Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

Abstract

Analysis of the time series Sea Surface Temperature (SST) variations is a key element in understanding the climate change impacts on the phenology, trophodynamics, distribution, and catch of commercial marine fish species. As SST projections are mostly model dependent, the discrepancy in the model and observed values needs to be elucidated so as to derive accurate interpretations and conclusions. In this paper, the decadal and seasonal variations of SST anomaly over the four coastal zones of India were analysed. The selected period of the study is from 1968 to 2017 and the data obtained from the International Comprehensive Ocean Atmosphere Data Sets (ICOADS) and Max Planck Institute Earth System Model (MPI-ESM) was used. The Coupled Model Intercomparison Project 5 (CMIP5) model data were used for comparison with the observed value. The linear trend of observed and modelled values was inferred using the least square method for four seasons. The seasonal variation of SST anomaly of observed data in four coastal zones of India reveals that Northeast zone exhibits the least trend of warming in all seasons, whereas Northwest zone shows the highest trend of warming. SST anomaly of the first decade in all zones exhibited negative values in all seasons, while last decade shows positive value, indicating warming trend. In other decades, no uniform SST trend for all seasons was observed. However, the Northeast zone is exceptional with negative anomaly during all decades. The warming trend was observed in all coasts for both model and observed values. The model and observed SST anomaly follows an almost similar trend, but with noticeable differences in values among both. Owing to the differences in the model and observed values, it could be emphasised that error corrections needs to be applied in futuristic SST projections and related studies of Indian fisheries.