Numerical Model Studies to Predict the Wind-Wave Climate Considering Climate Change Effects

Abstract

The waves propagating over an area under the action of the wind is termed as wind waves. The disturbances on the ocean surface by the wind are restored to a calm equilibrium position by the action of gravity. The fundamental element in the wind-wave generation is the interaction between air and ocean. During this interaction, there is an energy and momentum transfer between the atmosphere and ocean. The climate change affects the atmospheric temperature which in turn alters the wind patterns. The wave conditions change according to the wind pattern. Studies on global climate changes and extreme weather events have fascinated researches all over the world. Climate change, a global phenomenon, is a consequence of ever-increasing greenhouse gas concentration and is considered a serious threat to mankind. Climate change is a phenomenon triggered by natural and anthropogenic activities, which is one of the most discussed topics in the research community today. An increase in global sea level, changes in wind pattern and an increase in the frequency of extreme wave events which is caused by climate change have critical impacts on the coastal population around the world. Indian coast measures about 7500 km along with the nine coastal states which host marine and coastal biodiversity. Thirteen major ports and associated activities play a prominent role in coastal population concentration of about 14% along the Indian coast. The coastal and offshore structures are typically designed for the significant wave height (HS) corresponding to a specific return period and it is, therefore, necessary to know possible changes in their magnitudes at different locations of interest. Structures built in the sea are traditionally designed according to historical climate observations or hindcasted data. For structural safety, consideration of such climate change effects is highly desirable. Computational advancements in recent times have resulted in various General Circulation Models being developed and effectively used for assessing the atmospheric and ocean circulation. The performance of these modelled result can be compared with the in-situ measurements of shorter duration. Forecast of the climate parameters incorporating climate change effects are developed. These data products can be used to develop numerical wave models for long term analysis of wind and wave patterns which will aid in the design of coastal and offshore structures. i i In the present study, hindcasting from 1980 for the Indian domain is performed from reanalysed gridded global wind speed dataset called ERA-Interim. The performance of this global dataset is assessed by comparing it with in-situ measurements recorded at the east and west coast of India. As the ERA-Interim dataset showed a good match with the in-situ records these long-term wind speeds are used as an input to the numerical wave model. MIKE 21 SW numerical wave model is developed for the Indian domain with coordinates - 4º to 30º N 40º to 95ºE. Significant wave heights from this wave model driven by ERA-Interim wind speeds are extracted at locations nearshore to Karwar and offshore OB03 location for validation. After validation, the numerical model is used to perform longterm wave analysis, shoreline analysis, assessment of wind-wave climate along the Indian coast and wave climate predictions along Karnataka coast for the near future. The numerical model output depends on the input which is global wind speed dataset. Wind speed analysis is initially performed before using it in the numerical model. As ERA-Interim dataset does not provide forecasts, global wind speeds provided by the CMIP5 database is considered in this study. Wind speed projections from 38 different CMIP5 global models are compared against ERA-Interim global wind speeds for the Indian domain. The performance of datasets is graphically evaluated based on Taylor plots. Initially, statistical analysis of monthly wind speeds from 1980 to 2005 is performed to arrive at four best performing datasets for the Indian domain. Further, a nowcast study on daily wind speeds from 2006 to 2018 considering the four climate change scenarios termed as Representative Concentration Pathways (RCPs) is carried out. From the nowcast analysis, an Italian CMIP5 dataset called CMCC-CM for RCP 4.5 matched well with the real-time reanalysed wind speeds provided by ERA-Interim. Hence in the present study, wave climate predictions for the Indian domain is based on wind speeds driven by CMCC-CM RCP 4.5. The long-term analysis is performed based on the five probability distributions such as Log-normal distribution, Gumbel distribution, Fretchet distribution, Exponential distribution, and Weibull distributions to arrive at significant wave height with 10 and 50 year return period for New Mangaluru port location. Initially, long-term analysis is performed on in-situ records measured for 5 years near New Mangaluru Port. From this analysis, Weibull distribution with α=1.3 showed good performance and is used to arrive at significant wave heights with 10 and 50 year return period. The same approach is extended on the MIKE 21 simulated significant wave heights from 38-year ERA-Interim hindcast. The results showed 2.6% and 5.44% increase in significant wave height with 10 year and 50 year return period at the location studied. ii i A shoreline analysis is performed using LITPACK tool along the coast adjacent to the New Mangaluru Port. The volume of sediment transport is analysed and the shoreline changes from 1980 to 2015 is studied to understand the erosion and accretion patterns. The performance of the numerical model matched well with the satellite measurements. In an attempt to explore the renewable energy potential along the Indian coast the numerical wave model is also used to assess the wind-wave climate based on ERA-Interim wind speed data of 38 years. The results showed amongst the locations studied off Goa, Karnataka, Kerala, Tamil Nadu, and Andhra Pradesh had good potential to extract offshore wind energy from offshore wind turbines. MIKE numerical model driven by wind speeds from CMCC-CM RCP 4.5 up to the year 2070 is used to simulate the wave climate along the Karnataka coast. The monsoon wave climate is studied to arrive at wave parameters with 10 and 50 year return period at six locations along the Karnataka coast.