Statistical analysis of sea surface temperature for best fit

The meeting of the world's top governmental agencies at Paris for the COP21 summit, made it imminently clear that global warming was no longer a distant threat but a real and tangible one. Since then, there has been a noticeable impetus towards developing mechanisms for measuring the current state of different climate change indicators. In this paper, we have presented a statistical estimation of one such climate change indicator which is the sea surface temperature. Sea surface temperature is important because it not only gives us an idea about the sea level rise, the frequency of storms but also about the marine ecosystem as a whole. If we look towards technology which aims at reducing global warming and its effects in entirety, then we will come across renewable energy systems and among them are hydro energy systems. The systems among these which produce energy do so by the thermal & mechanical energy of the seas with thermal producing the bulk of the energy. Thus, it becomes an important task for one to measure and model the sea temperature so as to take effective measures for proper harnessing of hydro energy. In our review, we have used fundamental distribution functions to model the sea surface temperature, and have calculated error using various error detection tests thereby concluding the best fit for sea temperature data.