A new look of sea ice radiative forcing over Arctic

Abstract

Arctic sea ice is a crucial indicator of climate change, with far-reaching impacts on atmospheric circulation, weather patterns, oceanic heat and moisture exchange, marine ecosystems, commercial activities, and more. As a result of this rapid warming, there has been widespread loss of Arctic sea ice. To understand the mechanisms behind this phenomenon, this study focused on analyzing the Arctic in terms of Sea Ice Radiative Forcing (SIRF). SIRF is the instantaneous perturbation of Earth’s radiation at the top of the atmosphere (TOA) caused by sea ice. Previous studies focused only on the role of albedo on SIRF. Skin temperature is also closely related to sea ice changes and one of the main factors in Arctic amplification. In this study, I estimated SIRF considering both surface albedo and skin temperature using radiative kernels. The annual average net-SIRF, which consists of the sum of albedo-SIRF and temperature-SIRF, was calculated as -54.57 ± 3.84 W/m2 for the period 1982–2015. In the net-SIRF calculation, albedo-SIRF and temperature-SIRF made similar contributions. However, the albedo-SIRF changed over the study period by 0.12 ± 0.07 W/m2 per year while the temperature-SIRF changed by 0.22 ± 0.07 W/m2 per year. The SIRFs for each factor had different patterns depending on the season and region. In summer, rapid changes in the albedo-SIRF occurred in the Kara and Barents regions. In winter, only a temperature-SIRF was observed, and there was little difference between regions compared to the variations in albedo-SIRF. Based on the results of the study, it was concluded that the overall temperature-SIRF is changing more rapidly than the albedo-SIRF. This study indicates that skin temperatures may have a greater impact on the Arctic than albedo in terms of sea ice surface changes. This study examined how data characteristics and perspectives affect the estimation of SIRF in three different aspects. First, I evaluated the accuracy of kernel-based method for assessing surface changes on TOA radiation using CERES data. A high correlation coefficient of 0.924, was found for surface albedo changes on shortwave radiation. For longwave radiation, a correlation coefficient of 0.752, was observed, indicating lower accuracy than the shortwave region due to atmospheric factors such as clouds. Second, I analyzed areas not considered in the SIRF change calculation, specifically those where sea ice had disappeared completely. The largest area of sea ice was observed in 1982 and 1983, while September had the highest sea ice loss with a total area of 2,140,150 km2. The average net-SIRF during this period was -14.56 ± 16.52 W/m2, the lowest radiative forcing value per pixel. Finally, I compared the suitability of ERA5 skin temperature and satellite-based MODIS IST data for representing the actual surface temperature of the Arctic. There was a consistent difference between ERA5 and MODIS, with ERA5 consistently higher. By taking into account all the discussions presented, I will be able to more accurately and quantitatively determine the impacts of phenomena caused by sea ice surface changes on the Arctic. In conclusion, this study highlights the importance of considering both surface albedo and skin temperature when estimating SIRF in the Arctic. By examining the accuracy of the kernel-based method and suggesting the limitations of the data used, I provide a more comprehensive understanding of the factors influencing SIRF. My findings emphasize the need for accurate and detailed assessments of sea ice surface changes and their impacts on the region. Ultimately, this research contributes to previous understanding of the complex processes driving changes in the Arctic and supports the development of more effective strategies to mitigate and adapt to these changes.