Rapid decline in Arctic sea ice has been observed, and climate models predict even more dramatic changes in the near future. One of the key components that cause such rapid sea ice reduction is sea-ice floe (pieces) breakup in the margin of the ice area during spring and summer. At the edge of sea-ice area, sea-ice floes are exposed to waves and winds and break into smaller pieces. As they become smaller, they become easier to melt from the side and exposing more open water areas. More exposed open water areas then leads to warmer ocean as more sunlight absorb into the ocean, which in turn make sea ice more fragile and breakable. This process can accelerate sea ice retreat in summer and thus impact the minimum ice extent. To understand the effects of such process on summer sea ice retreat, reliable information on sea-ice floe size distribution (FSD) is necessary. Such observations can be made from satellite Synthetic Aperture Radar (SAR) that can observe sea ice through cloud and darkness. There is an increasing number of satellite SAR images being acquired in the Arctic, and often at spatial resolutions in the images as good as 1-20 m. More importantly satellite SAR images are being acquired over autonomous buoy systems and in conjunction with field campaigns. This provides the ideal framework to measure the full range of ocean, sea-ice and atmosphere parameters to investigate complex floe breakup process. However the challenge we have is a lack of proven-quality algorithms that can derive FSD from satellite SAR images fast and accurately. Thresholding algorithms previously applied to the problem are not adequate for quantitative analysis and the performance has not been precisely assessed. Recently NERC TPoC funded us to conduct research to develop cutting-edge algorithm for the retrieval of sea-ice FSD from SAR images. In this IOP proposal we expand our current project internationally in order to add values to the current NERC TPoC project as well as have impacts on wide research communities and commercial companies.

Globally averaged surface air temperature (SAT) during the 20th and 21st centuries displays a gradual warming and superimposed year-to-year and decadal-scale fluctuations. The upward trend contains the climate response to an anthropogenic increase of heat-trapping atmospheric greenhouse gases. The temperature ups and downs around the trend - that are particularly pronounced in the Arctic - mostly reflect natural variability. Natural climate variations are of two types, internal and external. The former is produced by the climate system itself, e.g. due to variations in ocean circulation. An example of the latter is solar-induced climate variability. Decadal-scale variability is of large societal relevance. It is observed, for example, in Atlantic hurricane activity, Sahel rainfall, Indian and East Asian Monsoons, Eurasian winter coldness and in the Arctic SAT and sea ice. The understanding and skillful prediction of decadal-scale climate variability that modulates the regional occurrence of extreme weather events will be of enormous societal and economic benefit. InterDec is an international initiative aiming at understanding the origin of decadal-scale climate variability in different regions of the world and the linkages between them by using observational data sets and through coordinated multi-model experiments. How can a decadal-scale climate anomaly in one region influence very distant areas of the planet? This can happen through atmospheric or oceanic teleconnections. Fast signal communication between different latitudinal belts within days or weeks is possible through atmospheric teleconnection, whereas communication through oceanic pathways is much slower requiring years to decades or even longer. Understanding these processes will enhance decadal climate prediction of both mean climate variations and associated trends in regional extreme events. Scientists from different European countries, from China and Japan will closely collaborate to disentangle the secrets of the inter-relations of decadal-scale variability around the globe.