the period 1969–75 has been noticed in all regions except the western Himalaya. Cold spells of shorter durations show decreasing trends in the northern regions of India. Again the western Himalaya is an exception. Such a discrepancy in the western Himalaya can be explained in the context of limited data availability in that hilly region. As in northern India, there is a decreasing trend in the frequency of occurrence of cold spells on the west coast. Changes in the frequency of warm and cold exceedances indicate maximum warming in the west coast as compared with all other regions. Results show that frequencies of the strongest warm events such as TX99p and TN99p are at a maximum during the last decade 1996–2005 as compared with earlier decades when India is considered as one complete unit. Regions in southern India show a rise in the frequency of warm days in summer, whereas regions in northern India do not indicate any change in the occurrence of warm days. Nevertheless, a significant decrease in the number of winter cold nights leads to relatively more warming in the northern regions of the country than in the south. Overall results indicate that the decreasing trends in the frequency of cold nights are more significant and prevalent than the increasing trends in warm days in India. The sample size of data spanning 37 years is not long in the statistical sense, however. Hence, some of the results obtained in this paper need to be confirmed when similar data for a longer time period are available in the future. Acknowledgments. The authors acknowledge the IMD for the use of their gridded temperature data. Thanks are also given to all three reviewers for their useful suggestions to improve the quality of the paper.

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