• Journal of the Korean earth science society
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• v.36 no.6
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• pp.501-511
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• 2015

This study suggests a method of alleviating global warming by the increase of the Earth surface albedo through Artificial Sea ice Increasing (ASI) over the Available Freezing Areas (AFA). The method is developed based on the fact that the large sea surface area in or near the Arctic and the Antarctic has no ice even though both water and air temperatures are below zero and the artificial sea ice generation is thus available. The mean energy of $0.85Wm^{-2}$, which was suspected of adding to the earth by the global warming effect was calculated to offset at once when the sea ice area about $4.09{\times}10^6km^2$ was additionally increased. In addition, three techniques for producing ice plates on the sea surface (using ships, installation apparatus, and floating matter such as Green Cell Foam) for ASI were proposed. According to the result of simple analysis using the energy balance model, when ASI was maximally operated only for 3 months (September, October, and November) over AFA, it is expected that the annual mean temperature of earth surface would be decreased about $0.11^{\circ}C$ in the following year. On the other hand, in case of generating the artificial sea ice in all four seasons, a risk of triggering snowball earth was detected.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.