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Government of India Ministry of Earth Sciences India Meteorological Department Met Monograph No. Environment Meteorology-01/2010 CLIMATE PROFILE OF INDIA S. D. Attri and Ajit Tyagi 1 9 POINT BINOMIAL FILTER TREND=+0. 560C/100 YEARS 0. 8 Annual Mean Temp Anomalies (°C) 0. 6 0. 4 0. 2 0 -0. 2 -0. 4 -0. 6 -0. 8 1901 1910 1919 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 2009 Y E A R S 2010 Met Monograph No. Environment Meteorology-01/2010 CLIMATE PROFILE OF INDIA Contribution to the Indian Network of Climate Change Assessment (NATIONAL COMMUNICATION-II) Ministry of Environment and Forests

S D Attri and Ajit Tyagi India Meteorological Department Ministry of Earth Sciences New Delhi 2010 Copyright © 2010 by India Meteorological Department All Rights Reserved.

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Disclaimer and Limitations IMD is not responsible for any errors and omissions. The geographical boundaries shown in the publication do not necessarily correspond to the political boundaries. Published in India By Environment Monitoring and Research Centre, India Meteorological Department, Lodi Road, New Delhi- 110003 (India) Phone: 91-11-24620701 Email: [email protected] om PREFACE The beginnings of meteorology in India can be traced to ancient times from the philosophical writings of the Vedic period, contain serious discussion about the processes of cloud formation and rain and the seasonal cycles caused by the movement of earth round the sun.

But, the Modern Meteorology is regarded to have had its firm scientific foundation in the 17th century after the invention of thermometer, barometer and the formulation of laws governing the behaviour of atmospheric gases.

India is fortunate to have some of the oldest meteorological observatories of the world like those at Calcutta (now Kolkata) in 1785 and Madras (now Chennai) in 1796 for studying the weather and climate of India India Meteorological Department (IMD) has progressively expanded its infrastructure for meteorological observations, communications, forecasting and weather services and it has concurrently contributed to scientific growth since its inception in 1875. One of the first few electronic computers introduced in the country was provided to IMD for scientific applications in meteorology.

India was the first developing country in the world to have its own geostationary satellite, INSAT, for continuous weather monitoring of this part of the globe and particularly for cyclone warning. It has ventured into new areas of application and service, and steadily built upon its infra-structure during its history of 135 years. It has simultaneously nurtured the growth of meteorology and atmospheric science in India for sectoral services. Systematic observation of basic climate, environmental and oceanographic data is vital to capture past and current climate variability.

IMD has provided climatic observations and products to the national requirements including National Communication (NATCOM). To meet the future need, it is in process of augmenting its weather and climate-related observation systems that underpins analytical and predictive capability which is critical for minimising extreme climate variability impacts. I am hopeful that this publication on “Climate Profile of India” will contribute to the “India’s National Communication-II” to be submitted to UNFCCC next year.

The publication is based on the work mainly carried out by IMD scientists. I extend my sincere thanks to Sh. A K Bhatnagar, Dr A Mazumdar, Dr Y E A Raj, Sh B. Mukhopadhyay, Sh N Y Apte, Dr Medha Khole, Dr M Mohapatra, Dr A K Srivastava and Dr J Sarkar for providing requisite inputs. October 2010 New Delhi Ajit Tyagi Director General of Meteorology INDIA METEOROLOGICAL DEPARTMENT DOCUMENT AND DATA CONTROL SHEET 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Document title Document type Issue No. Issue date Security Classification Control Status No. of Pages No. igures No. of reference Distribution Language Authors Originating Division/Group Reviewing and Approving Authority End users Abstract Climate Profile of India Met Monograph Environment Meteorology-01/2010 October 2010 Unclassified Uncontrolled 122 39 62 Unrestricted English S D Attri and Ajit Tyagi EMRC DGM Ministries / Departments of Central and State Governments, Research organisations, INCCA (NATCOM), Scientific community, Public etc. Normal climatic pattern and long term trends over India during last more than 100 years have been presented here.

The publication contains studies with latest data on various aspects of important weather / climate systems viz. Monsoons, Cyclone, Drought, Floods and observational weather / climate mechanism in the country. Status and trends in parameters of atmospheric environment viz. radiation, ozone, precipitation chemistry etc has been depicted. The publication is also intended to provide requisite details for Indian Network of Climate Change Assessment (INCCA) to address climate change issues. Climate Change, Cyclone, Monsoon, Drought, Flood, Environment 17 Key words CLIMATE PROFILE OF INDIA

CONTENTS 1. Climate profile 2. Systematic observations 1-8 9-16 3. Climate change scenario 17-32 4. Behaviour of Monsoons 33-54 5. Floods 55-61 6. Tropical cyclones 62-89 7. Drought 90-105 8. Environmental status in India 106-117 References 118-122 Chapter – I CLIMATE PROFILE 1. Introduction India is home to an extraordinary variety of climatic regions, ranging from tropical in the south to temperate and alpine in the Himalayan north, where elevated regions receive sustained winter snowfall. The nation’s climate is strongly influenced by the Himalayas and the Thar Desert.

The Himalayas act as a barrier to the frigid katabatic winds flowing down from Central Asia keeping the bulk of the Indian subcontinent warmer than most locations at similar latitudes. As such, land areas in the north of the country have a continental climate with severe summer conditions that alternates with cold winters when temperatures plunge to freezing point. In contrast are the coastal regions of the country, where the warmth is unvarying and the rains are frequent. The country is influenced by two seasons of rains, accompanied by seasonal reversal of winds from January to July.

During the winters, dry and cold air blowing from the northerly latitudes from a north-easterly direction prevails over the Indian region. Consequent to the intense heat of the summer months, the northern Indian landmass becomes hot and draws moist winds over the oceans causing a reversal of the winds over the region which is called the summer or the south-west monsoon This is most important feature controlling the Indian climate because about 75% of the annual rainfall is received during a short span of four months (June to September).

Variability in the onset, withdrawal and quantum of rainfall during the monsoon season has profound impacts on water resources, power generation, agriculture, economics and ecosystems in the country. The variation in climate is perhaps greater than any other area of similar size in the world. There is a large variation in the amounts of rainfall received at different locations. The average annual rainfall is less than 13 cm over the western Rajasthan, while at Mausiram in the Meghalaya has as much as 1141 cm. The rainfall pattern roughly reflects the different climate regimes of the country, which vary from humid in the northeast 1 about 180 days rainfall in a year), to arid in Rajasthan (20 days rainfall in a year). So significant is the monsoon season to the Indian climate, that the remaining season are often referred relative to the monsoon. The rainfall over India has large spatial as well as temporal variability. A homogeneous data series has been constructed for the period 1901-2003 based on the uniform network of 1476 stations and analyzed the variability and trends of rainfall. Normal rainfall (in cm) pattern of the country for the four seasons and annual are depicted in Fig 1 and Fig 2 respectively.

Normal monsoon rainfall more than 150cm is being observed over most parts of northeast India, Konkan & Goa. Normal monsoon rainfall is more than 400cm over major parts of Meghalaya. Annual rainfall is more than 200 cm over these regions. For the country as whole, mean monthly rainfall during July (286. 5 mm) is highest and contributes about 24. 2% of annual rainfall (1182. 8 mm). The mean rainfall during August is slightly lower and contributes about 21. 2% of annual rainfall. June and September rainfall are almost similar and contribute 13. 8% and 14. 2% of annual rainfall, respectively.

The mean south-west monsoon (June, July, August & September) rainfall (877. 2 mm) contributes 74. 2% of annual rainfall (1182. 8 mm). Contribution of pre-monsoon (March, April & May) rainfall and post-monsoon (October, November & December) rainfall in annual rainfall is mostly the same (11%). Coefficient of variation is higher during the months of November, December, January and February. India is characterised by strong temperature variations in different seasons ranging from mean temperature of about 10°C in winter to about 32 °C in summer season (Fig 3).

Details of weather along with associated systems during different seasons are presented as under: 1. 1 Winter Season / Cold Weather Season (January and February) India Meteorological Department (IMD) has categorised the months of January and February in winter season. However, December can be included in this season for north-western parts of the country. This season starts in early December 2 associated with clear skies, fine weather, light northerly winds, low humidity and temperatures, and large daytime variations of temperature .

The cold air mass extending from the Siberian region, has profound influence on the Indian subcontinent (at least all of the north and most of central India) during these months. The mean air temperatures increase from north to south up to 17°N, the decrease being sharp as one moves northwards in the north-western parts of the country. The mean temperatures vary from 14 °C to 27°C during January. The mean daily minimum temperatures range from 22 °C in the extreme south, to 10 °C in the northern plains and 6 °C in Punjab.

The rains during this season generally occur over the western Himalayas, the extreme north-eastern parts, Tamil Nadu and Kerala. Western disturbances and associated trough in westerlies are main rain bearing system in northern and eastern parts of the country. 1. 2 Pre-monsoon season/ Summer season/ Hot weather season/ Thunderstorm season (March, April and May) The temperatures start to increase all over the country in March and by April, the interior parts of the peninsula record mean daily temperatures of 30-35 °C. Central Indian land mass becomes hot with daytime maximum temperatures reaching about 40°C at many locations.

Many stations in Gujarat, North Maharashtra, Rajasthan and North Madhya Pradesh exhibit high day-time and low night-time temperatures during this season. The range of the daytime maximum and night-time minimum temperatures in found more than 15 °C at many stations in these States. Maximum temperatures rise sharply exceeding 45 °C by the end of May and early June resulting in harsh summers in the north and north-west regions of the country. However, weather remains mild in coastal areas of the country owing to the influence of land and sea breezes.

The season is characterised by cyclonic storms, which are intense low pressure systems over hundreds to thousands of kilometres associated with surface winds more than 33 knots over the Indian seas viz. Bay of Bengal and the Arabian Sea. These systems generally move towards a north-westerly direction and some of them recurve to northerly or northeasterly path. Storms forming over the 3 Bay of Bengal are more frequent than the ones originating over the Arabian Sea. On an average, frequency of these storms is about 2. 3 per year.

Weather over land areas is influenced by thunderstorms associated with rain and sometimes with hail in this season. Local severe storms or violent thunderstorms associated with strong winds and rain lasting for short durations occur over the eastern and north eastern parts over Bihar, West Bengal, and Assam. They are called norwesters or “Kal Baisakhis” as generally approach a station from the northwesterly direction. Thunderstorms are also observed over central India extending to Kerala along wind-discontinuity lines. Hot and dry winds accompanied with dust winds (“andhis” ) blow frequently over the plains of north-west India. . 3 South-west Monsoon/ Summer Monsoon September) The SW monsoon is the most significant feature of the Indian climate. The season is spread over four months, but the actual period at a particular place depends on onset and withdrawal dates. It varies from less than 75 days over West Rajasthan, to more than 120 days over the south-western regions of the country contributing to about 75% of the annual rainfall. The onset of the SW monsoon normally starts over the Kerala coast, the southern tip of the country by 1 June, advances along the Konkan coast in early June and covers the whole country by middle of July.

However, onset occurs about a week earlier over islands in the Bay of Bengal. The monsoon is a special phenomenon exhibiting regularity in onset and distribution within the country, but inter-annual and intrannual variations are observed. The monsoon is influenced by global and local phenomenon like El Nino, northern hemispheric temperatures, sea surface temperatures, snow cover etc. The monsoonal rainfall oscillates between active spells associated with widespread rains over most parts of the country and breaks with little rainfall activity over the plains and heavy rains across the foothills of the Himalayas.

Heavy rainfall in the mountainous catchments under ‘break’ conditions results flooding over the plains. However, very uncomfortable weather due to high humidity and temperatures is the feature associated with the Breaks. 4 (June, July, August and Cyclonic systems of low pressure called ‘monsoon depressions’ are formed in the Bay of Bengal during this season. These systems generally form in the northern part of the Bay with an average frequency of about two to three per month and move in a northward or north-westward direction, bringing well-distributed rainfall over the central and northern parts of the country.

The distribution of rainfall over northern and central India depends on the path depressions. SW monsoon followed by these current becomes feeble and generally starts withdrawing from Rajasthan by 1st September and from north-western parts of India by 15th September. It withdraws from almost all parts of the country by 15th October and is replaced by a northerly continental airflow called North-East Monsoon. The retreating monsoon winds cause occasional showers along the east coast of Tamil Nadu, but rainfall decreases away from coastal regions. 1. Post-monsoon or Northeast monsoon or Retreating SW Monsoon season (October, November and December) North-East (NE) monsoon or Post-monsoon season subcontinent. Meteorological subdivisions namely is transition season associated with the establishment of the north-easterly wind regime over the Indian Coastal Andhra Pradesh Rayalaseema , Tamil Nadu, Kerala and South Interior Karnataka receive good amount of rainfall accounting for about 35% of their annual total in these months. Many parts of Tamil Nadu and some parts of Andhra Pradesh and Karnataka receive rainfall during this season due to the storms forming in the Bay of Bengal.

Large scale losses to life and property occur due to heavy rainfall, strong winds and storm surge in the coastal regions. The day temperatures start falling sharply all over the country. The mean temperatures over north-western parts of the country show decline from about 38°C in October to 28°C in November. Decrease in humidity levels and clear skies over most parts of north and central India after mid-October are characteristics features of this season (NATCOM 2004, IMD 2010). 5 (a) (b) (c) (d)

Fig 1: Normal rainfall pattern (cm) during (a) Winter (b) Pre-monsoon (c) Monsoon and (d) Post-Monsoon seasons for the period 1941-90 6 Fig 2: Annual normal rainfall pattern (cm) during 1941-90 7 Fig. 3: Seasonal temperature distribution over India 8 Chapter – II SYSTEMATIC OBSERVATIONS Agriculture based economy under favourable climatic conditions of the summer monsoon has necessitated a closer linkage with weather and climate since the Vedic period. Ancient Indian literature by Varahmihir, the ‘Brihat-Samhita’, is an example of ancient Indian weather research.

Modernized meteorological observations and research in India was initiated more than 200 years ago, since 1793, when the first Indian meteorological observatory was set up at Madras (now Chennai). IMD was formally established in 1875 with a network of about 90 weather observatories for systematic observation and research. Agricultural-meteorology directorate was created in 1932 to further augment the observation network. Many data and research networks have been added during the 135 years for climatedependent sectors, such as agriculture, forestry, and hydrology, rendering a modern scientific background to atmospheric science in India.

The inclusion of the latest data from satellites and other modern observation platforms, such as Automated Weather Stations (AWS), and ground-based remote-sensing techniques, has strengthened India’s long-term strategy of building up a self-reliant climate data bank for specific requirements, and also to fulfill international commitments of data exchange for weather forecasting and allied research activities. The latest observational network in depicted in Table 1. 2. Institutional arrangements The Ministry and Environment and Forests (MoEF), Ministry of Earth Sciences (MoES), Ministry of Science and Technology (MST), Ministry of Agriculture (MoA), Ministry of Water Resources (MWR), Ministry of Human Resource Development (MHRD), Ministry of Nonconventional Energy (MNES), Ministry of Defence (MoD), Ministry of Health and Family welfare (MoHFW), Indian Space Research Organization (ISRO) and India Meteorological Department (IMD) promote and undertake climate and climate change-related research in the country.

The MoES, MoEF, MST, MHRD and MOA also coordinate research and observations in 9 many premier national research laboratories and universities. The IMD possesses a vast weather observational network and is involved in regular data collection basis, data bank management, research and weather forecasting for national policy needs. 2. 2 Atmospheric monitoring There are 25 types of atmospheric monitoring networks that are operated and coordinated by the IMD.

This includes meteorological/climatological, environment/ air pollution and other specialized observation of atmospheric trace constituents. It maintains 559 surface meteorological observatories, about 35 radio-sonde and 64 pilot balloon stations for monitoring the upper atmosphere. Specialized observations are made for agro-meteorological purposes at 219 stations and radiation parameters are monitored at 45 stations. There are about 70 observatories that monitor current weather conditions for aviation. Although, severe weather events are monitored at ll the weather stations, the monitoring and forecasting of tropical cyclones is specially done through three Area Cyclone Warning Centres (Mumbai, Chennai, and Kolkata) and three cyclone warning centres (Ahmedabad, Vishakhapatnam and Bhubaneswar), which issue warnings for tropical storms and other severe weather systems affecting Indian coasts. Storm and cyclone detections radars are installed all along the coast and some key inland locations to observe and forewarn severe weather events, particularly tropical cyclones. The radar network is being upgraded by modern Doppler Radars, with enhanced observational capabilities, at many locations.

In another atmospheric observation initiative, the IMD established 10 stations in India as a part of World Meteorological Organization’s (WMO) Global Atmospheric Watch (GAW, formerly known as Background Air Pollution Monitoring Network or BAPMoN). The Indian GAW network includes Allahabad, Jodhpur, Kodaikanal, Minicoy, Mohanbari, Nagpur, Port Blair, Pune, Srinagar and Visakhapatnam. Atmospheric turbidity is measured using hand-held Volz’s Sunphotometers at wavelength 500 nm at all the GAW stations. Total Suspended Particulate Matter (TSPM) is measured for varying periods at Jodhpur using a High Volume Air Sampler.

Shower-wise wet only precipitation samples are collected at all the GAW stations using specially designed wooden precipitation collectors fitted with stainless 10 steel or polyethylene funnel precipitation collectors. After each precipitation event, the collected water is transferred to a large storage bottle to obtain a monthly sample. Monthly mixed samples collected from these stations are sent to the National Chemical Laboratory, Pune, where these are analyzed for pH, conductivity, major cations (Ca, Mg, Na, K, NH4+) and major anions (SO42-, NO3-, Cl-).

The IMD established the glaciology Study Research Unit in Hydromet. Directorate in 1972. This unit has been participating in glaciological expedition organized by the GSI and the DST. The unit was established for the: (a) determination of the natural water balance of various river catchment areas for better planning and management of the country’s water resources; (b) snow melt run-off and other hydrological forecasts; (c) reservoir regulation; (d) better understanding of climatology of the Himalaya; and (e) basic research of seasonal snow cover and related phenomena.

The IMD has established observing stations over the Himalayan region to monitor weather parameters over glaciers. In view of the importance of data in the tropical numerical weather prediction, IMD has been in the process of implementing a massive modernization programme for upgrading and enhancing its observation system. It is establishing 550 AWS out of which about 125 will have extra agricultural sensors like solar radiation, soil moisture and soil temperature, 1350 Automatic Rain Gauge (ARG) stations and 10 GPS in 2009 and will increase these to 1300, 4000 and 40, respectively .

In addition to this, a network of 55 Doppler Weather Radar has been planned of which 12 are to be commissioned in the first phase. DWR with the help of algorithms can detect and diagnose weather phenomena, which can be hazardous for agriculture, such as hail, downbursts and squall. A new satellite INSAT-3D is scheduled to be launched during 2011. INSAT-3D will usher a quantum improvement in satellite derived data from multi spectral high resolution imagers and vertical sounder. In addition to above, IMD is also planning to install wind profilers and radiometer to get upper wind and temperature data.

It is also augmenting its monitoring capability of the parameters of atmospheric environment. Trace gases, precipitation chemistry and aerosols will be monitored in the country on a long term basis at 2 baseline and 4 grab sample 11 GHG monitoring stations, Aerosol monitoring at 14 stations using sky radiometers, Black carbon measurement at 4 stations using aethelometers, Ozone Radiation measurements in NE and Port Blair in addition to existing stations in India and Antarctica, Turbidity and Rain Water chemistry at 11 stations and measurements at 45 stations.

The data will be monitored and exchanged globally as per GAW / WMO protocols and quality control will be ensured as per international standards. Such data will be used in C-cycle models to accurately estimate radiation forcing and quantify source and sink potential for policy issues under UN framework. The IMD, in collaboration with the NPL plays an important role for climate change-related long-term data collection at the Indian Antarctic base-Maitri. Continuous surface meteorological observations for about 22 years are now available for Schirmacher Oasis with National Data Centre of IMD.

The IMD collects meteorological data over oceans by an establishment of cooperation fleet of voluntary observing ships (VOF) comprising merchant ships of Indian registry, some foreign merchant vessels and a few ships of the Indian Navy. These ships, while sailing on the high seas, function as floating observatories. Records of observations are passed on to the IMD for analysis and archival. 2. 3 Data archival and exchange The tremendous increase in the network of observatories resulted in the collection of a huge volume of data.

The IMD has climatological records even for the period prior to 1875, when it formally came into existence. This data is digitized, quality controlled and archived in electronic media at the National Data Centre, Pune. The current rate of archival is about three million records per year. At present, the total holding of data is about 9. 7 billion records. They are supplied to universities, industry, research and planning organizations. The IMD prepared climatological tables and summaries/ atlases of surface and upper-air meteorological parameters and marine meteorological summaries.

These climatological summaries and publications have many applications in agriculture, shipping, transport, water resources and industry. 12 The IMD has its own dedicated meteorological telecommunication network with the central hub at New Delhi. Under the WWW Global Telecommunication System, New Delhi functions as a Regional Telecommunication Hub (RTH) on the main telecommunication network. This centre was automated in early 1976, and is now known as the National Meteorological Telecommunication Centre (NMTC), New Delhi.

Within India, the telecommunication facility is provided by a large network of communication links. The websites of IMD viz. http://www. imd. gov. in / http://www. mausam. gov. in operational from 1 June, 2000 contains dynamically updated information on all-India weather and forecasts, special monsoon reports, satellite cloud pictures updated every three hours, Limited Area Model (LAM), Global Circulation Model (GCM) generated products and prognostic charts, special weather warnings, tropical cyclone information and warnings, weekly and monthly rainfall distribution maps, earthquake reports, etc.

It also contains a lot of static information, including temperature and rainfall normal over the country; publications, data archival details, monitoring networks and a brief overview of the activities and services rendered by IMD. 2. 4 Augmentation of Weather and Climate forecasting capabilities (short, medium and long range) In view of growing operational requirements from various user agencies, IMD has embarked on a seamless forecasting system covering short range to extended range and long range forecasts.

Such forecasting system is based on hierarchy of Numerical Weather Prediction (NWP) models. For a tropical country like India where high impact mesoscale convective events are very common weather phenomena, it is necessary to have good quality high density observations both in spatial and temporal scale to ingest into assimilation cycle of a very high resolution nonhydrostatic mesoscale model. A major problem related to skill of NWP models in the tropics is due to sparse data over many parts of the country and near absence of data from oceanic region.

Data from AWSs, ARGs, DWRs, INSAT-3D and wind profilers are available in real time for assimilation in NWP models. A High Power Computing (HPC) system with 300 terabyte storage has been installed at NWP Centre at Mausam Bhavan. All 13 the systems have started working in an integrated manner in conjunction with other systems, such as all types of observation systems, AMSS, CIPS, HPCS, synergy system etc. , in a real-time and have greatly enhanced IMD capability to run global and regional models and produce indigenous forecast products in different time scales.

It has also started running a number of global and regional NWP models in the operational mode . IMD also makes use of NWP Global model forecast products of other operational centres, like NCMRWF T-254, ECMWF, JMA, NCEP, WRF and UKMO to meet the operational requirements of day to day weather forecasts. Very recently, IMD has implemented a multimodel ensemble (MME) based district level five days quantitative forecasts in medium range. Climate-related risks are likely to increase in magnitude and frequency in future.

There is need to prioritize actions to improve monitoring of such extremes and refine models for their prediction and projection in longer scale. New levels of integrated efforts like Global Framework for Climate Services/ National Framework for Climate Services (GFCS/NFCS) are required to strengthen climate research at existing and newer institutions to: • • • • • • Develop improved methodologies for the assessment of climate impacts on natural and human system Characterize and model climate risk on various time and space scales relevant to ecision-making and refine climate prediction skills Enhance spatial resolution of climate predictions, including improvements in downscaling and better regional climate models Improve climate models to represent the realism of complex Earth system processes and their interactions in the coupled system Develop a better understanding of the linkages between climatic regimes and the severity and frequency of extreme events Enable progress in improving operational climate predictions and streamlining the linkages between research and operational service providers.

IMD is taking initiatives for creation of Indian Climate Observation System (ICOS) to support such services in long run. 14 A dynamical statistical technique is developed and implemented for the realtime cyclone genesis and intensity prediction. Numbers of experiments are carried out for the processing of DWR observations to use in nowcasting and mesoscale applications. The procedure is expected to be available in operational mode soon.

Impact of INSAT CMV in the NWP models has been reported in various studies. Various multi-institutional collaborative forecast demonstration projects such as, Dedicated Weather Channel, Weather Forecast for Commonwealth Games 2010, Land falling Cyclone, Fog Prediction etc. are initiated to strengthen the forecasting capabilities of IMD. 15 Table 1 Atmospheric monitoring networks 2 3 4 6 7 8 9 Surface observatories Pilot balloon observatories RS/RW observatories Aviation current weather observatories Storm detecting radar stations Cyclone detection radar stations High-wind recording stations Stations for receiving cloud pictures from satellites a Low-resolution cloud pictures b High-resolution cloud pictures c INSAT-IB cloud pictures(SDUC stations) d APT Stations in Antarctica e AVHRR station Data Collection Platforms through INSAT Hydro-meteorological observatories a Non-departmental rain gauge stations i Reporting ii Non-reporting b Non-departmental glaciological observations (non-reporting) i Snow gauges ii

Ordinary rain gauges iii Seasonal snow poles Agro-meteorological observatories Evaporation stations Evapotranspiration stations Seismological observatories Ozone monitoring a Total ozone and Umkehr observatories b Ozone-sonde observatories c Surface ozone observatories Radiation observatories a Surface b Upper air Atmospheric electricity observatories a Background pollution observatories b Urban Climatological Units c Urban Climatological Observatories Ships of the Indian voluntary observing fleet Soil moisture recording stations Dew-fall recording stations AWS ARG GPS 559 65 3 71 17 10 4 7 1 20 1 1 100 701 3540 5039 21 10 6 219 222 39 58 5 3 6 45 8 4 10 2 13 203 49 80 550 1300 10 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 16 Chapter – III CLIMATE CHANGE SCENARIO

India Meteorological Department (IMD) maintains a well distributed network of more than 500 stations in the country for more than a century. The salient findings of the IMD studies (IMD Annual Climate Summary, 2009, Tyagi and Goswami, 2009, Attri 2006) are summarized as under: 3. 1 Temperature Analysis of data for the period 1901-2009 suggests that annual mean temperature for the country as a whole has risen by 0. 560C (Fig 4) over the period. It may be mentioned that annual mean temperature has been generally above normal (normal based on period, 1961-1990) since 1990. This warming is primarily due to rise in maximum temperature across the country, over larger parts of the data set (Fig 5).

However, since 1990, minimum temperature is steadily rising (Fig 6) and rate of its rise is slightly more than that of maximum temperature (IMD Annual Climate Summary, 2009). Warming trend over globe of the order of 0. 740C has been reported by IPCC (2007) Spatial pattern of trends in the mean annual temperature (Fig 7) shows significant positive (increasing) trend over most parts of the country except over parts of Rajasthan, Gujarat and Bihar, where significant negative (decreasing) trends were observed (IMD Annual Climate Summary, 2009). Season wise, maximum rise in mean temperature (Fig 8) was observed during the Post-monsoon season (0. 770C) followed by winter season (0. 700C), Premonsoon season (0. 640C) and Monsoon season (0. 330C).

During the winter season, since 1991, rise in minimum temperature is appreciably higher than that of maximum temperature over northern plains. This may be due to pollution leading to frequent occurrences of fog. 17 Upper air temperatures have shown an increasing trend in the lower troposphere and this trend is significant at 850 hPa level, while decreasing trend (not significant) was observed in the upper troposphere (Kothawale and. Rupa Kumar, 2002). 3. 2 Precipitation Trends The country as a whole, the all India annual and monsoon rainfall for the period 1901-2009 do not show any significant trend (Fig. 9a & 9b). Similarly rainfall for the country as whole for the same period for individual monsoon months also does not show any significant trend.

The alternating sequence of multi-decadal periods of thirty years having frequent droughts and flood years are observed in the all India monsoon rainfall data. The decades 1961-70, 1971-80 and 1981-90 were dry periods. The first decade (1991-2000) in the next 30 years period already experienced wet period. However, during the winter season, rainfall is decreasing in almost all the subdivisions except for the sub-divisions Himachal Pradesh, Jharkhand and Nagaland, Manipur, Mizoram & Tripura. Rainfall is decreasing over most parts of the central India during the pre-monsoon season. However during the post-monsoon season, rainfall is increasing for almost all the sub-divisions except for the nine sub-divisions (Fig 10).

The analysis for the monthly rainfall series of June, July, August, and September (% variation) for all the 36 subdivisions (Guhathakurta, P. and Rajeevan 2008) shows significant variations on the regional scale (Fig. 11) which are summarized as under: • June rainfall has shown increasing trend for the western and southwestern parts of the country whereas decreasing trends are observed for the central and eastern parts of the country. subdivisions. Its contribution to annual rainfall is increasing in 19 subdivisions and decreasing in the remaining 17 18 • The contribution of July rainfall is decreasing in central and west peninsular India (significantly in South interior Karnataka (95%), East M. P. 90%) Vidarbha (90%), Madhya Maharashtra (90%), Marathwada (90%), Konkan & Goa (90%), and North interior Karnataka (90%)), but has increased significantly in the northeastern parts of the country • In August, four (ten) subdivisions have shown decreasing (increasing) trends in rainfall. It has increased significantly (at 95% significance level) over the subdivisions Konkan and Goa, Marathwada, Madhya Maharashtra, Vidarbha, West M. P. , Telangana and west U. P. • September rainfall is increasing significantly (at 95% level of significance) in Gangetic West Bengal and decreasing significantly (at 90% level of significance) for the sub-divisions Marathwada, Vidarbha and Telangana. During the season, three subdivisions viz. Jharkhand (95%), Chattisgarh (99%), Kerala (90%) show significant decreasing trends and eight subdivisions viz.

Gangetic WB (90%), West UP (90%), Jammu & Kashmir (90%), Konkan & Goa (95%), Madhya Maharashtra (90%), Rayalseema (90%), Coastal A P (90%) and North Interior Karnataka (95%) show significant increasing trends. The trend analyses of the time series of contribution of rainfall for each month towards the annual total rainfall for each year in percentages suggest that contribution of June and August rainfall exhibited significant increasing trends, while contribution of July rainfall exhibited decreasing trends. However, no significant trend in the number of break and active days during the southwest monsoon season during the period 1951–2003 observed (Rajeevan et al 2006). 3. 3 Extreme Rainfall events A large amount of the variability of rainfall is related to the occurrence of extreme rainfall events.

The extreme rainfall series at stations over the west coast north of 12°N and at some stations to the east of the Western Ghats over the central parts of the Peninsula showed a significant increasing trend at 95% level of (Fig 12) were 19 confidence. Stations over the southern Peninsula and over the lower Ganga valley have been found to exhibit a decreasing trend at the same level of significance. Various studies on extreme rainfall over India have found the occurrences of 40 cm or more rainfall along the west and east cost of India, Gangetic West Bengal and north eastern parts of India. Country’s highest observed one day point rainfall (156. 3 cm) and also world’s highest 2-day point rainfall (249. cm) occurred in Cherrapunji of northeast India in the year 1995 (IMD 2006). Significant increasing trend was observed in the frequency of heavy rainfall events over the west coast (Sinha Ray & Srivastava, 2000). Most of the extreme rainfall indices have shown significant positive trends over the west coast and northwestern parts of Peninsula. However, two hill stations considered (Shimla and Mahabaleshwar) have shown decreasing trend in some of the extreme rainfall indices (Joshi & Rajeevan, 2006). Increase in heavy and very heavy rainfall events and decrease in low and moderate rainfall events in India have been reported by Goswami et al (2006).

Rao et al (2010) have assessed the role of Southern Tropical Indian Ocean warming on unusual central Indian drought of summer monsoon – 2008. The recent exceptionally heavy rainfall of 944 mm over Mumbai (Santacruz) on 26th July, 2005 was very unprecedented in nature, which led to many more studies on frequency and variability of heavy rainfall events. The development of a high resolution (1° X1° lat. /long. ) gridded daily rainfall dataset for the Indian region by IMD is very helpful in undertaking such studies. Based on the amount of rainfall in a day, IMD has classified into six categories. However, for extreme event studies, rain has been regrouped into three broad categories viz. i) light to rather heavy rainfall (0 ; R ? 64. mm), ii) heavy rainfall (64. 4 ; R ? 124. 4 mm) and iii) very heavy to exceptionally heavy rainfall (R ; 124. 4 mm ). Rainfall ; 124. 4 mm will be referred hereafter as extreme rainfall events (Pattanaik and Rajeevan, 2010). The frequency of extreme rainfall (Rainfall ? 124. 4 mm) shows increasing trend over the Indian monsoon region during the southwest monsoon season from June to September (JJAS) and is significant at 98% level (Fig. 13). It is also found that the increasing trend of contribution from extreme rainfall events during JJAS is balanced by a decreasing trend in category-i (rainfall ? 64. 4 mm/day) rainfall events. Similarly on monthly 20 cale, the frequency of extreme rainfall events show significant (95% level) increasing trend during June and July, whereas during August and September the increasing trend is not significant statistically (Fig. 14). Like the frequency of extreme rainfall events, the contribution of extreme rainfall to the total rainfall in a season is also showing highly significant increasing trend during the monsoon season from June to September and during June and July on monthly scale. It is observed that the mean monthly contribution of heavy and extreme rainfall events (rainfall ; 64. 4 mm in a day) during June-July is 5 to 6% higher than that during August-September and hence contributes significantly to the total rainfall during the first half of the season (June and July). 3. Cloud cover over the Indian Seas Both total and low cloud cover over Arabian Sea and the equatorial Indian Ocean are observed to decrease during the ENSO events. However, cloud cover over Bay of Bengal is not modulated by the ENSO events. On inter-decadal scale, low cloud cover shifted from a “low regime” to a “high regime” after 1980 which may be associated with the corresponding inter-decadal changes of sea surface temperatures over north Indian Ocean observed during the late 1970s (Rajeevan et al. , 2000). 3. 5 Heat Wave and Cold Wave A significant increase was noticed in the frequency, persistency and spatial coverage of both of these high frequency temperature extreme events (heat and cold wave) during the decade (1991-2000) (Pai et al. 2004). 3. Discomfort indices It has been found that in general, there is an increasing trend (significant) in the discomfort indices from the last 10 days of April to June over most of the Indian cities (Srivastava, et al. 2007). This publication is confined to observed climate change. However, future scenario of climate change in India have been brought out by Indian Institute of Tropical Meteorology, Pune (NATCOM 2004, Rupa kumar et al 2006, Krishan Kumar 2009, INCCA 2009) 21 1 0. 8 9 P OINT BINOM IAL FILTER TREND=+0. 560C/100 YEARS 0. 6 Annual Mean Temp Anomalies(0C) 0. 4 0. 2 0 -0. 2 -0. 4 -0. 6 -0. 8 1901 1910 1919 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 Y E A R S Fig 4: All India annual mean temperature anomalies for the period 1901-2009 (based on 1961-1990 average) shown as vertical bars (The solid blue curve how sub-decadal time scale variations smoothed with a binomial filter) 22 1. 5 TREND=+1. 02 0C/100 YEARS 1 9 P OINT BINOM IAL FILTER Annual Max Temp Anomalies(0C) 0. 5 0 -0. 5 -1 -1. 5 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 Y E A R S Fig 5: All India annual maximum temperature anomalies for the period 1901-2009 (based on 1961-1990 average) shown as vertical bars (The solid blue curve show sub-decadal time scale variations smoothed with a binomial filter) 23 1 9 POINT BINOMIAL FILTER 0. 8 TREND=+0. 120C/100 YEARS 0. 6 Annual Min Temp Anomalies(0C) 0. 4 0. 2 0 -0. 2 -0. 4 -0. 6 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 Y E A R S

Fig 6: All India annual minimum temperature anomalies for the period 1901-2009 (based on 1961-1990 average) shown as vertical bars (The solid blue curve show sub-decadal time scale variations smoothed with a binomial filter) 24 Fig 7: Spatial Pattern of Trend (0c/ 100 years) in Mean Annual Temperature Anomalies (1901-2009). Areas where trends are significant are shaded (red : warming, blue : cooling) 25 2 1. 5 9 P OIN T B IN OM IA L F ILT ER WINTER (JAN-FEB) TREND=+0. 70 0C/100 YEARS 1 JnFb en e p nme( ) a+e M Tm o a sC a A l i 0 0. 5 0 -0. 5 -1 -1. 5 -2 1901 1910 1919 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 Y E A R S 1. 5 PRE-MONSOON (MAR-MAY) 1 P OIN T B IN OM IA L F ILT ER TRE ND=+0. 54 0C/100 Y ARS E M- a M ne p n a s ) aM e T A me( r y a m o l i C 0 0. 5 0 -0. 5 -1 -1. 5 1901 1910 1919 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 Y E A R S 1 0 . 8 9 P O I N T B I N O M I A L F I L T ER MONSOON (JUN-SEPT) TRE ND=+0. 330C/100 YE ARS J nSp en e p n mi s 0 ) u – e M Tm Ao ae( C a l 0 . 6 0 . 4 0 . 2 0 -0 . 2 -0 . 4 -0 . 6 -0 . 8 1 01 9 1 1 9 0 1 1 9 9 1 28 9 1 37 9 1 46 9 1 55 9 1 64 9 1 73 9 1 82 9 1 91 9 2000 2009 Y E A R S 1. 5 TREND=+0. 77 0C/100 YEARS 1 9 P OIN T B IN OM IA L F ILT ER POSTMONSOON (OCT-DEC) O- e M n e p nmi sC c Dc e TmAo ae( ) t a l 0 0. 5 0 -0. 5 -1 1901 1910 919 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 Y E A R S Fig 8: All India Mean Temperature Anomalies for the four seasons for the period 1901-2009 (based on 1961-1990 average) 26 (a) ALL INDIA ANNUAL RAINFALL(% DEP. ) 30. 0 ANNUAL Linear (ANNUAL) 20. 0 10. 0 RAINFALL (% DEP) 0. 0 -10. 0 -20. 0 y = -0. 0036x – 0. 0149 2 R = 0. 0001 -30. 0 1901 1905 1909 1913 1917 1921 1925 1929 1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2001 YEAR (b) ALL INDIA SEASONAL (MONSOON) RAINFALL(% DEP. ) 30. 0 JJAS Linear (JJAS) 20. 0 10. 0 RAINFALL (% DEP) 0. 0 -10. 0 -20. 0 y = -0. 0015x – 0. 1038 2 R = 2E-05 -30. 1901 1905 1909 1913 1917 1921 1925 1929 1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2005 YEAR Fig 9: Trend in all India rainfall data for country as a whole, a) annual b) monsoon season, for the period 1901-2009. 27 2005 Fig 10: Trend in sub- divisional rainfall data (increase/decrease in rainfall in mm) for different seasons season (1901-2003). Different levels of significance are shaded with colors. 28 Fig 11: Trend in sub- divisional rainfall data of monsoon months (increase/Decrease in rainfall in percentage) to annual rainfall (1901-2003). 29 Fig. 12 : Time series of active days (a) and (b) break days during the monsoon season (1951–2003). 30 Fig. 3 : Average frequency (count per day) of occurrence of different rainfall (R) events during monsoon season (June to September) from 1951 to 2005. (a) Category-i with ‘R’ ? 64. 4 mm in a day, (b) Category-ii with 64. 4 ; ‘R’ ? 124. 4 mm in a day & (c) Category-iii with ‘R’ ; 124. 4 mm in a day. 31 2. 5 Average frequency 2. 0 1. 5 1. 0 0. 5 0. 0 (a) Category-iii, Jun y = 0. 009x + 0. 571 R? = 0. 124 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2002 2002 year 3. 5 Average frequency 3. 0 2. 5 2. 0 1. 5 1. 0 0. 5 0. 0 (b) Category-iii, Jul y = 0. 011x + 1. 097 R? = 0. 112 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 year 2. 5 Average frequency 2. 0 1. 1. 0 0. 5 0. 0 (c) Category-iii, Aug y = 0. 001x + 0. 880 R? = 0. 002 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 year 2. 5 Average frequency 2. 0 1. 5 1. 0 0. 5 0. 0 (d) Category-iii, Sep y = 0. 001x + 0. 594 R? = 0. 005 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 year Fig 14: Average frequency (count per day) of occurrence of category-iii rainfall events (rainfall ; 124. 4 mm in a day) on monthly scale from (a) June to (d) September during the period from 1951 to 2005 32 2005 2005 2005 2005 Chapter – IV BEHAVIOUR OF MONSOONS Southwest / Summer Monsoon

The behavior of major features of Southwest Monsoon can be analysed for any characteristic trend exhibited by them over the period of years in terms of the following: • • • Dates of monsoon onset, duration of monsoon over the country in terms of number of days and dates of withdrawal (Mazumdar et al 2001) Quantum of monsoon rainfall over various meteorological sub-divisions during June to September. Frequency of monsoon low pressure systems viz. Depressions, well marked lows, low pressure areas etc. 4. 1 Variability in monsoon Onset and Withdrawal A study of mean dates of onset of monsoon for the period 1941-2000 (Mazumdar et al 2001) revealed that the mean onset dates over majority of subdivisions have been later than normal in both the 30 years’ time slots of 19411970 and 1971-2000.

The magnitudes of late onset during 1941 – 1970 have been higher than those during 1971 – 2000. Some of these deviations are statistically significant. The maximum deviation being 11 and 7 days over Andaman & Nicobar Islands during 1941 – 1970 and 1971 – 2000 respectively. Since, for India as a whole, the commencement of onset starts from Andaman & Nicobar Islands, the SW monsoon had a late start by about a week during the period of study. Based on 100 years (1901 – 2000) of data, the onset dates for the twentieth century, when compared to the existing normals, the differences for the period are marginal except for Andaman Nicobar Islands where it has been greater (late onset) by about 5 days. 33

The lowest Standard Deviation (SD) of date of onset of about 5 days is over Andaman & Nicobar Islands during 1971-2000 and the highest of 14 days is over Jammu and Kashmir. For every subdivision of India, the values of SD are higher during 1941-1970 as compared to 1971-2000. Generally, the SDs of onset dates are about one week over high rainfall area and North Eastern parts, increasing to one and half week towards low rainfall areas of West and North Western parts of India. Generally, decreasing trends are found over northern parts (North of 25? N) and increasing trends over southern parts of India. The mean withdrawal dates are found to be later than the existing normal, in both the 30 years slot of 1941 – 1970 and 1971 – 2000, by about one to one and a half week.

A general late onset, as concluded earlier coupled with late withdrawal suggests a shift in the monsoon activity. The SDs of withdrawal dates range from 11 to 14 days during 1941 – 1970 and from 7 to 10 days during 1971 – 2000. This indicates that the variability in the withdrawal of monsoon has been greater during the first 30 years period as compared to the later half, not only in temporal but also in spatial scales. The duration of southwest monsoon is found to be higher than normal almost in all meteorological sub-divisions in both the 30 years’ period. The duration is much higher in the first half as compared to that during the second half.

The SDs of duration of monsoon varies between 13 and 19 days and 7 to 15 days during 19411970 and 1971-2000, respectively. These results are shown in Fig. 15 and Fig. 16. Decadal and epochal variability indicates near 30 year’s periodicity in onset, withdrawal and duration of the monsoon. Trends in the sub divisional rainfall data for the individual monsoon months are depicted as under: • June rainfall has shown significant increasing trend for the western and southwestern parts of the country, whereas significant decreasing trend is observed for the central and eastern parts of the country. 34 • July rainfall has significantly decreased for most parts of the central and peninsular India the country. ut has increased significantly in the northeastern parts of • August rainfall has increased significantly for the subdivisions Konkan & Goa, Marathwada, Madhya Maharashtra, Vidarbha, West Madhya Pradesh, Telengana and West Uttar Pradesh. • September rainfall has shown significantly decreasing trend for subdivisions Vidarbha, Marathwada and Telangana and increasing trend for the subdivision Sub Himalayan Gangetic West Bengal (Guhathakurta and Rajeevan, 2008). 4. 2. Trend in Withdrawal of monsoon The mean withdrawal dates are found to be later than the existing normal, in both the 30 years slot of 1941 – 1970 and 1971 – 2000, by about one to one and a half week.

A general late onset, as concluded earlier coupled with late withdrawal suggests a shift in the monsoon activity. The SDs of withdrawal dates range from 11 to 14 days during 1941 – 1970 and from 7 to 10 days during 1971 – 2000. This indicates that the variability in the withdrawal of monsoon has been greater during the first 30 years period as compared to the later half, not only in temporal but also in spatial scales. 4. 3 Duration of SW monsoon The duration of southwest monsoon is found to be higher than normal almost in all meteorological sub-divisions in both the 30 years’ period. The duration is much higher in the first half as compared to that during the second half.

The SDs of duration of monsoon varies between 13 and 19 days and 7 to 15 days during 1941-1970 and 1971-2000, respectively. Major findings of analyses of onset, withdrawal and duration of SW monsoon as during the period 1941-2000 are: 35 (i) (ii) (iii) (iv) Slight shift of monsoon activity with late onset and late withdrawal. Increase in the duration of the monsoon by about a week, as compared to normal duration. Decreasing trends in onset dates, roughly north of 25 N and general decreasing trends in both withdrawal and duration of the monsoon ; Decadal and epochal variability indicate near 30 year’s periodicity in onset, withdrawal and duration of the monsoon. 4. 4 Monsoon Forecasting (Long Range/ Seasonal Forecasting) 4. 4. History IMD started issuing tentative forecasts from 1882 to 1885 utilizing the indications provided by the snowfall in Himalayas. The success achieved infused greater confidence and the first of the regular series of forecasts was given on the 4th June 1886 and is continuing practically till date but for changes in its format and content. In 1892, long range forecast (LRF) for the rainfall for the second half of the monsoon season (August-September) was also started. In December 1893, the first forecast for winter precipitation over the Northern and central India was issued. Various subjective methods such as analogue and curve parallels for the LRF of Indian Summer Monsoon Rainfall (ISMR).

The efforts for better forecasts continued during the period during 1904-1924 and IMD started the forecasts based on objective techniques using correlation and regression techniques for preparing long range forecasts and discovered importance of Southern Oscillation, North Atlantic Oscillation and North Pacific Oscillation for monsoons. In 1922, India was divided into three main homogenous areas, namely, i) Peninsula ii) N. E. India and iii) Northwest India. In 1935, forecast for NE India was discontinued. The issuance of forecast for two homogenous regions (NW India and Peninsula) was continued till 1987. In 1988, IMD introduced operational 16 parameter power regression for issuing quantitative forecasts and parametric models for qualitative forecasts (whether normal/excess or deficient) for the southwest monsoon rainfall over the country as a whole.

IMD introduced a new two stage forecast strategy in 2003 viz. the first stage forecast for the seasonal (June to September) rainfall over the country 36 as a whole is issued in April and the update for the April forecasts is issued in June. Along with the update forecast, forecast for seasonal rainfall over broad homogeneous rainfall regions of India and July rainfall over country as a whole are also issued. During the period 2003-2006, the first stage quantitative and 5 category probabilistic forecast for the season rainfall over the country as a whole were issued using 8-parameter power regression (PR) model and Linear Discriminant Analysis (LDA) model respectively.

Update for the first stage forecasts were issued using 10 Parameter PR and LDA models. In 2007, IMD introduced new statistical forecasting system based on ensemble technique for the south-west monsoon season (June to September) rainfall over the country as a whole. 4. 4. 2 Present forecasting system At present, the forecast for the South-West monsoon rainfall is issued in two stages. The first stage forecast for the seasonal (June to September) rainfall over the country as a whole is issued in April and the update of the April forecast in June. Along with the update forecast, forecast for seasonal rainfall over four broad geographical regions of India and July rainfall over country as a whole are also issued.

For issuing the forecast for the seasonal rainfall over the country, a new statistical forecasting system based on the ensemble technique was introduced in 2007 using 8 predictors for the new ensemble forecast system as summarized in Table 2. The predictors used for the April forecast and the updated forecast in June are presented in Table 3 and table 4 respectively. The model error of the April forecast system is 5% and for the June forecast system, it is 4%. For developing the models, two different statistical techniques viz. Multiple Regression and Projection Pursuit Regression were considered For the forecast of July rainfall over the country as a whole, a statistical model with 6 predictors was developed using Principal Component Regression (PCR) technique.

The predictors used are: North Atlantic Sea surface temperature (December of previous year), NINO 3. 4 Sea Surface Temperature (May +June), North Pacific mean sea level pressure (March), East Asia mean sea level pressure 37 (February + March), North Atlantic mean sea level pressure (May) and Equatorial Indian Ocean mean sea level pressure (November of previous year). The model error of the model for July rainfall is 9%. For forecasting of South-West monsoon season rainfall over the four broad geographical regions of India (NW India, Central India, South Peninsula and NE India), multiple regression (MR) models based on separate set of predictors are used.

All the four multiple linear regression models have model errors of 8% of LPA. IMD also prepares an extended range forecast for the onset of southwest monsoon rainfall over Kerala. This forecast was first issued in 2005 using indigenously developed statistical model with 6 predictors (Table 5). In addition, IMD prepares operational long range forecasts for the Winter Precipitation (Jan to March) over Northwest India and Northeast Monsoon rainfall (October to December) over South Peninsula. For this purpose, separate statistical models have been developed. Table 2 Predictors used in new ensemble forecast system of Monsoon over India S. N. 1 2 3 4 5 6 7 8 Predictor (Period) Used for the forecasts in April and June

North Atlantic Sea Surface Temperature (December + January) Equatorial SE Indian Ocean Sea Surface Temperature April and June (February + March) East Asia Mean Sea Level Pressure April and June (February + March) NW Europe Land Surface Air Temperatures (January) April Equatorial Pacific Warm Water Volume (February+March) Central Pacific (Nino 3. 4) Sea Surface Temperature Tendency (MAM-DJF) North Atlantic Mean Sea Level Pressure(May) North Central Pacific Wind at 1. 5 Km above sea level (May) April June June June 38 Table 3 Details of predictors used for the first stage forecast ( April) No. Parameter Period Spatial domain CC with ISMR (19582000) -0. 45 ** A1 A2 A3 A4 A5 A6

North Atlantic SST anomaly Equatorial SE Indian Ocean SST anomaly East Asia surface pressure anomaly Europe land surface air temperature anomaly Northwest Europe surface pressure anomaly tendency WWV anomaly December + January February + March February + March January DJF(0) – SON (-1) February + March 20N-30N, 100W-80W 20S-10S, 0. 52 ** 100E-120E 35N-45N, 0. 36 * 120E-130E Five 0. 42 ** stations 65N-75N, -0. 40 ** 20E-40E 5S-5N, -0. 32 * 120E-80W * Significant at and above 5% level ** Significant at and above 1% level Table 4 Details of predictors used for the second stage forecast (June) No. Parameter Period Spatial domain 20N? 30N, 100W? 80W 20S? 0S, 100E? 120E 35N? 45N, 120E? 130E 5S? 5N, 170W? 120W 35N? 45N, 30W? 41W 5N? 15N, 180E? 150W CC with ISMR (1958? 2000) ? 0. 45 ** 0. 52 ** 0. 36 * ? 0. 46 ** ? 0. 402** ? 0. 55 ** J1 J2 J3 J4 J5 J6 North Atlantic SST anomaly Equatorial SE Indian Ocean SST anomaly East Asia surface pressure anomaly Nino? 3,4 SST anomaly tendency North Atlantic surface pressure anomaly North Central Pacific zonal wind anomaly at 850 hPa December + January February + March February + March MAM(0) – DJF (0) May May * Significant at and above 5% level ** Significant at and above 1% level 39 Table 5 Prediction of monsoon onset date over Kerala No 1 2 Name of Predictor

SE Indian Ocean SST anomaly NW India Minimum Surface air Temperature Anomaly Zonal Wind Anomaly at 1000hpa over Equatorial South Indian Ocean OLR Anomaly Over IndoChina OLR Anomaly Over Southwest Pacific Pre-Monsoon Rainfall Peak Date Temporal Domain JAN Deesa, Rajko, Guna Bikaner, Akola, Barmer 1-15may 1-15may 1-15may Pre-monsoon Geographical Domain 24S-14S, 80E-100E C. C 1975-2000 0. 41 16th April to 15th May -0. 63 3 4 5 6 10S-0, 80E-100E 17. 5N-27. 5N, 95E105E 0. 52 0. 43 30S,20S, 145E-160E -0. 54 South Peninsula (8N-13N, 74E-78E) 0. 65 40 Fig 15: Decadal mean values of onset, withdrawal and duration of monsoon 41 Fig 16: Epochal variation of onset, withdrawal and duration of SW Monsoon 42 4. 5 Northeast Monsoon The southeast peninsular India which falls under the rain hadow region during the summer season due to the presence of the Western Ghats receives rainfall from the subsequent northeast monsoon (NEM) which supplements the is a small scale inadequate precipitation received during the SW monsoon. It monsoon experienced by the southern peninsular India during October to December. Meteorological features associated with the NEM have been described in detail by IMD (1973). The onset of NEM is well defined with dramatic reversal of low level winds from southwesterly to northeasterly during mid-October followed by the commencement of fairly widespread rainfall activity along the coastal districts in about a week.

The seasonal rainfall manifests high inter-annual variability characterised by the occurrence of years of large scale droughts and large scale floods. The intra-seasonal variation of NEM is frequently associated with long dry spells. 4. 5. 1 Climatology The meteorological subdivisions of Coastal Andhra Pradesh (CAP), Rayalaseema (RYS), Tamil Nadu (TN), Kerala (KER) and South Interior Karnataka (SIK) are the beneficiaries of the northeast monsoon (Fig. 17). These sub-divisions receive about 35% of their annual total during this season. Normal rainfall received by these 5 sub-divisions based on the long period (1941-1990) are presented in Table 6.

All the 5 sub-divisions receive good rainfall in October. The sub-divisions while, the rainfall is of TN, KER and CAP receive good rainfall in November, confined to TN and KER during December. This season is the main rainfall period for TN during which nearly 47% of the annual total of 91cm is received. Further, the coastal districts of Tamil Nadu normally receive about 75-100cm of rainfall during this season thereby constituting nearly 60% of their annual total. Onset and the various features associated with the onset of NEM have been described by Raj (1992,1998 & 2003). 43 4. 5. 2 Trends in NEM rainfall Seasonal and monthly linear rainfall trends in October, November and

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Climate Profile India Essay. (2019, May 01). Retrieved from https://graduateway.com/climate-profile-india/

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