An objective criterion for the identification of breaks in Indian summer monsoon rainfall

Transcription

1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 16: (2015) Published online 12 September 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: /asl2.536 An objective criterion for the identification of breaks in Indian summer monsoon rainfall U. Umakanth, 1 * Amit P. Kesarkar, 1 T. Narayana Rao 1 and S. Vijaya Bhaskar Rao 2 1 National Atmospheric Research Laboratory, Gadanki , India 2 Department of Physics, Sri Venkateswara University, Tirupati , India *Correspondence to: U. Umakanth, National Atmospheric Research Laboratory, Gadanki, Andhra Pradesh , India. uma.phys@gmail.com Received: 3 February 2014 Revised: 19 August 2014 Accepted: 21 August 2014 Abstract We proposed a new objective criterion for discerning breaks in Indian summer monsoon rainfall, which is based on grid-level rainfall threshold. The identified breaks reproduced all well-known breaks discussed in the literature, but are relatively fewer in number. The correlation analysis between the number of break days and all-india seasonal rainfall is not only high (R 2 = 0.77) but also reproduced well-known association between El Nino and bad monsoon years. The fraction of seasonal rainfall in break days is found to be smaller (<10%) over most part of India than earlier studies, indicating that the discerned spells are valid for a large part of India. Keywords: rainfall Indian summer monsoon; break monsoon; rain fraction; spatial variability of 1. Introduction India receives most of its annual rainfall during the summer monsoon season (JJAS) and major percentage of it occurs in peak monsoon months of July and August. The rainfall does not occur continuously rather occurs in quasi-periodic active spells, punctuated by spells of break conditions. The prolonged break conditions in the monsoon months are primarily responsible for the deficient seasonal rainfall and subsequent reduction in agricultural production of India (Gadgil et al., 2003). Identification of single criterion for prevailing drought condition for the whole country is difficult due to heterogeneous nature of rainfall in both spatial and temporal scales. Many efforts have been made to identify the break days based on the large-scale circulation features associated with motion of the monsoon trough (Ramamurthy, 1969; Ramanadham et al., 1973; Sikka and Gadgil, 1980; De et al., 1998; Webster et al., 1998; Goswami and Ajaymohan, 2001; Joseph and Sijikumar, 2004) and also to develop a criterion based on rainfall or proxy for rainfall (such as outgoing longwave radiation) during JJAS (Krishnan et al., 2000; Gadgil and Joseph, 2003; Ramesh Kumar et al., 2004; Rajeevan et al., 2010 (hereafter RGB10); Mohan and Rao, 2012; Ratan and Venugopal, 2013). A summary of the earlier studies is discussed in Ramesh Kumar et al. (2004) and RGB10. For identifying break days in summer monsoon season, earlier studies considered rainfall over a specific region of India. Gadgil and Joseph (2003) and RGB10 have considered the rainfall over monsoon trough region as it is well correlated with all India seasonal rainfall. Nevertheless, active or break conditions are not homogeneous across the country. The rainfall fraction for break days as determined by Gadgil and Joseph (2003) or RGB10 criteria is large over southeast peninsula and northeast India and is small over the central India (Rao et al., 2009). Therefore, these criteria are best suited to capture the variability of rainfall over central India and consequently the break or active conditions pertaining to central India. For the same reason, Mohan and Rao (2012) used rainfall over southeastern peninsular India for identifying break and active spells for that region. Ramesh Kumar et al. (2004), on the other hand, used all India daily rainfall for identifying active/break monsoon periods. Nevertheless, their scheme is also mostly biased towards rainy regions, such as Western Ghats and northeastern regions, and does not account for spatial variability. Therefore, there is a need to develop a methodology, which identifies break conditions in summer monsoon rainfall for whole India by considering the large spatial variability of rainfall. In this work, we have proposed a new objective criterion for selecting break spells in Indian Summer Monsoon Rainfall (hereafter referred to as ISMR). Instead of using all India rainfall or rainfall over a particular region, the criterion utilizes rainfall (and a threshold rainfall) at each grid (1 1 ) for identifying break spells for the whole country. The performance of this newly developed criterion has been evaluated (with reference to the existing similar analysis/techniques available in the literature) using two metrics: (1) correlation analysis and (2) rain fraction for break days. The paper is organized as follows: Section 2 discusses the data and methodology adopted for this study. The important results are discussed in Section 3 and concluded in Section Royal Meteorological Society

2 194 U. Umakanth et al. Table 1. Comparison of known historical break spells as identified by different studies employing different methods. Year Present study RGB10 Gadgil et al. (2003) Ramamurthy (1969) and De et al. (1998) A 24 29A 24 30A 24 29A A 22 28A 22 29A 21 25A J, 4 9A, 13 15A 6 11J, 1 14A 7 11J, 4 14A 6 8J, 4 15A J, 24 30A 2 12J, 21 31A 2 12J, 22 31A 2 11J, 23 27A A 25 31A 25 31A 25 29A J, 19J 3A 18J 3A 19J 3A 17J 3A A 15 20A 15 19A 15 18A J, 12 31A 2 6J, 14 29A 2 6J, 15 31A 17 23J, 15 31A J 1 8J 1 8J A 22 31A 22 31A 23 26A, 29 31A A 1 9A NA NA J, 24 31J 4 17J, 21 31J NA NA The during 1982 by Ramamurthy (1969) and De et al. (1998) indicates the absence of break spell. NA, not defined by the respective authors; A, August; J, July. 2. Data and methodology High-resolution daily gridded rainfall data for the period have been used for identifying break days in ISMR (Rajeevan et al., 2006). This data set has been developed by India Meteorological Department using quality-controlled daily gauge rainfall from 1800 stations spread across India. Shepard s interpolation method was adopted for gridding the data into regular grids of 1 1 in latitude longitude. To account for heterogeneous nature of rainfall and variability at regional scale in identifying break conditions for whole India, thresholds have been used for each grid, instead of all-india average rainfall. Rainfall rate (mm day 1 ) percentiles (5, 10, ) have been estimated at each grid point and the 10th percentile has been chosen as the rainfall threshold at each grid. Since the gridded data often show weak rainfall depending on the interpolation scheme adopted, a minimum rainfall rate needs to be fixed to minimize such data entering in our analysis and reducing the threshold rain rate (10th percentile). We, therefore, considered the days with rainfall rate values 1mm day 1 as rainy days and are only used for percentile calculation. We hardly find a day with rainfall at each grid below a grid-level threshold for the entire study region (whole India). Therefore, we considered a day as break day when break conditions prevail over most parts of India, i.e. at least over 70% of the total number of grids considered in the present study. Further, it is well known that the breaks in ISMR are associated with large-scale phenomena and therefore persists for few days. If the break conditions persist for longer than or equal to 3 days, then those days are considered as a break spell in the present study. The new criterion for identifying break spells, therefore, first checks break condition at each grid (daily rainfall < rainfall threshold obtained from 10th percentile of long-term daily ISMR), then whether or not such conditions prevail over most parts of India (>70% of total number of grids) and finally the persistence of break conditions for at least 3 days. As we considered the entire India for our analysis, the rainfall data during the peak monsoon months of July and August are used for the identification of break spells, because entire India will be under the influence of monsoon during the above period. Hereafter, unless mentioned, the rainfall during July and August is referred as seasonal rainfall. The rationale for selecting the above thresholds (10th percentile as grid-level rainfall threshold and also 70%) is based on the following factors: (1) number of break days and whether or not these thresholds reproduce all the known historical break spells reported in the literature (see Table 1), (2) small rain fraction (fraction of seasonal rainfall, in percentage) values during the break spells and (3) large correlation coefficient (negative) between number of break days and all-india seasonal rainfall (will be shown later in Table 3). Nevertheless, a sensitivity analysis has also been carried out to study the impact of the thresholds on our analysis in the next section. 3. Results and discussion The method of identifying a grid-level threshold based on percentiles instead of a fixed threshold is more meaningful as it accounts the spatial variability of rainfall. Figure 1(a) shows the spatial distribution of rainfall threshold (10th percentile), which clearly follows the climatological pattern of ISMR. It is high ( mm day 1 ) over climatologically wet regions like Western Ghats, northeast India and eastern parts of central India and is low (1 1.4 mm day 1 ) over semi-arid regions of northwest India and southeast peninsular India. Even though we have considered 70% areal coverage for identifying break days, due to spatial variability of ISMR, the grids constituting 70% area may not repeat for each break day. To understand which grid and how often is in break condition, we have quantified the probability of a grid to be in break condition. The probability is calculated as the ratio of the number of times a grid is in break condition to the total number of break days (Figure 1(b)). It is clear from Figure 1(b)

3 Criterion for the identification of breaks in monsoon 195 (a) Latitude (b) Latitude (c) 30 N 20 N 10 N 30 N 20 N 10 N Frequency of spells E 80 E 90 E 100 E Longitude Duration of spells Figure 1. (a) Spatial distribution of grid-level rainfall thresholds (mm day 1 ), estimated using 10th percentile of total data. (b) Spatial distribution of probability for a grid to be in break condition (1 corresponds to maximum probability) and (c) number and duration of break spells. that the probability of break to occur over the northwestern India is quite high (>0.9) and is relatively low over the rainy regions Western ghats, northeast India and extreme eastern parts of central India (<0.6). Except for a few grids along the west coast of India and northeast India, the probability of the grids to be in break condition is reasonably high (0.6) for almost all (>90%) grids in the study region, indicating that the break days obtained from this study are not biased towards any specific region A total of 311 break days are identified from 54 years (i.e. on average 6 ± 6 break days per year) using the criterion discussed above. The days are grouped in different categories based on the duration of the break, from 3 to 20 days (Figure 1(c)). Although the number of break spells decrease quite rapidly with the increase in duration of breaks, considerable number of break spells occurs with long duration. For instance, a significant number of break spells ( 5) lasted for 10 days and can have a large impact on the agricultural production of India (Gadgil et al., 2003). These long duration break spells are mostly observed during bad monsoon years (can be seen in Figure 2(a) and will be discussed later). For instance, the long duration breaks of 20 days occurred in 1979, 17 days in 1972 and 13 and 8 days in To study how well the distribution of break spells (as a function of their duration) obtained in the present study compares with earlier studies, occurrence percentages of break spells with different durations are estimated (Table 2). The occurrence percentage estimated by different studies in each duration category is nearly same. However, the number of break spells as well the break days identified in the present study is relatively smaller than that of earlier studies, mainly because of the consideration of larger area (>70%) to be in break condition. Since the area in earlier studies is limited to smaller area(s), i.e. either to monsoon zone (Ramamurthy, 1969; Gadgil and Joseph, 2003; RGB10) or rainy regions (Ramesh Kumar et al., 2004), the borderline cases that just satisfy the thresholds employed in those studies may miss out in the present study. Nevertheless, the long duration breaks, which generally spread over a larger area, are picked correctly by all algorithms. The performance of our criterion for identifying break spells is evaluated using two metrics. First, a correlation test (Spearman s rank correlation) has been performed between the number of break days in each season and all-india seasonal rainfall (Table 3). It clearly shows that they are negatively correlated with a correlation coefficient of 0.77 (significant at 95% confidence level), which is quite high compared to that of in earlier studies by Ramamurthy (1969), De et al. (1998), Gadgil and Joseph (2003) and RGB10. Also, the longest break spells with durations >10 days occurred in bad monsoon years of 1972, 1979 and Two points are noteworthy from this analysis. (1) The close association between the number of break days and seasonal rainfall. (2) It highlights the robustness of our criterion as it brings out the above association much more clearly than any earlier study. It is now known that both El Nino and La Nina influence the ISMR significantly. To examine how well the present criterion is reproducing the dry conditions prevailing during the El Nino years, Southern Oscillation Index (SOI) and standardized anomalies for all-india seasonal rainfall, which is an index for identifying good (>1) and bad (< 1) monsoon years and the number of break days are plotted in Figure 2(a). The SOI is calculated

4 196 U. Umakanth et al. Table 2. Comparison of frequency distribution of duration of break spells (in percentage) obtained by different studies. Duration (days) Present study RGB10 Ramesh Kumar et al. (2004) Gadgil and Joseph (2003) Ramamurthy (1969) > Table 3. Number of break days, their correlation with all-india seasonal rainfall and rain fraction for break days obtained by using a variety of thresholds (for both grid-level rainfall and areal coverage). Percentage area covered during break day condition th percentile No. of break days Correlation coefficient ( 0.80) ( 0.70) ( 0.58) ( 0.18) Rain fraction (<10%) 45.3% 73.4% 90.2% 97.0% 10th percentile No. of break days Correlation coefficient ( 0.79) ( 0.77) ( 0.62) ( 0.36) Rain fraction (<10%) 22.1% 73.6% 89.1% 97.8% 15th percentile No. of break days Correlation coefficient ( 0.79) ( 0.79) ( 0.64) ( 0.40) Rain fraction (<10%) 1.2% 66.1% 86.8% 97.8% for the months of July and August from the monthly values of SOI, available at climate/current/soihtm1.shtm1. Figure 2(a) shows that not all high negative/positive rainfall anomalies (or bad/good monsoon years) are matching with high positive/negative anomalies in number of breaks and SOI. But certainly high positive anomalies in the number of break days and bad monsoon years are seen in strong El Nino years of 1965, 1972 and 1987 (Meyers et al., 2007). The correlation of SOI with standardized values of number of break days and seasonal rainfall is significant (at 95% confidence level) with a correlation coefficient of 0.43 and 0.37, respectively. The second test performed to evaluate the performance of the criterion is based on rainfall fraction for break days (simply rain fraction). It is estimated from the ratio of the cumulative rainfall during break days to the all-india seasonal rainfall (July and August) multiplied by 100. Rain fractions are estimated using the break days obtained from our analysis and also as given by RGB10 for comparison (Figure 2(c) and (b), respectively). It is clear from the figure that the general pattern looks similar in both the analyses with very small percentage values over central and northwest India and moderate values over southeast India and northeast India. Nevertheless, the spatial extent of small rain fraction values (<10%) and magnitude of rain fraction differ in both analyses. For instance, rain fraction estimated with RGB10 criterion is in the range of 15 35% over a large region covering the foot hills of Himalayas, southeast India and northeast India, whereas it rarely exceeded 15% with our criterion. Overall, the rain fraction estimated with our criterion is smaller than that estimated by RGB10 criterion at almost all grids. Quantitatively, 74% (only 59%) of the study region has rain fraction <10%, when it is estimated with our (RGB10) criterion. Rain fraction values never exceeded 20% with our criterion, whereas about 15% of the study region shows rain fraction values in excess of 20% with RGB10 criterion. The two evaluation tests clearly suggest that our criterion to identify breaks in ISMR on all-india scale is better than other criteria employed in earlier studies. Further to examine the impact of the used thresholds on number of break days and their correlation coefficient with all-india seasonal rainfall, various thresholds for break condition (5th, 10th and 15th percentiles) and for area to be under break condition (60%, 70%, 80% and 90%) have been used for the analysis. Table 3 shows the number of break days, the correlation coefficient between seasonal rainfall and number of break days and number of grids (in terms of %) having rain fraction less than 10% for different combinations of thresholds. There is a drastic decrease in the number of break days for every 10% increase in percentage of areal coverage for all the percentiles. The usage of higher areal coverage (80% and 90%) produces small rain fraction values ( 95% of area shows rain fraction <10%), but with small correlation coefficients. They yield fewer break days and also miss some of the well-known breaks in ISMR. Breaks identified with 5th percentile and 60% percentage of areal coverage (593) contain all break days identified in earlier studies by Ramamurthy (1969), De et al. (1998), Gadgil and Joseph (2003) and RGB10. But the rain fraction calculated with these break days is quite high over most parts of India (only 45% of total area shows rain fraction <10%). Considering all above aspects, 10th percentile is chosen as a grid-level rainfall threshold to identify break condition and at least 70% for area to be in break condition. These thresholds produced good number of break days covering all known historical break spells in the literature (Table 1). They also depicted relatively high (negative) correlation between the number of break days and all-india rainfall and show less rain fraction (<10%) over most parts of India.

5 Criterion for the identification of breaks in monsoon 197 (a) 4 3 Rainfall Breakdays SOI 2 Standardized value (b) Year (c) Latitude 30 N 20 N 10 N E 80 E 90 E 100 E 70 E 80 E 90 E Longitude Longitude 100 E Figure 2. (a) Temporal variation of SOI (blue line) and standardized anomalies of rainfall (red bar) and number of break days (brown line). (b) and (c) Rain fraction (%) for break spells obtained from RGB10 analysis and our criterion, respectively. 4. Conclusions A new criterion has been developed for identifying break spells in ISMR by checking the break conditions at each grid by fixing a rainfall threshold (10th percentile) and then examining the areal extent and the persistence of prevailing break conditions. Such an approach (or criterion), i.e. adopting a variable rainfall threshold against a fixed threshold used in other studies, accounts the large spatial variability of rainfall. Since the criterion is based on actual rainfall values and considers the entire India for analysis, rather than restricting to central India or southeast peninsular India, it is more robust in identifying break spells on all-india scale. The present analysis clearly shows this feature. The probability of any grid to be in break condition is >0.6 for 90% of the area considered in the present study. The rationale for choosing the thresholds (10th percentile and 70%) has been provided with a sensitivity analysis. Two tests have been performed to evaluate the performance of the newly developed criterion. The correlation analysis between number of break days and all-india rainfall is not only found to be high (with a correlation coefficient of ( 0.77) but also reproduced the well-known associations between bad monsoon years and breaks and El Nino. The rain fraction is also found to be small (<10%) over most parts of India. The correlation and rain fraction analyses clearly indicate that the present criterion for identifying all-india breaks really works well. The built in flexibility in this approach (relative rainfall and area thresholds) makes it suitable for identifying break conditions in other data sets like numerical weather predictions, networks of rain gauge data, etc., and extend or limit the analysis to any area or time period. References De US, Lele RR, Natu JC Breaks in southwest monsoon. Pre-Published Scientific Report No. 1998/3, IMD, Pune, India, Gadgil S, Joseph PV On breaks of the Indian monsoon. Indian Academy of Sciences, Earth and Planetary Sciences 112: Gadgil S, Vinayachandran PN, Francis PA Droughts of the Indian summer monsoon: role of clouds over the Indian Ocean. Current Science 85: Goswami BN, Ajaymohan RS Intraseasonal oscillations and interannual variability of the Indian summer monsoon. Journal of Climate 14: Joseph PV, Sijikumar S Intraseasonal variability of the low level jet stream of Asian summer monsoon. Journal of Climate 17: Krishnan R, Zhang C, Sugi M Dynamics of breaks in the Indian summer monsoon. Journal of Atmospheric Science 57: Meyers GA, McIntosh PC, Pigot L, Pook MJ The years of El Niño, La Niña and interactions with the tropical Indian Ocean. Journal of Climate 20:

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