Zhihua Zhang (China) 1
1 - Shandong University
Due to global warming, the quantity of Arctic sea ice has been drastically reduced in recent decades, consequently navigating the Arctic is becoming increasingly commercially feasible and is viewed as a key climate change adaption strategy.
In this study, the authors assess the expansion of trans-Arctic maritime transportation in the context of big data mining.
Results and Conclusions
Because Arctic sea ice prone regions are a significant challenge for charting Arctic routes, sate-of-the_art remote sensing big data systems can provide an accurate and reliable approach to monitoring near real-time large-scale variability of Arctic sea ice conditions. Big data from dynamic climate modeling can provide a short-term forecast and long-term prediction on changes of Arctic sea ice and related climate conditions, which are indispensable for the exploitation of trans-Arctic maritime transportation. Based on these big datasets and data assimilation techniques, the authors proposed the framework of a ‘near-to’ real-time dynamic optimal trans-Arctic route system to guarantee safe, secure, and efficient trans-Arctic navigation. Such dynamic maps will help the pilots of vessels to maintain safe distances from icebergs and large-size ice floes and to save time, fuel and operational costs and risks.