National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019) 4-5 July 2019 Organized by Ocean Society of India At

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019) (Co-sponsored by India Meteorological Department, Govt. of India)

4-5 July 2019

Organized by Ocean Society of India at Centre for Atmosphere Sciences Indian Institute of Technology Delhi Hauz Khas, New Delhi-110016

Sponsors

India Meteorological Department (IMD), New Delhi, MoES, Government of India

Centre for Marine Living Resources and Ecology (CMLRE),

Kochi, MoES, Government of India CENTRE FOR MARINE LIVING RESOURCES AND ECOLOGY MINISTRY OF EARTH SCIENCES, GOVERNMENT OF INDIA

National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, MoES, Government of India

Science and Engineering Research Board (SERB), New Delhi, Government of India

Department of Science and Technology (DST), New Delhi, Government of India

National Centre for Polar and Ocean Research (NCPOR), Goa, MoES, Government of India

Indian Institute of Tropical Meteorology (IITM), Pune, MoES, Government of India

Indian National Centre for Ocean Information Services (INCOIS), Hyderabad, MoES, Government of India

National Centre for Coastal Research (NCCR), Chennai MoES, Government of India

National Institute of Ocean Technology (NIOT), Chennai MoES, Government of India

National Geophysical Research Institute (NGRI), Hyderabad, Government of India

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019) (Co-sponsored by India Meteorological Department, Govt. of India)

4-5 July 2019

Organized by Ocean Society of India

at Centre for Atmosphere Sciences Indian Institute of Technology Delhi Hauz Khas, New Delhi-110016

Foreword

It gives me an immense pleasure that Ocean Society of India (OSI) under the aegis of the current Governing Council is organizing a National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019) during 4-5th July 2019 at Centre for Atmospheric Sciences (CAS), IIT Delhi. Coastal marine systems are one among the most ecologically and socio- economically vital ecosystems on the planet. Given their regional and global importance, these environments are a major focus of concern, as they are highly vulnerable to the perturbations that may be natural or anthropegenic. It is generally accepted that the climate change induced by the human activities would profoundly affect the coastal ocean and the vast and increasing populations that dwell along the coast for their livelyhood. Therefore, a better understanding of this environmental setting is imperative for reasons that have direct bearing on ecosystems, coastal management, navigation, defense, fisheries and weather forecasting. Continuous updation of global ocean observations suggested that the earth’s climate system undergoes changes naturally across a range of temporal scales including seasonal cycles, inter-annual patterns such as the ENSO, inter-decadal cycles such as the North Atlantic and Pacific decadal oscillations and multi-millenial scale changes such as glacial to inter-glacial transitions. This natural variability is reflected in the evolutionary adaptations of species and large-scale patterns of bio-geography and the geological records. However, anthropogenic activities play an uncontestable role and directly influence the climate system. Anthropogenic climate forcing is seemingly mediated by greenhouse gases (predominantly CO) emissions. It is also known that elevated CO2 and the resultant increase in the global mean temperature have a cascading effect on physical and biogeochemical processes of marine systems.

The recent increase in the cyclonic activity in the Arabian sea and the flash floods during the last monsoon in Kerala triggered a discussion under the auspices of OSI in Kochi last year. It recommended a series of actions including detailed studies and a more comprehensive dialogue on the way forward. As the modelling studies require systematic data collection, continuous updation and near validation, a regular exchange of results amongst the stakeholders is very important for the better understanding of the coastal hazards and the ecosystems. Having recognised the need as above the OSI held discussions with the CAS experts of IIT, Delhi and arrived at a decision to jointly hold a seminar on this area of topical interest.

OSI takes this opportunity to extend its sincere thanks to the organisers from IIT, Delhi and their associated staff. Special thanks to all the institutes under the Ministry of Earth Sciences, Department of Science and Technology and other agencies of national importance that came forward to support this event. We are overwhelmed by the fact that the major players of the coastal ocean and allied subjects have expressed their readiness to participate and contribute to the success of this National Seminar, CCCOP-2019. I am sure that this seminar facilitates such a discussion so that the deliberations will provide a platform for devising national strategies and suggest the way forward to address the possible impacts of climate change and the precautionary approach that country need to take.

Dr. M. Sudhakar President, Ocean Society of India Director, CMLRE, Kochi

Preface

Coasts are often more scenic and contain abundant natural resources. India has a long coastline of about 7500km and about 30% of Indian population lives along within 10km from the coast. As a part of the global climate system, the oceans are profoundly affected by any climate change and are already responding in noticeable ways. Any changes in coastal geomorphological or dynamical coastal ocean processes, particularly, in perspective of global warming scenario, will have a direct impact on the coastal population.

It is a well-known fact that the sea surface temperature (SST) is increasing as a result of global warming. The projected changes in the tropical cyclone activity is strongly correlated with the projected changes in tropical SST. This will result in increase of tropical cyclone potential intensity and hence probability of having stronger cyclones. Sea level rise near the coast is an important area of concern due to thermal expansion and net melting of land ice and sea ice. Some of the important processes, which affect the coastal inhabitants are storm surges generated by tropical cyclones, particularly, in view of climate change. Understanding of regional sea level rise with its variability and coastal ecosystem are important environmental drivers in a changing climate scenario.

The Seminar focusses on recent developments in the application of numerical modelling and simulation in understanding coastal ocean processes and improving capabilities for their prediction. The Seminar covers probabilistic approaches and risk assessment, advanced numerical methods, climate change in the coastal ocean, physics-ecosystem interactions. It will also cover novel coastal oceanographic measurements and analysis techniques.

The main thrust of the present Seminar is focused on ‘Climate Change and Coastal Ocean Processes (CCCOP-2019)’. Based on the thrust area, the following scientific themes are identified to address in more focused way.

 Climate change and coastal hazards  Coastal currents, waves and tides  Coastal erosion / accretion processes and their modelling  Coastal ecosystem and their modelling  Climate change impact on coastal ocean processes  Adaptation to coastal hazards and climate change impacts The CCCOP-2019 has received an overwhelming response, especially from the research students and early career scientists. A total 45 abstracts are selected for oral presentations including 15 invited talks from renowned scientists working in various areas covering all the themes of the Seminar. This Seminar provides a platform to bring together scientists, academicians, researchers, engineers, technologists in addressing some of the issues related to the thrust area. It is expected from the deliberations that a road map to improve prediction capability of the coastal ocean processes as it is also becoming increasingly important in view of the climate change.

We welcome all the participants of CCCOP-2019 to IIT Delhi to make this event a scientifically memorable National Seminar. We appreciate and thank India Meteorological Department for co- sponsoring this important event of national importance. We also thank all the other sponsors who have extended their support to make this event to happen at IIT Delhi. Lastly, we appreciate very much and thank the Ocean Society of India (OSI) for selecting to organize this Seminar at IIT Delhi.

Prof A D Rao, Convener and Vice-President of OSI Prof Vimlesh Pant, Co-convener

Ocean Society of India Governing Council Members: 2018-2020

President Dr Maruthadu Sudhakar

Vice Presidents Dr K V. Jayachandran Dr A D Rao

General Secretary Dr C. Revichandran

Joint Secretaries Dr Jayakumar Seelam Dr Tata Sudhakar

Treasurer Dr Mani Murali

Members Dr M.A. Atmanand Dr M. Baba Dr V. Sanil Kumar Dr C. V. K Prasad Rao Dr N. P. Kurian Dr Roxy Mathew Koll Dr R. Venkatesan Dr Pratima kessarkar

Ex-Officio Members Dr M. V. Ramana Murthy Dr P. Chandramohan

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019)

National Advisory Committee Dr M Rajeevan, Secretary, MoES, Chairman Dr V Ramgopal Rao, Director, IIT Delhi Dr K J Ramesh, DGM, IMD, New Delhi Dr S C Shenoi, Director, INCOIS Dr M A Atmanand, Director, NIOT, Chennai Dr M V Ramana Murty, Director, NCCR, Chennai Dr M Ravichandran, Director, NCAOR, Goa Dr N P Rao, Director, NCESS, Trivandrum Dr S K Singh, Director, NIO, Goa Dr E N Rajagopal, Head, NCMRWF, New Delhi Dr M Baba, Former Director, CESS, Trivandrum Dr M Sudhakar, Director, CMLRE, Kochi & President, Ocean Society of India Dr Ravi S Nanjundiah, Director, IITM, Pune Dr Akhilesh Gupta, Advisor, DST, New Delhi Cmde. M K Singh, PDNOM, New Delhi Dr Raj Kumar, Deputy Director, SAC, Ahmedabad Dr V M Tiwari, Director, NGRI, Hyderabad

Technical Program Committee Dr M Baba, Ex-CESS, Trivandrum, Chairman Dr K V Jayachandran, Kochi Dr Vimlesh Pant, CAS, IIT Delhi Dr C V K Prasada Rao, Kochi Dr C Revichandran, NIO, Kochi Dr Jayakumar Seelam, NIO, Goa Dr N P Kurian, NCESS, Trivandrum Dr R Venkatesan, NIOT, Chennai Dr C Gnanaseelan, IITM, Pune Dr Pratima Kessarkar, NIO, Goa Dr K V S R Prasad, AU, Visakhapatnam Dr R Sajeev, CUSAT, Kochi Dr Prasad Bhaskaran, IIT Kharagpur Dr Arun Chakraborty, IIT Kharagpur Dr Rashmi Sharma, SAC, Ahmedabad Dr M Mohapatra, IMD, New Delhi

Local Organizing Committee Dr Manju Mohan, Head, CAS, IIT Delhi Dr A K Mitra, NCMRWF, Noida Dr Vimlesh Pant, CAS, IIT Delhi, Co-convener Dr A D Rao, CAS, IIT Delhi, Convener

Contents

Sl. Title Authors Page No. No.

1 Adaptation to Climate Change: Climate Resilient Baba Mytheenkhan 1 Coastal Protection 2 Changing pelagic ecosystems with potential adjustments Sudhakar, M and Asha 3 in its food chain components Devi, C.R. 3 Operational forecast of extreme events: Observations M. Mohapatra 4 and modelling with special reference to tropical cyclones 4 Shoreline management: a case study on identification of N.P. Kurian, K.B. 5 sediment cells Kulkarni, T.S. Rathod, Shreekantha P. and Joseph Mathew 5 Regional sea level rise in the Indian Ocean – present A.S. Unnikrishnan 6 understanding and future perspectives 6 Interaction between long and short waves during A D Rao 7 extreme weather events 7 Studies on the storm-induced surge variations and Jaya Kumar Seelam 8 sediment transport estimates 8 Impact of Climate Change on Wind-Waves and its Prasad K. Bhaskaran 9 Implications on Coastal Ocean Processes for the Indian Region 9 Characteristics of the Discontinuity of Western Arun Chakraborty, Bijan 10 Boundary Current in the Bay of Bengal Kumar Das, T S Anandh and J. Kuttippurath 10 Maritime perspective of climate change Commodore Manoj 11 Kumar Singh 11 Upwelling and Oxygen Minimum Zone interactions in G.V.M. Gupta 13 the Arabian Sea: Modelling perspective 12 Significance of sustained ocean observation network for R. Venkatesan 14 climate studies 13 Short-term Climate Prediction with NCMRWF Coupled Ashis K. Mitra, Imranali 15 Model: Bay of Bengal Features M., Ankur Gupta, P.P. Saheed, Nagarjuna Rao, A. Karmakar, D.K. Mohapatra and E.N. Rajagopal 14 Impact of Climate Change on Shoreline and its M.V. Ramana Murthy 17 Management 15 Atlantic Niño modulation of the Indian summer Ramesh Kumar Yadav, G. 18 monsoon Srinivas and Jasti S. Chowdary 16 Flood and drought years computation using IMD rainfall Sudip Kumar Kundu and 20 data over the coastal Karnataka region Charu Singh 17 Status of Bay of Bengal Upper Ocean stratification in Ananya Karmakar, I. M. 22 GODAS and NEMO ocean analysis Momin, A. K. Mitra 18 Coastal Currents from NEMO analysis and forecast Imranali M. Momin, A. K. 24 Mitra and E. N. Rajagopal 19 Assessment of Ocean Surface Wind Speed from CMIP6 Athira Krishnan and 26 Global Climate Models and its comparison with Prasad K Bhaskaran Scatterometer winds over the Indian Ocean 20 Simulation of coastal inundation during cyclonic storms Anup Kumar Mandal, 28 in Bay of Bengal Ratheesh R, Smita Pandey and A D Rao 21 Adaptation measures for Coastal Infrastructure B. Santosh kumar, K. 30 Murali, S.A. Sannasiraj, R. Sundaravadivelu and V. Sundar 22 Shoreline change due to an extreme weather event Mikkilineni Dhananjayan, 32 Sannasiraj S A, Murali K 23 The relationship between Indian Ocean Dipole and G. Srinivas, P.G. Remya 34 tropical Indian Ocean significant wave height in and T.M. Balakrishnan reanalysis and CMIP5 models Nair 24 Case study of a typical coastal flooding event along the Swathy Krishna P.S, L. 36 Southwest part of the Kerala coast Sheela Nair, Amrutha Raj 25 CMIP5 projected Changes of Mean and Extreme Ocean Prashant Kumar, 38 Wave Height in the Pacific Ocean under the RCP8.5 and Sukhwinder Kaur, Seung- RCP4.5 Ki Min, and Xiaolan Wang 26 Influence of Natural Climate Variabilities over the Sukhwinder Kaur, and 40 Extreme Wave Climate in Pacific, North Atlantic and Prashant Kumar Indian Ocean 27 Climate change impact on extreme wave analysis in the Mourani Sinha, Susmita 42 Arabian Sea Biswas and Mrinmoyee Bhattacharya 28 Comparison study of OW detection among pre- and Jiya Albert, Bishnupriya 44 post-monsoon cyclones in the Bay of Bengal Sahoo, Prasad K Bhaskaran 29 Long term wind-wave climate study for Indian Ocean Sreelakshmi S and Prasad 46 using satellite altimetry data Kumar Bhaskaran 30 Pre-emptive policy frame work for coastal region and Lieutenant K Manoj 48 islands post climate change impact based on scientific Kumar, Commander T S inputs Ramanathan 31 Shoreline Change Detection Analysis and its effects on Barnali Das, Anargha A. 50 Mangrove colony – A case study of Alibag Coast, Dhorde Raigad, Maharashtra 32 Effect of temperature rise on bacterial metabolic rates: Lata Gawade, Chaitali 52 An experimental approach Madkaikar 33 Rapid intensification and weakening of Cyclone Ockhi Jyothi Lingala, Sudheer 54 and its related oceanic responses using HWRF modelling Joseph, Lijo Abraham system Joseph, Akhil Srivastava, Sunitha P, Hyun-Sook Kim, Avichal Mehra, Sundararaman G. Gopalakrishnan 34 Siltation Studies in the Far-Shelf of coastal Bay of Dhanya S., H. V. Warrior 56 Bengal 35 Modelling studies of the acidification of Bay of Bengal A. P. Joshi, V. Kumar and 58 H.V Warrior 36 Remote sensing based detection and monitoring of Rip T Sridevi, Surisetty V V 60 current hot spots at RK Beach stretch, Visakhapatnam Arun Kumar and Raj Kumar 37 Estimating the Impact of Fani Cyclone along the coast of Seema Rani 62 Orissa, India 38 Probabilistic Rip current likelihood model coupled with Surisetty V V Arun 64 Satellite data assimilative wave prediction system Kumar, Seemanth M, Rashmi Sharma and Raj Kumar 39 Validation and Analysis of the HF Radar-derived Samiran Mandal, Saikat 66 currents in the Gulf of Khambhat, India Pramanik and Sourav Sil 40 Analysis of intensity and frequency of tropical cyclones Stefy Thomas 68 in relation to Sea Surface Temperature 41 The influence of boreal summer intra-seasonal P.G. Remya, G. Srinivas 70 oscillation on Indian Ocean surface wave variability and T.M. Balakrishnan Nair 42 Decadal variability of Indian Summer Monsoon rainfall Vivek K Pandey and Lav 72 under historical run of a climate model Kumar 43 Impact of Different Ocean Initial Stratification on the Tanuja Nigam, Kumar 74 Tropical Cyclone Generated Mixing over the Arabian Ravi Prakash, Vimlesh Sea: A Sensitivity Study Pant 44 Biogeochemical variability of the Indian Ocean using an Vivek Seelanki, Vimlesh 76 Ecosystem model Pant 45 Diurnal variability of ocean environment on sub-grid Yadidya Badarvada, 78 scale using 1-D coupled model Sachiko Mohanty, A D Rao

Adaptation to Climate Change: Climate Resilient Coastal Protection Baba Mytheenkhan Former Director, Centre for Earth Science Studies, Trivandrum [email protected]

Abstract Table 1: Environmental Softness Ladder for the selection of climate resilient coastal protection [2] abridged. Climate change adaptation guidelines for coastal protection and management have been developed for Environme- India. This paper provides some glimpses of the Scheme Methodology ntal Impact Category Guidelines. rating 1 Introduction Steep To protect the 12 Hard Projections of the Intergovernmental Panel on Climate Seawalls land Change [1] indicating a high likelihood of more frequent and Low To protect the sustained heat waves and rainfall events are well known. As Gradient 11 Hard land predicted it is being reported that the ocean is continuing to Seawalls warm and acidify and mean global sea level is on the rise. Headland longer than The storm waves, coastal erosion and storm surges are in the 10 Hard rise. The climate change impacts (over and above natural Groynes 300 m wave, wind, and physical coastal dynamics) threaten the Long, High- Indian coast further. Mitigation of global warming being a Longer than crested 9 Hard difficult task, adaptation is the only feasible option. 100-300 m Groynes Adaptation along the coast involves initiatives and measures that reduce the impact of climate change. Responses can Short, High- Less than 100 include relocation of coastal infrastructure, enforcing coastal crested 8 Hard m long regulations, strengthening natural coastal systems, Groynes protecting and/or recreating the natural systems Low-crested Series of short technologically. The ADB TA: ‘Climate Resilient Coastal 7 Moderate Protection and Management Project’ for Indian coastline has Groynes groynes generated Guidelines to achieve the above objectives. The Nearshore Built close to 6 Moderate basic premise for resilience is that the natural systems are the Reef shore most appropriate and time tested protection measures for the coast. Hence, the coastal protection and management Offshore Emerged measures shall try to emulate the natural systems while Islands / offshore 5 Moderate designing the measures suitable for climate change impacts. Breakwaters structure This requires strengthening of the link between science, Offshore Reef built engineering, ecosystems, management and economics for 4 Moderate Reefs offshore informed decision making using multidisciplinary teams. Major sand 2 Environmental Softness Ladder Nourishment 3 Soft A means to decide, administer and cross check projects nourishment; for adherence to the Guidelines is proposed in the Guidelines Dune Sand 2 Soft [2]. The Environmental Softness Ladder (ESL) defines the Restoration nourishment: softness/hardness of the adopted protection measure in Replanting, relation to potential environmental effects (Table 1). While Dune Care 1 Soft the terms ‘soft’ and ‘hard’ are used in coastal engineering to fencing, describe the coastal protection solution, the definition is sometimes confused with the construction materials being used. ESL distinguishes the potential environmental effects design fits all’ is not possible given the wide ranging physical of structures, rather than recommending preferences for parameters, geomorphology, sediment sources and sediment favoured methodologies. It recommends that the options characteristics. The basic input data on waves, currents, lower on the ladder must be fully considered and eliminated longshore transport and other physical factors needs to be with proper justification before proceeding to a harder determined for the site and analysed. Decisions also require a protection methodology which is higher. range of numerical and / or physical models. Coastal protection requires greater scientific investigation, data 3 Engineering Considerations collection and numerical modelling. While every section of coast will be different and detailed 4 Minimum Beach Level studies will be needed, the challenge is to find the best, cost- A methodology for determining acceptable Minimum effective solution for each problem region. ‘One Beach Level (MBL) for coastal structures is provided [2]. The MBL may be used to define dune heights and crest level of coastal protection structures. To define MBL the following parameters are to be accounted: tides, winds and waves

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 1

around the Indian coast; storm surge, sea level, tidal and wave data projections for climate change and computed surf zone parameters such as set-up and run-up using this data (Figure 1). These parameters determine the level of the

Figure 2: The location on the dune nourishment in Bhatye, . Ratnagiri, Maharashtra. Figure 1: The scheme for computing the MBL [2]. sea at the coast and the ultimate re-sculpting or retreat of the coast in response to waves and storms. The science can identify what to expect in the future, e.g. what water levels to prepare for, whether cyclones will migrate more south as seas become warmer, or monsoon seasons get longer, wetter, shorter etc. All of these factors and many more determine outcomes at the coast. Further the Minimum Floor Level (MFL) introduced, which is again based on the safety from coastal climate change impacts, for buildings or structures at a coastal location may be used while giving building permission. The MBL and MFL recommended in the Guidelines are two key additions to coastal protection, planning and regulations. 4 An Example of Climate Resilient Coastal Intervention The beaches and dunes are the natural protection measures Figure 3: The climate resilient dune nourishment at Bhatye. adopted by the coast. Re-nourishing the eroded beaches and dunes are widely accepted as nature and tourist friendly (iii) A better unification of science (to investigate nature) and practices. A similar demonstration using Guidelines was engineering (for construction design) is a high priority. attempted in the Ratnagiri district of Maharashtra. The Acknowledgements Bhatye site is located near the Ratnagiri town and the location is in the southern part of the bay (Figure 2). A low dune which The author thanks Dr. Kerry P. Black for the leadership in existed here got eroded over a course of time. The pilot sub formulating the Guidelines. MoWRRD&GR, CWC, ADB, project is re-nourishment of the dune for a 500 m length GEF and FCG ANZDEC supported the program. (Figure. 3), planting it with native vegetation employing the Dune Care Group and providing fencing around the dune for References its protection from trampling (Figure. 3). The dune height is 5.5 m above Chart Datum with slopes of 1:5 on both sides. 1. IPCC. (2013). Climate Change: the Physical Science The sand required for it is dredged from the shoal adjacent to Basis. Contribution of Working Group I to the Fifth the river entrance. Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.- K.Plattner, Recommendations M.Tignor, S.K. Allen,J . Boschung, A.Nauels, Y. Xia, V. (i) Establish a national multidisciplinary coastal research Bex and P.M. Midgley (eds.)]. Cambridge University Press, program on (i) surf zone dynamics on natural beaches and Cambridge, United Kingdom and New York, NY, USA, around structure, (ii) prepare large- scale overviews on 1535pp. India’s coastal dynamics and (iii) plan grand coastal protection and management schemes. 2. K.P.Black et al (2019) ‘Reference Manual on Climate Change Adaptation Guidelines for Coastal Protection and (ii) Strengthen the link between science, engineering, Management in India’ (Eds: K.P. Black, M. Baba and J. ecosystems, management and economics for informed Mathew), prepared by FCG ANZDEC (New Zealand) with decision making. the support of the Global Environment Facility (GEF) and Asian Development Bank (ADB) for CWC / MoWR, RD&GR, Volumes 1 (72p) and 2 (335p).

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 2

Changing pelagic ecosystems with potential adjustments in its food chain components Sudhakar, M and Asha Devi, C.R. Centre for Marine Living Resources & Ecology, Ministry of Earth Sciences, Kochi-682037 [email protected]

Abstract Plankton serves as indicator of environmental settings due to their rapid responses to the environmental perturbation. Studies have shown that planktonic community vary significantly from lower to higher latitudes but proved that they are the single largest community in terms of biomass and numerical abundance over any other organisms found in sea. This has made them a unique ecosystem component with potential to use as universal tool to evaluate sustainability and resilience any given ecosystem. Studies carried out to evaluate the changes in terms of community structure and food web dynamics for different ecosystems viz. Arabian Sea, Indian Ocean Sector of Southern Ocean and Arctic fjords have explained the structural adaptions and adjustments exhibited by the planktonic group in these diverse environments studied. In the case of Arabian Sea, an increase in the carnivorous groups like Amphipoda were observed. Further, the impact of various oceanographic processes such as stratification, eddies, bloom and non-bloom etc. on the microbial food web has been evaluated from tropics to Polar Regions. Results clearly indicative of the fact that change in the micro- and mesozooplankton community, being the link between the primary and tertiary trophic levels, could bring about discernible alteration in the food web dynamics of the region abruptly. Also with the climate warming and resultant increase in stratification, the role of microbial grazers amongst the other planktonic community appear to gain significance in establishing equilibrium in the pelagic food-chain in these ecosystems.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 3

Operational forecast of extreme events: Observations and modelling with special reference to tropical cyclones M. Mohapatra India Meteorological Department Mausam Bhavan, Lodi Road, New Delhi-110003 [email protected]

Abstract India experiences many types of extreme weather events like cyclones, floods, droughts, heat wave, cold wave, thunder squalls and tornadoes etc. Among these the tropical cyclones are the major high impact weather events causing loss of lives and properties. Though the loss of lives due to cyclones have been minimised in recent years due to effective early warning based on modernised observations and numerical modelling by Ministry of Earth Sciences (MoES)/IMD and improvement in mitigation measures at central and state levels through policy frame works, there are still challenges to reduce the loss of properties by reducing the risk. The risk reduction due to cyclones depends on several factors including (i) hazard analysis, (ii) vulnerability analysis, (iii) preparedness & planning, (iv) early warning, (v) prevention and mitigation. The early warning component of cyclone risk management includes (i) skill in monitoring and prediction of natural hazards, (ii) effective warning products generation and dissemination, (iii) coordination with emergency response units and (iv) public awareness and perception about the credibility of the official predictions and warnings. There is also scope for improvement of early warning at local level in terms of (i) improving the monitoring and detection of mesoscale hazard associated with cyclone, (ii) improving the spatial and temporal scale of forecasts through technological upgradation, (iii) sectoral applications of impact based forecast and risk based early warning, (iv) warning communication to last mile and disaster managers through state of art technology, (v) developing synergized standard operation procedure among the early warning agencies and user agencies and (vi) upgrading and enhancing the link between early warning service provider and disaster managers. All these aspects have been discussed with special emphasis on extreme weather characteristics (spatial and temporal distribution and intensity etc.), damage potential, modeling and prediction, Prediction skills, achievements in recent years, existing gap areas and future scope in view of MoES strategy for 2030.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 4

Shoreline management: a case study on identification of sediment cells N.P. Kurian, K.B. Kulkarni, T.S. Rathod, Shreekantha P. and Joseph Mathew Sustainable Coastal Protection and Management Investment Program Karnataka Department of Public Works, Ports & Inland Water Transport Mangalore 575 001 [email protected]

Abstract The primary natural planning boundary for shoreline management is the sediment cell. A sediment cell is defined as the length of coastline, which is essentially self-contained as far as the movement of sediment is concerned such that the interruption of such movement in one cell should not have a significant effect on adjacent sediment cells. The boundaries of the sediment cells generally coincide with geomorphic boundaries and not socio-political boundaries. The selection of sediment cell boundaries has been subjective. There is a need to have some basis for the selection of the cell and to be confident that the cells are truly independent of each other. Being self-contained with regard to sediment movement, sediment dynamics should be the effective tool for identification of sediment cells. The defining of sediment cells helps consideration of coastal processes as a system and assists coastal management plans by illustrating the links between inputs, components, stores, transfers and outputs. Human intervention in a coastal system, in the form of coastal defences for example, is likely to have repercussions elsewhere in the system.

In this paper the initiatives already made in the country in identifying sediment cells for the Indian coast are reviewed. Since the available works are lacking in terms of consideration of sediment dynamics, an attempt is made here to identify Primary and Secondary Sediment Cells for Karnataka coast based on coastal processes. For identifying the Primary Cell (PC) boundaries, the concept of Closure Depth which is the seaward limit of effective profile fluctuation over long-term (seasonal or multi-year) time scales is invoked. The Closure Depth was computed for the Karnataka coast using hindcast wave data for a 5-year period (20013-18). The offshore wave data pertain to around 45 m water depth off Suratkal, south Karnataka coast and Gokarna, north Karnataka coast. Based on the computations and analysis, headlands or other geomorphic units extending upto 12 m in the north and 11 m depth in the south can be taken as PC boundaries. Secondary Cells (SC) are cells that are semi-isolated. One criterion for identification of SCs could be the sediment transport during the non-monsoon season. The offshore extent of longshore sediment transport is confined to the breaker zone. Headlands or such other artificial structures that extend beyond the breaker zone during the non-monsoon season is proposed as boundaries of SCs. The offshore boundary of the longshore transport during the non-monsoon is computed as 5 m and 6 m water depth respectively for the southern and northern Karnataka coast. The PCs and SCs for the Karnataka coast are identified based on the above approach. The methodology derived can be replicated for the other coastlines of the country.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 5

Regional sea level rise in the Indian Ocean – present understanding and future perspectives A.S. Unnikrishnan CSIR-National Institute of Oceanography, Goa [email protected]

Abstract Global sea-level-rise trend in the past has been well studied and documented (Church et al., 2013). Recent studies (Nerem et al., 2019) using satellite altimeter data during the past 25 years found an acceleration in global mean sea level rise, which indicates increased flooding in future due to sea-level rise.

In the Indian Ocean, the tide gauge network improved since the commencement of Indian Ocean Tsunami Warning Centre after the occurrence of the 2004 tsunami. However, the past tide-gauge records having about 40 years of data are only about 20. Among them, the longest past tide gauge records having more than 100 years record are those of Mumbai (India) and Fremantle (Australia). Moreover, the coverage is not uniform, particularly poor in the Southern Ocean. In fact, Nidheesh et al. (2017) reported inconsistencies in decadal signals in the Indian Ocean, derived from various sea-level reconstruction products, presumably due to poor spatial coverage in the data. There have many studies on sea-level variability in the Indian Ocean, for example, Ravichandran et al. (2017), Shankar et al. (2010) etc.

One of the difficulties in obtaining estimates of accurate sea-level rise trends using tide-gauge data has been the non- availability of data on land movements in regions close to the location of tide gauges. Earlier studies (for example, Church et al., 2006, Unnikrishnan and Shankar, 2007, Unnikrishnan et al., 2015) made use of corrections for glacial isostatic adjustment using the results from ICE models (Peltier, 2015) to account for land movements. But measurements from GNSS need to be incorporated to account for land movements so that accurate assessment of net sea-level rise can be obtained.

In the coastal regions of Indian Ocean, some regions have been identified for deltaic subsidence. Besides, some regions experience increased groundwater extraction, which causes changes in net sea level rise (Veit and Conrad, 2016). For better estimates of sea-level rise, it is necessary to have measurements on land movement. These measurements need to be co- located with tide gauge measurements, as pointed out in earlier studies (for example, King, 2014).

References Church, J.A., Clark, P.U., Cazenave, A., Gregory, J.M., Jevrejeva, S., Levermann, A., Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, D., Unnikrishnan, A.S., Chapter 13 : Sea Level Change. In Climate Change 2013. The Physical Science basis. Working Group I Contributions to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Ed. by Stocker, T.F., Qin, D., Plattner, G.K., Tignor,M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Cambridge University Press, Cambridge, U.K., 2013, 1137-1216. Church J.A., White, N.J. and Hunter, J.R. (2006). Sea-level rise at tropical Pacific and Indian Ocean islands. Global and Planetary Change, 53, 155-168. King, M. 2014. Priorities for installation of continuous Global Navigation Satellite System (GNSS) near to tide gauges. Technical Report. University of Tasmania. October 2014. DOI: 10.13140/RG.2.1.1781.7049. Nidheesh, A.G., M.Lengaigne, J. Vialard, T. Izumo, A.S. Unnikrishnan, B. Meyssignac, B. Hamlington and C. de Montegut (2017). Robustness of observation-based decadal sea level variability in the Indo-Pacific Ocean, Geophysical Research Letters, 44, doi:10.1002/2017GL073955. Shankar, D., Aparna, S.G., McCreary, J.P., Suresh, I., Neetu, S., Durand, F., Shenoi, S.S.C., AlSaffani, M.A., Minima of interannual sea-level variability in the Indian Ocean. Progress in Oceanography, 2010, 84(3-4), 225-241. Peltier, W.R., Argus, D.F. and Drummond, R. (2015) Space geodesy constrains ice-age terminal deglaciation: the global ICE-6G_C (VM5a) model. J. Geophys. Res. Solid Earth 120:450-487. https//doi.org/10.1002/2014JB11176. Ravichandran, M. et al.(2017). Causes for the reversal of North Indian Ocean decadal sea level trend in recent two decades. Climate Dynamics. DOI:10.1007/s00382-017-3551-. Shankar, D., Aparna, S.G., McCreary, J.P., Suresh, I., Neetu, S., Durand, F., Shenoi, S.S.C., AlSaffani, M.A., Minima of interannual sea-level variability in the Indian Ocean. Progress in Oceanography, 2010, 84(3-4), 225-241. Unnikrishnan, A.S. and Shankar, D. (2007), Are sea-level-rise trends along the north Indian Ocean coasts consistent with global estimates ? Global and Planetary Change, 57, , 301- 307. Unnikrishnan, A.S., Nidheesh, A.G. and M. Lengaigne (2015). Sea-level-rise trends off the Indian coasts during the last two decades. Current Science. 108(5), 966-970. Viet, E. and C.P. Conrad (2016) The impact of groundwater depletion on spatial variations in sea level during the past century. Geophysical Research Letters, 10.1002/2016GL68118.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 6

Interaction between long and short waves during extreme weather events A D Rao Centre for Atmospheric Sciences Indian Institute of Technology Delhi Hauz Khas, New Delhi-110016 [email protected]

Abstract The frequency of landfall cyclones on the west coast of India is relatively few compared to that of for the east coast of India. The damage that results from any tropical cyclone is mainly due to combined effect of storm surges (SS) and local astronomical tides, which are basically characterized as long waves. The generation and propagation characteristics of SS are mainly influenced by the basin characteristics, translation speed of a cyclone and local tides. The basin characteristics include the coastline geometry, bottom relief features such as shelf slope and width of the continental shelf. The west coast of India has a larger continental shelf area compared to the east coast. The width of the shelf region is about 60km off Kochi in Kerala, and that gradually increases to about 330km off Daman (South Gujarat). The shelf breaks along the entire coast at about 130m water depth. The coast is also dominated by the wind waves which are short waves, generated during a cyclone period. Due to wave breaking effects in near shore regions, the transfer of momentum to water column results in varying elevations typically referred as ‘wave setup’ attributed by the wave radiation stress gradient (RSG). The wave setup has a direct impact on the total water level (TWL) near the coast.

The present study reports role and dependence of continental shelf geometry on the nonlinear interaction of storm surges, tides and wind waves along the west coast of India during a cyclone period. In the first idealized experiment, the coast is assumed as a straight coastline with varying continental shelf width as in the actual case. The reason for considering the straight coastline is to nullify the effect of coastline geometry in the computations of surge-tide-wave interaction. The bathymetry for the model domain is constructed such that the depth contours follow the coastline and continental shelf width increases from south to north. In the second experiment deals with the actual coastline geometry and bottom topography and a comparison is also made with that of experiment one. For the experiments, a hydrodynamic finite-element based Advanced Circulation 2D depth integrated (ADCIRC- 2DDI) model is used in standalone mode for computation of storm tides and coupled ADCIRC+SWAN model for surge-tide-wave interactions are used by considering 13 idealized cyclone tracks covering the study domain. It is noticed that an amplification of peak storm surge of ~ 12cm for every 10 km increase in the shelf width. The interaction of tides on surges and wind waves is observed maximum at low-tide and minimum at flood tide over the wide shelf regions. In general, the non-linear interaction is found to be 15% - 20% for any cyclone track or tidal phase. The wave setup caused by wind waves is marginally varies with change of amplitude and phase of the tide, however, its profile is significantly modified, particularly over the wider shelf widths. The impact of wave on TWL is increased by 45cm as the shelf width gets wider up to 120km and beyond this width, the effect of waves is minimal of about 15cm. The study highlights a threshold value of about 120km shelf width is crucial with regards to wave-induced stress on total water elevation.

The rise of total water levels at the coast is caused primarily by three factors that encompass storm surges, tides and wind waves. The accuracy of total water elevation (TWL) forecast depends not only on the cyclonic track and its intensity, but also on the spatial distribution of winds which include its speed and direction. In the present study, the cyclonic winds are validated using buoy winds for the recent cyclones formed in the Bay of Bengal since 2010 using Jelesnianski wind scheme. It is found that the cyclonic winds computed from the scheme show an underestimate in the magnitude and also a mismatch in its direction. Hence, the wind scheme is suitably modified based on the buoy observations available at different locations using a power law which reduces the exponential decay of winds by about 30%. Moreover, the cyclonic wind direction is also corrected by suitably modifying its inflow angle. The significance of modified exponential factor and inflow angle in the computation cyclonic winds is highlighted using statistical analysis. Using the ADCIRC model, the TWL is computed as a response to combined effect of cyclonic winds and astronomical tides. As contribution of wave setup plays an important role near the coast, a coupled ADCIRC+SWAN is used to perceive the contribution of wind waves on the TWE. The experiments are performed to validate computed surge residuals with available tide gauge data. On comparison of observed surge residuals with the simulations using modified winds from the uncoupled and coupled models, it is found that the simulated surge residuals are better compared, especially with the inclusion of wave effect through the coupled model.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 7

Studies on the storm-induced surge variations and sediment transport estimates Jaya Kumar Seelam Senior Principal Scientist, CSIR – National Institute of Oceanography, Goa [email protected]

Abstract One of the perennial natural phenomena the Indian coastline experience is cyclonic-storms. There have been considerable advances in the storm prediction and its landfall locations. Many studies on storms and associated surges are available. However, measured surge estimates for most storms are not readily available. Due to this shortcoming, numerical models are often used to estimate the storm surges. A case study of Thane Cyclone is considered to estimate the surge variations for different storm conditions. The different conditions considered were the maximum wind speeds, storm size, storm forward speed. These conditions were increased from 5% to 40% over and above the normal storm parameters. A slow-moving storm case is also considered to observe the effect of the slow-moving storm on the surge variation. The studies show that for the Thane Cyclone, increase in maximum wind speed magnitude by 40% resulted in an increase of maximum surge by 60%. A 40% increase in maximum wind radius resulted in reduction of maximum surge value by 10%, although the surge duration is observed to increase by 1 hour. An increased cyclone forward speed by 40% showed almost similar maximum surge height. However, the storm reached coast 18 hours earlier and a reduction in the storm surge duration by 2 hours was observed. Storm induced sediment transport along the nearshore region is of great importance for shoreline morphology changes. Although there is scarce information on the sediment transport rated during storms, numerical studies can be used to assess the variations in the sediment transport for storm conditions. The variations in storm conditions and their influence on the sediment transport estimates for the Thane Cyclone is studied using MIKE by DHI numerical modelling software. This paper presents the numerical modelling studies on the storm surges and sediment transport rates under varying storm conditions.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 8

Impact of Climate Change on Wind-Waves and its Implications on Coastal Ocean Processes for the Indian Region Prasad K. Bhaskaran Department of Ocean Engineering & Naval Architecture Indian Institute of Technology Kharagpur Kharagpur 721 302, West Bengal, India [email protected], [email protected]

Abstract The Intergovernmental Panel on Climate Change (IPCC) Assessment Report articulates on the effect of climate change across the global oceans. The likely futuristic climate change projections covering aspects on the increased frequency and intensity of extreme weather events are also covered under the IPCC report. The recent IPCC document beginning from the Fourth Assessment report highlights on the importance of wind-waves and its role in a changing climate scenario. Wind- waves contributes a major role in the climate system having direct implications on coastal processes such as wave setup during extreme weather events, flooding and erosion thereby playing a pivotal role in the sediment budget along the Indian coastlines. Increased storminess and activity of extreme waves in the open ocean generates active swell field and the energy level propagates long distances impacting the coastal environment. Also, wind-waves can influence the exchange of momentum flux, heat and mass transfer across the air-sea interface. The study presents on the impact of climate change on the variability of maximum significant wave heights and wind speeds over the Indian Ocean region. Daily observed data from multi-satellite missions covering a period of two decades are used to evaluate and assess variability of wind-wave characteristics over the Indian Ocean domain. The presentation will highlight the regions or sectors over the Indian Ocean belt that experienced maximum changes in wind-wave climate and also the influence of these changes on coastal wave climate for the East coast of India. The role of non-linear wave-wave interaction in modulating and modifying the local wind- waves over coastal regions will be discussed. The importance of energy flux density in modelling wind-waves and its role in coastal processes will be highlighted. Extreme weather events such as tropical cyclones during landfall can pose significant impact in the coastal environment. Recent research shows clear evidence on increased intensity and size of tropical cyclones that forms over the North Indian Ocean basin that is linked with climate change. In addition, the regional tropical cyclone activity over this basin experiences a paradigm shift in cyclogenesis. The need to revisit the existing parametric wind formulation and the necessary modifications that is required for operational needs will be emphasized. The presentation will cover the importance of a coupled wave-hydrodynamic model that improves coastal and nearshore extreme water levels during very severe cyclone cases. The study also presents a detailed validation of various parameters such as significant wave height, storm tide, and flooding scenarios associated with the extreme event against the field measured data. Finally, the key challenges, priority areas and knowledge gaps identified for a detailed research on wind-waves and tropical cyclones will be highlighted.

Keywords: Climate Change, Coastal Processes, Wind-Waves, Tropical Cyclones

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 9

Characteristics of the Discontinuity of Western Boundary Current in the Bay of Bengal Arun Chakraborty, Bijan Kumar Das, T S Anandh and J. Kuttippurath Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur 721302, India. [email protected]

Abstract The Western Boundary Current in the Bay of Bengal (BOB), also known as East India Coastal Current (EICC), is northward (southward) and continuous during pre- (post-) Indian Summer Monsoon (ISM), but discontinuous during ISM (June– September). This study investigates the features of this discontinuity and the role of eddies, local winds and southern open boundary lateral forcing using AVISO TOPEX Poseidon, ERS and Jason1 combined sea surface height anomaly, Ocean Surface Current Analysis Real–time (OSCAR) surface currents and high resolution Regional Ocean Modelling System (ROMS) simulations. The study shows that the discontinuity is governed by westward and southwestward propagating eddies between two opposite facing flows along the boundary; northward of 10°N and southward of 21°N. This northward flow of 10°N is wind driven with lateral influence during September. The southward flow of 21°N is influenced by Summer Monsoon Current but the local wind influence starts from July onwards. During ISM, about 79% cyclonic and 77% anticyclonic eddies move westward while 68% cyclonic and 69% anticyclonic eddies move southward. Three highly active eddy genesis areas, having more than 50% activities during ISM, are identified; northern and south-western bay for cyclonic and off Visakhapatnam (17.68°N, 83.21°E) coast for anticyclonic eddies. The pre– (post–) ISM is dominated by anticyclonic (cyclonic) eddies, which are distributed all over the western BOB and away from northward (southward) EICC. The ISM is dominated by westward and southwestward moving both cyclonic and anticyclonic eddies, which are confined to the boundary, where discontinuity is generally observed.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 10

Maritime perspective of climate change Commodore Manoj Kumar Singh PDNOM, Indian Navy [email protected]

Abstract Climate Change is one of the most formidable developmental challenges faced by humanity today. Global climate change, by its very nature, is a trans-national phenomenon. While its impacts will not respect political frontiers, the sources of climate related problems and those at risk from them might well be on different sides of national boundaries. This situation is further complicated when the boundaries themselves are unclear, contested or both. As states react to climate change issues in line with their self interests, asymmetries in risk perceptions and the existence of unresolved inter-state disputes are likely to complicate ongoing conflicts. The impact of climate change tends to be more pronounced for the disadvantaged, making them even more vulnerable to climate risks. The geopolitical consequences of climate change are determined by local political, social, and economic factors as much as by the magnitude of the climatic shift itself. As a rule, wealthier countries and individuals will be better able to adapt to the impacts of climate change, whereas the disadvantaged will suffer the most. An increase in rainfall, for example, can be a blessing for a country that has the ability to capture, store, and distribute the additional water. Whereas, it would be a deadly source of soil erosion for a country that does not have adequate land management practices or infrastructure. Developing countries are especially vulnerable, many with limited capacity to adapt to rising sea levels or recover from associated losses.

Over Oceans, incidence of extreme seas and intensity of cyclones with unusual movements are likely to increase. Climate change may also render the Indian Ocean nations vulnerable with more frequent and higher storm surges. In the developing world, even a relatively small climatic shift can trigger or exacerbate food shortages, water scarcity, destructive weather events, spread of disease, human migration, and natural resource competition. These crises are all the more dangerous because they are interwoven and self-perpetuating, for example, water shortages can lead to food shortages, which can lead to conflict over remaining resources, which can drive human migration, which can create new food shortages in new regions. Every day the news delivers more facts, figures and statistics that support that Climate Change is a reality and would affect each one of us with varying degree. Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report released in Nov 2014, highlighted disturbing trend observed in the Climate Change. Rising rates and magnitudes of warming, Sea Level Rise (SLR) and other changes in the climate system, accompanied by ocean acidification, increase the risk of severe, pervasive, and in some cases, irreversible detrimental impacts. Over half the population of the planet about 3.2 billion people lives and works in a coastal strip on just 10% of the earth’s land surface, whose livelihood is greatly linked with stable ecosystem. A mean SLR of 15-38 cm is projected along India’s coast by the mid 21st century and of 46-59 cm by 2100. India’s NATCOM I assessed the vulnerability of coastal districts based on physical exposure to SLR, social exposure based on population affected, and economic impacts. In addition, a projected increase in the intensity of tropical cyclones poses a threat to the heavily populated coastal zones in the country.

It is well documented that Climate change is likely to have a serious impact on the maritime domain. The warming of ocean water and melting of land ice brought on by the changing climate, will raise sea levels, change salinity and alter ocean water temperatures. The absorption of carbon dioxide in seawater will increase ocean acidity levels, and the melting polar ice will alter the flow of ocean currents. As the surface and undersea environment changes, it may force maritime forces to revise their operational regimes. Naval operational forces may soon realize that the changing environment has a bearing on all operational aspects: from Navigation, Maneuvers, to operational exercises, maintenance of ships, engines, and other equipment. If the environment continues on its present steep trajectory, regional navies will need to seriously study technologies, procedures and skills to deal with the changes in the maritime environment. Further, rising sea levels are likely to threaten to some degree On/Off-shore infrastructure/ installations necessary for maritime operations. With the weather conditions in the Indian Ocean getting worse with time there is an increased possibility of storms and associated storm surge in water levels. The maritime sector might find some of its coastal and island bases threatened by flood waters. The melting of glaciers will lead to the opening of the new sea routes. The opening up of Arctic sea routes, owing to melting of glaciers, threatens to complicate delicate relations between countries with competing claims to Arctic territory – particularly as exploration for oil and natural gas becomes possible in once inaccessible areas.

Studies bring out the alarming increase in the rate at which sea levels have risen over the past decade in the Bay of Bengal. Until 2000, the sea levels rose about 3 millimeters (0.12 inches) a year, but over the last decade they have been rising about 5 millimeters (0.2 inches) annually. Bangladesh, a low-lying delta nation of 150 million people, is one of the countries worst- affected by global warming. A nearby island, Lohachara, was submerged in 1996, forcing its inhabitants to move to the mainland, while almost half the land of Ghoramara island was underwater. At least 10 other islands in the area are at risk as well. Officials estimate that 18 percent of Bangladesh’s coastal area will be underwater and 20 million people will be displaced if sea levels rise 1 meter (3.3 feet) by 2050 as projected by some climate models. Maldives faces the similar problem. This could lead to climate induced transnational migration posing a serious threat to national Security.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 11

A volatile and sensitive climatic environment will lead to a profusion of security threats, today considered more of an ‘axiom’ than a logical ‘prediction’. In a climatologically fraught global scenario, the role of the armed forces in mitigating climate effects will only gain greater recognition and larger focus in years to come. As Climate Change becomes more pronounced overtime, there is a need to adapt its implications on equipment, systems, operations, and doctrines to the altering maritime environment.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 12

Upwelling and Oxygen Minimum Zone interactions in the Arabian Sea: Modelling perspective G.V.M. Gupta Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi [email protected], [email protected]

Abstract Summer upwelling and winter convective mixing are the two major physical forcing mechanisms that greatly influence the biogeochemistry and ecosystem dynamics of the Arabian Sea. Despite several efforts, modelling is still far from reality in properly evaluating the oceanic and coastal physical interactions to the biogeochemical and ecosystem responses. While surfacing of coastal upwelling is the main nourishing mechanism in enhancing the biological production and supporting fishery potential during summer monsoon along the west coast of India, many dynamics of upwelling which are important to predict the ecosystem responses are yet to be ascertained. The salient among them include (i) does forcing factors like remote forcing, winds, currents, etc. have sequential control from onset of upwelling at deeper depths to its advection to nearshore surface, (ii) why length and strength of upwelling varies spatially, (iii) does the upwelling source water depth and associated chemical characteristics remain uniform all along the west coast, (iv) the variation in interaction between convective mixing and upwelling, and offshore oxygen minimum zone (OMZ) and coastal deoxygenation, etc. Apart, are the global models prediction of OMZ expansion and reduction in primary production due to ongoing ocean warming a real concern in the Arabian Sea is to be debated? Despite the upwelling on the eastern Arabian Sea shelf is much weaker than on its western shelf, the seasonal anoxia is recurrently occurring over its eastern shelf only due to its proximity to the OMZ. Therefore, accurate modelling of OMZ, its interaction with upwelling and associated biogeochemical responses is highly needed to predict the reliable ecosystem changes over the Indian western shelf. Our ongoing basin-scale time-series project Marine Ecosystem Dynamics of eastern Arabian Sea (MEDAS) shall address our understanding on the above pointed gaps and also complement the model data needs within the exclusive economic zone.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 13

Significance of sustained ocean observation network for climate studies R. Venkatesan Scientist G & Head Ocean Observation Systems National Institute of Ocean Technology Ministry of Earth Sciences, Chennai-600100 [email protected]

Abstract The Global Climate Observation Systems (GCOS) of WMO and Global Ocean Observation Systems (GOOS) of UNESCO IOC calls for efficient effective accurate sustained ocean observations all over the world. The coastal ocean observation includes moored buoys, tide gauges, HF radars, gliders etc. The National Institute of Ocean Technology (NIOT) with the prime objective of collecting meteorological and oceanographic parameters maintains moored buoys in the Indian waters. The real time data from these moored buoys are useful for the forecast of track of cyclones, wave modelling studies etc. The rapid mode cyclone algorithm developed by NIOT is one of the innovative technologies to observe the oceans at higher sampling rates during extreme events like cyclones. This technology has been successfully implemented in coastal buoys deployed by NIOT and captured high temporal frequency observations during the passage of cyclones Roanu, Vardah Vayu etc. The coastal buoy systems equipped with surveillance systems which provide real time images and high definition videos are extremely useful for surveillance studies and also to check vandalism. Calibration Validation CALVAL buoys deployed jointly by Indian Space Research Organization and NIOT provide valuable data seta to validate the satellite products like SST, winds etc. The Robo Coastal Observer (RCO) and Robo Boat are yet another novel technology which can be used for real-time data collection of oceanographic parameters in coastal waters. More than two decades of sustained ocean observations with experience and expertise in providing real time quality data in global standards, Validation exercises in particular or water level data for Tsunami early warning are highly appreciable and poised to grow to improve instrumentation to meet the growing demand This paper describes advances in ocean science and technology in developing new platforms to collect high frequency spatial and temporal resolution in-situ data to help the coastal community on the coastal threats.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 14

Short-term Climate Prediction with NCMRWF Coupled Model: Bay of Bengal Features Ashis K. Mitra, Imranali M., Ankur Gupta, P.P. Saheed, Nagarjuna Rao, A. Karmakar, D.K. Mohapatra and E.N. Rajagopal NCMRWF, MoES, A-50, Sec 62, Noida, UP, India 201309 [email protected]

Abstract In last one decade due to availability of enhanced computing resources, weather/ocean observed data and improved assimilation-forecast system, the skill of tropical wind and rainfall forecasts have improved in medium range time scale. High capacity computers are aiding ensemble forecasting systems at high resolutions and enhanced ensemble manners. This has helped us in dealing with uncertainties in water forecasting by issuing probabilistic forecasting from global and regional ensemble prediction systems. By inclusion of exclusive Ocean, Land-Surface, Sea-Ice and Bio-Geo-Chemistry into the prediction model, a complete Earth System Model is now possible for going beyond medium range to a season and even climate projects. Particularly for India where the monsoon rainfall information is the lifeline of people the water forecasted at sub-seasonal to seasonal scale is very important for agriculture and water management. Due to improved model skill, services at short-term climate scales (up to a season) have become possible. AT NCMRWF a seamless modeling system is implemented for providing weather/climate information across scales from hours to a season. The advantage of such seamless modeling systems is its fast development cycle. The field campaign data collected at local scales can be experimented with meso-scale and large-scale process studies in model to improve the model. While the models are gradually improving there is enhanced demand of rainfall forecast from various sectors of economy for a variety of applications. Keeping pace with the demand, developing and demonstrating applications for water sector is a challenge for weather/climate modeling community. The 'THORPEX Interactive Grand Global Ensemble' (TIGGE) is an implementation of ensemble forecasting for global weather forecasting and is part of THORPEX, an international research programme established in 2003 by the WMO/UN to accelerate improvements in the utility and accuracy of weather forecasts up to two weeks ahead. NCMRWF 12 km global model output is being shared with TIGGE. WMO's 'Sub-seasonal to Seasonal Scale (S2S) prediction project is working towards improving forecast skill and understanding on the S2S timescale with special emphasis on high-impact weather events.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 15

A state-of-art global ocean data assimilation system has been implemented at NCMRWF for daily real-time use for initializing the global coupled model. Bay of Bengal is one of the important regions where complex air-sea interaction influences the Monsoon systems and the tropical cyclone systems. A thin fresh water layer in the northern Bay region influences the heat potential and provides feedback to monsoon systems and cyclones. The recent Fani cyclone in the Bay of Bengal region showed higher heat in the coastal regions of the Bay. Details of the changes in the Bay associated with the cyclone will be discussed in the workshop from the NCMRWF coupled model.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 16

Impact of Climate Change on Shoreline and its Management M.V. Ramana Murthy Director, National Centre for Coastal Research, NCCR, Ministry of Earth Sciences [email protected]

Abstract Global sea level is influenced by the expansion of ocean waters as they warm, the addition of freshwater to the ocean and from melting land-based ice sheets and glaciers, Most studies on sea level rise tend to rely on large-scale models that give global projections, such as those used in the Intergovernmental Panel on Climate Change (IPCC) reports, and then down- scale these models to regional or local levels. At these smaller scales, sea level is additionally affected by factors such as coastal ocean temperatures and circulation patterns, and tectonic activity. Because sea level does not change uniformly around the globe, modifications for regional or local conditions can be used to refine future projections for adaptation plans. The most recent global-scale assessment of sea level rise is the 2013 IPCC 5th Assessment Report (AR5). Depending on the emissions scenario used, global average sea level projections indicate a rise by 10-39 in (26-98 cm) by the year 2100 relative to mean sea level from 1985-2005. These projections are approximately 50% higher than previously-reported global projections because only the most recent models account for increases in sea level due to the loss of land-based ice sheets, which are a major reservoir for the global water supply.

As sea level rises, it will adversely affect coastal communities and resources. The main physical effects of sea level rise include increased flooding, inundation, wave impacts, coastal erosion, changes in sediment dynamics, and saltwater intrusion to groundwater supplies. These impacts are interrelated and often occur together. Climate change could also contribute to increased storm frequency or intensity (i.e., storminess). Extreme events stand to interact with sea level rise in ways that further intensify its effects. The extremes associated with high-intensity events may be particularly devastating since they have the potential to cause broad-scale damage. Extreme events are of particular concern in the examination of coastal vulnerability and damage because they tend to cause the greatest community upheaval and can result in irreversible changes to the coastal landscape.

India has a coastline of 7,500 km and nearly 560 million, or 43%, live in the coastal areas of the country. With the rising sea level the population and assets projected to be exposed to coastal risks also increase. Coastal erosion has become an alarming threat for the population and, if we do not take immediate steps, we would end up losing more land and infrastructure to the sea. The damage will be irreversible. Coastal population will bear the maximum brunt, especially villages and recent habitations, including buildings, hotels and resorts which are at risk. While climate change and rising sea-levels have exacerbated the problem, dams on river basins have reduced the flow of sediments to the coasts. “Increasing construction activities along the coast involving dredging which tend to dispose sediments beyond littoral area (water depth more than 6- 7m) have further worsened the situation. National Centre of Coastal Research has undertaken extensive research on studying the shoreline changes of the country and it is estimated that about 33% of coastline is under varying degree of coastal erosion, 29% is of accreting nature condition and the remaining 38% falls stable state. The state wise analysis suggests that the more that 40% of erosion is noticed in four states/UT i.e. West Bengal (63%), Pondicherry (57%), Kerala (45%) and Tamil Nadu (41%) coast. While accretion is exceeding to 40% along Odisha (51%) and Andhra Pradesh (42%) coast. The west coast of India (except Kerala) is mainly stable condition, along with isolated pockets of eroding coast. Land loss and land gain analyses revealed that West Bengal coast has lost about 99 sq km land during last 26 years.

Many countries, including India, is in the process of responding to this challenge by understanding processes at Global seal level (GSL), Regional seal level ( RSL) and Local Coastal Sea Level. A detailed analysis on existing flood and coastal protection strategies are being reviewed and in the process of developing innovative approaches climate resilience coast. Some of such interventions include Demonstration of Innovative protection measures by National Institute of Ocean Technology (NIOT) to restore the lost beaches in Puducherry and Tamil Nadu and Flood forecasting/Multihazard mapping of coastal areas by National Centre for Coastal Research (NCCR).

Reference

IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

R S Kankara, M V Ramana Murthy & M Rajeevan (2018), National Assessment of Shoreline Changes along Indian Coast, National Centre for Coastal Research, Ministry of Earth Sciences, Govt. of India.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 17

Atlantic Niño modulation of the Indian summer monsoon Ramesh Kumar Yadav1*, G. Srinivas1 and Jasti S. Chowdary1

1Indian Institute of Tropical Meteorology, Pashan, Pune 411008, India [email protected]

Abstract 2 Results The Atlantic Niño influences north India summer The Indian summer monsoon rainfall (ISMR) series rainfall (NISR) by intensifying the ITCZ. This provokes is significantly anti-correlated with Atlantic Niño. The meridional stationary wave resulting in the consecutive positive phase of Atlantic Niño intensifies the inter- anomalous negative, positive, and negative GPH over tropical convergence zone (ITCZ) over Atlantic and west the tropical east Atlantic, Mediterranean, and north– Africa. In response to large atmospheric heating due to west Europe, respectively. The north–west Europe acts intensification of ITCZ stationary wave is generated as the center of action for the propagation of a Rossby which travel meridionally, as meridional transfer of wave train zonally oriented toward central Asia energy is strong, as the influence of background jets are reinforcing positive GPH anomaly over there. The minimal over North Africa and Europe. The stationary positive GPH anomaly reduces the Asian subtropical wave generates positive and negative pressure anomalies westerly jetstream east of the Caspian Sea, owing to the over tropics and extratropics, respectively (Fig. 1). This reduction in the upper-troposphere divergence toward increases the climatological background steep pressure the Indian subcontinent and below normal NISR. gradient between tropics and extra-tropics consisting of 1 Introduction anomalous negative anomaly over north-west Europe and The role of Atlantic Niño in modulating the vice versa for negative phase of Atlantic Niño. The interannual variability of Indian summer monsoon rainfall positive GPH anomaly over north-west of Europe acts as (ISMR) have been studied by Kucharski et al. (2007, a center of action for the propagation of a Rossby wave 2008, 2009) and Barimalala et al. (2012). They found that train from north-west of Europe to north-west of India via the influence of Atlantic Niño extends further to the east, north of Kazakhstan and Black Sea (Fig. 2). This produces to the Indian peninsula. The atmosphere responds to an anomalous positive GPH anomaly over north-west of anomaly localized in the south tropical Atlantic of the India, which intensifies the Tibetan High over north-west Atlantic Niño develops a Gill-Matsuno-type (Gill 1980) of India. The intensification of Tibetan High reinforces quadrupole in the eddy stream function with Kelvin and the outbreak of convection and monsoon activity over Rossby waves transporting the signal respectively to the central and north-west India. east and to the west. The response to a warm (cold) SST The horizontal Rossby wave activity flux anomaly in the tropical Atlantic consists of upper-level depicted in Figure 2 is calculated by velocity potential minimum (maximum) in the equatorial   2   Atlantic/African region and compensating velocity pcos U   2 V   2  Wx          potential maximum (minimum) in the central-western 2U a2cos2   2 a2cos           Pacific. The response is baroclinic with opposite  anomalies at low levels. In the gradient of the upper-level   2  pcos U   2  V   2 velocity potential anomalies a pair of upper-level Wy          2U a2cos     a2   2  cyclones (anticyclones) develops in the subtropical        regions about 20°–30° both north and south of the Equator because of Sverdrup balance. At low levels, a pair of here Wx,y are the wave flux in the horizontal x and y anticyclones (cyclones) forms, leading to a high (low) direction, a is the earth's radius, (φ, λ) are latitude and surface pressure anomaly over India that triggers longitude, respectively, Geostrophic streamfunction is anomalous low level divergence (convergence) with defined as ψ =φ/f, where φ is geopotential and f (=2Ωsin weakened (strengthened) Somali jet and weak (strong) φ) the Coriolis parameter with the earth's rotation rate , is ISMR. perturbation streamfunction, U is zonal wind, V is meridional wind, |U| is wind magnitude, and p is pressure The effect of Atlantic Niño on ISMR through normalized by 1000-hPa (=pressure/1000-hPa). In theory, Eurasian wave has not been studied so far. This study the flux W is independent of wave phase and parallel to hypotheses the mechanism by which Atlantic Niño the local group velocity of planetary waves. The flux tends modulates the ITCZ. The ITCZ is the region of intense to diverge out of forcing regions. The details of the convection and rainfall which heats up the troposphere by individual terms of W are explained by Takaya and the convectional heating. This tropospheric heating Nakamura (2001). generates a stationary wave in the meridional direction up to extra-tropics where the jetstreams are weaker. A Past studies by Kucharski et al. (2007, 2008, 2009) zonally oriented Rossby wave in the extra-tropics and Barimalala et al. (2012) have shown the role of reinforcing opposite GPH anomaly over central Asia, Atlantic Niño in modulating the inter-annual variability of which modifies the Asian subtropical westerly jetstream ISMR. They hypothesised that the atmospheric response east of the Caspian Sea, and hence ISMR. to the south tropical Atlantic SST anomaly develops a Gill-Matsuno-type (Gill 1980) quadrupole in the eddy

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 18 streamfunction with Kelvin and Rossby waves transporting the signal respectively to the east and to the west. The response to a warm SST anomaly in the tropical Atlantic consists of upper-level velocity potential minimum in the equatorial Atlantic/African region and compensating velocity potential maximum in the central- western Pacific. The response is baroclinic with opposite anomalies at low levels. In the gradient of the upper-level velocity potential anomalies a pair of upper-level cyclones develops in the subtropical regions both north and south of the Equator because of Sverdrup balance. At low-levels, a pair of anticyclones forms, leading to a high surface pressure anomaly over India that triggers anomalous low level divergence with weak Somali jet and weak ISMR, and vice versa for negative phase of Atlantic Niño. These studies do not talk about the Atlantic Niño influence on the extra-tropical atmosphere by the Figure -2 meridionally travelling stationary wave. Similarly, Ding and Wang (2007) studied the teleconnection between Figure 2: July-August 250-hPa level wave activity Rossby wave train across the Eurasian continent and the fluxes (black vectors, m2s−2) and anomalies of 250-hPa summer monsoon convection in northwestern India and GPH (shaded, m) for the years 1983. Pakistan on intra-seasonal timescale using leading singular vector decomposition patterns. The time-lagged Acknowledgements singular vector decomposition analyses had shown that The data have been taken from Web sites. Computational the mid-latitude wave train originates from the and graphical analyses required for this study have been northeastern Atlantic and transverse Europe to central completed with the free softwares xmgrace, NCL and Asia which strengthens the Tibetan High and hence active Ferret. ISMR. In this study, the linkage between Eurasian wave train and east equatorial SST anomaly on inter-annual References time-scale have not been established. The present study is of its first kind in which the effect Barimalala R, Bracco A, Kucharski F (2012) The of Atlantic Niño on ISMR through Eurasian wave on representation of the South Tropical Atlantic interannual time-scale have been studied. The results from teleconnection to the Indian Ocean in the AR4 coupled this investigation may help in improving the understanding models. Clim Dyn 38:1147–1166. and prediction of the Indian summer monsoon on interannual time scales. The Atlantic Niño and Eurasian wave Ding Q, Wang B (2007) Intraseasonal teleconnection can aid in determining the seasonal flood or drought between the summer Eurasian wave train and the Indian conditions over India. Monsoon. J Clim 20:3751–3767.

Gill AE (1980) Some simple solutions for heat-induced tropical circulations. Q J Roy Meteor Soc 106:447–462.

Kucharski F, Bracco A, Yoo J, Molteni F (2007) Low- frequency variability of the Indian Monsoon-ENSO relationship and the Tropical Atlantic: the weakening of the 1980s and 1990s. J Clim 20:4255–4266.

Kucharski F, Bracco A, Yoo J, Molteni F (2008) Atlantic forced component of the Indian monsoon interannual variability. Geophys Res Lett 35:L04706.

Kucharski F, Bracco A, Yoo J, Tompkins A, Feudale L, Ruti P, Dell’Aquila A (2009) A simple Gill-Matsuno-type Figure 1: Simultaneous correlation of ISMR with gridded Figure -1 mechanism explains the tropical atlantic influence on 250-hPa geopotential height (shaded). Absolute CC larger African and Indian Monsoon rainfall. Q J Roy Meteor Soc than 0.33 are 95 % statistical significant and indicated by 135:569–579. contours. Contour interval is 0.1 and greater than and equal to 0.33 and less than and equal to −0.33. Takaya K, Nakamura H (2001) A formulation of a phase- independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J Atmos Sci 58(6):608–627

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 19

Flood and drought years computation using IMD rainfall data over the coastal Karnataka region Sudip Kumar Kundu1 and Charu Singh2

1 Project Assistant, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 2 Scientist/Engineer – SE, Marine and Atmospheric Sciences Department, Indian Institute of Remote Sensing, Dehradun [email protected]

Abstract at that time. The coastal region of Karnataka, which is also called Canara coast having about 320 km coastline along The present study has been planned to compute the the Arabian sea, adversely affected due to the flood and drought years over the coastal Karnataka industrialization, improper land use, unsustainable region from 1951 to 2014 using area average IMD economic activities and overexploitation of natural rainfall data. To serve that purpose, the Normalized resources [5]. Decline in mangroves and coastal wetlands index (NI) has been utilized to find out whether the have eroded its pollutant-filtering capacity. Therefore, the particular year is fallen under drought year or flood present study has been planned to compute the flood and year? The area average rainfall data during the drought years using IMD rainfall data over the coastal principal rainy season, June to September (JJAS) has been extracted to calculate JJAS mean rainfall for each Karnataka region to understand the relationship of year, climatological mean and the standard deviation. extreme wether events like floods and droughts with the After the calculation, it has been plotted by the changing rainfall pattern. diagrams (or graphs) for visual interpretation- some of 2 Study area the years associated with flood years, some are drought The study area is coastal Karnataka region which is also years and rest are neutral. known as Canara coast. It encompasses three coastal 1 Introduction districts, namely Dakshina Kannada, Udupi district Due to widely reported global warming, the events (South Canara) and Uttara Kannada (North Canara). The like melting glaciers, rising sea level and changing geographical extension of this region is varies in between weather patterns are being observed across the globe. The 12.80° North to 14.80° North latitudes and 74.20° East to climatic system of Indian region is primarily 75.20° East longitudes. distinguished by tropical monsoon climate. As a great 3 Datasets and Methodology physiographic divide, the Himalayas affecting a large The areaaverage IMD rainfall data has been utilized for systems of water circulation as well as air circulation the months June, July, August, September (JJAS) during which help to determine the climatic condition in the 1951 to 2014 over that region. The month wise rainfall Indian subcontinent to the south and mid Asian highlands data of JJAS has been converted into total monsoon to the north. It creates obstacles by defending rain-bearing season rainfall during 1951 to 2014. south-westerly monsoon to give up maximum We have used the method Normalized Index (NI) to precipitation in that area in monsoon season and also find out the flood and drought years. The NI can be defend chill continental air from north side into India in defined as follows: winter [1]. India receives more than 75% of rainfall during Normalised index (NI) = (Mean rainfall of JJAS – southwest monsoon [2]. Indian economy is highly Climatological mean of JJAS rainfall)/Standard dependent on agriculture which is closely associated with Deviation. the monsoon season rainfall [3]. Extreme weather events such as heavy precipitation, flood, drought, cyclone, 4 Results and Discussions storm, etc. are happening often and often in various parts To compute the flood and drought years we have of India and coastal areas are extremely vulnerable in that followed NI method for the present study. For that context. Coastal zones are highly dynamic area of purpose the JJAS mean rainfall of each year, the interaction between terrestrial and marine processes and climatological mean, and the standard deviation of the also known as most fragile and productive ecosystem [4]. same have been calculated for the coastal karnataka. We Although, coastal zones constitute only about 10% of the have treated those years flood years where the NI value is land surface, these are densely populated, sustaining as greather than one. On the other hand, if the NI value is much as 60% of the world’s population. The entire stretch less than minus one (-1), it has been considered as drought of coastal zone along 7, 500 km (including island) long year. coast line of India assumes its importance because of high For the year 1952, productivity of its ecosystems, exploitation of natural The mean JJAS rainfall = 219.10 mm, Climatological resources, industrial and port development, discharge of mean of JJAS rainfall = 338.7922 mm, Standard waste effluents and increased tourism activities [4]. So, Deviation = 63.57784. the investigation of the changes that are happening on the Therefore, NI = (219.10-338.7922)/63.57784 = -1.88. coast especially due to the climate change are very crucial So, the year 1958 is treated as drought year (NI<-1) National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 20

On the othe hand, in the year 1961, The flood years receives more rainfall than the mean The mean rainfall of JJAS = 405.80 mm, monsoon season rainfall which is 339 mm and the drought Climatological mean of JJAS rainfall = 338.7922 mm, years less rainfall than the mean monsoon season rainfall Standard Deviation = 63.57784. over that region. The year 1985 receives highest monsoon Therefore, NI = (405.80 -338.7922)/ 63.57784 = 1.05. season rainfall, approximately 480 mm. on the other hand, So, the year 1961 is flood year (NI>1). 2002 receives lowest rainfall which is about 175 mm. The calculation NI for rest of the years over the coastal 5 Conclusions and Recommendatios Karnataka region have been also done using the same The present study deals only with the computation of methodology to compute the flood and drought years. flood and drought years using Normalised Index over the coastal Karnataka region. The occurrence of flood and drought are closely related to the rainfall over that region That region receives one severe flood year in 1985 (NI>2) and one severe drought years in 2002 (NI<-2). The computation of flood and drought years can be computed over rest of the Indian region. The happening of flood and drought years can also be correlated with the large-scale phenomena like ENSO and IOD. Acknowledgements We are very grateful to Indian Meteorological Department (IMD) for providing the area-average Figure 1. Graphical representation of NI value using area monthly rainfall data and also introduce the parameter average IMD JJAS rainfall data for the period 1951 to Normalized Index to define flood and drought year over 2014 over the coastal Karnataka region. the Indian region. We also thank Head MASD, GD ER For the coastal Karnataka region (fig. 1), the years &SS G and Director IIRS for providing support to carry 1962, 1966, 1975, 1981, 1983, 1985, 1988, 1996, 1998, out the present work. The IMD data is downloaded from and 2000 can be considrerd as flood years. On the other https://data.gov.in/catalogs/ministry_department/india- Figure -1 hand, the drought years are 1952, 1957, 1969, 1980, 1982, meteorological-department-imd. 1990, 1994, 1999, 2019, 2002, 2006, and 2012. The occuree of drought years (twelve) are greater than flood References years (ten) over the study region but there is tendency of drought in recent years after 2000. [1] Britannica.com, “Himalayas - Climate | mountains, Asia,” pp. 1–3, 2019. The time series (1951 to 2014) plots for total monsoon season rainfall along with mean monsoon season rainfall [2] S. Sharmila, S. Joseph, A. K. Sahai, S. Abhilash, (fig. 2) have also been introduced over the coastal and R. Chattopadhyay, “Future projection of Karnataka region. Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models,” Glob. Planet. Change, vol. 124, pp. 62–78, 2015. [3] B. Wang, Q. Ding, and P. V. Joseph, “Objective definition of the Indian summer monsoon onset,” J. Clim., vol. 22, no. 12, pp. 3303–3316, 2009. [4] Ajai and S. Nayak, “Coastal Zones of India,” 2011. [5] C. Karnataka, “Dakshina Kannada Udupi Uttara kannada,” pp. 3–5, 2019.

Figure 2. Graphical representation of total monsoon season rainfall (JJAS) in mm/year using area average IMD rainfall data over the coastal Karnataka region during 1951 to 2014.

National Seminar on Climate Change and Coastal Ocean Processes [CCCOP-2019), IIT Delhi, 4-5 July 2019 21

Status of Bay of Bengal Upper Ocean stratification in GODAS and NEMO ocean analysis Ananya Karmakar1,*, I. M. Momin1, A. K. Mitra1 1National Centre for Medium Range Weather Forecasting, Noida UP-201309, India. [email protected]

Abstract component of coupled forecasting system. It is very important to see that these ocean analyses can capture the The Bay of Bengal [BoB] upper-ocean physical oceanic features properly or not. The present study parameters are examined in the two ocean analysis analyzes the vertical structure of temperature, salinity and Modular Ocean Model [MOM] Global ocean data currents compared to RAMA Buoy and INCOIS-ARGO assimilation system [GODAS] and Nucleus for and ocean surface current analysis real time [OSCAR] European Modelling of the Ocean [NEMO]. Study over BoB. analyzed the upper ocean stability structure and investigates its intraseasonal error with the daily data 2 Dataset used 2016-2018 in BoB region compared to in situ In the present study the following ocean analysis used. observations [BUOY and ARGO]. The dominant 2.1 MOM [NCEP-GODAS] intraseasonal current variability regions i.e., East coast GODAS is based on the Modular Ocean Model version of India [R1] and Sri Lanka Dome [SLD] [R2] is well 3 with a three-dimensional variational [3DVAR] data captured in NEMO whereas in GODAS is failed do same assimilation scheme. It assimilates profiles of temperature for 2016-18. The vertical profile of temperature and and synthetic salinity. It is forced by the momentum flux, salinity bias display reduction of error especially up to 0- heat flux, and freshwater flux from the NCEP atmospheric 100m for NEMO compared ARGO and RAMA buoy in reanalysis version 2 with horizontal resolution 1° × 1° R1 and R2 regions. This bias leads to the MLD bias in [1/3° meridional at the tropics] and 40 vertical levels. pre-monsoon season [Mar-Apr-May] for GODAS. Our study concludes that NEMO could represent the oceanic 2.2 NEMO [NCMRWF] parameters more realistically for the north BoB which is NCMRWF-NEMO ocean analysis uses Nucleus for a challenging region to capture the monsoonal European Modeling of the Ocean [NEMO] model. Three intraseasonal variability. This may lead to realistic and dimensional variational assimilation [3D-Var] algorithms better East India coastal processes [currents, waves, with FGAT scheme [NEMOVAR] are used to produce ecological system] for NEMO than GODAS. reanalysis of the global ocean. It is forced by the NCMRWF atmospheric fluxes; horizontal resolution is 1 Introduction 0.25°x0.25° and 75 vertical levels. NEMOVAR In the Bay of Bengal [BoB], improper stratification assimilates temperature and salinity profiles, and along- can significantly weaken the convective mixing and wind track altimeter-derived sea-level anomalies. driven processes which are responsible for transport of nutrient to the euphotic zone thus affecting the coastal 3 Results processes. The variability of BoB is governed by a 3.1 Intraseasonal variability of BoB seasonal reversal of surface wind [1,2] and high fresh Fig 1 displays the dominant intraseasonal variability of water flux input via precipitation and river runoff with current for BoB region from OSCAR and models [MOM contrast relatively lesser evaporation [2,3]. The shallow and NEMO] during 2016-17 JJAS. The observed halocline and enhanced stratification in the upper ocean variability is showing the strong intraseasonal variability are maintaining the high heat content and SST in the BoB for SMC and Eastern coast of India [denoted as R1 and [4]. In June 2016, the southern BoB was under the R2]. NEMO current could capture the variability more influence of a convectively active phase of the Boreal realistically for R1 and R2 whereas NCEP-GODAS is Summer Intraseasonal Oscillation [BSISO] [5]. failed to do same. NEMO is 0.25x0.25° eddy permitting Simulation of BoB upper ocean properties is one of the and higher vertical resolution model so the R1 eddy system challenging issues to the modeling community due to the captured well in it. complexity arising from the unique physical and 3.2 The Vertical structure of Temperature, Salinity dynamical processes in this basin. The strong and MLD bias with gridded ARGO and RAMA Buoy stratification of BoB causes rapid variations in SST that influences the deviation of monsoon rainfall forecast Temperature and salinity bias for both models with system. This stratification is driven by salinity difference respect to gridded ARGO for R1 and R2 are displayed in the northern bay and salty water of SMC. So the realistic fig 2 [a-h]. R2 [SLD] NEMO temperature bias [Figure2a- representation of SMC in ocean models is a challenging b] is greater than 1°C upto 0-60m. GODAS show strong issue for modelling community. positive bias [1-2°C] during 2016 and 2018 JJA for upper 60m. At subsurface 60-150m GODAS display both In this study, characteristics and mechanisms of negative and positive bias ranging from 2-3°C. For R1 BoB upper-ocean [100 m] physical parameters are [Figure2c-d] NEMO displayed an upper ocean [0-40m] analyzes in two ocean model outputs from MOM and warming bias periods and subsurface [40-150m] cold bias NEMO ocean model. These models are widely used for during winter monsoon [JFM] of 2016-2018. GODAS various diagnostic studies and also initializing the ocean

National Seminar on Climate Change and Coastal Ocean Processes [CCCOP-2019), IIT Delhi, 4-5 July 2019 22 display strong cold and warm subsurface bias 60-150m [>2°C].

Figure 3. Mixed Layer Depth bias for NEMO and GODAS with respect to ARGO during 2016-18. In fig 4a-f vertical temperatures and its bias shown with three RAMA buoy locations for NEMO and GODAS. GODAS display strong positive bias in upper Figure 1. Standard Deviation of 30-120day filter of ocean 0-60m about 1-2°C. Fig 4g-l display salinity from OSCAR, NEMO and GODAS of Zonal, meridional both models and its bias with respect to the three buoy current and current speed during 2016-17 JJAS. locations for 2016-18. Temperature and salinity biases are increasing as going to south to north BoB. These Figure 2[e-h] showed salinity bias for R1 and R2 for biases are higher during the summer monsoon period NEMO and GODAS with respect to ARGO observations. [JJAS] [~1.5 psu] when the fresh water input is SLD region [R1] NEMO display mostly negative bias at increasing for the north BoB and the crucial period of upper ocean 0-40m [0.5psu]. GODAS also display strong BoB stratification. Both models are suffering from negative bias for upper ocean 0.5 psu. R2 displays stronger negative salinity bias which indicates stratification issue bias than the SLD region [R1] as it is near to the head bay for NEMO and GODAS. This may affect to the Mixed region. NEMO display lesser bias compare to GODAS Layer depth and other stratification factors of BoB. with respect to ARGO. GODAS display salinity bias >1psu for upper BoB 0-100. This salinity bias contributes in most stratification issue of BoB.

Figure 4. Temperature [a-d] and Salinity bias [e-h] for NEMO and GODAS with respect to RAMA buoy during 2016-18. 4 Conclusions Intraseasonal variability underestimated in GODAS whereas NEMO could represent it well. The model temperature and salinity bias in the BoB could be due to a combination of inaccurate surface forcing and the incorrect physical representation of the mixing. Figure 2. Temperature [a-d] and Salinity bias [e-h] for References NEMO and GODAS with respect to ARGO during 1. Schott F, McCreary JP [2001] Prog Oceanogr 51:1– 2016-18. 123. MLD bias [Fig3a] in SLD region both models showing 2. Shankar, D., P. N. Vinayachandran, and A. S. similar kind of bias pattern though GODAS is mostly Unnikrishnan. Progress in oceanography 52.1 [2002]: 63- displaying 10-20m deeper bias than NEMO. For R2 120. near to head bay GODAS showing upto 40m deeper 3. Sengupta D, Bharath Raj GN, Shenoi SSC [2006] bias whereas NEMO MLD is closer to observations. Geophys Res Lett. doi:10.1029/2006GL027573. This indicates the major mixing problem with the both 4. Shenoi SSC, models in which NEMO performs better Intraseasonal 5. Vinayachandran, P. N., Shankar D, Shetye SR [2002] J variability compared with observed one. Geophys Res 107[C6]:3052.et al [2018]. Bulletin of the American Meteorological Society, 99[8], 1569-1587.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 23

Coastal Currents from NEMO analysis and forecast Imranali M. Momin1, A. K. Mitra1 and E. N. Rajagopal1

National Centre for Medium Range Weather Forecasting, Earth System Science Organization, Ministry of Earth Sciences, A-50, Sector-62, Noida, UP – 201 309, India [email protected]

Abstract system were compared against satellite estimates for two Nucleus for European Modelling of the Ocean different southwest monsoon seasons (2017 & 2018) along (NEMO) is a state-of-the-art European modelling east coast of India. framework used by a large community for research, 2 Model and Data Used ocean forecasting and climate sciences. National Centre 2.1 NEMO Global Ocean Model: for Medium Range Weather Forecasting (NCMRWF) configured the global NEMO based three dimensional The Global NEMO ocean model [5] is based on the variational assimilation system at ¼ degree resolution ORCA025 tripolar grid developed by Mercator Ocean. and 75 vertical levels for monitoring the global ocean The horizontal grid spacing is varies with a 1/4◦(28 km) at circulation and provides the time evolution lower the Equator reducing to 7 km at high southern latitudes. boundary condition to the coupled weather-climate The vertical coordinate system is prescribed using a model. The NEMO ocean model is initialized and run double-tanh function distribution with more surface levels using the global NCMRWF Unified Model (NCUM) to better resolve shallow mixed layers. The NEMO model surface boundary fluxes to produce the stand alone used a primitive equation model with variables distributed properties of upper ocean forecast up to 10 days. In this on Arakawa C grid. The model uses a free-slip lateral study, the surface current from this analysis is first momentum boundary condition and linear filtered free compared against the in-situ and the satellite estimated surface. The model bathymetry is based on the surface current from Ocean Surface current Analysis ETOPO2v2data set. The turbulent kinetic energy (TKE) Real-time (OSCAR) which shows good mutual scheme is used as vertical mixing [6]. The detail agreement. NEMO forecast were compared against the description of NEMO physical model, and its short-range OSCAR current along the east coast of India during ocean forecast assessment is well described by [7]. southwest monsoon 2017 and 2018. The forecast skill is 2.2 Data used for NEMO forecast verification: evaluated in term of mean, bias, root mean square The Ocean Surface Current Analysis Real-time (RMS), and correlation average over the Tamil Nadu (OSCAR) is satellite estimates near surface current which (Tnco; 80-82o E; 10.5-13.5o N), Andhra Pradesh (Anco; is derived from the sea surface height, surface wind vector, 80.5-83o E; 14-17o N); Orissa (Orco; 84-87o E; 17-21o N). and sea surface temperature. The global OSCAR current is In future, the coastal current from the High Frequency generated by the Earth Space Research (ESR) at two (HF) Radar will be used to evaluate skill of the NEMO different grid resolutions (1 degree, & 1/3 degree). This surface current forecast along the east coast of India. current is more accurate over the tropical regions due to 1 Introduction the geostrophic, Ekman, and Stommel shear dynamics [8]. The Bay of Bengal (BoB) is unique due to its 3 Results geographic location, seasonal reversal of monsoon winds, and also controlling the monsoon weather system. Over We first compared the mean surface current from the the BoB, the coastal current is useful for search and rescue NEMO forecast (Day1, Day3 and Day5) against OSCAR current along east coast of India. Figure 1 shows the mean operation, oil-spill monitoring/tracking, and tracer surface and its magnitude from NEMO day1, day3, and transport study. Recently, the BoB Boundary Layer day5 forecast with respect to OSCAR current during Experiment (BoBBLE) field experiment shows the southwest monsoon (2017 & 2018). NEMO forecast importance of coastal current which transport high saline captures the surface current and its magnitude very well water into BoB during earlier southwest monsoon [1]. with respect to OSCAR current. However, the NEMO National Centre for Medium Range Weather forecast have systematic bias over the Sri Lanka Coast, and Forecast (NCMRWF)/Ministry of Earth Science is east coast of India from day1 to day5. working in close collaboration with the Unified Model Further, we compared the NEMO surface zonal (UM) consortium partners for implementation and and meridional current forecast with respect to OSCAR improvement of the unified modelling (UM) based current over the different coastal regions such as Tamil seamless modeling system which requires the initialization Nadu (Tnco; 80-82o E; 10.5-13.5o N), Andhra Pradesh of global ocean. The global Nucleus European Modelling (Anco; 80.5-83o E; 14-17o N); Orissa (Orco; 84-87o E; 17- of Ocean (NEMO) based assimilation system [2] is 21o N). Table-1 and Table-2 shows the mean, root mean configured at NCMRWF [3] which is forced with the square error (RMSE) and correlation of NEMO day1 and surface boundary fluxes from the operational UM global day3 forecast with respect to OSCAR current during atmospheric model [4] to produce the global ocean southwest monsoon 2017 and 2018. NEMO forecast analysis and stand-alone NEMO forecast up to 10 days. In captures the surface zonal and meridional current very this study, The surface current from NEMO forecast

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 24 well. However, the surface current forecast is much higher than observation and further increases with forecast length.

Table-1: Mean, RMSE, and correlation of surface zonal current (cm/s) from NEMO forecast for monsoon 2017 & 2018). Regions Mean RMSE Correlation Obs Day1 Day3 Day1 Day3 Day1 Day3 Tamil -4.99 2.1 2.3 12.59 13.12 55 53.1 Nadu -4.6 2.4 2.47 13.26 13.53 49.67 43.74 Orissa 13.27 12.43 13.82 14.62 14.69 77.2 75.89 14.85 12.22 11.92 19.91 19.92 54.79 42.1 Andhra -9.1 0.7 0.8 16.2 16.72 85.49 83.75 Pradesh 1.5 2.9 4.04 16.26 18.1 81.95 79.28 Table-2: Mean, RMSE and correlation of surface meridional current (cm/s) from NEMO forecast system for southwest monsoon 2017 & 2018. Regions Mean RMSE Correlation Figure 2: Systematic bias of Surface current and its Obs Day1 Day3 Day1 Day3 Day1 Day3 magnitude for NEMO Day1, Day3 and Day5 forecast for southwest monsoon 2017 & 2018. Tamil -7.24 -6.67 -8.1 14.62 14.96 66.0 64.4

Nadu 9.1 12.71 13.47 19.52 19.96 58.9 57.5 Acknowledgements Orissa 9.83 10.7 10.97 22.28 22.53 57.6 54 Figure -2 The NEMOVAR code & data were obtained under the -8.85 -4.1 -3.36 22.84 24.39 65.7 60.3 Andhra -9.1 0.7 0.8 16.2 16.72 85.49 83.75 collaboration with Met Office, UK. OSCAR is a NASA

Pradesh -9.6 -8.5 -8.1 13.4 14.57 77.47 73.1 funded research project and is freely available from Earth Space Research (ESR) web site Due to this,, RMSE of surface current forecasts increase References with respect to observation. Both southwest monsoons, the [1] P. N. Vinayachandran et al., 2018, BoBBLE: Ocean– correlation of surface zonal and meridional current is high Atmosphere Interaction and Its Impact on the South Asian over the northeast BoB than southeast BoB region. Monsoon. Bull. Amer. Meteor. Soc., These regions are useful because the high frequency https://doi.org/10.1175/BAMS -D-16-0230.1. radar data is available along the Indian coast and Andaman [2] J. Waters, D. J. Lea, M. J. Martin, I. Mirouze, A. T. Islands known as Indian Coastal Ocean Radar Network Weaver, and J. While, 2015. Implementing a variational

(ICORN) under the Indian Ocean Observation Network data assimilation system inan operational 1/4 degree

(IOON) project of Ministry of Earth Sciences which global ocean model, Q. J. Roy. Meteor.Soc., 141(687), provides the two dimensional high resolution surface 333-349. DOI:10.1002/qj.2388. current for oceanographic study, climate research, storm [3] I. M. Momin, A. K. Mitra, J. Waters, M. J. Martin, and surge, tsunami early warning and also for validating the E. N. Rajagopal, 2019. Impact of Altika Sea Level coastal current from global and regional model. Anomaly data on a variational assimilation system. J. Coastal Res. (Accepted). [4] S. Kumar, A. Jayakumar, M. T. Bushair, Buddhi Prakash J., Gibies George, A. Lodh, S. Indira Rani, S. Mohandas, J. P. George and E. N. Rajagopal, 2018. Implementation of New High Resolution NCUM Analysis-Forecast System in Mihir HPCS, NMRF/TR/01/2018. [5] Madec, G., 2008. Nemo ocean engine. In Note du Ple de modlisation, number 27. [6] P. Gaspar, Y. Grégoris, and J. M. Lefevre, 1990. A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: tests at Station Papa and long- term upper ocean study site. J. Geophys. Res., 95:16179– 16193. doi:10.1029/JC095iC09p16179. [7] E. W. Blockley, et al., 2014. Recent development of

the Met Office operational ocean forecasting system: an Figure 1: Mean Surface current and its magnitude for overview and assessment of the new Global FOAM OSCAR, NEMO Day1, Day3 and Day5 forecast for southwest monsoon 2017 & 2018. forecasts. Geosci. Mod. Dev., 7, 2613–2638. [8] F. Bonjean, and G. S. E. Lagerloef, 2002. Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J. Phys. Oceanogr., 32(10):2938–2954. Figure -1

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 25

Assessment of Ocean Surface Wind Speed from CMIP6 Global Climate Models and its comparison with Scatterometer winds over the Indian Ocean Athira Krishnan and Prasad K Bhaskaran Indian Institute of Technology Kharagpur

[email protected]

Abstract Wind speed measurements from the ERS-1, ERS-2, QuikSCAT, and ASCAT satellites are used to evaluate the monthly mean wind speed simulated by Phase 6 of the Coupled Model Intercomparison Project (CMIP6). This study focuses on the Indian Ocean region with various sub-domains covering the Arabian Sea (D1), Bay of Bengal (D2), South Indian Ocean (D3) and Southern Ocean (D4). The study utilized 10 Global Climate Models (GCM) to perform the comparison for a period of 23 years (1992-2014). The best performing models for the domain estimated are CanESM5 CESM2-WACCM and BCC-CSM2-M. The mean 10-m winds exhibit temporal and large spatial variability, with higher wind speeds spread over much of the South Indian and Southern Oceans. Trends also exhibit spatial variability, with an Figure 1. Study area (Indian Ocean domain). upward trend in all the domains except the Arabian Sea. 3 Results 1 Introduction The monthly mean wind speed from Scatterometers There has been a growing concern regarding changes in and the ensemble (r1i1p1f1) average of the 10 CMIP5 the global climate due to increased concentration of models for the four sub-domains are shown in Figure 2. greenhouse gases, in particular, CO2. For planning and Spatial variability in the wind speed is evident from the operational perspective, proper knowledge on the wind time series plots with a higher range of 8-14 m/s observed speed and its variability over the ocean basins are very for D4 sector. In addition, the models also well correlated crucial. An appropriate assessment on the potential impact with the scatterometer winds with a correlation coefficient of climate change on the basin-scale variability of wind ranging between 0.7-0.9. Furthermore, the scatterometer speed is essential [1]. The Intergovernmental Panel on winds are also found to overestimate the models for the Climate Change (IPCC) well documents the latest findings higher and lower extremes at a small rate. and knowledge on the scientific, technical, and socio- economical aspects of climate change. The CMIP6 overview [2] presents the background and rationale for the new structure of CMIP, providing a detailed description on the DECK (Diagnostic, Evaluation, and Characterization of Klima) and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs (Model Intercomparison Projects). 2 Data and Methodolgy We analyzed the wind speed from the historical simulations of 10 models in CMIP6 that are available at https://esgf-node.llnl.gov/search/cmip6/. Scatterometer wind field from 4 satellites has been used as a reference data obtained from http://apdrc.soest.hawaii.edu/. Datasets Figure 2. Time series plot representing the monthly wind were downscaled to a uniform grid of 1010 using bilinear speed distributions from Scatterometer and mean of 10 interpolation. The data analysis focuses first on the CMIP6 models. performance of GCMs evaluated using Taylor diagram [3], interannual variability as measured using linear trend 3.1 Statistical analysis using Taylor Diagram calculated using linear regression [4]. Linear regression Taylor diagrams were plotted for each of the domain was applied to the time series of variables for each spatial to quantify the performance of global climate models in point in the domain. The slope of the linear fitting time terms of the statistical measures such as correlation series (y = mx + c; where m is the slope, c is the intercept, coefficient, standard deviation, and root mean square error. x is the time in years, and y is the value of the parameter) It is apparent that there are deviations for individual provides the change per year, and total linear change is the models against the observations. From Figure 3, it can be product between the number of years and slope. interpreted that the best performing models common for

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 26 all the sub-domains are CanESM5 CESM2-WACCM and exhibits a positive trend with a maximum of 0.25 ms-1 yr- BCC-CSM2-M. 1 over the eastern and western boundaries of Southern Ocean, in the domain D4 and a negative trend of -0.15 ms- 1 -1 yr . The positive trend is not significant for the model CESM2-WACCM in the Bay of Bengal.

4 Conclusion

The effect of climate change on wind speed 1 BCC-CSM2-M 2 BCC-ESM1 variability can be appropriately assessed using the long- 3 CanESM5 term data available from the global climate models. The 4 NCAR.CESM2 5 NCAR.CESM2- performance of various GCMs that participated in the WACCM 6 GISS-E21-G Coupled Model Inter-Comparison Phase 6 (CMIP6) 7 GISS-E21-H 8 MIROC6 project was critically evaluated by comparing the model 9 MRI-ESM2 simulated near-surface wind speed against scatterometer 10 SAM0- UNICON data for the Indian Ocean region. The study reveals a good

degree of agreement obtained in the wind speed comparison with a correlation factor of 0.9 and RMSE of 1 ms-1 for the study domain. The study reveals that the Figure 3. Taylor diagram representing the statistics of best performing models were CanESM5, BCC-CSM2-M, monthly wind speed distributions from Scatterometer as and CESM2-WACCM from the analysis of 23 years data. along with 10 CMIP6 models. In a quantitative sense, the analysis of two-decadal These three GCMs show a high correlation with an satellite scatterometer data shows the highest variability -1 -1 average of 0.8, and a root mean square error of 1 m/s for with a rise in wind speed of about 0.25 ms yr . The all sub-domains. In terms of standard deviation D3 and future study will focus on the analysis of projection data D4 shows the least with 1.5 m/s and 1.2 m/s respectively, with various socio-economic pathways. whereas the other domains show a deviation of around 2.3 Acknowledgements m/s. The authors sincerely thank the Department of Science and 3.2 Trend Analysis Technology (DST), Government of India for the financial From the analysis of altimeter data for the Indian support. This study was conducted under the Centre of Ocean region, a study [5] reported that the Southern Excellence (CoE) in Climate Change studies established at Ocean belt encompassing the geographical coordinates IIT Kharagpur funded by DST, Government of India. The between 40°S – 55°S experienced the highest variability study forms a part of the ongoing project ‘Wind-Waves in wind speed due to the impact from climate change. and Extreme Water Level Climate Projections for East In order to quantify the variability from GCMs and Coast of India' under the CoE in Climate Change at IIT scatterometer data, trend analysis was performed for the Kharagpur. domain. The linear trends in the monthly mean 10-m wind speed for the 23 years are calculated using linear References regression, and the results for only two best performing [1] Tahereh A-T, Nasser H-Z, Bahareh K. 2017. models are shown in Figure 4. Assessment of CORDEX wind field in the Persian Gulf. First International Conference on Oceanography for West Asia, 30 -31 October 2017. [2] Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J. and Taylor, K.E., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization.

Geoscientific Model Development (Online), 9(LLNL- JRNL-736881). [3] Taylor KE. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Figure 4. Spatial distribution of linear trends in the 10-m Geophysical Research, 106, 7183–7192. wind speed (ms-1 yr-1) [4] Patra, A., Bhaskaran, P.K. and Jose, F., 2018. Time Significant upward trends of about 0.15 ms-1 yr-1 evolution of atmospheric parameters and their influence on occur in areas covering the Bay of Bengal, the north- sea level pressure over the head Bay of Bengal. Climate eastern sector of South Indian Ocean and the eastern dynamics, 50(11-12), pp.4583-4598. boundary of the Southern Ocean from CanESM5 model. [5] Bhaskaran, P.K., Gupta, N. and Dash, M.K., 2014. While significant downward trends of -0.15 ms-1 yr-1 Wind-wave climate projections for the Indian Ocean from occur over regions near the equator and especially in the satellite observations. J Mar Sci Res Dev S, 11, p.005. areas between increasing trends. CESM2-WACCM

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 27

Simulation of coastal inundation during cyclonic storms in Bay of Bengal Anup Kumar Mandal1, Ratheesh R1, Smita Pandey2 and A D Rao2

1EPSA, Space Applications Centre, Ahmedabad- 380015 2Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi-110016 [email protected]

Abstract In their study, Rao et al. (2012) [2] have reported to have achieved realistic condition of coastal inundation Cyclone induced coastal inundation along the East simulation for three test cases of cyclones which were putational domain is prepared by integrating digital compared to post-cyclone survey reports of IMD. coastal bathymetric chart with ETOPO-2 and the Bhaskaran et al. (2014) [3] and Srinivasa et al. (2015) [4] topography of the coastal land region was created by have validated their inundation models using field merging fine resolution airborne DEM with measurements based on basic GPS observations. In our CARTOSAT-2 and SRTM DEM. Simulation results of present study, we have used the integrated DEM of three cyclones are discussed, where the cyclones Phailin airborne observations (spatial resolution 1m, vertical (2013) and Hudhud (2013) are simulacoast of India is accuracy 10 cm), CARTO2 DEM (spatial resolution 10m, simulated using the state of the art storm surge (ADCIRC vertical accuracy 50 cm) and SRTM DEM to represent the + SWAN) model. Bathymetry of the comted in a hind cast coastal topography. The present study also has attempted mode while the coastal inundation due to Titli (2018) is to use the satellite SAR image to validate the coastal forecasted with a lead period of 48 hrs. The storm tide inundation forecast. and surge residual for Phailin and Hudhud were compared with available INCOIS tide gauge 2 Methodlogy observations at Paradip and Visakhapatnam stations In the present study, the advanced circulation respectively. Root Mean Square Error (RMSE) between (ADCIRC) model coupled with Simulating Waves Near the model simulated and observed surge-residual for Shore (SWAN) model is used which integrates domain- Phailin and Hudhud cyclone were found to be 0.06m and specific tools, standard grid, and portal tools to provide an 0.11m respectively. The inundation extent at Ganjam integrated environment for forecasting. Here, the district was 1 km along the coastline which is observed to ADCIRC model (Luettich et al. 1992 [5]) is configured for be matching with the IMD records. The coastal the entire east coast of India starting from Trivandrum inundation forecast due to Titli cyclone is validated by (77.08°E,8.36°N) till Myanmar (93.36°E,19.80°N) for assessing the spatial coherency of simulated inundation computation of storm tides and associated inland with the water logged area demarcated from inundation (Figure 1). RADARSAT-2 SAR image. The results clearly indicate the acceptability of the model performance with the potential to predict the coastal inundation during a cyclone for operational early-warning.

1 Introduction Tropical cyclones are one of the most devastating natural disaster which periodically impact human Figure 1: Major Lagoons and River system along the east population and imparts a major loss in the state economy. coast of India, which are represented as water bodies in the Even though the recent cyclones are predicted with domain. acceptable accuracies about the intensity, track and landfall, the operational forecast of the coastal inundation 2.1 Data used for the Indian subcontinent have not reached its maturity. The topographic information (within 10 m topo- Decision support system has been established at Indian contour) is obtained by integration of Airborne DEM with National Centre for Ocean Information System (INCOIS) CARTO-DEM and SRTM30 DEM. Airborne DEM is for real time storm surge and inundation prediction given the priority and CARTOSAT and SRTM DEM are (Murty et al. 2017 [1]). The major lacuna in coastal assigned to fill the gap, due to narrow ALTM observation inundation forecast is the lack of accurate coastal DEM and non-availability of ALTM beyond Chilika mouth in and updated coastal bathymetry. Small creeks and rivers the north and Cuddalore in the south. Major rivers and act as feeders which carries the storm surge inland, often Lagoons are demarcated using 2014 LISS4 satellite enhancing the surge height and inducing devastating images and available bathymetry values are given as effects towards the inland. In this context, in order to shown in Figure 1. simulate a realistic coastal inundation, very high 2.2 Model set up resolution of the geometry is required especially along the coast, which can capture the creeks, rivulets and lakes Prerequisite for any storm surge model setup is to assess adjoining the coastal area. the model’s ability to accurately simulate the phase and amplitude of pure astronomical tide. Spin up simulations with 30-day duration (for each test cases) are carried out

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 28 to initialize the model for simulating storm tide. The spin land water boundary demarcated from the analysis of up simulations are carried out in stand-alone ADCIRC RADARSAT-2 satellite image of 12th October, 2018 has model considering only astronomical forcing. For Phailin been used to compare the simulated coastal inundation (2013) and Hudhud (2014) cyclone, we have used forecast. Figure 4 shows the land water boundary from “modified Jelesnianski wind scheme” (described in RADARSAT-2 image overlaid on the simulated coastal Pandey et al. 2018 [6]) to prepare wind fields from IMD inundations corresponding to RADARSAT-2 pass, where archived best track data (www.rsmcnewdelhi.imd.gov.in), the permanent water bodies are masked from the analysis. which is further used as primary forcing in storm tide Total inundated area near Rajapuram in our simulation simulation. However, while generating coastal inundation was 30.20sq km as compared with 40.22sq km in SAR forecast for Titli (2018) cyclone, we have used WRF image. model wind fields at 5 km spatial resolution. 3 Results 3.1 Phailin and Hudhud cyclone Storm tide for Phailin cyclone in a hind cast mode was simulated from 08/10/2013 18:00 till 12/10/2013 21:00 using the validated ADCIRC model set up coupled with SWAN model. Simulated Storm tide is compared with the INCOIS tide gauge observation at Paradip station as shown in Figure 2(a). The simulated surge residual is compared with the observed surge residual (Figure 2b). During Phailin cyclone, the simulated maximum storm tide was about 2.5m and coastal inundation was observed near Ganjam district, where the inundation was about 800 Figure 4: Inundated area from simulation and m to 1 km towards the land. RADARSAT-2 SAR on 12/10/2018

References (1) P.L.N. Murty, J. Padmanabham, T. Srinivasa Kumar, N. Kiran Kumar, V. Ravi Chandra, S.S.C. Shenoi, M. Mohapatra; Ocean Engineering 131(2017) 25-35: Real-time storm surge and inundation forecast for very severe cyclonic storm ‘Hudhud’. Figure 2: Validation carried out at Paradip station for (2) A. D. Rao, P.L.N. Murty, Indu Jain, R.S. Kankara, Phailin cyclone (a) storm tide (b) surge residual. S.K. Dube, T.S. Murty; Nat Hazards (2013) 66:1431- 1441: Simulation of water levels and extent of coastal Validation of storm tide and surge residual for Hudhud inundation due to a cyclonic storm along the east (2014) cyclone is shown in Figure 3. Simulated maximum coast of India. storm tide was about 2.5m and coastal inundation was observed near Bheemunipatnam and Konada districts (3) Prasad K. Bhaskaran, R. Gayathri, P.L.N. Murty, where the surge has inundated inland to about 500m to SubbaReddy Bonthu, Debabrata Sen; Coastal 550m. Engineering 83 (2014) 108-118: A numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal. (4) T. Srinivasa Kumar, P.L.N. Murty, M. Pradeep Kumar, M. Krishna Kumar, J. Padmanabham, N. Kiran Kumar, S.C. Shenoi, M. Mohapatra, Shailesh Nayak, Prakash Mohanty (2015); Mar. Geod. 38, 345–360: Modeling storm surge and its associated

inland inundation extent due to very severe cyclonic Figure 3: Validation carried out at Visakhapatnam storm Phailin. station for Hudhud cyclone (a) storm tide (b) surge (5) L Luettich Jr RA, Westerink JJ, Scheffner NW residual. (1992) ADCIRC: an advanced three dimensional 3.2 Titli Cyclone circulation model for shelves coasts and estuaries, The coastal inundation due to Titli cyclone is simulated report 1. in a forecast mode with a lead time of 48 hours. Maximum (6) Smita Pandey, A. D. Rao; Nat Hazards (2018) 92:93– simulated storm tide was around 2.2m during the landfall. 112: An improved cyclonic wind distribution for Coastal inundation is observed along the low lying areas computation of storm surges. like Kalingapatnam, Rajapuram, Sompeta and Ichchapuram in Srikakulam district. The surge is observed to have inundated about 400m to 4.5 km. Maximum inundation (area wise) is observed near Rajapuram. The

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 29

Adaptation measures for Coastal Infrastructure B. Santosh kumar1, K. Murali2, S.A. Sannasiraj2, R. Sundaravadivelu2 and V. Sundar2 1Research Scholar, 2Professor, Department of Ocean Engineering, Indian Institute of Technology Madras, India [email protected]

Abstract It is well established that the effect of climate change infrastructure. It also affects the performance of the on coastal infrastructure is likely to become severe in the structures associated, by altering the deterioration and next few decades. Coastal infrastructures are always causes severe damage at the time of extreme events. The subject to a severe environment. The impacts of climate continuous rise in sea level would alter the splash zone change, therefore, have the potential to accelerate the and atmospheric zone of a structure and may increase the deterioration of the structure and their usability. The environmental loads. The climate change and sea level rapid change in climate will directly affect the structural rise affects the wave climate making the waves stronger elements and eventually lead to a reduction in the life of in the coastal regions as the waves are influenced by the structure. Adaptive measures are necessary to tackle variation in water depth and fetch of wind [7]. future climate change in order to increase the life of 3 Adaptation measures existing as well as new building structures and also for sustainable development of the nation. This paper deals Natesan and Parthasarathy [4] suggested some with the general adaptation measures for coastal mitigation measures that include both soft measures and infrastructure. The adaptation measures are drawn from hard measures. Adaptation strategies broadly classified as the detailed study conducted on two typical berthing (i) planned or retreat which means moving away from structures, and post cyclone survey of recent cyclone coast with planned development to minimise the human named “Fani” along with the knowledge from literature. impacts, (ii) accommodation, that is adjusting to the coastal changes, (iii) defending by means of soft or hard

measures, and (iv) attacking in the form of land 1 Introduction reclamation [8]. The climate induced sea level rise is becoming a 3.1 Coastal Infrastructure threat to low lying coastal cities. The coastal Health check-up study has been carried out on two infrastructure in the developed and developing countries typical berthing structures; one is in Paradip, Odisha and has to be protected against the impacts of rising seas. The the other is in Cochin, Kerala. The observations are shown projected global sea level rise is 0.26 to 0.98cm by 2100 in Table 1. In general, a structure tends to deteriorate with as per IPCC AR5 but Svetlana[1] and Bambar [2] have age and is also affected by environmental factors like estimated that the sea level can exceed 2m by the end of temperature, humidity, sea water in case of marine 21st century with warming of 20C and with no mitigation structures, etc. The existing structures which are not efforts. Nicholls [3] stated that 0.5m to 2m is the practical designed considering the impact of climate change may range of global sea level rise with the present temperature deteriorate faster [9]. The change in climate alters the rising and also a large investment has to be made to environmental factors which ultimately affects the protect the coastal infrastructure. However, the sea level deterioration process. Several conventional techniques rise tends to increase due to the thermal expansion and like increasing cover and strength grade, cathodic melting of glaciers given that emission of greenhouse gas protection for corrosion, etc. can be used for enhancing is stabilized [4]. In addition to this, some coastal regions durability of structures. However, different structures have may experience even greater rises in sea level due to different exposure conditions, construction and relative sea level rise [1]. The IPCC AR5 and church [5] maintenance and so adaptation measures must be structure estimated that combined effect of Mean Sea Level Rise specific [10]. and changes in storminess will affect the future sea level extremes and the occurrence of future extreme would Observations from the berthing structures. increase by 2100. So, the coastal infrastructure has great need of adapting to the future scenarios of climate induced Berthing Berthing sea level rise by implementing certain adaptation or structure - structure - mitigation measures. The present study emphasizes the Paradip port Cochin port general adaptation measures that can be considered in Age nearly 50year old 43year old order to mitigate the effect of climate induced sea level On the banks of rise on the major coastal infrastructure. On the shore of Location backwaters at 2 Impacts of Sea Level Rise BoB at Paradip Cochin Sea level rise possess major threat to the coastal infrastructures in terms of immediate and long term Exposes to Exposes to impacts. According to the past tide gauge data, the rate of Exposure aggressive sea aggressive sea Sea Level Rise (SLR) along the Indian coast is slightly environment environment less than 1mm/yr and the rate is different in different places due to relative sea level rise [6]. It causes flooding, inundation and affects the functionality of the

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 30

Structure is in Severe corrosion Fig. 1 shows the coast of Puri, cyclone hit area, the surge Remarks good condition, in almost every height was more than 1.5m above the astronomical tide, from visual corrosion of element of the measured based on the information by locals. The wall survey conveyor bridge structure constructed along the roadway acted like a hard measure by obstructing the flow of surge. In another case, (see Fig. Test 2) the effect of surge was minimized due to the presence Impact test, NDT NDT conducted of vegetation along the coast. It should be mentioned here, that the projected sea level rise will have more effect at Foundation Pile Column place in Fig. 2 where the roadway lies lower than present Rate of 0.75mm/yr sea level. Whereas in case of the place shown in Fig. 1, 1.14 mm/yr SLR approx. there is a possibility of severe erosion at the base of the wall. Currents 0.21 to 0.62 m/s 1.54 to 2.83 m/s Acknowledgement This work is supported by the Department of Better design considering the future climate scenarios can Science & Technology, India Grant No. avoid much of the problems and makes the infrastructure DST/CCP/CoE/141/2018C under SPLICE – Climate resilient towards impacts of climate change and sea level Change Programme. rise. References 3.2 Coastal areas [1] J. Svetlana et al., “Coastal sea level rise with The very recent cyclone named “Fani”, categorized warming above 2oC,” Proc. Natl. Acad. Sci. USA, as extreme severe cyclonic storm caused a lot of damage 2016, 113 (47), pp. 13342-13347. along the coast of Odisha, India. From the post cyclone [2] J. L. Bamber et al., “Ice sheet contributions to future study, it is observed that the gusting winds of around sea-level rise from structured expert judgment,” 200kmph are responsible for most of the damage. Proc. Natl. Acad. Sci. USA, 2019. Available: doi: However, the damage caused by waves is less due to 10.1073/pnas.1817205116 various factors that include soft and hard measures. [3] R. J. Nicholls et al., “Sea-level rise and its possible impacts given a ‘beyond 4◦C world’ in the twenty- first century,” Philos Trans A Math Phys Eng Sci, 2011, 369, pp. 161-181. [4] U. Natesan, and A. Parthasarathy, “The potential impacts of sea level rise along the coastal zone of Kanyakumari District in Tamilnadu, India,” J Coast Conserv, 2010, 14, pp. 207-214. [5] J. A. Church et al., “Sea Level Change,” In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker, T.F., et al., Eds. (Cambridge University Press, Cambridge, UK),

2013, pp. 1137-1216. Figure 1: Example of hard measure (Wall along the [6] A. S. Unnikrishnan et al., “Sea level changes along the roadway) at Puri, Odisha. Indian coast: Observations and projections,” Current science, 2006, vol. 90, no. 3. pp. 362-368. [7] B. G. Reguero et al., “A recent increase in global wave power as a consequence of oceanic warming,” Nature communications, 2019. [8] R. J. Nicholls, “Adapting to Sea Level Rise”, Resilience-The Science of Adaptation to Climate Change, 2018, Ch. 2, Elsevier, pp.13-29. [9] X. Wang, M. G. Stewart, and M. Nguyen, “Impact of climate change on corrosion and damage to concrete infrastructure in Australia,” Climate change, 2011, 110: 941. [10] X. Wang et al., “Analysis of Climate Change Impacts on the Deterioration of Concrete Infrastructure – Synthesis Report,” Published by CSIRO, Canberra, Figure 2: Example of soft measure (Vegetation) at 2010. Konark, Odisha.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 31

Shoreline change due to an extreme weather event Mikkilineni Dhananjayan1, Sannasiraj S A2, Murali K2

1 Project Associate, 2 Professor, Department of Ocean Engineering, Indian Institute of Technology, Madras [email protected]

Abstract to February’19 on a monthly basis. The data collected With the climate being unpredictable and changing its using RTK-GPS has been plotted and its corresponding course vividly, the coast is subjected to continues slope, width, the quantity of sediments and the shoreline changes. Also the occurrence of extreme weather events change has been calculated. in the Bay of Bengal region becoming quite regular, making the coasts more vulnerable. It is essential to understand the changes in the coastal environment after an extreme weather event. Field measurements provide precise data which help us in a better way to interpret the changes in coastal processes before and after an extreme weather event. Karaikal coast located in the East Coast of India was continuously monitored on a monthly basis from field surveys using Real Time Kinematic Global Positioning System (RTK-GPS). Recently this coastal area was hit by a severe cyclonic storm named Gaja in November 2018. Shoreline study was carried out before and after the storm crossing the coast. This study discusses the erosion pattern, sediment deposition and disruption of the coastal environment after the cyclone crossing the coast. 1 Introduction East coast of India is prone to severe cyclones more often compared to the west coast of India. From historical data analyzed, over 58% of the cyclones developed on the east coast of India approach and cross the mainland during the months of October and November (NCRMP). 308 cyclones affected the east coast from 1891-2000 of which Figure 1. Site map of Karaikal. 103 were severe while the west coast faced 48 cyclones of The coastal processes follow the pattern of northern which 24 were severe (NCRMP). Karaikal, located in the drift and southern drift. Erosion has been taken place on east coast of India in the union territory of Pondicherry has the north side of the training walls during South-west been chosen as the study area. This town is hit by severe monsoon (May to September) and from September cyclones at least once a year. This coastal town is a onwards accretion started. In contrast to this, beach to the relatively low lying area compared to the adjacent areas. south of the training walls experienced accretion during The coast of the site is comprised of coastal dunes, south-west monsoon (May to September) while erosion reserved forest cover and floral vegetation. Arasalar River, during North-east monsoon (October to December). This a tributary of river Cauvery flows through the town and is due to the littoral drift pattern on the east coast of India has an artificial fishing harbor for the fishing community throughout the year. It is also observed that during GAJA of the town. In order to provide safe passage for the fishing cyclone the coast faced accretion on both sides of the boats, river training walls have been constructed. With the training walls. This is due to the deposition of the construction of training walls, the coast adjacent to it sediments adjacent to the training walls and by affecting undergone various changes throughout the year. This town the regular littoral drift pattern. th was hit by severe cyclonic storm named GAJA on 16 The foreshore slope of the seven transects have been November 2018. The wind speed was 112kmph and the compared for the months of October (before cyclone), measured and the measured storm surge was 1.98m from November (during cyclone) and December (after cyclone) mean sea level. Due to the severity of cyclone, several crossing the coast. The slope was steep before the cyclone changes had occurred along the coast. The data was crossed the coast. Once the cyclone crossed the coast, the collected using RTK-GPS with a position accuracy of slope of the beach became flat. The transects located to the 1cm. north of the Training walls saw an increase in the length of 2 Results and Discussions the beach while the transects located to the south of the In order to understand the coastal processes happening Training walls faced a decrease in the length of the beach. along the Karaikal coast, 7 transects (Fig.1) have been Though there is erosion to the south of the training walls, established along the coast with 5 to the north of the there was an accretion to the north of the training walls. training walls while two to the south of the training walls. The sediments got deposited adjacent to the northern This coast has been monitored for a year from March’18 training wall and thereby increasing its width. National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 32

Figure 3. Pre-cyclone GAJA (08 November 2018).

Figure 2. Shoreline Change before and after cyclone. Fig.2 represents the change in shoreline before, during and after the cyclone GAJA. It was observed that the shoreline to the north of the training walls, on an average shifted 20m towards the baseline during the cyclone while to the south of the training walls an average shift of 40m was observed during cyclone. After the cyclone, north of the training walls experienced erosion while south of the training walls faced erosion. Also, this coast is comprised of coastal dunes as well as floral vegetation. Once the cyclone crossed the coast, the dunes got disrupted and the floral cover was destroyed. Fig.3 and Fig.4 represents the beach before and after the cyclone respectively. vegetation Figure 4. Post-cyclone GAJA (17 November 2018). was covering the sand dunes. Due to the impact of the cyclone, the dunes become flattened but a major loss of Acknowledgments sediments was prevented by the vegetation cover over it. They prevented the sediment movement to a large extent. This work is supported by the Department of Science & Technology, India Grant No. DST/CCP/CoE/141/2018C under SPLICE – Climate Change Programme. .

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 33

The relationship between Indian Ocean Dipole and tropical Indian Ocean significant wave height in reanalysis and CMIP5 models G. Srinivas1, P.G. Remya1 and T.M. Balakrishnan Nair1 1Indian National Centre for Ocean Information Services (INCOIS), Hyderabad-500090 [email protected]

Abstract 2 Data and Methodology This study addresses the relationship between tropical we have utilised the oceanic surface wave parameters such as Indian Ocean (TIO) significant wave heights (SWH) and SWH, mean wave period [MWP] and mean wave direction Indian Ocean Dipole (IOD) during boreal summer season [MWD] from the ERA-Interim for the period 1979 to 2017 (JJA) in reanalysis and Coupled Model Intercomparison [5]. Atmospheric variables such as zonal and meridional wind Projects phase 5 (CMIP5) forced simulations. IOD plays an components at 10 m and 850 hPa level, surface wind speed important role in the variability of wave climate in the TIO. and sea level pressure also used. Monthly Sea Surface The composites of SWH anomalies during positive IOD Temperature [SST] is used from the Extended Reconstructed years display negative anomalies over the eastern EIO Sea Surface Temperature [ERSST] version 4. Anomalies are region and positive SWH anomalies over the head Bay of computed based on the climatology of 1979 – 2017. Bengal and south-eastern TIO during JJA. Significant Statistical methods such as composite analysis and negative anomalies over the southern tip of India covering correlation coefficients are used for analysis. from south-eastern EIO region is due to the strong easterly wind anomalies over eastern EIO which is driven by the In addition, we have used the output from wave climate IOD conditions. In addition, during the negative IOD years simulations carried out using a 1-degree global most of the TIO experiences the positive SWH anomalies implementation of Wave Watch III [v3.14] forced from the especially over southern Arabian Sea, Southern TIO and latest version of CMIP5 coupled general circulation models. EIO. This study is important to understand the CMIP5 Wave model forcing consisted of 3 hourly surface winds and models ability in representing the waves over the TIO. linearly interpolated monthly sea-ice concentration fields. The CMIP5 models used for this study are ACCESS1.0, 1 Introduction CNRM-CM3, HadGEM2, INMCM4, BCC-CSM1.1, Indian Ocean (IO) holds unique characteristics among MIROC5, GFDL-CM3, MRI-CGCM3. From all the model world oceans due to the reversal of winds namely monsoon simulations, variables such as SWH, MWP and MWD from and associated oceanic phenomena. The monsoons of the IO historical runs covering the period 1980 – 2005 has been are a result of strong atmosphere–ocean interaction over utilized for analysis [6]. basin scales and it affects the coastal population and 3 Results and Discussion maritime economy of India. The variabilities of monsoon affects wind variabilities over the ocean and hence the ocean The climatology of the SWH over TIO display strong surface waves. variability during the JJA. The seasonal mean anomalies of Waves are the important characteristic of the surface SWH display strong interannual variability with the stronger ocean and lead a major role in human activities in both the heights ranging from -2.5 to 2.5 m/s over the regions AS, BoB ocean and coastal zones. Hence, comprehensive and TIO [Fig. 1a]. It is noted that, the interannual variability understanding of the properties of the waves and their of SWH significantly correlated with the Dipole mode index potential changes is the major knowledge required for [DMI] with the correlation of 0.5. This motivated us to study sustainable management of both the coastal and offshore the impact of Indian Ocean Dipole [IOD] on TIO SWH. The regions. The interannual variation of waves in NIO has been IOD events are identified if the normalized SST anomaly related to monsoon wind [1], tropical cyclone activity [2] gradient between Western Equatorial Indian Ocean [WEIO; and submarine topography. In the equatorial Indian Ocean, 50-70oE and 10oS-10oN] and Eastern Equatorial Indian warm (cold) water is usually on the eastern (western) side Ocean [EEIO; 90-110oE and 10oS to equator] exceed the during normal years [3]. standard deviation ± 1 during the JJA are considered for Indian Ocean Dipole (IOD), defined as an interannual analysis. variability with dipole-shaped signature in the Sea Surface Temperature (SST) field over the tropical IO, is one of the major ocean-atmosphere coupled phenomena in the tropical Indo- Pacific sector [3]. Reference [4] investigated the influence of IOD on the wave climate of the eastern AS by measured, modelled wave data and reanalysis wind data during October. Thus, it is important to study the influence of IOD on TIO surface waves during the boreal summer season which is important for the Indian coastal lands.

Figure 1: JJA seasonal mean anomalies of a] SWH [m] over TIO, AS and BOB. b] Unseasonal Dipole mode index [DMI] computed from the SST anomalies [oC] during the JJA. To examine the relationship between IOD and SWH, we have plotted the composites of Ws, SWH and MWP for the National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 34 both positive and negative IOD years are illustrated in figure In addition, we have looked these teleconnections 2. The anomalous Ws displays significant negative anomalies between IOD and TIO SWH in the historical simulations of with the significant easterly wind anomalies over south- CMIP5 model wind forcing to 1-degree global WaveWatch eastern Arabian Sea to eastern EIO region during the positive III model. It is noted that CMIP5 models able to capture the years. Furthermore, positive Ws anomalies are noticed over mean features of TIO SWH and IOD teleconnections with northern Arabian Sea and Bay of Bengal region including the considerable biases. Furthermore, we have plotted the Indian subcontinent with anticyclonic circulation anomalies correlation between DMI and SWH anomalies for model and over NIO. In addition, the south of the equator displays strong observations [Figure 3]. The SWH anomalies show negative positive Ws anomalies with anticyclonic circulation from the correlation over south-eastern EIO during JJA. Some models equator to 15oS [Fig. 2a]. The Ws anomalies during the are reproducing these teleconnections but most of the models positive IOD years over eastern TIO display a meridional having difficulty in representing these correlations. However, tripole like pattern with negative anomalies over the eastern the negative SWH anomaly correlation over TIO is mainly EIO and positive anomalies over the southeast Indian Ocean due to the eastern IOD box. The western IOD box correlation and Head Bay of Bengal [Fig. 2a]. with SWH displayed positive SWH anomalies over TIO. This suggest that IOD playing the significant role in SWH anomalies over TIO. Models are able to failed to show these teleconnections due to the improper wind forcing. To improve the teleconnections, proper wind forcing to wave models are necessary.

Figure 2: Composites of positive IOD years anomalies of a] WS, b] SWH and c] MWP. d] to e] are same as a] to c] but for negative IOD year composites. Contours and green vectors indicates the significant at 95 % confidence level. The composite of SWH anomalies for positive IOD years over TIO display positive anomalies over southern TIO and north Bay of Bengal region and negative anomalies over south-eastern Arabian Sea to eastern EIO region. It is noted that, the meridional tripole pattern as seen in the Ws is also reflected in the SWH anomalies [Fig. 2b]. Furthermore, the Figure 3: The correlation between DMI and SWH for a] MWP anomaly composite for positive IOD years show observations, b] MME, c] ACCESS1.0, d] BCC-CSM1.1, e] weaker positive wave period anomalies over the eastern EIO CNRM-CM5, f] GFDL-CM3, g] HadGEM2-ES, h] and intensified negative wave period anomalies over the INMCM4, i] MIROC5 and j] MRI-CGCM3. south-eastern TIO region is noted [Fig. 2c]. It is important to 4 Conclusions note that, the weak SWH anomalies over eastern EIO are due to decrease of short-period waves which are influenced by the The IOD influencing the SWH anomalies over TIO with weak easterly surface wind anomalies [Fig. 2a-c]. Therefore, positive SWH anomalies over EIO during the negative IOD the positive SWH anomalies over north Bay of Bengal and conditions and during the positive IOD conditions most of the southern TIO are due to strong winds and weak period waves TIO experiencing the negative SWH anomalies. during the positive IOD years. The composites of Ws for the Furthermore, the correlation analysis also supporting the IOD negative IOD years display significant positive anomalies influence on TIO surface waves. with westerly wind anomalies over the central EIO and References negative anomalies with cyclonic circulation over Bay of 1. Anoop et al. 2015, J Atmos Ocean Technol 32:1372–1385 Bengal and near Sumatra region [Fig. 2d]. The composites of 2. Shanas PR, Kumar VS (2014) Nat Hazards Earth Syst Sci SWH anomalies shows significant positive anomalies over the most of the equatorial and south Indian Ocean region 14:1371–1381. during the negative IOD years [Fig. 2e]. Interestingly, during 3. Saji et al. 1999, Nature 401:360–363 the negative IOD years, the western and southern tip of India 4. Anoop et al. 2016, Ocean Sci 12:369–378. experiences the significant positive SWH anomalies which could influence the most of the coastal regions with 5. Dee et al. 2011, Q.J.R.Mete. Soc., 137(656), 553–597. intensified inundation. The negative weak MWP anomalies 6. Hemer et al. 2012, Nat. Clim. Change, 3(5), 471–476. over the south of the Indian land mass replicates that the SWH anomalies are generated due to the wind-sea waves. The turbulent sea state generated by the dominance of short- period waves during the negative phase of IOD leads to an increased decay rate of. Furthermore, it is also noted that MWP anomalies are positive over Bay of Bengal and Sumatra Island regions during the negative IOD events [Fig. 2f]. National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 35

Case study of a typical coastal flooding event along the Southwest part of the Kerala coast Swathy Krishna P.S., L. Sheela Nair, Amrutha Raj National Centre for earth Science and Studies (NCESS) [email protected]

characterised by monsoon high waves, steep beach face and Abstract medium sized beach sand. Baba and Kurien in 1988 reported This is a case study on the typical flooding event occurs on that the coast experiences the maximum wave height of the Southwest Kerala coast and to delineate the causative nearly 6 m. The net annual long shore sediment transport is factors responsible for it. By the end of September 2018, Southerly in the inner shelf and northerly in the surf zone the coast witnessed a relatively non-destructive flash and these counter directional transports are linked by flooding event without any precursors or any kind of local seasonally reversing the cross shore transports (N.P Kurien wind activity. The model, Mike 21 was used to simulate the et al., 2008). The coast is highly dynamic in nature with only identified flooding event in two stages- (global and a small percentage of the coast remaining in stable state. The regional). Wind data taken from ECMWF Era interim was study area (model bathymetry) is shown in fig 1. Mike 21 is given as the forcing input to the global model. The model [m]used to simulate the flooding event of September 26 2018 results were validated using in situ measurements from 4000000with 2 model bathymetries (Global and regional).The global WRB wave data. Wave run up was then calculated from the model bathymetry was derived from the ETOPO 1 global 20° 0' N model output and was cross checked with the field relief model National Geophysical data centre (NGDC). It is 3000000 observations taken for specified stations. It was observed a 1 arc minute global relief model for the Earth surface. The from the model studies that the significant wave height and regional bathymetry incorporated the data available with peak wave period has exceeded 1.6 m and 18 sec, criteria 2000000NCESS. The global SW (spectral wave) model is forced with proposed by Kurien et al in 2005 for the flash flooding ECMWF wind data and the output wave data is fed to the event to occur. 1000000regional model (Trivandrum coast). In situ measurements collected form WRB buoy located at a depth of 20 m in 0° 0' N 1 Introduction Valiathura 0 is used for the validation of model data. The Coastal flooding refers to the incidence of high water model was calibrated and validated with WRB buoy data level events of marine rather than fluvial origin, affecting -1000000stationed at Valiathura. The correlation coefficient for Hs= coastal or estuarine shores (Hunt, 2005) [2]. An array of 0.772, Tp = 0.8934. Fig 2 shows the validation result for Tp. environmental factors can contribute to coastal flooding, [m] -2000000 including astronomic, atmospheric, thermal, oceanographic 4000000 20° 0' N (a) and geophysical factors. Coastal flooding occurs whenever 3000000 20° 0' S [m] -3000000 there is a rise in sea water level which can be attributed to 2000000 Above 0 -400 - 0 -800 - -400 wave setup due to high wave activity spring tide and coastal 1000000 -1200 - -800 -1600 - -1200 flooding events associated with meso-scale weather systems. -4000000 0° 0' N 0 -2000 - -1600 It can become worse when it coincides with spring tides and -2400 - -2000 -1000000 -2800 - -2400 -3200 - -2800 coastal wind condition which can generate significant wind -5000000 -2000000 -3600 - -3200 set up. Local or remote storms produce large wind or swell -4000 - -3600 20° 0' S [m] -4400 - -4000 -3000000 Above 0 waves, which can overtop coastal defences/beaches leading -400 --4800 0 - -4400 -800 - -400 -6000000 -1200 --5200 -800 - -4800 -4000000 -1600 - -1200 -2000 --5600 -1600 - -5200 to flooding and erosion. In low-lying areas, large annual 40° 0' S 120° 0' E -2400 - -2000 100° 0' E -2800 -Below -2400 -5600 -3200 - -2800 -5000000 variations can also contribute to nuisance flooding, which -3600 -Undefined -3200 Value -4000 - -3600

-4400 - -4000

40° 0' E 0' 40° -4000000E 0' 20° -2000000 0 2000000 4000000 -4800 - -4400 occurs during clear sky conditions due to combination of -6000000 -5200 - -4800 -5600 - -5200 40° 0' S 120° 0' E [m] 100° 0' E Below -5600

Undefined Value

40° 0' E 0' 40° high mean sea level and spring tides (H.R Moftakhari et al., -4000000E 0' 20° -2000000 0 2000000 4000000 [m] 2015) [4]. Kurian et al in 2009 proposed the term (b) “Kallakadal “to refer the flash flooding event which occurs without any precursors or any kind of wind activity [3]. The major causative factor of the Kallakadal event has been identified as remote forcing from Southern Ocean swells. This is somewhat similar to the coastal flooding events experienced by the English Channel coast (Andrew Sibley and Dave Cox, 2014) [1]. Mostly Kallakadal occurs during pre-monsoon period and the flooding takes severe when it coincides with the spring tide.

2 Region of study and methodology

This study considers two regions viz: Global and regional. The global region extending from 30 N to 50 S covers the whole Indian Ocean and taken in such a way that the effect of Southern ocean swells could be well captured to Figure 1: Model bathymetry (a) Global (b) regional. the regional scale. Trivandrum coast stretching 90 km coastline is the regional model which is a high energy coast

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 36

3 Results and Discussion It is inferred that the wave parameters like significant wave The flooding event was identified from the Model height (Hs) and peak wave period (Tp) are correspondingly output and the wave set up was calculated using the output high. During the event, the Wave height reached up to values model parameters and was cross checked with the field of 1.6 m and Tp up to 17.5 sec. Another interesting observations taken for specified stations (Valiathura, observation was that the high values persisted for 12 hrs and Shangumugham, and Adimalathura). Even though this coincides with the high tide. This certainly has led to the September is considered as the fag end of South West flooding of the low lying coastal area of Trivandrum monsoon season, Trivandrum coast witnessed a moderate to particularly the Adimalathura coast. The tide data and AWS high coastal flooding event on 27 th early morning which wind data (Fig 4) was analysed and found that, it was a tide lasted for almost 1 day. It was observed from the field that favouring flooding event where the local wind was not high waves of almost 2 m were approaching the coast strong enough to make such a high wave set up. The wave suddenly without any warning of high wind or storm events. run up at Adimalathura was calculated using empirical Whole Kerala coast was surrounded by high period swell formula and the run up height was calculated to be 0.4298 th waves ( Tp- 16.5 s – 18 s and Hs- 2.4 m – 3.2 m) on 11 m. The beach was inundated and the inundation level was September (from global Model output). This was a clear spotted from field observations and beach profile taken signature of the monsoon swell waves. The wave during the event. parameters: significant wave height (Hs), peak wave period

(Tp) a during the mentioned event is shown in fig 3.

Time period Tp (s) Bouy data Model data 20

15 10

Timeperiod (s) 0

06-09 11-09 16-09 21-09 26-09 01-09

Figure 2: Model validation with WRB buoy data.

1.6 (a) 1.5 1.4 1.3 1.2

1.1

1 Hs (m) Hs 0.9 Figure 4: (a) Mike generated tide; (b) AWS wind data. 0.8 0.7 References

0.6 1) Andrew Sibley, Dave Cox. 2015. Flooding along

24 00 24 12 27 00 01 25 00 25 12 25 00 26 12 26 00 27 00 28 12 28 00 29 12 29 00 30 12 30 24 12 24 English Channel coast due to long period swell waves. Date hours Weather- Vol. 69, No. 3.

19 (b) 2) J.C.R Hunt.2005. Inland and coastal flooding –

17.5 developments in prediction and prevention. Phil. Trans. R.

16 Soc. A (2005) 363, 1475–1491

14.5 doi:10.1098/rsta.2005.1580. 13

Tp (s) Tp 11.5 3) N.P. Kurien, N. Nirupama, M Baba, K.V. Thomas. 10 2009. Coastal flooding due tom synoptic scale meso scale 8.5 and remote forcing. Springer Science+Business Media B.V. 7 2008

26 00 26 00 27 00 28 00 29 24 00 24 12 24 00 25 12 25 12 26 12 27 12 28 12 29 00 30 12 30 00 01 Date hours 4) Hamed. R. Moftakhari, Brett.F. Sandres, David. L. Feldman. 2015. Increased nuisance flooding along the coasts Figure 3: Model output 24 Sep -01 Oct 2018 (a) of United States due to sea level rise: past significant wave height, (b) Peak wave period. andfuture.AGUpublications.10.1002/2015GL066072.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 37

CMIP5 projected Changes of Mean and Extreme Ocean Wave Height in the Pacific Ocean under the RCP8.5 and RCP4.5 Prashant Kumar1*, Sukhwinder Kaur1, Seung-Ki Min2, and Xiaolan Wang3 1 Department of Applied Sciences, National Institute of Technology Delhi 2School of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang,

Gyeongbuk, Korea 3 Climate Research Division, Environment Canada, Toronto, Ontario, Canada) [email protected]

latitudes of northern hemisphere (particularly in northeast Abstract North Atlantic) and decreases over the midlatitude in winter [5]. Further studies reported that these trends in wave height Ocean waves have a significant impact on the population are projected to continue depending on regional and seasonal that lives along the coastal and offshore regions. The study variations [6]. Studies based on CMIP3 and CMIP5 about extreme significant wave height (SWH) in Pacific multimodel suggest, an increasing trends in projected wave region in winter and summer seasonal is significant for height over the tropics (mainly in eastern tropical pacific) future projection under the different scenario. In order to and southern high latitudes [7]. Referring to projected reduce the risk in these regions, it is necessary to have a changes in wave height, although several studies have better understanding of ocean wave climate based on the focused on the projected change in wave height based on Coupled Model Intercomparison Project Phase 5 (CMIP5) CMIP3 and CMIP5 model [7-8], but still there is a need to multimodal. This study assesses the ensemble mean of improve the knowledge about the seasonal and regional CMIP5 model projected change in mean and extreme SWH variations in projected wave climate. under the RCP8.5 and RCP4.5 – concentration pathways This study examined the projected changes in seasonal (DJF over the period 2080–2099 to the historical period 1980 – and JJA) mean and extreme wave height in PO under the 1999 in the Pacific Ocean (PO) (400 S - 800 N, 1250 E – 850 RCP4.5 and RCP8.5 scenarios using CMIP5 models for 20 W). In the PO, the projected wave height has strong years period 2080-2099 with respect to 1980-1999. regional and seasonal variation which is comprehensively reliable with past reviews. The significant changes in the 2 Data and Methodology mean and extreme wave height are projected in central This study considered the Northern hemisphere summer North Pacific during December to February (DJF) and in (JJA) as well as winter (DJF) seasons for analyzing the western North Pacific during June to August (JJA) seasons projected changes in mean and extreme wave height (Havg, under the RCP8.5. These projected changes in mean and and Hmax, respectively) based on 20 CMIP5 model extreme wave height in central North Pacific (CNP) during simulation (Table. 1). We derived the seasonal (DJF and JJA) winter are strongly associated with ENSO influence. mean and extremes of SWH from the monthly wave height However, tropical cyclones in JJA season enhance the data for 20 CMIP5 model under historical (1950-2005), SWH in western North Pacific under the RCP8.5 while with RCP4.5 (2017-2099) and RCP8.5 (2017-2099) scenario. the RCP4.5 scenario, the projected change in SWH show Statistical downscaling approach has been used for similar patterns but are flimsy. A significant correlation constructing CMIP5 model wave climate data [5]. between seasonal mean and extreme wave height is observed with both RCP4.5 scenario and RCP8.5 scenario. 3 Result and Discussion Further, the standard deviation of CMIP5 projected future 3.1 Climatology and Variability changes in seasonal mean and extreme wave height is given The climatology and variability patterns of 20 – Multi under the RCP8.5 and RCP4.5. The significant increase in Model ensemble (MME) mean is calculated for DJF as well projected wave height is observed in the PO under the as JJA seasonal mean and extreme SWH under historical runs RCP8.5 scenario as compared to the RCP4.5 scenario. over the period 1980 – 1999, RCP8.5 and RCP4.5 scenario for the period 2080 – 2099 (Fig.1, and 2). Here 20 – Multi 1 Introduction Model ensemble (MME) mean refers the arithmetic mean of 20 CMIP5 model. The climatology patterns of MME for Ocean waves are an important factor that is responsible historical runs exhibit large Hmax values over the CNP for climate change and significantly affect the coastal as well during DJF and over the high latitude of Southern PO during as offshore activities and structures. In spite of this, there JJA. Similar seasonal variations are observed in MME of exists limited information about the ocean wave behaviour RCP8.5 and RCP4.5 scenarios, but RCP4.5 patterns are based on climate change as such information is not simulated weaker. Further, significant increase in Hmax variability in the global climate models [1]. Thus, either dynamic patterns is observed in Pacific Ocean that varies based on downscaling or statistical downscaling approach [2] is used season under RCP8.5 scenario as compare to RCP4.5 to examine the projected changes in wave climate. scenario. Several researchers have investigated the trends in 3.2 Projected Changes in Wave Height wave height based on different models or/and scenarios, over different period, by using different technique [3-4] and Ensemble mean of twenty CMIP5 models are utilized to observed that extreme wave height increases over the high determine the projected changes in seasonal Havg and Hmax under the RCP8.5 and RCP4.5 scenario over the period 2080– National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 38

2099 to the period 1980 –1999 in the PO (Fig. 3). The hatch lines represents the region with statistically significant response of SWH at 80% model agreement for Havg and with 20 year return value (RV20) for Hmax. The significant changes in the mean and extreme wave height are projected in CNP during DJF and in western North Pacific JJA under the RCP8.5 scenario. 3.3 Figures and Tables The detail of 20 CMIP5 climate models.

Figure 3: Projected changes in mean and extreme SWH climatology under RCP8.5 and RCP4.5 to the historical. References [1] M. A. Hemer, X. L. Wang, R. Weisse, and V. R. Swail, “Advancing wind-waves climate science”, Bull. Am. Meteor. Soc., 93, pp.791–796, 2012. [2] X. L. Wang, V. R. Swail, and A. Cox, “Dynamical versus statistical downscaling methods for ocean wave heights”, Int. J. Climatol., 30, pp.317–332, 2009. [3] X. L. Wang, and V. R. Swail, “Changes of extreme wave heights in northern hemisphere oceans and related atmospheric circulation regimes”, J. clim.,14, pp.2204- 2221, 2001. Figure 1: Climatology of seasonal mean and extreme SWH for CMIP5 models historical, RCP8.5, and RCP45 [4] S. Caires, V. R. Swail, and X. L. Wang, “Projection and scenario. analysis of extreme wave climate”, J. Clim., 19, pp.5581–5605, 2006. [5] X. L. Wang, Y. Feng, and V. R. Swail, “North Atlantic wave height trends as reconstructed from the twentieth century reanalysis”, Geophys. Res. Lett., 39, L18705, 2012. [6] Y. Fan, I. M. Held, S. J. Lin, and X. Wang, “Ocean warming effect on surface gravity wave climate change for the end of the 21st century”, J.Clim., 26, pp. 6046– 6066, 2013. [7] X. L. Wang, Y. Feng, and V. R. Swail, “Changes in global ocean wave heights as projected using

Figure 2: Variability of seasonal mean and extreme SWH multimodel CMIP5 simulations”, Geophys. Res. Lett., for CMIP5 models historical, RCP8.5, and RCP45 41, pp. 1026–1034, 2014. scenario. [8] M. A. Hemer, K. L. McInnes, and R. Ranasinghe, “Projections of climate change-driven variations in the offshore wave climate off south eastern Australia”, Int. J. Climatol., 33, pp.1615-1632, 2013.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 39

Influence of Natural Climate Variabilities over the Extreme Wave Climate in Pacific, North Atlantic and Indian Ocean Sukhwinder Kaur1*, and Prashant Kumar1

1Department of Applied Sciences, National Institute of Technology Delhi [email protected]

extreme wave climate, several studies have examined the Abstract relationship between Hmax and variabilities at regional [7] and at global scale [8] but, still there is a need to examine Knowledge of wave climate at regional scale is essential how regional extreme wave climate is effected by the for the activities and wellbeing of shipping, offshore dominant variability modes. industries and coastal advancement. To understand the This study examined the seasonal influence of natural regional wave climate, it is important to investigate the climate variabilities such as ENSO, NAO, IOD and SAM, atmospheric circulation patterns which derive the wave on Hmax in the regions where these variability modes are climate and its variability. This study examined the impact dominant. of natural climate variability such as El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), 2 Data and Methodology Indian Ocean Dipole (IOD), and Southern Annual Mode 2.1 Data (SAM) over the seasonal extreme significant wave height (SWH) of Pacific, North Atlantic and Indian Ocean. The This study considered the four boreal seasons (i.e. analysis is carried out for 54 year period from 1957 to 2010 December–February (DJF), March–May (MAM), June– using ERA-20 Century reanalysis data and the non- August (JJA), and September–November (SON)) for stationary generalized extreme value (GEV) distribution is analyzing the Hmax over 54 years period (1957- 2010). The used to examine the extreme SWH responses. The strongest seasonal Hmax data is derived from the 6–hourly SWH data ENSO impact on extreme SWH (Hmax) is evident over taken from ERA–20C [9]. northeast North Pacific, increases during El Niño and 2.2 Methodology decreases during La Niña in December–February (DJF) The need to comprehend the frequency or intensity of season. In DJF season, Hmax response to NAO occurs over natural hazard has promoted the extreme value theory. The the Icelandic and Azores regions. Further, the significant ultimate objective of this theory is to provide the estimation IOD influence on Hmax observed over eastern slice of of the probability of events larger than any on record. Tropical Indian Ocean (TIO) is associated with the Extreme value theory provides the statistical description of maturing (and larger amplitude) of IOD events in the maxima of stationary and non stationary process. September–November (SON) season. The SAM is not only However, in context of environmental variables, non- affecting the Southern Indian Ocean (SIO) significantly stationarity is used. In non stationary process, the maxima but also has weaker influence in Northern Indian Ocean behavior is described by the three extreme value distributions (NIO), and TIO during all seasons. The significant region that arose from the limiting theorem of [10]. These three with 5% significance is also determined. distributions are nested into a single unit, called non stationary GEV distribution and its distribution function is 1 Introduction given as: Extreme ocean waves are an essential component in the  x  t exp[ exp()],0 t development of several natural processes. Yet, mostly, they  t Fx( ,,,tt ) t    xx1 have negative impact on coastal and offshore structures, exp[ (1)],0,10  ttt  ttt activities and community. Thus, in order to understand the  tt regional wave climate, it is essential to have a good Where, µt, σt, and ξt denotes the location, scale, and shape knowledge of the atmospheric system which is responsible parameters, respectively. Here, the climate variability υt that for wave generation. This not only helps us to understand the varies with time are taken as covariate of GEV parameters, natural processes but also helps in preventing social and which are estimated by using log likelihood ratio test. economic disasters. 2.3 Variability Indices Natural climate variability indices are used to describe the The climate variability indices of ENSO, NAO, IOD, and atmospheric circulation patterns to apprehend weather and SAM for the period 1957-2010 are obtained from the various ocean climate conditions such as drought, water level rise, sources as ENSO and NAO indices are downloaded from wave climate variability etc. In Pacific Ocean, it has been National Oceanic and Atmospheric Administration observed that mean SWH is associated with ENSO [1], and (NOAA)/Climate Prediction Center (CPC) in North Atlantic with NAO [2]. Reference [3] correlates the (http://www.cpc.ncep.noaa.gov/data/indices/). The IOD variations in monthly average SWH in Mediterranean Sea index is constructed by using SST anomalies obtained from with NAO and Indian Monsoon. IOD influence on SWH in the HadISST dataset and observationally based Marshall eastern Arabian Sea (AS) has been reported by [4]. A strong index is used to examine SAM influence. correlation between SWH and SAM in Southern Ocean (SO) was reported by [5]. Reference [6] provides the detail of the SAM impact on SWH over the global ocean. Alluding to National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 40

3 Results and Discussion 3.1 Climatology Climatology patterns of seasonal extreme SWH for Pacific (top), Atlantic (middle), and Indian Ocean (bottom) by using ERA-20C data over the period 1957-2010 are displayed in Fig. 1. Location parameter of GEV distribution is used to represent the climatology of extreme responses. Large Hmax values are found over the Southern Pacific, Southern Atlantic, and SIO throughout the months, while all over the Indian Ocean during JJA and all over the Pacific and Atlantic Ocean during DJF season. In general, JJA season experiences strongest wave height and DJF less as compare to other seasons in Indian Ocean, however counter response is observed in Pacific, and Atlantic Ocean. 3.2 Influence of natural climate variability over

extreme wave height Figure 2: Natural climate variabilities influence over The regression patterns of seasonal extreme SWH onto Hmax for ERA-20C (1952–2010). The statistically different climate variability modes (ENSO, NAO, IOD, and significant regions at the 5% level are hatched. SAM) for ERA-20 reanalysis data over the period 1957-2010 References are shown in Fig. 2. The extreme responses of Hmax are acquired by using the GEV analysis and variability indices are used as covariates. Areas with statistically significant [1] S. K. Gulev, and V. Grigorieva, “Last century changes responses in Hmax at 5% level are marked with hatch lines. in ocean wind wave height from global visual wave The strongest ENSO impact on Hmax is evident over data”, Geophys. Res. Lett., 31, L24302, 2004. northeast North Pacific, increases during El Niño and [2] D. K. Woolf, and P. G. Challenor, “Variability and decreases during La Niña in DJF season. However, in rest of predictability of the North Atlantic wave climate”, J. the months enhance in wave height is evident over equatorial, Geophys. Res., 107(C10), 3145, 2002. southwest and northwest Pacific Ocean. In DJF season, [3] P. Lionello, and A. Sanna, “Mediterranean wave climate variability and its link with NAO and Indian monsoon”, Hmax response to NAO occurs over the Icelandic and Azores Clim. Dyn., 25, pp. 611–623, 2005. regions. Further, the significant IOD influence on Hmax [4] T. R. Anoop, V. S. Kumar, P. R. Shanas, J. Glejin, and observed over eastern slice of TIO is associated with the M. M. Amrutha, “Indian Ocean Dipole modulated wave maturing of IOD events in SON season. In other seasons (i.e. climate of eastern Arabian Sea”, Ocean Sci.,12, pp. JJA, MAM, and DJF), IOD influence on Hmax is seen over 369–378, 2016. the South China and Philippines seas, and western TIO. The SAM is not only affecting the SIO significantly but also has [5] M. A. Hemer, J. A. Church, J. R. Hunter, “Variability and trends in the directional wave climate of the weaker influence in NIO, and TIO during all seasons. Southern Hemisphere”, Int J Climatol 30, pp. 475-491, 2010. [6] A. G. Marshall, M. A. Hemer, H. H. Hendon, and K. L. McInnes, “Southern annular mode impacts on global ocean surface waves”, Ocean Modelling, 129, pp. 58- 74, 2018. [7] C. Izaguirre, F. J. Méndez, M. Menéndez, A. Luceño, and I. J. Losada, “Extreme wave climate variability in southern Europe using satellite data”, J. Geophys. Res., 115, C04009, 2010. [8] P. Kumar, S. K. Min, E. Weller, H. Lee, and X. L. Wang, “Influence of climate variability on extreme ocean surface wave heights assessed from ERA-interim and ERA-20C”, J. Clim., 29, pp.4031–4046, 2016.

[9] P. Poli, and Coauthors, “The data assimilation system Figure 1: Climatology patterns of seasonal extreme SWH and initialperformanceevaluation of the ECMWF pilot for Pacific (top), Atlantic (middle), and Indian Ocean reanalysis of the 20th-century assimilating surface (bottom). observations only (ERA-20C). ERA Rep. Series,14, pp. 62, 2013. [10] R. A. Fisher, L. H. C. Tippett, “Limiting forms of the frequency distributions of the largest or smallest member of a sample”, Proceedings of the Cambridge Philosophical Society, 24, pp.180–190, 1928.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 41

Climate change impact on extreme wave analysis in the Arabian Sea Mourani Sinha1, Susmita Biswas2 and Mrinmoyee Bhattacharya2

1Department of Mathematics, Techno India University, West Bengal, Kolkata-700091 2Department of Computer Science and Engineering, Techno India University, West Bengal, Kolkata-700091 [email protected]

2 Methodology Abstract The analysis regions covering the the AS extends from 55º E to 75º E and 25º N to 5º N. Surface wind generated Prediction of cyclonic wave heights using numerical significant wave height (SWH) data is extracted for the wave models are always a challenge due to inaccurate wind period 2006-2015, for the above region from the NOAA speed values during extreme conditions. Thus the use of WAVEWATCH III global wave model. The six hourly probabilistic approach for extreme wave analysis becomes global wave data is generated by a multi-grid forecast model important. Surface wind generated significant wave height with two way nesting capability and with 0.5º global grid. (SWH) data is extracted for the period 2006-2015, for the Empirical orthogonal function (EOF) analysis has been Arabian Sea (AS) region. Empirical orthogonal function carried out over the AS region for the above 10 years to analysis has been carried out and the first principal extract the dominant mode representing maximum variability component is fitted with the Generalized extreme value of the total variance. The first principal component (PC1) is distribution (GEV). The maximum wave height that can be fitted with the Generalized extreme value distribution (GEV) obtained for a given return period is estimated for 2, 5, 10, and the location, scale and shape parameters are calculated. 25, 50 and 100 years. The Weibull distribution was fitted for The maximum wave height that can be obtained for a given a particular grid through which Ockhi cyclone passed. return period is estimated for 2, 5, 10, 25, 50 and 100 years. The expression derived for the return period analysis is given 1 Introduction in details in [2]. Then a grid (72E longitude and 9N latitude) is chosen in the AS through which Ockhi cyclone passed. Recent studies reveal ocean warming leading to Data is extracted for the grid from 2006-2016 and maximum increase in wave energy. Due to upper ocean warming there wave height estimated for given return periods using GEV is change in the global wind patterns which are making the and Weibull distribution. wind generated waves stronger. Also due to ocean warming there is intensification and expansion of tropical cyclones. 3 Results and discussions Reference [3] suggest due to weak winter monsoon SWH data extracted for the AS region for the period 2006- circulation resulting from anthropogenic emissions there is 2015 is subjected to EOF analysis. Figure 1 shows the an increased number of cyclones in the Arabian Sea (AS) in temporal pattern of the PC1 having 90% variance and annual the post monsoon season. The impact of climate change on periodicity. tropical cyclones frequency and intensity is prominent on ocean basins with increased sea surface temperatures. Tropical cyclones may develop any time of the year in the AS due to warm sea surface temperatures prevailing, but the monsoon circulation and strong vertical wind shear limits the period for cyclogenesis [4]. Prediction of cyclonic wave heights are always a challenge due to the inaccurate wind speed values during extreme conditions. Thus the use of probabilistic approach for extreme wave analysis becomes important. Ockhi cyclone formed near southern India and Sri Lanka on November 30, 2017, moved out over the AS, Figure 1. First principal component of Arabian Sea SWH data intensified to category 3 strength on December 2-3, but then for the period 2006 -2015. weakened as it moved closer to land. Reference [1] studied The PC1 fitted with GEV distribution is given in Figure 2. the inter-annual variability of the warming of the The scale parameter σ, the location parameter µ, and the southeastern Arabian Sea during the spring transition shape parameter ξ are 16.6124, -22.5903 and 0.574085 months from 2013 to 2015. Using heat budget analysis, they respectively. highlighted the role of the net heat flux and the entrainment of cold subsurface waters in controlling the mixed layer temperature. Reference [2] applied Generalized Pareto distribution (GPD), Generalized extreme value distribution (GEV) and Weibull distribution to approximate design wave parameters and examine extreme wave heights for given return periods. The evaluation conducted and outcome attained shall help in the construction of a long term maximal wave chart for the study region, which may aid as a fast escort to recognize most vulnerable seaside areas. Figure 2. PC1 of SWH data fitted with GEV.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 42

Table 1 gives the maximum wave height that can be obtained Acknowledgements for given return periods. Irrespective of the spatial grids, the This study forms a part of the SAMUDRA project under probabilistic estimation of the maximum wave height with the Space Application Centre of the Indian Space Research respect to the first PC is predicted to be approximately 6 Organization. The authors of the study wish to thank Indian meters. Space Research Organization for sponsoring the project. Table 1. Extreme or maximum wave heights using GEV Return Period in Expected SWH using References years GEV 2 6.25106 [1] Simi Mathew, Usha Natesan, Ganesan Latha and 5 6.29689 Ramasamy Venkatesan, 2018, Dynamics behind warming of the southeastern Arabian Sea and its interruption based on in 10 6.31439 situ measurements, Ocean Dynamics, 25 6.32803 (https://doi.org/10.1007/s10236-018-1130-3). 50 6.33429 100 6.33845 [2] Jobin George Varghese and Mourani Sinha, 2019, “Non- stationary and non-linear time series analysis using multiple probability distributions to estimate extreme wave return Finally a grid (72E longitude and 9N latitude) is chosen in the values”, International Conference on Computer, Electrical & AS through which Ockhi cyclone passed. The time series data Communication Engineering (ICCECE 2019), hosted by: for the period 2006-2016 is fitted with GEV and extreme Techno India University, West Bengal, Salt Lake Sector V, wave heights calculated for given return periods which are Kolkata 700091, on 17th-19th January, 2019. underestimated. Hence Weibull distribution is chosen. The scale and shape parameters are estimated to be 1.64059 and [3] A T Evan, J P Kossin, Chul ‘Eddy’ Chung and V 1.92703 respectively. Table 2 gives the estimated extreme Ramanathan, 2011, Arabian Sea tropical cyclones wave heights calculated from Weibull distribution. intensified by emissions of black carbon and other aerosols, Table 2. Extreme or maximum wave heights using Weibull Nature 479, 94-97. Return Expected SWH Period in years using Weibull [4] A T Evan and S J Camargo, 2011, A climatology of 2 5.43108 Arabian Sea cyclonic storms. Journal of Climate 24, 140- 5 5.74098 158.

10 5.9379 25 6.17837 50 6.3513 100 6.51874

The maximum wave heights predicted were between 5.5 meter and 6.5 meter for given return periods. Maximum model computed SWH was 8.02 meter (NOAA WW3) on December 02, 2017, 00 hours, during Ockhi cyclone at the particular grid. Thus there is an increase in the wave height value during extreme condition in comparison to the probabilistic estimated value. 4 Conclusions In the present work 10 years of SWH data is considered for the entire AS region and a time series SWH data for 11 years through which a cyclone passed. For basin scale estimation EOF analysis is performed for the AS region and the first PC having 90% variance is fitted with GEV. Extreme wave heights are calculated for given return periods upto 100 years which estimated to 6 meters approximately for AS region. Then a particular grid is chosen through which a cyclone passed to assess the methodology. GEV underestimated the SWH values whereas Weibull gave an acceptable estimation. For the time series data the scale parameter (1.6 for Weibull and 0.4 for GEV) maybe responsible for the underestimaton. It can be said from the work done there is an increase in the wave height value during extreme condition in comparison to the probabilistic estimated one. This may be attributed to climate change related ocean warming leading to increased wind speed values and hence wave height values. National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 43

Comparison study of OW detection among pre- and post-monsoon cyclones in the Bay of Bengal Jiya Albert1, Bishnupriya Sahoo1 Prasad K Bhaskaran2

Department of Ocean Engineering & Naval Architecture Indian Institute of Technology Kharagpur Kharagpur 721 302, West Bengal, India [email protected]

Abstract the seasonal and differential characteristics of prevalent atmospheric eddies over the study domain over the pre- The present knowledge on tropical cyclone detection is genesis time. The Okubo-Weiss (OW) parameter formulated only possible through remote sensing techniques based on [3 or 4 (check)] using the zonal and meridional wind satellite imagery of cloud bands only after the formation of components obtained from WRF model were utilized for this a warm core depression on the ocean surface. The present study. Mathematically, the OW parameter can be represented study is an effort to examine the atmospheric pre-cyclonic as: eddies prior to cyclogenesis formation in the Bay of Bengal 2 2 2 OW = Sn + Ss − W (1) (BoB) basin. The analysis reveals significant evidence of S = ∂u/∂x − ∂v/∂y (2) intensified OW parameters about four days for Vardah n cyclone and six days for Aila cyclone prior to the detection Ss = ∂v/∂x + ∂u/∂y (3) by satellites. The study brings to light the role and influence W = ∂v/∂x − ∂u/∂y (4) of atmospheric eddies on the detection of pre-cyclonic Sn and Ss are the normal and shear components of strain, disturbances and their significance during the pre- and and W is the relative vorticity. The study performed a post-monsoon cyclone seasons. comprehensive analysis of the OW parameters thereby 1 Introduction providing details on the lead time for detection, based on the spatio-temporal evolution of atmospheric eddies as well their Detection of the tropical cyclones is considered as one translation along the vertical layers of the atmospheric among the embolden topics amongst the scientific column. To investigate the mesoscale atmospheric features, community attributed due to their immense impact on the the present study fixed the domain of 27 km resolution coastal community and wide socio-economic implications. covering 40º × 30º domain in the BoB and WRF simulations Research studies have advocated on the development of were conducted for a month long period corresponds to time several theories and methodologies to comprehend tropical period of respective cyclone case studies. The chosen cyclone cyclone initiation and instability mechanisms in the vertical case studies are described in the proceeding section. atmospheric column prior to satellite detection over the ocean surface [5]. Theories provide physical insight starting with the initiation of cyclonic eddies in the upper atmosphere, its evolution and thereafter the associated sinking mechanism as a cluster of eddies to the atmospheric boundary layer over the ocean surface[1,5]. It is anticipated that both atmospheric dynamics at various vertical levels as well water mass characteristics over the ocean basin can Figure 1: Cyclone case studies ‘Aila’ and ‘Vardah’. contribute to differential air-sea flux exchange thereby 2.1 Cyclone Aila (2009) playing a role in modulating and modifying the atmospheric strata. As the cyclogenesis is partly influenced by both Aila cyclone reported initially by satellite as a depression atmospheric and ocean parameters, it is imperative to area over central area of Bay of Bengal on 23rd May 2009. It understand the role and influence of mesoscale eddies and further intensified to category of severe cyclonic storm (SCS) its variability during a pre- and post-monsoon episode. The quickly with 3-min sustained wind speeds of 110 km/h (70 popular eddy detection technique Okubo-Weiss (OW) mph). After the extensive destruction made by cyclone Sidr parameter along with its robustness and efficacy of OW on 2007, with in a gap of two years, Bangladesh was again parameter as a detection tool is widely recognized in many worst affected by the cyclone Aila [2]. studies and this technique has been used for the detection [1] 2.2 Cyclone Vardah (2016) and identification of pre-cyclonic vortices in the present Cyclone Vardah, the second catastrophic event after 2011 study which intent to investigate the lead time prior to Thane and the final cyclone put in to a category of Very satellite detection. Severe Cyclonic Storm (VSCS) under 2016 North Indian 2 Data and Methodology Ocean Cyclone Season. It was detected as low pressure rd The present analysis initiates a study focuses over the system over Malay Peninsula on December 3 and further prevalent atmospheric conditions for two catastrophic intensified to a Category 1 (130 km/h, 80 mph) cyclone cyclonic storms made enough destructions over the system. After Vardah made the land fall at Chennai around th landmasses which it made the landfall. One that developed 12 of same month [2]. Even though after dissipation into a during the pre-monsoon season (Aila cyclone during 2009) DD, the system propagated across the land mass of Indian and other during the post-monsoon season (Vardah cyclone subcontinent and continued to move as a remnant low over during 2016). WRF simulations were carried out to examine Arabian Sea.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 44

3 Results and Discussions initial disturbances in case of post-monsoon period are found Investigation of results were mainly emphasised over the to wide spread over a two degree by two degree spatial two primary facts. First over the lead time and second extend. Secondly, the presence of the maximum intensity towards atmospheric column heights in which the signals over the upper atmospheric column around (400-500 disturbances are mainly clustered. The characteristic mb) (Figure.4). The much disturbed atmosphere after the detailing of these factors and its uniqueness and a comparison monsoonal circulations is the main reason for the large extend among the pre-monsoon and post-monsoon cyclonic case of OW pattern distribution comparative to the calm pre- studies are described in this section. monsoon atmosphere and the intensification rate is also shows a higher value than that of previous case discussed. Cyclone detection and lead time detailes.

Season of Detection Cyclones occurrence Satellite OW Lead time 23rd May 17th May Aila Pre-monsoon 138hr 2009 2009 3rd Dec 30th Nov Vardah Post-monsoon 105hr 2016 2016 3.1 Pre-monsoon cyclone Aila

The signal of OW parameter in Aila cyclone is clearly giving a compact structure of mild disturbance positioned over the lower and middle part (750–850 mb) of atmospheric column (Figure.3). Another salient observation is the occurance of the precyclonic signs much in an advanced time than the normal post-monsoon cyclones.

Figure 3: OW parameter for cyclone Vardah. References 1 Dunkerton, T.J., Montgomery, M.T., and Wang, Z. (2009). Tropical cyclogenesis in tropical wave critical layer: Easterly waves. Atmospheric Chemistry and Physics, 9, 5587–5646. 2 Preliminary Report Month Wise For Year 2016 and 2008-IMD (Vardah and Aila) 3 Okubo, A. (1970, June). Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. In Deep sea research and oceanographic abstracts (Vol. 17, No. 3, pp. 445-454). Figure 2: OW parameter for cyclone Aila. Elsevier. 4 Peng, M. S., Fu, B., Li, T., & Stevens, D. E. (2012). On an average around 30hr lead time advancement is Developing versus non-developing disturbances for existing for the pre-monsoon cyclone Aila based on its tropical cyclone formation. Part I: North announcement as a Deep Depression (DD). The initial Atlantic. Monthly weather review, 140(4), 1047-1066. indication of the cyclonic disturbance which led to Aila 5 Prasad K. Bhaskaran, Bishnupriya Sahoo and Jiya cyclone were found around 138hr prior to the DD warning Albert (2018): Tropical Cyclogenesis for the North by the satellite sources over the ocean surface. The Indian Ocean region (Book chapter), Title: Climate occurrence of relatively clear atmospheric column in pre- Change and Coastal and Marine Ecosystems in India, monsoon gives out the genesis of unique and well Ministry of Environment, Forest & Climate Change-U clustered OW patches over the lower atmospheric heights. UNFCCC CoP-24,Katowice, Poland December 2018, 3.2 Postmonsoon cyclone Vardah 55-72.

Cyclone Vardah represents a case study of the detection scheme using OW parameter of a post-monsoon cyclone. The

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 45

Variability analysis of wind-waves for Indian Ocean using satellite altimetry data Sreelakshmi S and Prasad Kumar Bhaskaran Department of Ocean Engineering & Naval Architecture, Indian Institute of Technology Kharagpur Kharagpur 721 302, West Bengal, India [email protected]

dominant modes of variability. Due to variability in winds Abstract over basin-scale, from north hemisphere to south hemisphere, the IO region was divided into 3 domains as shown in Figure This study examines the spatio-temporal variability of 1, grouped as D1 (North Indian Ocean), D2 (South Indian wind-waves over the Indian Ocean region covering a period Ocean), and D3 (Southern Ocean). The EOF and PCA of 26 years (1992-2017). Advanced data analysis techniques analysis was carried out to understand the dominant modes of such as the Empirical Orthogonal Function (EOF) and variability over these three regions as well to assess the major Principal Component Analysis (PCA) were used to identify variability in spatial-temporal scales. the dominant modes of variability in the observed datasets. The first modes were analyzed with the highest significance, covering a major percentage of wind-wave variability and has further investigated. 1 Introduction The Indian ocean region due to its unique geographic setting has been considered in this study to assess and evaluate the variability of wind-waves utilizing satellite measured data. In particular, the observed changes in wind- wave climate over the Southern Ocean belt has direct implications on the local wind-wave climate over the Northern hemisphere. There are studies that reported on the modulation of long distant swells propagating from Southern Ocean influencing the local wind-wave climate of the North Indian Ocean region [1][2]. In that context wind-wave variability at different geographical regions are important to be highlighted. Recent advancements in space-borne technology are highly remarkable and the potential of Figure 1: Sub-domains of the Indian Ocean region used satellite data is being considered beneficial for basin-scale to assess the variability of wind-waves. studies. A study [3] addressed the limitation of uniformity in datasets obtained from various altimeters and proposed 3 Results and Discussion necessary bias correction of datasets for research use. The high dimensionality of such data is challenging to make a 3.1 Spatial variability of Wind-Waves better sense of the overall dataset. The empirical orthogonal function (EOF) and pricipal component analysis (PCA) are powerful mathematical tools used in various fields such as image processing, climate analyses etc., where reduction of dimensionality is required. Through this study, we aim to identify the geographical regions that exhibited the highest variability as well as to understand the dominant trend of wind-waves over the recent years for the Indian Ocean (IO) region using satellite altimetry data and advanced data analysing tools. 2 Data and Methedology The merged and quality checked space-borne data from nine satellite altimeter missions (ERS-2, TOPEX/POSEIDON, GEOSAT Follow- ON (GFO), JASON-1/2, ENVISAT, CRYOSAT, and SARAL) are available from the French Research Institute for Exploitation of the Sea (IFREMER/CERSAT) and used for this study. The Basic Radar Altimetry Toolbox (BRAT) is employed to convert the raw swath data extracted from the above- Figure 2: First EOF mode of annual averaged SWH for mentioned satellites into a gridded form and used for further domains 1, 2, and 3. analysis. The final processed monthly data obtained from BRAT comprises of significant wave height (SWH) at a Spatial variability of wind-waves obtained from the first spatial homogeneity of 0.33° × 0.33° grid resolution. The EOF mode for the three sub-domains are depicted in Figure annual averaged data for 25 years was used to examine the 2. This figure depicts the potential geographical regions where the variability is dominant over 26 years on inter- National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 46

annual scale. First mode of EOF is the dominant mode covering the highest amplitude of total variance in domains 1, 2, and 3. The areas exhibiting highest variability identified in all the three domains are, western Arabian Sea, northern Bay of Bengal, western Australia, and central Southern Ocean (coordinates between 60E to 100E). As compared to the other two regions, the highest variability range is noticed for the region D1 (0.008-0.02 m) shown in Figure 1, attributed due to high seasonal variability in the annual wind- wave. For the western Arabian Sea sector, the variability range indicates that the seasonal change being the dominant mode of variability in NIO [4], is significant. The variability of wind-waves noticed for the northern Bay of Bengal clearly indicates the influence of swell propagation towards the NIO from Southern Ocean. Figure 4: PC1 for monthly averaged SWH for D1, D2, and 3.2 Temporal variability D3. The PC1 of annual averaged SWH for D1, D2, and D3 are 4 Conclusion shown in Figure 3. The three domains exhibited a stabilizing trend within the range of -0.3 m to 0.2 m over a period of 26 Satellite altimeter data for a period of 26 years obtained years. Moreover, the annual variability in all three domains from IFREMER/CERSAT was used in this study to evaluate are found to be weak for the period of 2007 to 2017 for D1 the variability of wind-waves in the Indian Ocean basin. and D3. The PC1 for monthly averaged SWH for all the three Estimates on the annual variability of SWH in terms of spatial domains are shown in Figure 4. The variability range was and temporal scales over Indian Ocean domain has been found maximum for D1 ranging between -0.1 to -0.02 m, and carried out using EOF and PCA analysis. It was noticed that the values ranged between -0.075 to -0.04 m respectively for the first mode of EOF as well as the PC1 values were the D1 and D2 regions. dominant modes covering significant variability. The study identified geographical regions that experienced the highest variability viz; western Arabian Sea, northern Bay of Bengal, western Australia, and central Southern Ocean (coordinates between 60E to 100E). Principal component time series (PC1) provided more information on the temporal variability of annual averaged and monthly averaged SWH over D1, D2, and D3 within a range -0.2 to 0.2 m and -0.08 to 0.04 m respectively.

References [1] Kurian, N.P., Rajith, K., Shahul Hameed, T.S., Sheela Nair, L., Ramana Murthy, M.V., Arjun, S., & Shamji, V.R. Figure 3: PC1 of annual averaged SWH for D1, D2, and D3 (2009). Wind waves and sediment transport regime off the covering 26 years from 1992 to 2017. south-central Kerala coast, India. Natural Hazards, 49, 325- 345. [2] Nayak, S., P. K. Bhaskaran, R. Venkatesan, and S. Dasgupta (2013), Modulation of local wind-waves at Kalpakkam from remote forcing effects of Southern Ocean swells, Ocean Eng., 64, 23–35. [3] Queffeulou, P. (2004). Long term validation of wave height measurements from altimeters, Mar. Geodynam., 27, 495–510. [4] T. R. Anoop,V.Sanil Kumar, P. R. Shanas, and Glejin Johnson (2015). Surface wave climatology and its variability in the North Indian Ocean Based on ERA-Interim Reanalysis, Journal of Atmospheric and ocean technology.,32,1372-1385.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 47

Pre-emptive policy frame work for coastal region and islands post climate impact based on scientific input Lieutenant K Manoj Kumar, Commander T S Ramanathan [email protected], [email protected]

Abstract 2.1 Grid wise Analyses- SST Anomalies

Tuvalu and Kiribati are examples of states in the Pacific Ocean that are being affected by climate change and are projected to disappear by 2050. The Global Climate Model (GCM)/ National Oceanographic and Atmospheric Administration (NOAA) reanalyses plot for Sea Surface Temperature (SST) anomalies and increasing atmospheric Carbon Dioxide (CO2) indicates significant sea level rise over these Small Island Developing State (SIDS) 1. The Climate change models over Indian coastal regions especially over Gulf of Mannar, Andaman Islands, coastal regions of West Bengal etc. also indicates similar trends. A strategic and holistic approach with a standby policy frame work is hence essential for addressing climate change whilst preparedness for an estimated worst condition, based Figure 2: SST anomalies. on these scientific climate model indications. Latitude 4N-8S, Longitude 171E-173E 1 Introduction The paper intends to cover the scopes which are to be exercised to react to the worst conditions. A standard set of policy for the Climate Change Refugees including provisions for sovereignty, relocation and funding to cope the scenarios is established. Based on Scientific inputs deduced from the SST anomalies over submerging Islands1 and comparing the same with Indian Coastal region/island, indicates similar trend in the climate change exile situation. Therefore, precautionary measures guided by policies and strategies that integrate with broader national outlook are essential for safe and timely displacement of Climate Change refugees. Figure 3: SST anomaly Over Indian region.

2 Scientific Analyses Geographical location Specific SST anomaly also shows NOAA Sea surface Temperature (SST) plot averaged similar trend of increase. Fig 2.2 and 2.3 indicates the SST over 100 years indicates similar values over Indian coastal anomaly (based on 1981-2010 climatology) showing similar region in comparison with the submerging islands (Fig 2.1). values in both the regions. 2.2 Comparison The detailed location specific Scientific Study/analyses to assess the worst Climate Scenarios may include the following parameters (Table 2.1) in comparison with SIDS. The progressive trend of these parameters based on climatological data may be studied to device an ensemble scientific model for Indian Coastal region/ island which would alert and address the community in advance to cope with worst case climate Scenarios. Table 2.1 Parameters - Comparison between Indian Region and SIDS S/N Parameters SIDS Indian Coastal Region/ Islands Figure 1: Mean Global SST. (a) Latitude, Kiribati Andaman and Nicobar

longitude 2 N , 173E island 1. Tuvalu and Kiribati as Case Studies Illustrating the Tuvalu 3N -16 N, 89-93 E Need for a Climate Refugee Treaty Rana Balesh* 8 S, 171 E (b) Average MSLP 1.5 – 2 Andaman islands -16m Height meters (average) Car-Nicobar – 5-6m.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 48

 Indian Coastal S/N Parameters SIDS Region/ Islands

Categorized Low ( c) High Moderate Level Island ( LLI )

(d) Average SST >26.5 >25.5 Increase Rate (e) 6-8 per year 4-5 per year Cyclones/Flood

Bleaching Alert High Alert Moderate (f) Area Zone zone Alert zone Flooding due Flooding due to Natural Figure 5: Policy frame work plan. to Storm (g) Extensive erosion disaster Surge (Tsunami 3.2 The strategies and Policies should encompass (Hurricanes) 2004) the following

2.3 Alert Zone for High Inundation (a) International cooperation: Meeting international commitments and cooperation at all levels essential for achieving standard set of operating procedures to cope the worst scenario of climate change.

(b) Identification and declaration of Vulnerable Zone: Based on Scientific input from the climate model, Policy frame work may include timely declaration of risk/alert zone with possibility of high inundation which would enable safe transfer of men and essential material to safe zone.

(c) Displacement Plan of refugees: Pre-emptive policy must include the location specific guidelines / real-estate for migration, temporary and permanent Figure 4: Global bleaching area alert zones. reallocation of climate change refugees.

(a) The NOAA bleaching alert area plot and analysis (d) Technology Transfer:- Strengthened capacity to (Fig 2.4) indicate watch- warning zone with moderate apply analytical frameworks, models and tools vulnerability over Indian Coastal Region/ island in (appropriate to the Pacific) to assess national and comparison with warning -alert zone located over regional climate change vulnerability Kiribati and Tuvalu islands. (impacts/vulnerability) to current and projected climate changes

(e) Finance and capacity development: Pre-emptive policy must invite revenue/funding from various sources/parties to the Convention for transfer /settlement of Climate change refugees.

References (a) Pacific Islands framework for action in climate change. (b) www.coast.noaa.gov/srl, (c) www.esrl.noaa.gov/psd Figure 2.5 Interactive Plot of sea level Prediction. (d) Climate Change, Sea Level Rise, and Artificial

Islands: Saving the Maldives’ Statehood and Maritime (b) Sea level rise/flood prediction derived from Claims through the ‘Constitution of the Oceans’ Interactive plot (Fig 2.5) based on NOAA data indicates 5 feet sea level rise over majority of Indian Coastal *Michael Gagain*. region by 2070. (e) 1.Submerging Islands: Tuvalu and Kiribati as Case Studies Illustrating the Need for A Climate Refugee 3 Policies, Objective and Strategies Treaty *RanaBalesh* 3.1 A stand by policy frame work / declared set of Standard (f)www.ss2.climatecentral.org, operating Procedure (SOP) to be exercised to cope the worst ww.nooa.coastalmangement / Bleach Alert zone scenarios of climate change is proposed based on the above analyses.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 49

Shoreline Change Detection Analysis and its effects on Mangrove colony – A case study of Alibag Coast, Raigad, Maharashtra Barnali Das1, Anargha A. Dhorde2 1 Research Scholar, Savitribai Phule Pune University 2 Associate Professor, Nowrowjee Wadia College, Pune [email protected]

Abstract

The present study examines shoreline change detection over span of last eighteen years (2000-2018) and its impact on Mangrove colony along Alibag coast. The study area extends from Karanja creek in north to Kundalika River in south. For shoreline change detection analysis remotely sensed data have been used. Least mean square method of shoreline change detection shows that there is -3.567m/yr of erosion. Straight line distance of the present study is 35Km (approx.). An attempt has also been made to understand erosion / deposition patterns. Rate of denudation along this area is 0.65m/yr. and accretion is 0.16m/yr. As far as the mangrove colonies are concern it has observed that over the span of eighteen years Figure 1: Accretion and Denudation from 2000 to 2018. specifically over northern part of the study area there in increase in mangrove patches. This is probably due to shoreline shift and denudation process which leads to salt Similarly it has been observed that shore line is showing water intrusion in that area. in land shift specifically over Revdanda creek. Shoreline is showing positive change i.e. accretion over northern part of Keywords: Remote Sensing; GIS; Shoreline Changes; the study area[3]. Among many shoreline changes detection Landsat. analysis technique End Point Rate technique (EPR) has been 1 Introduction applied for the present study (Fig 2). Raigad is a coastal district situated on the western coast of Maharashtra. Present research work includes Karanja creek, Dharamantar creek, Revdanda creek and Kundalika river. The area extends from 18°52’50.53” N to 18°29’44.24” N and 72°54’45.65” E to 72°53’59.38” E. Particularly this region has been selected because lot of spatial variation with respect to mangrove patches has been observed over the span of 18 years in this region. Shoreline along this region is also very susceptible to erosion [1]. 2 Methodology Methodology for the present study can be divided in to three parts. First, for accretion and denudation rate calculation through Digital image processing (DIP) [2]. Second, for shore line change detection analysis. This has been done by taking 2000 and 2018 shoreline as baseline in Figure 2: EPR Shoreline change detection analysis. GIS environment. Third, mangrove colony delineation with Mangroves colony for both the year has been extracted the help of supervised classification technique. through image segmentation technique in GIS environment [4, 5]. Mangroves are observed along the creek, over mouth 3 Data analysis and results of the creek as well as in few pockets over inland area. In the Landsat 8 data has been used for the present study. year 2000 (54.836 sqKm) mangrove patches are less as February month data for both the year has been used i.e. compatred to 20018 (106.3 sqKm). Mangoroves have almost 17/2/2000 and 18/2/2018. DIP technique is used to calculate doubled in eighteen years. There is almost this can easily be rate of accretion and denudation over span of 18 years i.e. corelated with the shoreline change analysis. Over the 2000 to 2018 [1]. From the result it has been observed that northern part of the study area i.e. over Revdanda creek, rate of accretion is 0.156m/yr. and rate of denudation is Alibag, shore line is observed to show fluctuation and shift 0.65m/yr. Rate of denudation is quite high than that of rate of towards inland. This leads to saltwater intrusion. Mangroves accretion. Along Karanja creek accretion is taking place. Rest being salt tolarant species has grown profusely over this of the study area is showing denudation (Fig 1). region over eighteen years (Fig3 and 4).

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 50

This increase in mangroves colony has direct relation with salt water ingression which has a negative impact on quality of drinking water [6, 7, 8]. References [1] A. Nawal and W. Kristine (2018), Groundwater Overexploitation and Seawater Intrusion in Coastal Areas of Arid and Semi-Arid Regions. (Water 10, 143, doi:10.3390/w10020143) [2] B. Umrikar and S.S. Thigale (2007), Sea Water Ingress Study Along the Guhagar Coast of Maharashtra with Reference to the Harmonious Water Resource Development, (Journal Geological Society Of India,Vo1.69, pp.933-942). [3] A.G. Chachadi and J.P.L. Ferreira (2005), Assessing Figure 3: Mangroves colony in the year 2000. aquifer vulnerability to sea-water intrusion using GALDIT method: Part 2 –GALDIT Indicators Over rest of the study area there is also increase in the Description. (The Fourth Inter-Celtic Colloquium on mangrove colony, but over mouth of the Revdanda creek Hydrology and Management of Water Resources, changes are quite significant. Portugal July). [4] G. Felisa et.al., (2013), Saltwater Intrusion in Coastal Aquifers: A Primary Case Study along the Adriatic Coast Investigated within a Probabilistic Framework. (Water 5, 1830-1847; doi: 10.3390/w5041830). [5] M. Barbarella et al., (2014), Satellite data analysis for identification of groundwater salinization effects on coastal forest for monitoring purposes (Remote Sensing and GIS for Hydrology and Water Resources Proceedings RSHS14 and ICGRHWE14, Guangzhou, China, August 2014) doi:10.5194/piahs-368-325-2015 [6] M.A. Marfai (2011), Impact of coastal inundation on ecology and agricultural land use case study in central Java, Indonesia, (QUAESTIONES GEOGRAPHICAE, ISBN 978-83-62662-75-3. ISSN 0137-477X. DOI Figure 4: Mangroves colony in the year 2018. 10.2478/v10117-011-0024-y). [7] M. Mohan Babu et al., (2013), Assessment of Saltwater Intrusion Along Coastal Areas of Nellore District, A.P. 4 Conclusion (International Journal of Scientific & Engineering It has been observed in the present study that there Research, Volume 4, Issue 7), ISSN 2229-5518. is considerable increase in the mangroves specifically over [8] V. Lenin Kalyana Sundaram et al., (2008), Vulnerability the northern part of the study area. There is direct relation assessment of seawater intrusion and effect of artificial with the shoreline change with increase in the mangrove recharge in Pondicherry coastal region using GIS. colony. Where the shoreline is more or less stable at that part (Indian Journal of Science and Technology, Vol.1 No mangrove patches are also stable but over the region where 7). there is considerable shift i.e. along the mouth of Revdanda creek there is considerable increase in mangrove colony. It can easily be related that with shoreline shift inland there is saltwater ingression which leads to increase in mangroves.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 51

Effect of temperature rise on bacterial metabolic rates: An experimental approach Lata Gawade1, Chaitali Madkaikar2

1CSIR-National Institute of Oceanography, Dona Paula, Goa, India-403 004 2 Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Tathawade, Pune, India- 411033 [email protected]

1000 ml water sample was filtered through 0.2 m Abstract polycarbonate filter [6]. Inorganic nutrients were measured Laboratory experiments were conducted to simulate the following standard colorimetric procedures [7] using auto effect of sea surface warming on bacterial metabolic rates. analyser. BR rates were measured in filtrate (0.7 m The link of these bacterial metabolic rates to variation in filtered) as the difference in dissolved oxygen at 0 and 24 h inorganic nutrients concentration has been examined. [8, 9]. Dissolved Oxygen (DO) was analysed using -1 During the 24 h incubation under warming condition, Winkler’s titration method of [10]. Oxygen mL L was bacterial respiration and carbon demand increased converted to carbon units using the respiratory quotient (RQ) significantly by  46% and  34% respectively whereas of 1[11]. Samples for BA were preserved with 5% increase in productivity (32%) rate and growth efficiency formaldehyde and kept at 4°C until analysis (less than 15 (34%) was not statistically significant. Inorganic nutrient days). Bacterial cells were counted by 4'-6'-Diamidino-2- phenylindole (DAPI) method [12] using epifluorescence concentrations were higher by 1, 0.7, and 2.2 times for microscope (Olympus BX-51). BP rates were calculated by nitrite + nitrate, phosphate and silicate respectively at converting the increase in cell number with time [8, 13], it elevated temperature, suggesting the efficient modification was converted to carbon units using a conversion factor of of organic matter by bacteria. Observations suggest that 20 x 10-15 g C cell-1 [14]. BGE was calculated as BGE%= with increase in temperature, microbial loop will act as an [BP/ (BP+BR) x 100], BCD was calculated as efficient link of the organic matter to the higher trophic BCD=BP+BR. level. 3 Results and discussion 1 Introduction Hydrographical and bacterial metabolic rates variation has Heterotrophic life style of bacteria makes them been shown in table 1. Variations in salinity and other important for mineralisation and degradation of organic hydrographic features at both the stations might alter the matter thereby leading the biological role in carbon cycling. bacterial metabolic rates at both the stations. During 24 h Their production contributes 30% to that of primary incubation, ammonium concentrations decreased by 1.2 productivity [1] and consumes 50-60% of the total fixed time for both the stations, at the same time nitrite+nitrate carbon [2], thus sustaining many important ecosystem concentrations increased by 1 time having higher rate of functions including biogeochemical cycling of carbon and increase at elevated temperature. Therefore suggesting, inorganic nutrients and transferring it to the higher trophic warming makes the conducive conditions for the efficient level. Rising sea surface temperature and global warming is expected to have impact on microbial metabolic rates- nitrification. Phosphate increased by 0.7 times at elevated altering the heterotrophic bacterial activities like respiration temperature, whereas silicate concentrations increased [3] and production rate [4]. Therefore it is important to drastically both at ambient (1.9 times) and elevated (2.2 estimate the magnitude of this impact on bacterial metabolic times) temperature with higher concentrations in unfiltered rates. The estimation and understanding of effect of warming i.e. total community (TC) samples. Decrease in on the heterotrophic bacterial metabolic rates and carbon concentrations of inorganic nutrients was inversely turnover will help for the future predictions that can be proportional to size. Therefore suggesting under warming considered for large scale carbon budgets. condition mineralization of organic matter to inorganic Present study has been carried out in order to (i) investigate nutrients like nitrate, phosphate and silicate will be faster. the effect of warming on bacterial metabolic rates, (ii) to Bacterial abundance increased 32.7% at elevated understand the link of these metabolic rates to organic matter temperature. Filtration process decreased the BA by 25% mineralization and inorganic nutrient concentrations. in filtrate. It is observed that increase in temperature increases the BP rate by 0.9 times at station 2. Respiration 2 Materials and methods rates were higher at elevated temperature by 0.8 and 0.7 Surface water samples were collected at two stations from times at station 1 and 2 respectively, they were within the the Mandovi estuary, one at mid of the estuary and other at range of literature values reported for tropical estuaries and mouth of the estuary, during the pre-monsoon season (Jan- coastal waters [15] and temperate estuaries [16]. Bacterial May months). Water samples were analysed for bacterial respiration increased significantly by 46% at elevated metabolic rates (respiration rate (BR), production rate (BP), temperature ( Fig.1). BR accounted for 58% of total growth efficiency (BGE) and carbon demand (BCD), total community respiration which is similar to the observations suspended matter (TSM), inorganic nutrients and of [17] i.e., 50-80%. Spatially, BR contribution is higher at Chlorophyll-a; (Chl-a) concentrations. The temperature (ºC) mid estuarine region than at mouth region (Table 1). and salinity (psu) of the water sample were measured using Average BP was low by a factor of 1.5 to that of BR. laboratory thermometer and refractometer of ATAGO Average BR:BP ratio was 6.6 during the study period. S/Mill-E (salinity 0-100%) respectively. Chl-a was extracted Observed BGE is in similar range to previous reports of in to 90% acetone following the filtration by GF/F filter and tropical estuarine and coastal region [8,9] and temperate measured spectrophotometrically [5]. To estimate TSM, National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 52

regions like Hudson river [16]. According to the hypothesis Table 1. Bacterial metabolic rates variation. of [15], < 8% BGE makes the system net heterotrophic. Our observed BGE values are quite high (avg.16%) by an order

)

) )

1 1

of 2, therefore suggesting moderate heterotrophy in present cells

- -

1 d

study region. Carbon demand by bacteria increased 

1 d 1 d

- - significantly by 34% at elevated temperature. Increased

carbon demand associated with higher production would

g C L g C

)

g C L g C L g C



 

lead to higher flux of carbon through bacteria. (

Filtrate Filtrate ( fraction Station size BA ( No. x 10 L BP BR ( BGE (%) BCD ( Our observation suggets that under warming conditions AT ET AT ET AT ET AT ET AT ET micrbial loop will play prime role in carbon cycling thus acting as a link for making the organic carbon available to 0.7 1 11.1 2.5 46.2 - 232 232 16.6 278 60.3 next trohic level, thereby sustaining and mantaining the 1 0.5 10.9 21.1 136 232 426 8.3 24.3 253 563 ecosystem structure healthy. Observed effect of temperature on metabolic rates is in corroboration with that reported for 1 4.1 - 50.4 - 77.5 - 39.4 - 127 - temperate region [18] but the increase in rates is an order of 2 1.2 2.8 29.0 33.2 116 193 20.0 14.6 145 226 magnitude higher in present study. If these results were of general applicability, they would have implications in the 2 0.7 1.5 3.1 15.8 368 213 0.8 6.9 371 228 estuarine and coastal waters carbon cycle and inorganic 2 3.3 6.2 68.2 57.4 271 658 20.1 8.0 339 716 nutrients mineralisation. Present observations could be applied to other tropical estuarine and coastal regions too. TC 1 2.7 17.7 -8.5 301 387 581 -2.2 34.1 378 882 In tropical estuaries the hydrographic pattern is mainly controlled by well defined seasonal/monsoonal pattern [19]. 1 2.7 11.6 -8.9 158 - 891 - 15.1 -8.9 1049 Hence the present work and observations should be tested on 1 3.6 - 151 - 736 - 17.1 - 887 - annual scale. 2 3.5 3.4 -19.6 -2.1 77.5 232 -33.9 -0.9 57.9 230 2 2.7 2.3 29.9 -7.3 523 193 5.4 -3.9 552 186 2 1.9 5.2 126 66.1 309 774 28.9 7.9 436 840 Acknowledgement: We thank the Director, CSIR-NIO for providing facilities and support. References 1. Es, FBvan & Meyer-Reil LA. (1982). Adv. microb. Ecol.6: 111-170 2. J.A. Fuhrman & F. Azam (1982). Mar. Biol. 66: 109–120. 3. R.B. Rivkin & L. Legendre (2001). Science, 291, 2398- 2400. 4. R.B. Rivkin et al. (1996). Aquat. Microb. Ecol.10, 243- 254.

5. T.R. Parsons et al. (1984). Pergamon Press, New York. Figure 1: Percentage change in bacterial metabolic rates at 6. L. Gawade et al. 2017. Geomicrob. 34: 628-640. elevated temperature (warming condition) to that of ambient temperature. 7. K. Grashoff, Ehrhardt M, Kremling K (eds) (1992). Verlagchemie, New York, p 419. 8. L. Gawade et al. (2018). Estuar. Coast. Shelf Sci. 215:20- 29.

9. A. S. P. Ram et al. (2007). Microb. Ecol. 53, 591–599. 10. DE. Carritt & J.H. Carpenter (1966). J.Mar. Res.24, 286. 11. B. Biddanda et al. (1994). Limnol.Oceanogr.39:1259- 1275. 12. K.G., Porter & Feig, Y.S. (1980). Limnol. Oceanogr. 25:943-948. 13. Toolan T. (2001). Limnol. Oceanogr. 46, 1298–1308.

14. J.E. Hobbie et al. (1977). Appl Environ Microbiol 33:1225-1228. 15. C.W. Lee et al. (2009). Appl. Envir. Microb. 75 (24); 7594-7601.

16. R.J. Maranger et al. (2005). Ecosystems 8, 318-330 17. X. Yuan et al.(2010). Aquat. Microb.ecol.58:167-179. 18. Vázquez-Domínguez & Vaque D, Gasol JM. (2007). Glob. Change Biol. 13:1327-1334. 19. VVSS. Sarma et al. (2012). Tellus B,64, 10961, 2011.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 53

Rapid intensification and weakening of Cyclone Ockhi using HWRF modelling system Jyothi Lingala1, Sudheer Joseph1, Lijo Abraham Joseph1, Akhil Srivastava2, Sunitha P3, Hyun- Sook Kim4, Avichal Mehra4, Sundararaman G. Gopalakrishnan5

Indian National Centre for Ocean Information Services1, Indian Meteorological Department2, Andhra University3, NOAA/ Environmental Modeling Center4, AOML/Hurricane Research Division5 [email protected]

Abstract

The Hurricane Weather Research and Forecast (HWRF) is coupled to HYbrid Coordinate Ocean Model (HYCOM) to understand the evolution of Cyclone Ockhi. It is hypothesized that the presence of thick Barrier Layer (BL) and substantial amount of ocean heat content extended up Figure 2: Track error (km) at 06 hourly forecast intervals to 60-80m depth reduced the sea surface cooling which led calculated against the IMD observations. to the Rapid Intensification (RI) of Ockhi. The abundant availability of Relative Humidity (RH) and weak Vertical Wind Shear (VWS) laid a more conducive environment to mature the storm in a short duration. The system started weakening before making the landfall due to the lower Sea Surface Temperature (SST), strong VWS and intrusion of the dry air which disturbed the convective activity. The steering flow in the upper troposphere has directed the storm towards the Gujarat coast. Figure 3: Comparison of the intensity (wind speed in knots) forecast against the IMD observations. 1 Introduction 1.2 SST cooling comparison HWRF is a state of the art operational model coupled to either HYCOM or Princeton Ocean Model (POM) used Figure 4 depicts the model simulated SST (HWRF- for track and intensity prediction of Tropical Cyclones HYCOM and HWRF-POM) at different forecast hours (TCs). Using this coupled system, a case study is carried out (12hr, 36hr and 60hr) compared against the REMSS satellite on a Very Severe Cyclonic Storm Ockhi originated over the observations. It is evident that HYCOM coupled SSTs are in south-west Bay of Bengal on 29th November 2017. Figure 1 good agreement with the observations than the POM displays the simulated forecast tracks with GSI Data coupled. As HWRF-HYCOM system generated better SST Assimilation (DA) and without DA using HYCOM and response, track and intensity forecasts the simulations from POM as ocean components. All runs were initialized on 1st it are used to analyze the oceanic and atmospheric factors December 2017 00 hours. contributed to the intensification and weakening of Ockhi.

Figure 1: Comparison of the model simulated forecast tracks from various experiments against the IMD track. Figure 4: Comparison of the model simulated (HWRF- 1.1 Track and intensity evolution HYCOM, HWRF-POM) SST response against the REMSS SST at different forecast hours overlaid by the TC tracks and Figure 2 and Figure 3 shows the comparison of model storm locations at the respective forecast hours. simulated track errors and intensity forecasts against the IMD observations, respectively. The DA runs of the coupled 2 Thermohaline Structure models (HYCOMcoupled_GSI and POMcoupled_GSI) Figure 5a) - 5b) shows the horizontal section of warm- have shown improvement in reducing the track errors and fresh waters near the south-eastern Arabian Sea (SEAS) and intensity forecasts than the non-assimilated runs. cold-saline waters in the northern Arabian Sea (NAS). The

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 54

equatorward flowing East India Coastal Current carried low saline waters into SEAS increased the stratification. The vertical cross-section of temperature and salinity in Figure 5c) - 5d) displays the warm waters extending from surface to 80m depths and a band of saline waters sandwiched between the low saline waters. Hence, the oceanic conditions are in favour of intensification over SEAS region and weakening over NAS region.

Figure 7: Mid-tropospheric RH (shaded) on selected hours of 4th - 5th December 2017 overlaid by wind vectors at mid- tropospheric level. As the storm approached Gujarat coast, strong VWS disrupted the heat and moisture flow which led to its weakening. The system had a clockwise re-curving track before making the landfall due to the strong upper level steering flow that pushed the storm towards Gujarat as Figure 5: Spatial distribution of a) Temperature b) Salinity shown in Figure 8. overlaid by model currents on 1st December. Vertical cross section of c) Temperature and d) Salinity sampled along the black line marked in 5a).

The BL hinders the entrainment at the base of the mixed layer by limiting the TC induced SST cooling associated with upwelling. Higher Tropical Cyclone Heat Potential (TCHP) values restrict the TC induced cooling near the surface. Therefore, the TC intensification is largely influenced by substantial TCHP and thick BL [1]. From Figure 6a) and 6b) it is evident that the BL thickness is nearly 40m in the SEAS region and TCHP is much higher than the threshold (60 kJ/cm2) which can decrease the impact of negative SST feedback and thus favors for RI.

Figure 8: VWS (shaded) on selected hours of 4th - 5th December 2017 overlaid by steering flow averaged between 850-250mb. 4 Conclusions Track and intensity forecasts from HWRF-HYCOM are in better agreement with the observations. High TCHP, thick BL and favorable atmospheric circulation helped Ockhi to undergo RI. Ockhi weakened from 4th December due to the Figure 6: a) BL and b) TCHP distribution on 1st December unfavorable environmental factors. The steering flow in the 2017 00 hours. Colour along the track represents the upper troposphere recurved the storm towards Gujarat. intensity of the storm. 3 Atmospheric Factors References Figure 7 displays the intrusion of dry air into the centre [1] Balaguru, K., Chang, P., Saravanan, R., Leung, L. R., Xu, of the storm at mid-tropospheric level reducing the RH from Z., Li, M., & Hsieh, J. S. (2012). Ocean barrier layers’ effect ~95% to ~60% on 5th December. The dry air has a negative on tropical cyclone intensification. Proceedings of the influence on TC intensification, as it suppresses the National Academy of Sciences, 109(36), 14343-14347. convective activity by inhibiting the latent heat release and increasing the asymmetry of convection [2]. [2] Ge, X., Li, T., & Peng, M. (2013). Effects of vertical shears and mid-level dry air on tropical cyclone developments. Journal of the Atmospheric Sciences, 70(12), 3859-3875.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 55

Siltation Studies in the Far-Shelf of coastal Bay of Bengal Dhanya S.1, H. V. Warrior1 1Department of Ocean Engineering & Naval Architecture, IIT Kharagpur, West Bengal, India [email protected]

2 Methodology Abstract Figure 1 shows the methodology of our numerical scheme. In one sentence, our idea is to augment the velocity in the In this paper, we propose a novel technique to study the shelf with the basin scale velocity (taken from measured siltation rates of far-shelf of Bay of Bengal. The technique values like OSCAR) and then apply the ANN to predict the is mainly to point out the errors produced by the commercial enhanced siltation rates. codes in predicting siltation rates in the far-shelf regions. The current method involves the very widely used Artificial Neural Network (ANN). We show that using a commercial code augmented by the ANN for far-shelf regions of Bay of Bengal gives a much more realistic siltation rates than the code alone. This method can be adopted in other far-shelf regions as well. 1 Introduction The Bay of Bengal is about 2090 km long and 1610 km wide, bordered on the west by Sri Lanka and India, on the north by Bangladesh, and on the east by Myanmar and Thailand. This unique semi-enclosed basin experiences seasonally reversing monsoons and depressions, severe cyclonic storms (SCS), and consequently receives a large Figure 5: Methodology. amount of rainfall and river run-off in the tropics. The 2.1 Numerical Model- DELFT3D impacts of the enormous discharge of riverine fresh water Delft3D-FLOW is a multi-dimensional (2D or 3D) and sediments are least understood. The dynamics of the hydrodynamic (and transport) simulation program which sediment dynamics are so complex that we need to come up calculates non-steady flow and transport phenomena that with new and better technical tools to study the sediment result from tidal and meteorological forcing on a rectilinear transport and our paper is a work in that direction. or a curvilinear, boundary fitted grid. In 3D simulations, the As mentioned before, despite numerous studies vertical grid is defined following the σ co-ordinate approach. worldwide, it is still a challenge to estimate suspended 2.2 Artificial Neural Network (ANN) sediment concentration and its siltation characteristics in the continental shelf of the Bay of Bengal. The Ganges- Neural networks are composed of simple elements Brahmaputra River System carries the world’s highest operating in parallel. These elements are inspired by annual sediment load at one billion tons (Milliman and biological nervous systems. As in nature, the network Meade, 1983; Milliman and Syvitski, 1992), and yet because function is determined largely by the connections between of its remote location, research on sediment transport and elements. A neural network can be trained to perform a accumulation in the delta has been limited (Barua et al., particular function by adjusting the values of the connections 1994; Goodbred and Kuehl, 1999). (weights) between elements. Commonly, neural networks are adjusted or trained, so that a particular input leads to a There are many codes like DELFT 3D, Mike 21, etc specific target output (Deo 2007). which are routinely used to predict the siltation in the 3 Numerical Modelling continental shelf. In this paper, we show the error of one basic premise of these models- the neglect of basin-scale 3.1 DELFT-3D FLOW Model velocity in the Bay, which gives larger errors for the siltation The study area ranges from 20.55°N to 23.77°N latitudes rates on continental shelf, especially between the 50m and and from 87.74°E- 91.78°E longitudes covering around 420 500m isobaths. kms in the x-direction and 140 kms off the coast. The model For the purpose of our study, we divide the total grid was generated in Delft3D suite, with grid size 0.01º x continental shelf into 3 regions- 1. Region with depth less 0.01º, with the number of grids being 407 x 325. The than 50m; Region 2 with depths from 5m to 500m isobaths; maximum water depth in the area is 130m except near to the and Region 3 which is mainly the deep ocean region. Swatch of No Ground (SoNG)- a submarine canyon where the depth goes beyond 130m up to 1100 m (Mohanty et al 2008), as taken from GEBCO 08. The model was run for 10 months from 01-01-2018 to 13-10-2018 with 120 minutes time interval and 1-minute time step.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 56

Figure 6: Grid and Bathymetry of the study area. 4 Results and Discussions The DELFT3D output file was post processed using the QUICKPLOT tool. The required output parameters are the depth averaged velocity and cumulative erosion/ sedimentation. The output parameters were extracted from 01 January to 13 October with a time interval of 120min for different depths from 2m to 700m. Figure 4. Difference between DELFT3D and ANN outputs. The current velocity at a depth of around 150m in the study From the plot, it could be seen that for the shallow areas, the area was extracted for the entire year from Ocean Surface difference is minimum and hence DELFT3D is the reliable Current Analysis Real-time (OSCAR), and was combined model for finding the nearshore characteristics. And in the with Delft3D, to understand the effect of the currents from open basin of depth beyond 500m, where the basin velocity the Bay basin. plays an important role, a large scale model like the Princeton Ocean Model (POM) (Ahmuda et al., 2007) could be used. In A neural network had to be set up for predicting the the intermediate depths, from 50m to 500m, since there is a cumulative erosion/sedimentation for the new velocities (a combined effect of both the velocities, the DELFT3D model sum of the Delft predicted velocity and the basin velocity). and POM will be erroneous. In these regions, an appropriate For that, MATLAB Neural Network Toolbox is used. A set combination of DELFT3D predicted velocity and the basin of data, with time, depth and old velocity as inputs and scale velocity will work best and the most efficient of the cumulative erosion/ sedimentation as output was created. techniques to combine them is ANN.

References [1] J.D. Milliman and R.H. Meade. World-wide delivery of sediment to the oceans. Geology. 1983, pp: 1-21. [2] J.D. Milliman, and J.P.M. Syvitski, Geomorphic/tectonic control of sediment discharge to the ocean: the importance of small mountainous rivers. Journal of Geology, 1992, pp: 525-544. [3] D.K. Barua et Al, Suspended sediment distribution and residual transport in the coastal ocean off the Ganges-Brahmaputra River mouth. Marine Geology, 1994, pp: 41-61 [4] S.L. Goodbred Jr. and S.A. Kuehl, Holocene and modern sediment budgets for the Ganges- Brahmaputra River System: evidence for highstand dispersal to floodplain, shelf, and deep-sea depocenters. Geology. 27 (6), 1999, pp: 559-562. Figure 3: Velocity variation for January, May and October. [5] P.K. Mohanty et al, Sediment Dispersion in Bay of Bengal, Monitoring and Modelling Lakes and Figure 3 shows the comparison between the velocities from Coastal Environments, 2008, pp: 50-78. DELFT3D alone and from DELFT3D and OSCAR [6] G.R. Lesser et. al, Three-dimensional combined, with varying depth. It is observed that DELFT3D morphological modelling in Delft3D-FLOW, gives better values for nearshore calculations for upto a depth SASME Book of Abstracts (2001) of 50m. From 50m to 500m, there is a combined effect of the [7] M. A. Ahumada and A. Cruzado, Modeling of the tidal current velocities as well as basin velocity and, beyond circulation in the Northwestern Mediterranean Sea 500m water depth, the influence of the basin velocity with the Princeton Ocean Model, Ocean Science, dominates. 2007, pp: 77–89. The plots in Figure 4, shows the difference between the [8] M.C. Deo, Artificial Neural Networks in Coastal cumulative erosion/ sedimentation (m) as obtained from the and Ocean Engineering, Indian Journal of Geo- DELFT3D and that predicted using ANN. Marine Science, 2007, pp: 589-596.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 57

Modelling studies of the acidification of Bay of Bengal A. P. Joshi1, V. Kumar1and H.V Warrior1 1 Department of Ocean Engineering and Naval Architecture, IIT Kharagpur, West Bengal, India [email protected]

Monsoon [5]. The period is one of winds along the East coast Abstract of India in the south-westerly direction with wind speeds close to 8 m/s. The winds bring moist warm air from the The present paper looks the development of pCO2 and pH tropical Indian ocean to produce rainfall in the west coast. through numerical modeling techniques. Simulation of the The period of SWM is one of sharp changes in the oceanic South West monsoon in Bay of Bengal shows that the pH circulation pattern in the BoB with the seasonal reversal of of the waters decrease by 0.0004 units in the three months. East Indian Coastal Current [5]. This is the average value over a transect through the mid- Dissolution of CO2 in seawater forms carbonates, + ocean of the BoB. In addition, the pCO2 of surface waters bicarbonates and H ions as per (1), which decreases the pH in the coastal regions (near Orissa) reaches 400 µatm at of seawater in turn making it acidic. The reactions are as that time. This can be attributed to the strong upwelling due follows: to the southwest monsoon winds. All these modelling  2 COHOHCOHCO 2 (1) results are validated by observations reported by previous 2233 authors. There is also a buffering effect as a result of the carbonates turning into bicarbonates thus absorbing H+ and 1 Introduction reducing acidity. The main concern over the increase in CO in oceans 2 3 Model Used is mutifold; it increases the pCO2 of waters thus making more CO2 available for photosynthesis, but decreases the POM-C has been developed by incorporating the carbonate formation, and it lowers the aquatic pH which in above-mentioned chemistry of the carbonates and turn makes the waters acidic. IPCC [1] reports that the bicarbonates by including them as tracers into the Princeton average pH of the world ocean surface waters has already Ocean Model (POM). POM is a sigma coordinate model in fallen by about 0.1 units from an average value of about that the vertical coordinate is scaled on the water column 8.21 to 8.10 since the beginning of the industrial revolution depth. It contains an embedded second moment turbulence and the decrease is more in the inshore region of continental closure sub-model to provide vertical mixing coefficients. shelf. This could be attributed to the fact that there is The turbulence closure sub-model is one that was introduced increased CO2 loading in the coastal waters due to my George Mellor, 1973 and was significantly advanced in anthropogenic effects. In this paper, we attempt to model collaboration with Tetsuji Yamada [6, 7]. A 3-tier vertical these carbonate and pH changes as a result of increasing eddy diffusivity for CO2 for the mixed layer, thermocline and partial pressure of CO2 in BoB waters using a chemical deep waters was applied in this simulation. The region model incorporated into the Princeton Ocean Model. The selected for the study is the whole BoB, i.e., from 770 E to primary objective is to predict changes in pH, partial 1000 E and 50 N to 230 N. The surface region has been divided pressure of CO2 and total carbon (DIC). This is then viewed into 230x150 grid points and vertical is divided into 20 levels in the futuristic scenario of various RCPs [1] and the with 6 log layers near the surface and 1 log layer near the magnitude of CO2 increase till 2100 projected. bottom surface. The ocean is divided into sigma layers in the In the present study, the main focus is on physical and vertical direction, which enables capturing of the uneven chemical processes, whereas the biological processes have bottom surface of the ocean. been neglected. Thus it is a one-way coupled model with 4 Forcing Data the influence of phytoplankton/zooplankton on physical POM-C is forced with wind stress derived from QSCAT and chemical properties of water ignored. monthly climatology data for the year 2001, 1998 and 1996. 2 Material and Methods The other inputs like temperature (SST), heat flux and short- The study area chosen at present is Bay of Bengal wave radiation are taken from Wood Hole Oceanographic (BoB) (770E to1000E and 70N to 230N) in its entirety. We Institution (WHOI) OAFlux. focus on both the coastal and open ocean processes due to The initial values for the partial pressure of CO2 and which the mesh has been taken as coarse such that the whole other Dissolved Inorganic Carbon content have been derived BoB is covered. The motivation of this area is that a and averaged over the whole region from cruise data [8]. The significant amount of study has been carried out in Arabian monthly air-sea fluxes of CO2 (we neglect CO2 input from Sea [3] but the northern BoB remains less explored. There rivers) were computed using (2). are very few studies of this region as of date and so 퐹퐶푂2=퐾w푆[푝퐶푂2water−푝퐶푂2air] (2) observational data is also sparse. We have compared our Where Kw is the transfer velocity (cm/h) which is computed modelling results with the limited amount of data available from: from various cruises conducted by National Institute of 2 0.5 (3) Oceanography, India [4]. Kw  0.39 u [ Sc / 660] The region of BoB is mainly characterized by two Where u is the wind speed (m/s), S is the CO2 solubility is major monsoons: South West Monsoon (SWM; June- seawater, Sc is the Schmidt number, and pCO2 is the partial August) and North East Monsoon (NEM; November- pressure of CO2 in water and air respectively. January). The current paper is focused on the Southwest

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 58

5 Results and Discussion In the BoB a transect has been chosen at 88° E to study the variation of pCO2 during the SWM. Average value of pCO2 variability over the whole basin is calculated as an average over this transect. The surface pCO2 of 250 µatm was taken as initial condition of the surface waters and the model allowed to forecast with time. It is seen after the simulation that, along the transect there is an increase in partial pressure of surface waters of BoB of 0.95 µatm in the three months of simulation. This is an average value taken over the entire transect and is taken as representative of the surface waters of BoB. Concurrently, we also calculate the DIC content, and pH along the transect. There is a change of -0.0004 pH units in the three months. We have chosen the transect above to coincide with the track of ORV Sagar (a) (b) Kanya [8] during an expedition so that values can be Figure 8: Surface values of (a) pCO2 (µatm) and (b) DIC compared. The values of pCO2 as predicted by our model (µmol/kg) in the coastal regions of BoB. and the cruise measurements are given in Fig 1. 6 Conclusion To summarize, an attempt has been made in predicting acidification of Bay of Bengal during the SWM using an ocean model. The pCO2, DIC and pH were modelled and predicted by adding chemical processes to the physical processes of advection and diffusion in the POM. From our simulations, we note that the BoB has one of the highest acidification rates in world oceans with a corresponding increase in pCO2 of the waters. Results were validated with data from cruises. References 1. Pörtner, H. O., et al. "Ocean systems. Climate change 2014: Impacts adaptations and vulnerability. Part a: global and sectoral aspects. Contribution of working group ii of the fifth assessment report of the international panel on climate change." (2014): 411-484. 2 2. Yates, K. K., and R. B. Halley. "CO 3 concentrations and pCO2 thresholds for calcification and dissolution on the

Molokai reef flat, Hawaii." Biogeosciences Discussions 3.1 Figure 7: Comparison of model predictions with (2006): 123-154. observations after the SWM of 2001 (along 88 ͦ E). 3. Sarma, V.V.S.S. "Monthly variability in surface pCO2 and In view of their importance, the values of pCO2 and net air‐sea CO2 flux in the Arabian Sea." Journal of DIC exhibited in the coastal region (Fig. 2) are plotted. The Geophysical Research: Oceans 108, no. C8 (2003). values of pCO in the West Bengal coast (Fig 2) is about 150 2 4. Sarma, V.V.S.S., V. R. Kumari, T. N. R. Srinivas, M. S. µatm more than in open ocean (about 400 µatm compared to Krishna, P. Ganapathi, and V. S. N. Murty. "East India 250 µatm in the transect) due to the seasonal upwelling there. Coastal Current controls the dissolved inorganic carbon in Patches of weak upwelling has been noted along the coast of the coastal Bay of Bengal." Marine Chemistry 205 (2018): Orissa (south of Puri) in this year (2001), and can be 37-47. attributed to strong off shore surface Ekman transport 5. Krishna, V. V., and J. S. Sastry. "Surface circulation over the associated with dominant eastward component of the EICC. shelf off the east coast of India during the southwest The colder water upwelling from the thermocline at depths monsoon." (1985). of 100 m brings up more acidic waters to the surface 6. Mellor, George L., and Tetsuji Yamada. "A hierarchy of increasing the pCO2, concentration of DIC and acidity there. turbulence closure models for planetary boundary This value for pCO2 (as is the concentration of DIC) can be layers." Journal of the Atmospheric Sciences 31.7 (1974): compared favorably with the observations taken during 1791-1806. cruises in [9]. In these periods, the coastal BoB becomes a 7. Mellor, George L., and Tetsuji Yamada. "Development of a source of CO2 to the atmosphere. Arabian sea has been seen turbulence closure model for geophysical fluid to be a source of CO2 by many scientists but due to the problems." Reviews of Geophysics 20.4 (1982): 851-875. paucity of data, this has not been seen in BoB. 8. Sardessai, S., et al. "Environmental controls on the seasonal carbon dioxide fluxes in the northeastern Indian Ocean." (2010). 9. Sarma, V. V. S. S., et al. "Sources and sinks of CO2 in the west coast of Bay of Bengal." Tellus B: Chemical and Physical Meteorology 64.1 (2012): 10961.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 59

Remote sensing based detection and monitoring of Rip current hot spots at RK Beach stretch, Visakhapatnam T Sridevi, Surisetty V V Arun Kumar and Raj Kumar Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA) Space Applications Centre (ISRO), Ahmedabad, Gujarat, India [email protected]

for the period 2004 to 2017. Multi-date high-resolution Abstract satellite imageries from Google Earth Pro® have also been Rama Krishna Beach (RK Beach) in Visakhapatnam along used in this study. Approximately 231 channels were the east coast of India is known for Rip current existence. identified using these datasets. In addition to this, drowning Since 2000, around 243 drowning cases were reported due data reported in the Police First Information Reports (FIR) to rip currents along this stretch. In this study, 53 multi- at RK beach stretch were collected from the Visakhapatnam temporal high-resolution satellite imageries from 2004 to Police Commissioner’s office. Drowning data contains the 2017 have been analysed to identify the bathymetrically information of Name of the beach, Date and time of induced rip channels. The analysis showed that the entire drowning, Demographic information of report (e.g., age and beach stretch is highly dynamic with the existence of quasi- gender), Number of victims. All the data have been carefully permanent rip channels during all the seasons. It has been filtered and only such cases reported due to rip currents were observed that beach near Kurusura submarine museum considered in this study. has most dangerous permanent rip channels during 80% of the year, while at other nearby beaches the rips shift alongshore to and fro with the season. 1 Introduction Rip currents are fundamentally generated due to the action of breaking waves owing to alongshore gradients in wave-induced radiation stresses and pressure [1]. The offshore flow velocities of rip current ranges between 0.3 - 2 m/s [2]. Rip currents can occur along any coastline due to the interaction between waves, currents, water level and nearshore bathymetry and these currents flow faster near the water surface [3]. Rip currents can quickly carry the surf zone bathers to the offshore with all swimming abilities [4] Figure1: Monthly distribution of drownings at RK beach Hence, rip current studies have been taken up for both stretch during 2000 - 2017. scientific and societal interest.. Rip currents are one of the 4 Results and Discussion most common causes of drownings at the beach in south Figure 1 shows the monthly distribution of rip current related India. It was estimated that around 320 beach users drowned drownings at RK Beach stretch. It can be observed that most due to rip currents in the Visakhapatnam beaches during of the drownings were reported during October month (38) 2000-2010 [5]. Out of all beaches, Rama Krishna (R.K.) followed by May (36). Seasonal distribution of fatalities beach stretch is declared as the most dangerous for having shows that during pre-monsoon (MAM), 79 people were 243 drowning deaths. However, due to limited studies, the reportedly drowned, whereas during the post-monsoon rip current variability with the season is less understood. In seasons (ON) around 50 people were drowned. A large this paper, multi-temporal high-resolution satellite imageries number of fatalities were reported during the summer from 2004 to 2017 have been analysed to detect the monsoon (JJAS), where around 66 people were drowned bathymetrically induced rip channels, where wave breaking (second largest after pre-monsoon season). This shows that is generally absent. the rip current risk is high during MAM, followed by JJAS and ON. The same has been observed in the earlier study at 2 Study Area o o this stretch [5]. Rip current hotspot maps would help the The port city of Visakhapatnam (17 41' N and 83 17' E) is town planners and coastal authorities to take preventive located almost midway between Chennai and Kolkata on the action to restrict the public from entering such risk zones. east coast of India. The coast has an orientation of about 68° Each high-resolution satellite image was carefully digitised and oriented towards the southeast. It is a wave-dominated in ArcMap to separate the zones of rip channels, beach, coast with wave heights ranges from 0.5 – 2.0 m; higher breaker zone and water. Later, all the rip channels shape files during monsoon and cyclonic conditions and lower during obtained from different imageries of the respective season other seasons. were added and analysed for frequency of occurrence. The 3 Data and Methods number in the Figure 2 legend represents the frequency (how many times) of rip current persistence. As the optical Rip currents are very small-scale features and therefore their satellite data is obstructed by clouds during JJAS (summer signatures or signals cannot be observed in coarser monsoon), they were not considered in the study. However, resolution satellite (LISS-III, LANDSAT etc.) imageries. there could be potentially dangerous rip currents possible Therefore, high-resolution satellite imageries have been during that season due to higher wave attack. Nonetheless, procured from the National Remote Sensing Centre (NRSC) few people only venture into the sea during high sea state. National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 60

They are equally thankful to NRSC for providing satellite imageries. We acknowledge the support of Google Earth for providing a platform for digitising rip channels from satellite imageries. The first author is thankful to DST-SERB for providing fellowship.

References [1] A. J. Bowen, “Rip currents: 1. Theoretical investigations,” Journal of Geophysical Research, vol. 74, pp. 5467-5478, 1969.

[2] J. H. MacMahan, A. J. H. M. Reniers, E. B. Thornton and T. P. Stanton, “Surf zone eddies coupled with rip current morphology,” Journal of Geophysical Research: Oceans, vol. 109, 2004.

[3] K. A. Haas and I. A. Svendsen, “Laboratory measurements of the vertical structure of rip currents,” Journal of Geophysical Research: Oceans, vol. 107, 2002.

Figure2: Rip current hotspots from 2004-2017 (a) Post [4] D. Drozdzewski, W. Shaw, D. Dominey-Howes, R. monsoon, (b)Winter monsoon and (c) Pre-monsoon Brander, T. Walton, A. Gero, S. Sherker, J. Goff and B. seasons. Edwick, “Surveying rip current survivors: preliminary During all seasons, rip channel persistence is high along insights into the experiences of being caught in rip the Kurusura Submarine Museum and its surroundings. currents,” Natural hazards and earth system sciences, Beaches at Vuda Park and Palm Beach are least vulnerable vol. 12, pp. 1201-1211, 2012. for rip currents. It can be seen that immediately after summer monsoon i.e. in post-monsoon, the rip current hotspots are [5] S. V. V. A. Kumar and K. V. S. R. Prasad, “Rip current- concentrated near the Submarine Museum and the stretch related fatalities in India: a new predictive risk scale for south of it (Figure 2a). Later, during the winter monsoon, the forecasting rip currents,” Natural hazards, vol. 70, pp. hotspots were shifted southward towards RK Beach 313-335, 2014. converting the entire stretch from RK Beach to Submarine museum beach as “High risk” zone. During pre-monsoon [6] M. M. B. Talbot and G. C. Bate, “Rip current season, the rip current danger is localised to few small charactersitics and their role in the exchange of water stretches within this region. From the FIRs, the most and surf diatoms between the surf zone and nearshore,” numbers of drownings during post and pre-monsoon were Estuarine, Coastal and Shelf Science, vol. 25, pp. 707- reported at Submarine museum and north of RK beach (not 720, 1987. shown here). 5 Conclusions and Further study This study clearly shows that the RK Beach stretch is highly dynamic in nature with the existence of quasi-permanent rip channels during all the seasons. Rip current hot spot maps help to evaluate the rip current related danger along this coast. The existence of rip current hot spots was compared with the actual drowning cases reported along this stretch and the reasonable match was found. In general, it has been noticed that rip channels are active during post, pre-monsoon and winter monsoon season and they shift temporally alongshore. Protective measures have to be taken along the above stretches of RK beach to mitigate the rip current related drownings in the future. Acknowledgements The authors are thankful to the Director, Space Applications Centre (ISRO) for his constant support and encouragement.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 61

Estimating the Impact of Fani Cyclone along the coast of Orissa, India Seema Rani Department of Geography, Miranda House, University of Delhi, New Delhi [email protected]

Abstract

A cyclonic storm named as 'FANI' developed over southeast Bay of Bengal and adjoining eastern equatorial Indian Ocean on 27th April 2019 and turned into a severe cyclonic storm on 29th April, 2019. This storm blows through Puri and Bhubaneshwar in Odisha state on 3rd May. It has caused fairly widespread rainfall activity with the strong wind of about 175 km/hr and done severe damage to the property. This study is an attempt to estimate the inundated area caused by this cyclone in Puri district of Odisha. District wise daily rainfall data for the period 27 April – 6 May, 2019 is taken from India Meteorological Department. The study has used remote sensing data of Sentinel SAR data of pre (27 April 2019) and post (5 May, 2019) cyclone. Land cover mapping of the area has been done using decision tree method. Estimated inundated area of the Puri district is about 37 km2 in the coastal areas of Odisha. The study has found that inundated area is mainly in the coastal areas of the district. Inland area of the district has less effect. Figure 1: Location of Puri district in Odisha, India. Keywords: Cyclone FANI, Puri, inundated area 3 Data and Methods assessment District wise daily rainfall data (27 April-6 May 2019) of 1 Introduction the Odisha state was collected from the India Meteorological Eastern coastal regions of India are vulnerable to Department (IMD). Synthetic aperture radar (SAR) data of severe cyclonic activity. It has been found that cyclonic Sentinel 1A (27 April 2019) and Sentinel 1B (5 May, 2019) activity in the Indian Ocean has increased over the years and of the Puri district were obtained from Copernicus Open becomes more intense [1], [2]. Climate change is considered Access Hub (https://scihub.copernicus.eu/) [7]. Collected as one cause of increasing cyclonic activity [3]. Severe rainfall data were categorized into seven categories, based on cyclones lead to damages of crops and infrastructure. Active rainfall intensity, given by IMD [8] and the spatial remote sensing data have provided an opportunity to distribution map was prepared by choropleth technique. Pre- estimate the loss mainly inundated area [4]. This data has processing (subset, radiometric calibration, single product high spatial resolution and not affected by cloudy weather. speckle filter and range Doppler terrain correction) of Sentinel data was done in a sentinel application platform A cyclonic 'FANI' developed over southeast Bay of (SNAP 6.0) [9]. Then, the image of both the dates was Bengal in the Indian Ocean on 27th April 2019 and landfall classified in binary code into water and land. Inundated area occurred near Puri in Odisha on 3rd May 2019. It was of the district was estimated by an overlay technique in accompanied by heavy rainfall activity with strong wind. ArcMap 10.5. Cyclone Fani is considered as severe cyclone which has led to damage to property in the district. Hence, the aim of the 4 Results and Discussiom present study is to estimate the impact of Fani cyclone in Puri 4.1 Rainfall Conditions district of Odisha by calculating the inundated area through Fani cyclonic storm has a landfall near Puri in Odisha on active remote sensing data. 3rd May 2019. It has caused fairly widespread rainfall activity 2 Study Area with the strong wind of about 175 km/hour and done severe The study has been carried in the Puri district of the state damage. The coastal districts of the state of Odisha have of Odisha (Figure 1) which situated along the coast. The area received very heavy (Kandhamal - 88.8 mm, Puri-78.37 mm, of the district is about 3396 km2 and total population is Khordha -72.7 mm) and extremely heavy rainfall (Ganjam – 419 mm, Gajapati 148.1 mm) on 3rd May 2019 (Figure 2). 1698730 [5]. It is comprising of Puri Sadar sub-division only th and has more than 150 km long coastline. Largest brackish Similar rainfall was received other district on 4 May 2019. water lakes of Chilika in India is also located in the district. Very heavy rainfall led to inundated area and damage to Cyclone is ranked second in disaster faced by the district. crops. About 10 cyclones have occurred in the district since 1967 [6].

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 62

inundated area is around Chilika Lake. Though the inundated area is about 1% of the district, but heavy rainfall and high velocity wind have caused severe damage to livestock and crops. Acknowledgements

Author is thankful to European Space Agency for providing Sentinel SAR data in the public domain.

References [1] K. Hoarau, J. Bernard and L. Chalonge (2012). Review Intense tropical cyclone activities in the northern Indian Figure 2: District wise spatial distribution of intensity of Ocean. Int. J. Climatol. 32: 1935–1945. rainfall in Odisha. [2] M. Balaji, A. Chakraborty and M. Mandal (2018). 4.2 Inundated Area Changes in tropical cyclone activity in north Indian Ocean Cyclone led to very heavy rainfall in Puri district. Area during satellite era (1981–2014). Int. J. Climatol. 38(6), covered by water before the arrival of cyclone (27 April 2819-2837. 2019) is estimated about 21 % of the district and it becomes [3] A. Mishra (2014). Temperature Rise and Trend of 22 % on 5 May 2019. It shows that about 1 % (37 km2) area Cyclones over the Eastern Coastal Region of India. J Earth of the district was inundated due to cyclonic storm (Table 1). Sci Clim Change, 5:9, 1-5. Figure 3 shows the spatial distribution of inundated area. It is [4] F. Greifeneder, W. Wagner, D. Sabel and V. Naeimi mainly near the coast and around main water bodies. Area of (2014). Suitability of SAR imagery for automatic flood Chilika Lake which near to the coast has faced high mapping in the Lower Mekong Basin, Int J Remote Sens. inundation. There is need of detailed study of damages to 35:8, 2857-2874 crops and infrastructure at block scale for better [5] Census of India (CoI) (2011) District Census Handbook, understanding. Puri, Odisha. Available: http://www.censusindia.gov.in/2011census/dchb/DCHB_A/ Inundated area of the Puri district, Odisha 21/2118_PART_A_DCHB_PURI.pdf [6] District Disaster Management Plan 2017-18, Puri,

Area Km2 Area % Odisha District Disaster Management Authority (DDMA) 27 April 2019 710 21 Puri, Odisha. Vol 1, (2017) Available: 5 May 2019 746 22 https://cdn.s3waas.gov.in/s3e836d813fd184325132fca8edc dfb40e/uploads/2018/05/2018051650.pdf Inundated Area 37 1 [7] Copernicus Sentinel data (2019) for Sentinel data S1A_IW_GRDH_1SDV_20190427T001326_20190427T0 01351_026968_030910_4DE6; S1B_IW_GRDH_1SDV_20190505T122702_20190505T1 22731_016109_01E4DE_F7A0; S1B_IW_GRDH_1SDV_20190505T122731_20190505T1 22805_016109_01E4DE_C01C. Processed by ESA. [8] India Meteorological Department (IMD) Terminologies and Glossary. Available: http://imd.gov.in/section/nhac/termglossary.pdf [9] SNAP - ESA Sentinel Application Platform v6.0. Available: https://step.esa.int/main/download/snap- download/ Figure 3: Water bodies and cyclone inundated area (red colour) of Puri district of Odisha. 5 Conclusion

The present study is an attempt to estimate the effect of the Fani cyclone in the Puri district of Odisha. The study has found that the coastal district of Odisha has received extremely heavy rainfall of more than 244.4 mm when the cyclone crossed the area. Extremely heavy rainfall caused inundation of the area. Finding indicated that an area of about 2 37 km was inundated due to the cyclonic storm. Main

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 63

Probabilistic Rip current likelihood model coupled with Satellite data assimilative wave prediction system Surisetty V V Arun Kumar, Seemanth M, Rashmi Sharma and Raj Kumar

Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA) Space Applications Centre (ISRO), Ahmedabad, Gujarat, India [email protected]

2 Data and Methods Abstract 2.1 Wave hindcast setup

Rip currents are one of the most dangerous coastal hazards accounting for the highest portion of beach In order to provide input for the development of the rip rescues/drownings worldwide. Validated hindcasted model current likelihood model, the WAVEWATCH III (WW3) outputs from a Wave Watch III (WW3) and predicted tides hindcast run was conducted for the Indian Ocean domain over from FES2014 global tidal model for the period 2000 – 2016 2000-2016. The model setup included the definition of the were utilised. A probabilistic rip current prediction model model grids, their offshore wave boundary conditions, as well was created using a logistic regression formulation using as reanalyzed 25-km wind fields from the European Centre lifeguard reports from Goa beaches and obtained Brier for Medium Weather Forecasting (ECMWF) over the model Score of 0.19, which means good performance. Later, the domain (Figure 9). high resolution (2.5 km) WW3 model assimilated with altimeter data was used to predict the rip current risk for the 2.2 Rip current likelihood (ReLieF) model next 5 days and implemented experimentally on MOSDAC. formulation This forecast is first of its kind in India and would be helpful to reduce the rip-related drowning cases at beaches. A logistic regression predicts the best-fit model relating a 1 Introduction dependent response variable to one or more independent predictor variables [4]. A total of 972 lifeguard rescues from Rip currents are one of the most dangerous coastal Goa were considered in this study. An ADASYN algorithm hazards, which are typically few meters wide and extend (Adaptive Synthetic sampling using SMOTE (Synthetic 100-500 m within the surf zone or more towards offshore Minority Oversampling Technique) has been used [5] to with velocities generally 1 m/s or less, occasionally may make 0s (no rescue) and 1s (rescue) balanced. The logistic sometimes exceed 2 m/s [1]. They account for the highest regression model is defined as portion of beach rescues. In India, there is an average of 39 푒푔(푥) drownings per year estimated from online agencies and 휋(풙) = 푔(푥) (1) newspapers [2]. Many countries like US, Australia, Brazil, 1+푒 Israel and Columbia have reported rip currents as major where, 푔(푥) = 훽0 + 훽1푥1 + 훽2푥2 + 훽3푥3 + ⋯ + 훽푛푥푛 is hazards [3]. However, an accurate forecast system is still the logit function, for n predictors. A maximum likelihood unavailable in India to alert the public. It could be a valuable method has been used to estimate the values for the resource for public outreach, lifeguards and beach patrol. coefficients 훽0, 훽1 … 훽푛 for n predictors. There has been remarkable efforts in the world over the past For the model creation, the hindcast WW3 modelled two decades to develop a feasible rip current forecast model. outputs of breaker waves and predicted tides have been used This paper discusses on the development of a new rip current (refer Table 1). The output is the likelihood of hazardous rip likelihood-forecasting model using modelling and satellite currents occurring from 0 to 1. The input variables to the data. model were chosen based on importance to rip current dynamics and statistical significance [6]. 3 Results and Discussion 3.1 Validation of waves and tides Modelled waves and predicted tides have been validated with the wave buoys and tide gauges respectively. The RMSE and bias of significant wave height is around 0.27 m and 0.045 m respectively and the mean wave period has negligible bias of 0.64 s (Figure 10). The assimilated run has maximum positive impact of around 8-9% on SWH at 0-hr model forecast and reduces up to 3% for 24-hr forecast. After 24 hour, there is no significant impact of assimilation observed. Similarly, predicted tides have been validated with the measured tides at few stations along the Indian coast (not Figure 9: Nested domains chosen for WW3 model, shown). shaded with bathymetry (depths in meters). Data from blue rectangle (0.1o×0.1o) is considered for training and the data from red region (0.025o×0.025o) is used for rip current forecasting in this study.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 64

Contingency table. A threshold of 0.5 has been assumed to have higher probability of rip current risk in the present study. The results show that POD is 0.571 and FAR is 0.487. An Architecture of Rip current Likelihood Forecasting System (ReLieF) on MOSDAC (https://mosdac.gov.in/rip_current_forecast) is shown in Figure 12.

Figure 10: Comparison of WW3 modelled wave heights SWH (a) and mean wave period MWP (b) with wave buoys for the year 2016.

3.2 Performance of rip model

The model was trained with 80% of data and the remaining 20% data was used for evaluating its performance. As many causative factors are involved in rip current generation, three different configurations (3, 5 and 7 predictors) of the prediction model have been used. It has been observed that the model with 7 predictors (Hb, Tp, m, Spr, , Ω and tide) outperform all the other schemes evident Figure 12: Architecture of Rip current Likelihood from least p - values. The statistical computations for this Forecasting System (ReLieF) assimilated with satellite case are given in the Table 1. data. 4 Conclusions and Future work Table 1: Statistical parameters of , p - value and Standard In this paper, development and validation of a new error (SE) with 7 predictors Probabilistic Rip current likelihood model coupled with the

Parameter  p - value SE satellite assimilated wave model is presented. The model is -41 Intercept 2.959 9.3 × 10 0.221 capable to predict the rip current risk with a reasonable Hb 2.137 6.4 ×10-57 0.134 Tp -0.063 1.17 ×10-05 0.014 accuracy. As this forecast is coupled with other weather m -0.034 4.2 ×10-33 0.003 parameters and sky condition, this would provide improved Dsp -0.013 8.9 ×10-30 0.001 guidance to both lifesaving agencies and the general public  0.271 0.0 0.007 through “Safe Beach App” and hopefully aid in reducing both Ω -1.758 4.2 × 10-143 0.069 -13 rip current related rescues and drownings.  -0.119 3.4 ×10 0.016 References Comparison of predicted risk vs lifeguard records is shown in the Figure 11. During March to April 2016, more [1] J. H. MacMahan, E. B. Thornton and A. J. H. M. number of rescues are noticed. It is common that people Reniers, “Rip current review,” Coastal Engineering, generally prefer clam sea state for recreation. However, after vol. 53, pp. 191-208, 2006. June, up to August, very few rescues were recorded due to high sea state. This does not mean that rip current risk is not [2] S. V. V. A. Kumar and K. V. S. R. Prasad, “Rip current- prevailed. If more data is available, a better prediction is related fatalities in India: a new predictive risk scale for forecasting rip currents,” Natural hazards, vol. 70, pp. possible. 313-335, 2014. [3] A. D. Short, “Australian rip systems--friend or foe?,” Journal of Coastal Research, pp. 7-11, 2007. [4] F. Lo, M. C. Wheeler, H. Meinke and A. Donald, “Probabilistic forecasts of the onset of the north Australian wet season,” Monthly Weather Review, vol. 135, pp. 3506-3520, 2007.

[5] H. He, Y. Bai, E. A. Garcia and S. Li, “ADASYN: Figure 11: Rip current likelihood with 7 predictors. Red Adaptive synthetic sampling approach for imbalanced circles denote actual rescues reported by lifeguards. learning,” in 2008 IEEE International Joint Conference The Brier score (BS) was computed when the lifeguard on Neural Networks (IEEE World Congress on observations of 0 indicate no hazardous rip currents (i.e. o=0) Computational Intelligence), 2008. and observations indicate a hazardous rip current occurs (i.e. [6] G. Dusek and H. Seim, “A probabilistic rip current o=1). In the above case, the BS =0.19, which suggests good forecast model,” Journal of Coastal Research, vol. 29, performance. To assess the accuracy of this method, statistical pp. 909-925, 2013. verification like probability of detection (POD) and false alarm rate (FAR) has been computed by using 2×2

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 65

Validation and Analysis of the HF Radar-derived currents in the Gulf of Khambhat, India Samiran Mandal*, Saikat Pramanik and Sourav Sil

Ocean Analysis and Modelling Laboratory, School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Odisha. [email protected]

2 Quantification of Tidal Currents Abstract The hourly HFR-derived currents are used to extract and This work presents the results from the analysis of the quantify the tidal currents using harmonic analysis [4] during High Frequency Radar (HFR) – derived hourly ocean 2018. Particularly, the May and September months have been surface current observations in the Gulf of Khambhat, India highlighted, based on the seasonality of temperature and salinity during 2018. The work is mainly divided into two parts: i) the structure in the Gulf. The domain experiences maximum fresh extraction of high-frequency tidal currents, and, ii) the influxes during the post-monsoon season (September) from investigation of the impact of bathymetry and stratification Tapti, Narmada and Sabarmati rivers. The harmonic analysis of on tidal currents. The HFRs could capture stronger currents the HFR currents show highest amplitudes of the M2 tidal (~2.0 m/s) nearby the head gulf. The harmonic analysis of currents (104.35 – 120.23 cm/s), followed by S2 (30.41 – 49.39 surface currents clearly indicated the dominance of M2 tidal cm/s), N2 (23.55 – 32.29 cm/s) among the semi-diurnals, K1 currents (~1.04–1.20 m/s), which are the highest in the (12.39 – 24.91 cm/s) and O1 (~10 cm/s) among the diurnals. northern Indian Ocean. Among the shallow water tidal Moreover, the shallow water tidal constituents, M4 (2.95 – 4.00 constituents, the M4 tidal currents are the strongest with cm/s) and MS4 (3.18 – 4.77 cm/s) are observed with significant magnitudes of ~4.0 cm/s. The amplification of tidal currents amplitudes. in nearshore regions is due to the interaction with shallow Table 1: Semi-Major and Semi-Minor Axes of the Tidal bathymetry as well as the geometry of the Gulf. Higher values Constituents obtained from Harmonic Analysis. of Brunt-Vaisala Frequency (N2) indicate stratified upper layers during September, which is responsible for the highest (1.20 m/s) amplitude of M2 tidal currents. Tidal Semi- Major Axes Semi- Minor Axes Constit (cm/ s) (cm/ s) uents 1 Introduction May September May September The continental shelf in the western coast of India, M2 104.35 120.23 -1.30 2.56 widens along the northern part (~250 km off Mumbai coast), S2 30.41 49.39 0.78 3.98 with a gradual narrowing towards the head region (popularly N2 23.55 32.29 -0.27 -0.64 recognized as the Gulf of Cambay). The same shelf further leads into two gulfs: Gulf of Kutch (GoKu) and Gulf of K1 24.91 12.39 2.20 1.46 Khambhat (GoKh), which are well-known regions for the O1 10.18 10.60 -0.02 -0.20 significant amplification of tides. The GoKh on the western M4 4.00 2.95 1.20 -1.57 shelf of India is an inverted funnel-shaped region with shallow MS4 3.18 4.77 0.83 0.61 bathymetry (<40 m) between the Saurashtra Peninsula and mainland of Gujarat. * Only significant constituents with Signal to Noise Ratio >1 are noted. The GoKh is rarely investigated, which owes to the less availability of datasets and observation platforms due to the The amplitudes of the M2 tidal constituents increases from maximum tidal heights as well as stronger currents [1]. The the open ocean towards the head gulf. It can also be depicted Indian Coastal Ocean Radar Network (ICORN) covers the that the amplitudes of M2-driven tidal currents are almost 4.5 coastal regions of India, mainly Odisha [2], Andhra Pradesh times the amplitudes of diurnal constituents. The percentage [3], Tamil Nadu, Gulf of Khambhat and Andaman Sea. The total tidal variance in this region ranges between 90 – 96 %, High Frequency Radars (HFR) have been installed at Wasi which indicates that the region is strongly driven by tidal Borsi and Jegri lighthouse (black dots, Figure 1), Gujarat by currents. the National Institute of Ocean Technology (NIOT), India. 3 Role of Bathymetry The present study is carried out using HFR – derived ocean Ratio of semi-major axis amplitudes of the tidal currents to surface currents, which are available hourly and at a spatial the bathymetry is shown in figure 1 during both May and resolution of ~6 km during 2018. September. An evident increase in non-linearity is observed as The objectives of the present study are; (i) extracting the the bathymetry becomes shallow (<50 m). However, the ratio is high-frequency tidal currents using harmonic analysis in the higher in the middle of the domain, which indicates higher tidal GoKh, (ii) investigating the impact of bathymetry, and (iii) the current amplitudes. impact of seasonal stratification on the variability of tidal currents.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 66

Figure 2. Variation of Brunt-Vaisala Frequency (left) and M2 tidal current amplitudes during May and September. 5 Conclusions The study presents the analysis of HFR-derived surface currents in the GoKh during 2018. The high-frequency tidal variability has been investigated during pre-monsoon (May) and post-monsoon (September). The M2 tidal currents dominate the entire gulf with the highest amplitudes (~1.20 m/s), which is ~4.5 times the amplitudes of the diurnal constituents (0.24 m/s). The bathymetry constantly plays a significant role in the intensification of tides in the nearshore regions during both the seasons, whereas, stratification is responsible for tidal amplification during the post monsoon seasons (September). References [1] R.K. Nayak, and S.R. Shetye, Tides in the Gulf of Khambhat, west coast of India. Estuar. Coast. Shelf Sci. 57, Figure 1. Seasonal variations of the ratio of semi-major axis of tidal currents and bathymetry during May (upper) 249–254, 2003. and September (lower). [2] S. Mandal, S. Sil, A. Gangopadhyay, T. Murty, and D. 4 Role of Stratification Swain, On Extracting High-frequency Tidal Variability from HF Radar data in the Northwestern Bay of Bengal. J. Oper. The M2 tidal currents are stronger (120.23 cm/s) during September and slightly weaker during May (104.35 cm/s). The Oceanogr. 11 (2), 65-81, 2018. region experiences maximum freshwater influxes during [3] S. Mandal, S. Pramanik, S. Sil, K.S. Arunraj, and B.K. Jena, September, leading to the higher values of Brunt–Vaisala Sub-mesoscale circulation features along the Andhra Pradesh 2 Frequency (N ) (Figure 2). coast, Bay of Bengal: Observations from High Frequency The internal tides are primarily affected by the seasonal Radar. J. Coast. Res., 2019. (accepted). stratification. The perturbation at the subsurface is maximum due to the highly stable nature of water column (higher [4] R. Pawlowicz, B. Beardsley, and S. Lentz, Classical tidal stratification) and the lower values of eddy viscosity. This harmonic analysis including error estimates in MATLAB using stable nature leads to the reduction in the loss of tidal kinetic T_TIDE. Computat. Geosci. 28 (8), 929–937, 2002. energy and hence higher tidal currents are observed during [5] M. Müller, J.Y. Cherniawsky, M.G.G. Foreman, and J.S. September [5]. Hence, stratification is the main reason behind Von Storch, Seasonal variation of the M2 tide. Ocean Dyn. 64, the higher intensity of tidal currents during September than 159–177, 2014. May, when the upper layers show well-mixed conditions leading to more loss of tidal energy and comparatively less amplitudes of tidal currents are observed. The barotropic and baroclinic effects cannot be separated from the HFR currents. Hence, the higher amplitudes of the tidal currents during September at the center of the domain can definitely be attributed to the higher stratification.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 67

Analysis of intensity and frequency of tropical cyclones in relation to Sea Surface Temperature Stefy Thomas Department of Atmospheric Sciences, CUSAT, Kochi-16 [email protected]

Abstract depressions with a slight decrease in 1991-2017. Frequency of cyclonic storms decreased in 1931-1960 but increased The study objective is to analyze the trends in frequency constantly thereafter. SCS show a continually increasing and intensity of tropical cyclones in relation to sea surface trend over the whole period. The increasing SST and higher temperature for 117 years from 1900-2017, analyzed in a SST contours enveloping higher latitudes can be related to 30 years’ time interval, for east and west coast of North slight decrease in frequency of depressions and increase in Indian Ocean. The results show an increasing sea surface frequency of SCS. temperature in both the basins correspondingly increasing Bay of Bengal (BoB) the frequency of intense cyclonic activities, with the only exception of Bay of Bengal in 1991-2017 time interval. The increase in SST over the BoB basin is steeper than the Additionally, as the ocean gets warmer the cyclone AS basin. The variation in average maximum SST is as intensity category shifts from depressions to severe follows . Minimum cyclonic storms. SST over BoB is as follows 1 Introduction (Figure.3). Annual Indian Ocean cyclones are getting stronger and more average frequency of depressions shows a consistent frequent under present warming environment [1, 2, 3]. The decrease over the period of observation, from 6 in 1900- 1930 formation, maturity and dissipation of cyclones depend on to 3.5 in 1991-2017. Cyclonic storms also show a decreasing factors like wind direction, Coriolis force, vertical wind shear trend. SCS show a continuously increasing trend with an etc. but the most favorable condition is a warm sea surface. exception in 1991-2017.

The climatological character of North Indian Ocean (NIO) Tropical Cyclone (TC) frequency and intensity is gathering importance in recent years [4]. Therefore, a long term analysis of this factor in relation to SST can point to its future trend thus helping the forecast. 2 Data and Methodology The study area is NIO region with latitude and . This covers the west coast (Arabian Sea basin) and east coast (Bay of Bengal basin) of Indian subcontinent. SST and frequency and intensity of TC are analysed for 117 years (1900-2017) in 30 years time interval (1900-1930, 1931-1960, 1961-1990, 1991-2017). The last time interval consists of 26 years due to unavailability of cyclone data. SST data for NIO is obtained from NOAA ERSSTv5 (2o×2o) derived from ICOADS for the latitude 0oN to 30oN and longitude [5]. Cyclone data for the same time period and location is taken from IMD (Cyclone e-atlas) [6]. Yearly average SST of each period is compared with the yearly average frequency of cyclones in the corresponding period. Comparison is done for intensity categories namely depression, cyclonic storms and severe cyclonic storms (SCS). 3 Findings Arabian Sea (AS) SST shows an increasing trend for the entire period of observation. The maximum SST over AS increases from over the 30 years time interval. The minimum SST is consistently increasing; variation over the period of observation is (Figure.1). Figure 2 shows an increase in annual average frequency of Figure 1: Yearly average SST over AS from 1900-2017.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 68

4 Conclusions Over the period of 1900-2017, global SST has increased. An increase is SST, can resultantly trigger tropical cyclones, as it is the most favourable factor for cyclogenesis. The SST increase in AS shows a linearity with frequency and intensity of SCS in AS basin. BoB basin follows same linearity in SCS from 1900 to 1990. The declining trend after 1990 can be due to missing data after 2017. For both AS and BoB, as the ocean gets warmer the intensity category moves from depression to SCS. The minimum SST required for formation of depression is 26.5 degree Celsius. The area covered by high SST contours increases with time and most of the regions near the eastern coast is at 27 degree Celsius or more which facilitates formation of severe cyclonic storms. Of late, the western coast also exhibits a similar character. However, tropical cyclone is a complex phenomenon which is also enhanced by various atmospheric and ocean processes like El Nino, La Nina, ENSO and Indian Ocean Dipole [3,7]. Analysis of these processes alongside SST would be the future scope of this study. References 1. IPCC (2007) Climate Change 2007. Synthesis Report. Working group I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland 72. 2. M.K, Roxy, K. Ritika, P. Terray, and S. Masson, “The Curious Case of Indian Ocean Warming,” Climate, vol.27, no.22, pp. 8501-8509, Nov.2014, doi:10.1175/JCLI-D-14-00471.1. 3. A. Mishra, “Temperature rise and trend of

cyclones over the Eastern Coastal Region of Figure 2: Yearly average SST over BoB from 1900-2017. India,” J Earth Sci Clim Change, vol.5, no.9, pp.227 Oct,2014, doi: 10.4172/2157- 7617.1000227. 4. Y. JunPeng, A. Jie, “North Indian Ocean tropical cyclone activities influenced by the Indian Ocean Dipole mode,” Science China Earth Sciences, vol.56, no.5, pp.855-865, May.2013, doi: 10.1007/s11430-012-4559-0. 5. B. Huang, P.W. Thorne, V.F. Banzon, T.Boyer, G. Chepurin, J.H. Lawrimore, M.J. Menne, T.M. Smith, R.S. Vose, and H.M Zhang, “NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. [Monthly Mean]” (2017). NOAA National Centers for Environmental Information. doi:10.7289/V5T72FNM. 6. Cyclone eAtlas, Indian Meteorological Department, May.2019. [Online]. Available: http://www.rmcchennaieatlas.tn.nic.in 7. M.S, Girishkumar and M.Ravichandran, “The influence of ENSO on tropical cyclone activity in the Bay of Bengal during October-December,” Figure 3: Frequency and intensity of TC from 1900-2017. Journal of Geophysical Research, vol.117, no.C2, pp. 2033, Feb.2012, doi:10.1029/2011JC007417.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 69

The influence of boreal summer intra-seasonal oscillation on Indian Ocean surface wave variability P.G. Remya G. Srinivas and T.M. Balakrishnan Nair Indian National Centre for Ocean Information Services (INCOIS), Hyderabad-500090 [email protected]

Abstract

We assess the impact of northward propagating intraseasonal oscillations which is dominant during the boreal summer namely, the Boreal summer intra-seasonal oscillation (BSISO) on Indian Ocean surface waves. Steep monsoon waves always impact all the marine related activities in the Indian Ocean and hence the present study deals with the influence of BSISO on the tropical Indian Ocean (TIO) surface waves during the season June- August. Hs anomalies show strong and significant response to the wind anomalies of BSISO. Both AS and BOB shows an increase of ~50 cm increase in the wave height in BSISO phases 5-7 during monsoon. 1 Introduction The intra-seasonal mode which is dominant during the boreal summer, the Boreal summer intra-seasonal oscillation plays a vital role in the development and propagation of the Indian summer monsoon. As far as Indian Ocean concern, the highest wave heights are seen during monsoon and these steep monsoon waves always impact all the marine related activities in the IO. Madden Julian oscillation (MJO) influence on global weather and its impact on wave is Figure 1: Composites of wind speed anomalies (m/s) for the already addressed [2].However, the BSISO impact on waves 8 BSISO phases over Jun-August season. in the IO is not addressed explored in the previous studies and hence the present study investigates the influence of BSISO on surface waves in the IO. 2 Data and Methodology For the analysis, the OLR data from the NCEP-NCAR reanalysis is used for BSISO index. The ocean surface wave parameters from the ERA-5 reanalysis are used for the period of 1979 to 2017. We diagnose impacts of BSISO on surface waves by carrying out composite analysis of the eight phases of BSISO cycle on daily wave parameters as provided by the BSISO index [1]. 3 Ocean wave modulations Initially we provide the composite wind anomalies for the eight phases of BSISO in figure 1.In phase1 positive anomalies are seen in the southern tip of India and over eastern tropical southern Indian Ocean (ETSIO). The intensification of the wind anomalies is seen over the southwestern Arabian Sea (AS) from phase 3. Northward shift of positive anomalies is noticed during the phases 3-7 (figure 1). Intensified positive anomalies (~2m/s) are seen in both AS and Bay of Bengal (BOB) in phases5-7 (figure 1e- g). The composite of wave anomalies shows good association with surface wind patterns (figure 2). Negative Hs anomalies (~40cm) are seen in phases 1-3 in both AS and BOB (figure 2a-b). High positive anomalies of significant wave height (Hs) (~20 cm) is seen in the south western AS Figure 2: Same as figure 1 but for Hs anomalies (m). in phase 4. Both AS and BOB shows high positive Hs anomalies (~50 cm) in phases 5-7 as seen in the case of wind (figure 2e-g). National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 70

4 Conclusions There exists a significant relationship between the active phase of Boreal summer intra seasonal oscillation and the surface wave variability of north Indian Ocean. Hs anomalies show strong and significant response to the wind anomalies of BSISO. Both AS and BOB shows an increase of ~50 cm increase in the wave height in BSISO phases 5-7 during monsoon.

References

1. Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal representation of the tropical intraseasonal oscillation. Clim.

Dyn., 38, 1989-2000, doi:10.1007/s00382-011-1159-1.

2. Marshall A G et al., Madden julian oscillation impacts on global ocean surface waves, Ocean Modelling (2015), http:// dx.doi.org/10.016/j.ocemod.2015.06.002.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 71

Decadal variability of Indian Summer Monsoon rainfall under historical run of a climate model Vivek K Pandey and Lav Kumar K. Banerjee Centre of Atmospheric and Ocean Studies (KBCAOS), University of Allahabad, Prayagraj-211002 [email protected]

modeling. The Empirical approach makes use of information Abstract obtained from past climate analogues; the semi empirical The summer monsoon mean rainfall for the months of approach is based on statistical downscaling GCM fields; June-July-August-September (JJAS) is simulated over and modeling approaches of utilize physical models such as India and its adjoining regions for the period between 1982 nested regional models and variable resolution global and 2006. The study has been done using Regional models [7]. Various national and international projects are Climate Model (RegCM) version 4.6 at a resolution of using RCMs for the better understanding of regional climate 25km. The simulated mean JJAS monsoon rainfall is change and they use different regional climate scenarios to validated against the observational IMD data. Comparison assess climate change impacts and vulnerability with of JJAS seasonal mean summer rainfall for the first decade different atmospheric. 1982 to 1991, and the later decade 1997 to 2006 indicate The objective of this study is to find out the conditions of that the intensity of rainfall increases over Indian land- intensity JJAS rainfall and other climatic parameters in two mass in the later decade under the historical scenario. The decades over India and its adjoining regions, for the period observed JJAS mean rainfall indicates two maximum of 1982 to 2006 under the historical run of RegCM) version rainfall areas i.e. the Western Ghats and the Himalayan 4.6 at the horizontal resolution of 25 km and its validation region. There is a significant bias is present in the central for use this model in futuristic run. and J & K region. The JJAS mean seasonal ground 2 Model description and experiment setup temperature distribution at 0.25 x 0.25-degree grids We have used the Regional Climate Model version 4.6 resolution shows a decreasing trend of temperature over (RegCM4.6) in the present study. The detailed description of the Indian land-mass. the model and its features are highlighted by [8]. The model is run from 1st January 1982 until 31st December 2006 at 25 1 Introduction km horizontal resolutions with the biosphere-atmosphere The Indian climate is dominated by South Asian Monsoon transfer scheme (BATS) and Emanuel CP scheme for which remains effective for 122 days every year. It is the convection over land and ocean areas, which covers a broad most vigorous intra-seasonal oscillation which occurs during South Asia region and adjacent ocean areas, 46.2° E to the four months from June to September. Studies have 113.74° E and -3.22° S to 40.78° N for 50 km horizontal shown its dependence on various regional and global factors. resolution and 49° E to 110.64° E and 12.5° S to 46.5° N 25 However, it is still believed to be influence by the Indian and km horizontal resolution. EIN15 dataset (from IPCC) is used Pacific Ocean the most as the heat content of the nearby to produce simulation with the initial and lateral boundary ocean plays an important role in regulating and driving conditions for the RegCM4.6. This simulation consists the monsoon winds over India. A cooling and warming historical period covering from 1982 until 2006. phenomena observed in tropical Indian ocean know as a 3 Results Indian Ocean dipole mode which has its effect on Indian 1. The simulated mean Indian summer monsoon rainfall for Ocean dynamics which have resultant effect on Indian the JJAS season for the period between 1982 and 2006 using Monsoon [1]. El Nino Southern Oscillation (ENSO) and the the Regional Climate Model (RegCM) version 4.6 at 25 km Indian Ocean Dipole (IOD) are known to be the major horizontal resolutions. The simulated mean JJAS monsoon factors influencing the Indian monsoon (Agrawal et al. 2017. rainfall by RegCM4.6 is validated against the Indian The changes in ocean properties correspond to further Meteorological Department (IMD) data set and for another variability in atmospheric conditions. The Indian Ocean case ran with 25 km horizontal grid resolution. Comparison responds to either weaker or stronger monsoon winds, of JJAS seasonal mean summer rainfall for the case of first through changes in upwelling and mixing which forms a decade i.e 1982 to 1991, and the second decade i.e. 1997 to major component. Thus, the Indian Ocean may have a very 2006 indicate that the intensity of rainfall increases over active and independent role in climate variability [2]. India in case of later decade under the HISTORICAL Studies have shown that RCMs simulate weather scenario. statistics more realistically when compared to the 2. An increment is shown in wind intensity at Bay of Bengal observations [,3,4, 5]. The performance of RCMs depends and south equatorial easterlies. Similarly, at upper level (200 mainly upon the chosen physical parameterizations which hPa), it is observed that Tibetan anticyclone has a weakening are nothing but the mathematical formulations of different tendency. physical processes. Regional models add value by describing 3. RegCM4.6 provides satisfactory simulation over the fine mesoscale variability compared to the global reanalysis, Indian and adjoining regions and identifies occurrence of especially when they are run for the larger spatial extent. the two maximum rainfall zones, the first one is the Bay of This is useful for representing heterogeneous variables such Bengal including the north east India and the second one is as near-surface temperature, than nearly homogeneous sea Western Ghat. The rainfall distribution is seen to be robust level pressure [6]. There are three basic approaches for over the rest of the Indian region. RegCM4.6 rainfall regional climate simulation: empirical, semi empirical and National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 72

simulation correlates well with the IMD observation data sets over the Indian region. 4. The location of Somali jet and cross equatorial flow is realistically captured at lower troposphere. Comparison of wind magnitude for the two decades shows the weakening of westerlies over Arabian Sea and Somali jet strength is reduced at lower level in later decades. Figures and Tables

Figure 4: JJAS mean Vector plot and Magnitude of U,V wind at 850hPa (0.25x0.25 deg grids); (a) for the period 1982-1991; (b) for the period 1997-2006; (c) and change of magnitude of U,V wind at 850hPA (Fig.4(b-a)).

Figure 1: (a) IMD observed dataset JJAS mean rainfall (mm/day) (0.25x0.25) deg grid during the period 1982-2006; (b) RegCM4.6 model dataset JJAS mean rainfall (mm/day)(1982-2006) (0.25x0.25 deg grid); (c) Bias between IMD and RegCM4.6 dataset for JJAS mean rainfall (mm/day)(1982-2006).

Fig.5. JJAS mean vector plot and magnitude of U, V wind at 200hPa (0.25x0.25 deg grids); (a) for the period 1982-91; (b) for the period 1997-2006; and (c) change of magnitude of U, V wind (Fig.(b-a)). Acknowledgements Author thanks DST for assistance through the research project. References [1] Saji, N., B. Goswami, P. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical indian ocean. Figure 2: RegCM4.6 model dataset JJAS mean rainfall Nature, 401, 360-363 (e.g., Früh et al. 2010; (mm/day) (0.25x0.25 deg grid) (a) for the period 1982-1991, [2] Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. (b) and for the period 1997-2006; (c) bias between Leben, 1999: Coupled ocean-atmosphere dynamics in the RegCM4.6 (1982-1991) and RegCM4.6 (1997-2006) for indian ocean during 1997-98. Nature, 401, 356-360 JJAS mean rainfall (mm/day). [3] Früh, B., H. Feldmann, H.-J. Panitz, G. Schädler, D.

Jacob, P. Lorenz, and K. Keuler, 2010, Determination of

precipitation return values in complex terrain and their

evaluation. J. Climate, 23, 2257–227.

[3] Kunz. M., S. Mohr, M. Rauthe, R. Lux, and C. Kottmeier,

2010, Assessment of extreme wind speeds from regional

climate models—Part 1: Estimation of return values and their evaluation. Nat. Hazards Earth Syst. Sci., 10, 1–15. [3] Semmler, T., and D. Jacob, 2004, Modeling extreme precipitation events—A climate change simulation for

Europe. Global Planet. Change, 44, 119 –127. Figure 3: JJAS mean Vector plot and Magnitude of U,V [4]. wind (0.25x0.25 deg grids) (a) at 850hPa; (b) (b) at 200hPa. [5] Feser et al., 2011, Regional Climate Models Add Value To Global Model Data, American Meteorological Society,1181-1192. [6] Giorgi F, Mearns LO, 1999, Introduction to special section: regional climate modeling revisited. J Geophys Res 104: 6335−6352. [7] Giorgi et al., 2012, RegCM4: model description and preliminary testsover multiple CORDEX domains, Clim Res, Vol. 52: 7–29. National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 73

Impact of Different Ocean Initial Stratification on the Tropical Cyclone Generated Mixing over the Arabian Sea: A Sensitivity Study Tanuja Nigam1, Kumar Ravi Prakash1, Vimlesh Pant1

Indian Institute of Technology Delhi, Hauz Khas, New Delhi [email protected]

2 Model and Methodology Abstract 2.1 Model

The Regional Ocean Modeling System (ROMS) is The weather research and forecasting (WRF-ARW) utilized to estimate the cyclone generated mixing for four model used to generate atmospheric forcing for the ocean different ocean initial stratification conditions. For this model ROMS. The WRF model’s boundary and initial study, the two consecutive strong cyclones (Phet in June conditions are provided from National Center for 2010, Nilofar in October 2014) over the Arabian Sea are Environmental Prediction Final Analysis (NCEPFNL, 2000) selected. To understand the impact of the changes on the data. The initial condition was taken at 00:00 GMT, 1 June seasonal stratification for the same cyclonic forcing, the 2010 and at 00:00 GMT, 24 October 2014 for Phet and ocean initial conditions are exchanged to these two cases. Nilofar respectively. The WRF model grid horizontal and The WRF model is used to provide the atmospheric surface vertical resolution was taken as 9 km and 27 vertical sigma forcing data during cyclone. It has been observed that the levels and it was interpolated by ocean model to make it strong initial stratification conditions enhances the inertial suitable for the ocean model resolution. The model time step mixing associated with cyclone up to a deeper depth. for simulating atmospheric forcing was set as 90 seconds. 1 Introduction Methodology An idealistic approach is utilized to understand the same Evan et al., 2011 stated that the Arabian Sea (AS) is cyclone’s response in different seasons over the Arabian Sea experiencing increasing number of Tropical Cyclones (TC) by providing the constant ocean state of June and October and their interaction with upper ocean column changes the for both Phet and Nilofar consecutively. The cyclone local circulation. The TC occurrence over the AS has found generated baroclinic velocity shear and the baroclinic kinetic during the pre- and post-monsoon seasons (Gray, 1968). The energy estimates are analyzed for the Phet and Nilofar in cyclones that normally form in the AS in the month of May their actual occurrence time periods in June and October, and October originate in the South East Arabian Sea (SEAS) respectively as well as for the idealistic switching of ocean while; the June cyclones usually form over the east or central stratification between months October and June, AS region (Mahapatra et al., 2007). AS has a variable upper respectively. The ocean model, ROMS was run for Phet for ocean circulation in short and long time scales. The the duration of 7 days from 1st June 2010, 01:30 GMT to 7th variability in the ocean state can alter the interaction process June 2010, 04:30 GMT for both Phet(R) and Phet(I) of an individual storm and its associated response. experiments. In case of Nilofar, it was run for the duration of The aim of the current study is to establish an understanding 12 days from 24th October 2014, 01:30 GMT to 4th regarding the upper ocean response towards the TC and its November 2014, 19:30 GMT for both Nilofar(R) and impacts on the dynamical and thermo dynamical fields of the Nilofar(I) experiments. All experiments’ details are ocean. The study also highlights the importance of provided in Table1. translational speed of the storm and the effect of Table1: Details of Experiments. stratification of the Sea to the cyclones Phet and Nilofar. According to Saffir-Simpson Scale, two category 4 cyclone Model TC Name Initial and Atmospheric cases, The Phet (June) and Nilofar (October) are chosen for Experim (Realistic/ Boundary Forcing the present study. The Phet cyclone occurred over AS in the ent Idealised) Conditions at surface month of June during 31st May to 7th June, 2010. This was a Exp1 Phet(R) 1 June,2010 1-7 June, 2010 Very Severe Cyclonic Storm (VSCS) that is also termed as Exp2 Nilofar(R) 24 Oct, 2014 24-31Oct, 2014 category 4 storm according to India Meteorological Exp3 Phet(I) 24 Oct, 2014 1-7 June, 2010 Department (IMD) report. The Phet was formed on central AS as a low pressure area on 30th May, 2010. It was turned Exp4 Nilofar(I) 1 June,2010 24-31Oct, 2014 into depression on 31st May, 2010. But the Phet turned into The Baroclinic Kinetic Energy (BKE) shows the energy a cyclone on 1st June, 2010 and got its maximum intensity associated with vertically penetrating near inertial on 2nd June, 2010 with a maximum wind speed of 43.7 m s- oscillations. Data from numerical simulations is used to 1. It was continuously moving towards north and made its calculate daily BKE profiles per unit mass (m2 s-2). The landfall on 4th June, 2010 with a wind speed of 33.4 m s-1 formula used for the calculation of BKE is given below— The Nilofar cyclone was also a category 4, VSCS. It was 1 22 st developed over SEAS on 21 October, 2014 and started BKE( ubaroclinic  v baroclinic )     (1) moving northward. The cyclone was intensified as Severe 2 Cyclonic Storm (SCS) on 27th October, 2014. The Nilofar The energy transferred through BKE to the vertical attained its peak intensity on the night of 28th October, 2014 baroclinic velocity shear used up to work against the upper with a wind speed of 56.9 m s-1. ocean stratification which leads to cyclone generated mixing. The combined effect of cyclone-induced upwelling and mixing due to near-inertial oscillations leads to cooling National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 74

of sea surface. The vertical baroclinic velocity shear (S in s- 2) was also estimated by utilizing the following formula- uv S 2(baroclinic ) 2  ( baroclinic ) 2     (2) baroclinic zz 3 Result Analysis and Conclusion 3.1 Result Analysis The analysis of vertical temperature profiles suggested that the initial temperature for Phet(R) at 2nd June, 2010 and Nilofar(R) at 24th October, 2014 was 29.8 °C and 27.8 °C respectively. As both cyclones proceed in the model domain, MLD increased due to the influence of cyclonic wind generated inertial mixing as 50 m & 100 m that was initially at 15 m and 22 m for Phet(R) and Nilofar(R). It is evident from 6th June, 2010 and 28th October, 2014 temperature Figure 1: The Model domain and Track for the selected profiles that this cyclone mixing had cooled the upper ocean cyclones. by 5.9 °C and 7.4 °C in case of Phet(R) and Nilofar(R) respectively. This excess sea surface cooling is also explained by the translational speeds of Phet(R) and Nilofar(R). The Phet(I) and Nilofar(I) temperature profiles show decrement in the magnitude of cyclonic upwelling 1.6 °C and 2.1 °C respectively. This decrement of sea surface cooling for both cases has depicted that the change in the initial ocean state is an important aspect to get a greater amount of inertial mixing imparted by tropical cyclones. The influence of upper ocean stratification is also analyzed to understand this excess magnitude of cyclone upwelling and sea surface cooling due to mixing associated with Nilofar. The upper ocean density stratification conditions over the selected boxes for all four experiments are analyzed. The strongest Brunt-Vaisala frequency is found as 51 cycle day- 1 at a layer of 15 m to 35 m from 1st June to mid of 2nd June, 2010 for the Phet(R) whereas the Nilofar(I) initial upper ocean stratification became comparatively weaker due to Figure 2: Baroclinic Velocity Shear (s-2) for all four strong winds associated with Nilofar and higher experiments. stratification band is only limited up to 25 m to 45 m. Almost after this depth, stratification is vanished in Phet(R). But in 4 Conclusion case of Nilofar(I), from 70 m - 115 on 4th to 6th June, 2010 From the numerical experiments it can be inferred that and at 115 m to 135 m on 1st to 2nd June, 2010 21 cycle day- the lower stratification, slower translational speed, energetic 1 Brunt-Vaisala frequency is observed. The other two cases inertial oscillations, and available subsurface velocity shear Phet(I) and Nilofar(R) show a stratification of 33 to 35 cycle are responsible for the degree of sea surface cooling under the day-1 at 35 – 70 m and 25 – 85 m respectively. surface cyclonic wind stress. Both the idealized TC Figure 2 portray the interaction mechanism of inertial BKE simulations, i.e. Phet(I) and Nilofar(I), have depicted that the associated with different experiments with their respective altered oceanic state (i.e. the initial stratification) has strong upper ocean stratification conditions. In case of Phet(R) and influence on the cyclone-induced upwelling which, in the Phet(I), most of the kinetic energy is utilized to erode the present study, is found to decrease the degree of upwelling in initial day’s stratified layer further from the third day both in comparison to their realistic ocean states. Presence of higher June and October the remaining BKE is converted as the stratification to a deeper depth that causes more mixing due vertical baroclinic velocity shear of 0.45 m s-2 at 15 m to 60 to strengthening of inertial oscillations. m and 0.35 m s-2 at 40 m to 90 m. References Evan, A.T., Camargo, S.J., (2011), A Climatology of Arabian Sea Cyclonic Storms. J. Climate, 24, 140- 158, https://doi.org/10.1175/2010JCLI3611.1 Gray, W. M., (1968), Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700. Mahapatra, D.K., Rao, A.D., Babu, S.V., Srinivas, Chamarthi, (2007), Influence of coastline on upper oceans response to the tropical cyclone. Geophysical Research Letters 34, L1760.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 75

Biogeochemical variability of the Indian Ocean using an Ecosystem model Vivek Seelanki 1, *, Vimlesh Pant 1 1 Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi [email protected]

and NE monsoons water column is more stratified and Abstract nutrients are not pumped to the surface layers resulting in the Basic physical processes that impact the biological activity decreased primary production and development of chl-a are the near-surface processes such as upwelling, bloom. The presence of large fresh water discharge in the entrainment, detrainment, advection, and subsurface Bay of Bengal makes the sea water less saline and stratified, circulations including shallow overturning cells and sub- which restricts the upwelling of nutrients [Jyothibabu et al thermocline currents. These processes are examined in the 2004]. In the equatorial Indian ocean, the presence of Wyrtki present study over the Indian Ocean using Regional Ocean jet in the inter-monsoon periods, helps the nutrients to come Modelling System (ROMS) model coupled with a biological to the surface resulting in the moderate production of chl-a model. The two different water masses in the northern in the region. Cyclonic activities can also enhance the Indian Ocean, namely the Arabian Sea and Bay of Bengal biological productivity. Seasonal analysis of surface behave differently in terms of circulation due to seasonal chlorophyll has been done in detail by Levy et al (2007) by reversal of monsoonal winds. The biological productivity incorporating biology the model ORCA05-Pelagic of Arabian Sea is very high as compared to Bay of Bengal. Interaction Scheme for Carbon and Ecosystem Studies During the time of upwelling, there exist phytoplankton (PISCES). They studied the onset of the blooms as well as bloom near the coasts of Somalia and Oman which results the amount of increase in the chl-a from the onset to the peak. in the faster development of other biological variables. Even though the productivity is less in Bay of Bengal when Freshwater fluxes can also play an important role in the compared to Arabian Sea, phytoplankton blooms appears evolution of phytoplankton bloom in the Bay of Bengal and during NE monsoon in the southwestern BoB Arabian Sea. Due to the unavailability of observational [Vinayachandran et al 2003]. Based on the observational data for the phytoplankton and related biological variables, data, the sub-mesoscale biophysical activities are not well explored. In order to have a detailed investigation of the 2 Methodology & Model Description chlorophyll, other biological variables and their seasonal variability are to be illustrated by the coupling In the present study, the ROMS model coupled with biogeochemical model with the physical model. biogeochemical model is configured over the Indian Ocean from 30E-120E and 30S-30N. This experiment carried out In the present study we utilized the ROMS model which is with a high-resolution of grid size of 1/4 degree so that it is widely used by the ocean modelers globally. The ROMS efficient for resolving meso-scale features. ROMS is a free- model simulated physical parameters (sea surface surface, terrain-following, S-coordinate, primitive equation ocean model widely used by many scientific communities in temperature, salinity and surface currents) are validated the world. The model has 40 vertical levels. For the model against available observations. The main research interest topography, 2-minute Gridded Global Relief Data ETOPO2. is to couple the physical model with the biogeochemical Model is initialized with temperature and salinity data from model to understand the variability of the oceanic World Ocean Atlas (WOA2005). The model was started biogeochemistry over the Indian Ocean. The results from from a state of rest and was spun up for 10 years daily the numerical experiments are discussed in terms of the climatology for the period 2000 - 2008. For this experiment, the daily climatological surface forcing for, Wind stress from spatial and temporal variability in the biogeochemical QUICKSCAT (from IFREMER parameters in the Indian Ocean. (www.ifremer.fr/cersat/en/data/data.htm), precipitation data from TRMM of NASA 1 Introduction (http://daac.gsfc.nasa.gov/precipitation), air temperature, sea level pressure, specific humidity net long wave and Northern Indian Ocean exhibits two distinct cycle of shortwave radiation from NCEP were used. The model is chlorophyll-a (chl-a) bloom [Banse 1987], one during forced by climatological monthly forcing for river runoff norther summer or southwest (SW) monsoon and another using point-source method. As tracer advection scheme, during winter or northeast (NE) monsoon season. In the SW Recursive flux corrected MPDATA scheme has been used monsoon, winds trigger an intense bloom due to the and for momentum, third order upstream-biased and fourth- order centered schemes are implemented for the horizontal combined effect of coastal upwelling, lateral advective and vertical direction respectively for the momentum. For transport and offshore Ekman pumping. During NE the surface boundary layer mixing, K-profile monsoon, nutrients are brought to the surface layers by parameterization (KPP) scheme is applied. convective mixing resulting in surface chl-a concentration mostly in northern and central Arabian Sea. In between SW

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 76

The physical model is coupled with Bio-fennel Figures and Tables biogeochemical model. The initial conditions and boundary conditions are taken from WOA2005. All other biological variables like phytoplankton, zoo-plankton, Large and small Detritus, Ammonia and Chlorophyll are set initially to a small homogenous value of 0.1 mmol N m-3. 3 Model Validation Results and Discussion Remotely sensed surface chlorophyll data from Seawifs sensor from 2000-2008 is used to derive the monthly climatological data from 8-day data, and Sea surface temperature (SST) and Sea surface salinity (SSS) data from NIO climatology. Surface Currents data Oscar currents during 2000-2008 is used to derive the monthly climatological data. These data sets are used for the comparison of 10th year climatological Chla, SSS, SST, and currents from model. Remotely sensed surface chlorophyll data from Seawifs sensor from 2000-2008 is used to derive the monthly climatological data from 8-day data. This data Figure 1: Selected monthly mean climatological of sea is used for the comparison of 10th year climatological Chla surface temperature (oC). from model. The model has simulated the spatial patterns well for the physical parameters SSS, SST. Figure 2 shows the selected mean monthly cycle of SST. When Compare with Oscar currents, the model currents are strong in south of Srilanka. Estimates of surface chlorophyll from model outputs and SeaWiFS showed a similar picture overall, though the magnitude and spatial extent of simulated phytoplankton blooms are to some degree overestimated, while intense coastal blooms in SeaWiFS records are lacking in the model. Figure 2 shows the mean monthly cycle of surface chlorophyll in the model and observations. During the northeast monsoon in boreal winter, elevated Chl concentrations are found over the northwestern part of the Arabian Sea and the northern BoB. These features are relatively well reproduced by the model, although the intensity of the bloom is overestimated along Somalia and Figure 2: Selected monthly mean climatological Surface around Sri Lanka. The spring inter-monsoon is characterized Chlorophyll (mg m-3). for both model and observations by oligotrophic conditions and reduced chlorophyll in most of the Indian Ocean basin. During the summer/southwest monsoon and fall inter- References monsoon, the model correctly simulates phytoplankton Banse, K. (1987). Seasonality of phytoplankton chlorophyll blooms along the coasts of Somalia and the Arabian in the central and northern Arabian Sea. Deep Sea Peninsula, at the southern tip of India, around Sri Lanka, and Research Part A. Oceanographic Research Papers, 34(5- in the southeastern Indian Ocean. 6), 713-723. In order to investigate the physical processes involved, three Jyothibabu, R., Maheswaran, P. A., Madhu, N. V., Ashraf, boxes which are having unique orographaphic features and T. M., Gerson, V. J., Haridas, P. C., & Gopalakrishnan, external forcing. Over the northern Arabian sea the model T. C. (2004). Differential response of winter cooling on has captured fairly well the prominent features for instance the two chlorophyll blooms during the SW monsoon and NE biological production in the northeastern Arabian Sea monsoon is well simulated when compared to the Seawifs and northwestern Bay of Bengal. Current Science, 783- climatology Unlike the other regions of northern Indian 791. ocean, the southern tip of India is having only one prominent Lévy, M., Shankar, D., André, J. M., Shenoi, S. S. C., bloom during the SW monsoon which is mainly due to the Durand, F., & de Boyer Montégut, C. (2007). Basin‐ upwelling by Ekman suction and also the transportation of wide seasonal evolution of the Indian Ocean's Chl-a rich water by the South West monsoon current. phytoplankton blooms. Journal of Geophysical Research: Oceans, 112(C12). Vinayachandran, P. N., & Mathew, S. (2003). Phytoplankton bloom in the Bay of Bengal during the northeast monsoon and its intensification by cyclones. Geophysical Research Letters, 30(11).

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 77

Diurnal variability of ocean environment on sub-grid scale using 1-D coupled model Yadidya Badarvada, Sachiko Mohanty, A D Rao

Centre for Atmospheric Sciences, Indian Institute of Technology Delhi [email protected]

The model is initialized by using the temperature and Abstract salinity from WOA13. All the open boundaries of the domain are forced with the barotropic velocity components of tidal The Price-Weller-Pinkel one-dimensional mixed layer constituents extracted from the TOPEX/Poseidon global tidal model and Garret-Munk model are coupled to simulate model (TPXO8) [3]. At the surface, the model is forced with the diurnal variation in the temperature and salinity high-resolution surface fluxes (6 hourly interval) from profiles due to mixed layer processes and presence of National Centres for Environmental Prediction (NCEP). internal waves at RAMA buoy locations in the Bay of 2.2 PWP model Bengal. The coupled model is initialized with RAMA buoy observations and 3D MITgcm simulated The PWP model, which is an ocean-surface mixed layer temperature and salinity. The coupled model simulated one-dimensional model, uses surface heat and momentum outputs are compared with the buoy data for different fluxes along with the initial temperature and salinity profile months. to simulate the structure and evolution of the mixed layer in the ocean. It simulates the temperature and 1 Introduction salinity of the mixed layer using three stability conditions: Solar heat fluxes and wind stress primarily govern the static stability, mixed layer stability (bulk Richardson physics of the upper ocean [1]. They affect the mixed layer number ≤ 0.65), and shear flow stability (gradient Richardson in the ocean, which in turn influences the thermocline where number ≤ 0.25). internal waves are present. Internal waves are formed in 2.3 GM model stably stratified fluids when some external force ousts a parcel of water from its position and then restored by The description of the Garrett-Munk model is given in buoyancy forces. The restoration force depends upon [4]. The eigenvalues and modes of the internal waves are gravitational force and the density difference between the obtained with the help of “finite-difference-Sturm sequence- two layers in the ocean. They play a significant role in the bisection-inverse iteration method” [5]. The displacements thermodynamics of the ocean, submarine navigation, induced by the internal waves are generated using these offshore oil drilling, etc. internal wave eigenvalues and modes and statistics derived from the Garrett-Munk power spectrum [6]. Kumar et al. (2010) [2] used a similar coupled Price- Weller-Pinkel (PWP) and Garrett-Munk (GM) model to 2.4 Coupled PWP & GM model simulate the variability in the temperature in the coastal The temperature and salinity profile from RAMA buoy at waters of the Arabian Sea. They found that by coupling these 0030 IST for all the days in May 2014 are used to initialize models, there has been a significant improvement in the PWP model by forcing it with hourly shortwave radiation, simulated vertical profiles of temperature. longwave radiation, latent heat flux, sensible heat flux, zonal The present study aims to simulate the diurnal and meridional wind stress, and precipitation for 24 hours. variability of temperature and salinity using a coupled 1-d The same profiles are used in the GM model to simulate the model, which in turn can be used to calculate the internal wave displacements in the thermocline and then they transmission losses in the acoustic propagation on a diurnal are imposed on the mixed layer modified temperature and scale. salinity profiles derived from the PWP model by using 2 The model and methodology T(r,z,t) = T(z) + ζ(r,z,t) ∂g Tp; S(r,z,t) = S(z) + ζ(r,z,t) ∂g Sp 2.1 3D MITgcm model where ζ is the displacement. The temperature and salinity profiles and the sound speed profile derived from them are The MITgcm model is a hydrostatic/non-hydrostatic, z- thus simulated within a 10 km range and for 24 hours. The coordinate finite volume model that solves the flowchart of the coupled model is given in Figure 1. This set incompressible Navier-Stokes equations with Boussinesq of experiment is defined as Expt-1. approximation on an Arakawa-C grid. The model domain extends from 4°N to 15°N in the meridional and 88°E till 3 Results and Discussion 99°E in the zonal direction with a grid resolution of 2.7 km 3-D MITgcm is run for 45 days starting from 15th April and its bathymetry is derived from General Bathymetric 2014 till 31st May 2014, and the output of May is used for Chart of the Oceans. The model domain is shown in Figure further experiments with the coupled 1-D model. The vertical 1. There are 48 levels in the vertical. No slip and free slip are profiles of temperature and salinity at 0030 IST in each day applied at the bottom and lateral boundaries, respectively. of May 2014 are extracted from MITgcm and are given as an The horizontal and vertical eddy viscosity and diffusivity are input to the 1-D PWP model. It is forced with hourly heat and parameterized by using the Smagorinsky formulation and K- momentum fluxes from RAMA buoy data and mixed layer profile parameterization scheme, respectively. The value of modified temperature and salinity profiles are extracted from the bottom drag coefficient (Cd) is kept constant at 0.0025 in the PWP model. The displacement calculated from GM by this configuration. initializing it with the output of MITgcm is then imposed on

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 78

them to give the final output. This set of experiment is defined as Expt-2.

Figure 3: RMSE for (a) coupled model initialized with RAMA buoy data and (b) coupled model initialized with MITgcm. 4 Conclusion The temperature and salinity profiles of the MITgcm are improved by the incorporation of NCEP surface heat and salt fluxes. PWP model is coupled with the GM model and Figure 1: Flowchart of coupled PWP & GM model. implemented at RAMA buoy location. Using improved MITgcm vertical profiles of temperature and salinity as initial The average RMSE for Expt-1 where the coupled model conditions, the coupled model is integrated for different is initialized with RAMA buoy data is 0.71o C, whereas for months. The coupled model simulated spatio-temporal Expt-2 when it is initialized with the output of MITgcm, it is variability of temperature and sound speed profiles are 1.45o C. The depth-wise RMSE error is plotted in Fig. 3 to validated with RAMA buoy data. The ocean environment show that the 1-D coupled model is able to simulate the simulated using the coupled model can be further used to diurnal variability of the ocean environment reasonably well. calculate the transmission losses in the sound propagation on The temperature anomaly profiles plotted for every 24 hours the spatial domain and on the diurnal scale. also show good agreement between the observations and the coupled model simulations. The vertical structure of sound References speed is also calculated using the simulated temperature and salinity profiles from the coupled model for both experiments 1 and 2. [1] Price, J. F., Weller, R. A., & Pinkel. R. (1986). Diurnal cycling: Observations and models of the upper ocean

response to diurnal heating, cooling, and wind mixing. Journal of Geophysical Research, 91:8411-8427. [2] Kumar, P. H., Lekshmi, S., Jagadeesh, P. S. V., Anilkumar, K., Krishnakumar, G. V., & Rao, A. D. (2010). Internal tides in the coastal waters of NE Arabian Sea: observations and simulations. Marine Geodesy, 33(2-3), 232-244. [3] Egbert, G. D., & Erofeeva, S. Y. (2002). Efficient inverse modeling of barotropic ocean tides. Journal of Atmospheric and Oceanic Technology, 19(2), 183-204. [4] Sridevi, B., Murty, T. R., Sadhuram, Y., & Murty, V. S. N. (2011). Impact of internal waves on the acoustic field at a coastal station off Paradeep, east coast of India. Natural hazards, 57(3), 563-576. [5] Wilkinson, J. H. (1965). The algebraic eigenvalue problem (Vol. 662). Clarendon: Oxford.

[6] Flatte, M., Dashen, R., Munk, W. H., Watson, K. M. & Zachariasen, F. (1979). Sound transmission through a Figure 2: The model domain over the Andaman Sea with fluctuating ocean. Cambridge University Press. RAMA buoy locations.

National Seminar on Climate Change and Coastal Ocean Processes (CCCOP-2019), IIT Delhi, 4-5 July 2019 79

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