1 Long-term trends of instability and associated parameters 2 over the Indian region obtained using radiosonde network 3 Rohit Chakraborty, Madineni Venkat Ratnam* and Shaik Ghouse Basha 4 National Atmospheric Research Laboratory, India. 5 Correspondence to: M. Venkat Ratnam ([email protected]) 6 7 Abstract 8 Long-term trends of the parameters related to convection and instability obtained from 27 radiosonde 9 stations across 6 sub-divisions over the Indian region during the period 1980-2016 is presented. A total of 16 10 parcel and instability parameters along with moisture content, wind shear, and thunderstorm and rainfall 11 frequencies have been utilized for this purpose. Robust fit regression analysis is employed on the regional 12 average time series to calculate the long-term trends on both seasonal and yearly basis. The Level of Free 13 Convection (LFC) and Equilibrium Level (EL) height is found to ascend significantly in all Indian sub- 14 divisions. Consequently, the coastal regions (particularly the western coasts) experience increasing in Severe 15 Thunderstorm (TSS) and Severe Rainfall Frequencies (SRF) in the pre-monsoon while the inland regions 16 (especially central India) experience an increase in Ordinary Thunderstorm (TSO) and Weak Rain Frequency 17 (WRF) during the monsoon and post-monsoon. The 16-20 year periodicity is found to dominate the long-term 18 trends significantly compared to other periodicities and the increase in TSS, and Convective Available Potential 19 Energy (CAPE) is found more severe after the year 1999. The enhancement in moisture transport and associated 20 cooling at 100 hPa along with dispersion of boundary layer pollutants is found to be the main cause for the 21 increase in CAPE which leads to more convective severity in the coastal regions. However, in inland regions 22 moisture-laden winds are absent and the presence of strong capping effect of pollutants on instability in the 23 lower troposphere has resulted in more Convective Inhibition Energy (CINE). Hence, TSO and weak rainfall 24 occurrences have increased particularly in these regions. 25 Key words: Instability, Convection, Long-term trends, Radiosonde 26 27 1. Introduction 28 Intense convective phenomena are a common climatic feature in the Indian tropical region which occurs 29 during the pre-monsoon to post-monsoon seasons (April–October) (Ananthakrishnan, 1977) and they are 30 generally accompanied by intense thunderstorms, lightning, wind gusts with heavy rainfall. Hence, they are 31 known to induce immense socio-economic hazards including loss of life and property. Several reports have 32 shown an increase in the climatic extreme occurrence and intensity of these phenomena throughout the world 33 (Webster et al., 2005; Emanuel, 2006). In this connection, the traditional surface-based parcel theory has been 34 utilized to understand convective processes using atmospheric soundings as it calculates the atmospheric 35 instabilities and other parameters at various heights (Huntrieser et al., 1997; Santhi et al., 2014; Nelli et al, 36 2018a). 1 37 Considering the importance of studying the long-term trends in climatic extremes, a series of research 38 attempts have been orchestrated world-wide in the last two decades. Using multiple tropical stations and re- 39 analysis data, Gettleman et al. (2002) and Riemann-Campe et al. (2009) have shown that Convective Available 40 Potential Energy (CAPE) has been increasing very strongly with a growth rate of ~20% per decade during the 41 period 1958-1997 due to increase in surface heating and moisture. Gensini and Mote (2015) projected a 236 % 42 increase in severe thunderstorm frequency from 1980-1990 to 2080-2090 over the eastern United States (US). 43 Further, Brooks (2013) used various combinations of CAPE and Vertical Wind Shear (VWSH) products and 44 results hinted towards a probable increase in the severe thunderstorms over the US. It was also observed that the 45 effect of increasing CAPE is more dominant on convective severity than in case of decreasing shear. On the 46 other hand, studies by Prein et al. (2017) showed that a recent increase of temperature has led to a rise of 47 moisture ingress and consequently the frequency and severity of extreme precipitation events associated with 48 intense convection have shown a steep rise everywhere in the world. At the same time, an increase in 49 thunderstorm severity and instability has also been reported by many attempts over the Asian region (Wang et 50 al., 2011; Saha et al., 2017). 51 Over the Indian region, Manohar et al. (1999) studied the latitudinal variation and distribution of 52 thunderstorm frequency and CAPE over 78 Indian stations during 1970-1980 and they postulated that the 53 ambient temperature at 100 hPa pressure level has a strong relationship with it. Dhaka et al. (2010) utilized 54 radiosonde observations during 1958-1997 and obtained very prominent anti-correlations on both yearly and 55 seasonal basis between convection strength (CAPE) and upper troposphere temperatures at 100 hPa (T100). 56 Later, Murugavel et al. (2012) studied the long term trends of CAPE from 32 radiosonde stations during 1984- 57 2008 and revealed an alarming growth in monsoon CAPE over India with a slope of 38 J/kg/year. However, 58 they additionally stated that the low-level moisture and solar cycle can have additional impact on the increasing 59 CAPE. Recently from reanalysis datasets, Chakraborty et al. (2017a) and Saha et al. (2017) reported that lower 60 lower tropospheric instability is reducing over few Indian stations after 1980 due to increasing levels of 61 pollution. Apart from that, some studies have also attempted to correlate convective severity with boundary 62 layer phenomena, surface fluxes, solar effect and precipitation; (Murthy and Sivaramakrishnan, 2006; 63 Allappattu and Kunnikrishnan, 2009; Xie et al., 2011, Nelli et al. 2018b). 64 Previous studies over India have shown the distribution of CAPE only whereas other parameters like 65 Convective Inhibition Energy (CINE), Mixed Layer CAPE (MLC), Lifted Index (LI), Total Totals Index (TTI), 66 and Precipitable Water Vapor (PWV) are also important as they explain how the atmospheric instability and 67 moisture changes at various levels of the atmosphere. In addition, the influence of climatic oscillation (Quasi- 68 Biennial Oscillation (QBO), El-Nino Southern Oscillation (ENSO) and Solar Cycle) on the seasonal and annual 69 variation of convective parameters was also not studied in detail. Therefore in the present study, long-term 70 variation of parcel parameters (Lifted Condensation Level (LCL), Level of Free Convection (LFC), Equilibrium 71 Level (EL), CAPE and CINE), with instability (LI, Vertical Totals Index (VT)), moisture (PWV, and PWV at 72 low levels (PWL)), thunderstorm and rainfall severity frequencies (Thunderstorm-Severe (TSS), Thunderstorm- 73 Ordinary (TSO), Weak Rain Fall (WRF) and Strong Rain Fall (SRF)) followed by Temperature at 100hPa 74 (T100) and Wind Shear (WSH) is investigated using 27 radiosonde stations along with gridded rainfall data over 75 India. This article is structured as follows: Section 2 describes the datasets and methodology adopted for the 76 present study. Section 3 presents the long-term analysis of parcel and instability parameter over Chennai 2 77 (13.08oN, 80.27oE) and 6 sub-divisions of the Indian subcontinent on both annual and seasonal basis, followed 78 by the periodicity and split trend analysis. Finally, a discussion on the results and conclusions is appended in 79 Section 4 and 5, respectively. 80 81 2. Dataset and Methodology 82 Radiosonde observations from 27 stations over the Indian region from 1980-2016 are obtained from 83 Integrated Global Radiosonde Archives (https://www1.ncdc.noaa.gov/pub/data/igra/derived/derived-por/). 84 These datasets provide daily temperature and humidity profiles from 1538 stations around the world in fixed 85 pressure levels after doing quality checks (Durre et al., 2006; Ferreira et al. 2018). These studies have concluded 86 that the radiosonde data quality from IGRA has faced certain problems from time to time, but such cases are not 87 so prominent over the Indian region, especially after the year 1980. It is mainly because of the higher accuracy 88 and reliability of this in-situ measurement technique that these datasets are widely used worldwide nowadays for 89 calibrating other continuous profiler instruments (Chakraborty and Maitra, 2016). In accordance with data 90 availability and reliability, only 27 stations have been considered out of 37 IGRA Indian radiosonde stations 91 thereby providing descent data availability for carrying out this study. When an in depth investigation is done on 92 the data continuity by plotting the temperature and humidity profiles for all days, a set of gaps in datasets were 93 noticed. Most of the utilized stations have intermittent data gaps of 2-7 days in certain months only but, on the 94 whole, except only a very few cases, the duration of these individual data gaps are mostly limited to less than 1 95 month. However, these small data gaps are not expected to provide any significant impact on the long-term 96 seasonal or annual average variations of 37 years x 12 months span. 97 In addition to the data availability, homogeneity also acts as a common concern before using the data. 98 However, such issues should not be considered serious as all three types of homogeneity issues namely: volume, 99 instrument type and quality have been addressed before commencing the study. First, about 5000 radiosonde 100 profiles are available in majority of IGRA stations which are uniformly distributed among all years and seasons 101 (except monsoon); hence it provides a decent data volume for investigation of yearly trends. Secondly, the data 102 of all Indian IGRA stations come from a single type of IM-MK3 radiosondes which has not undergone any 103 change in radiosonde accuracies in the last years and so this addresses the instrument type related issue.