Status: this preprint has been withdrawn by the authors.
Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data
Yifan Ding,Xiao Cheng,Jiping Liu,Fengming Hui,and Zhenzhan Wang
Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.
This preprint has been withdrawn.
How to cite. Ding, Y., Cheng, X., Liu, J., Hui, F., and Wang, Z.: Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-208, 2019.
This study develops a new melt pond fraction (MPF) data set over sea ice on Arctic-wide scale, using a method of ensemble-based deep neural network. Based on the new dataset, we analyze the spatial-temporal variations of MPF on different ice types and the prediction of MPF to the Arctic sea ice extent in recent years. The new dataset may help improve the prediction of the Arctic sea ice minimum by assimilating the MPF in models.
This study develops a new melt pond fraction (MPF) data set over sea ice on Arctic-wide scale,...