Collaborative Research: Spatiotemporal variability of solar radiation partitioning in the sea ice system: Improving climate models using observations from the MOSAiC field campaign

University Of Washington, Seattle WA

Investigators

Abstract

Improved Arctic climate predictions are critical for society. They are needed to adapt to and plan for present and future change, both in the Arctic and for the rest of the planet. Climate models show that warming temperatures and Arctic sea ice loss will continue with increasing greenhouse gas concentrations. However, these models show large differences in the rate of warming and sea ice loss. These model differences largely come from imperfect representation of the interactions among the atmosphere, sea ice, and ocean. One of the key interactions is the reflection of sunlight by sea ice. In the spring, sea ice is snow-covered and reflects 85% of sunlight keeping the surface cool. However, moving into summer, the ice melts and the amount of sunlight reflected decreases, warming the surface resulting in more melt. It is critical to accurately represent this process in models. This project will combine observations and climate models to better simulate the processes controlling the interaction of sunlight and sea ice and their impact on sea ice melt. This modeling effort will lead to refined representation of sea ice in climate models, which will improve predictions of sea ice loss and global warming. This project will use observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign (Oct 2019 - Sept 2020) to improve key parameterizations for simulating sea ice physics and feedbacks through the analysis of relevant, process-oriented observations to enhance the predictive skill of climate models. It will focus on building model parameterizations that more accurately represent the variability of ice-albedo feedback processes. The methods include the integration of spatially and temporally coordinated field observations, single column modeling, and global climate modeling. We will synthesize field observations to produce a complete forcing/test dataset and iterate with single column modeling to develop parameterizations that improve the treatment of the spatial distribution of snow and melt ponds, light transmittance through sea ice, and responses to events such as summer snowfall and rain-on-snow events. Parameterizations will be incorporated in an open-source community climate model and used to evaluate improvements in sea ice predictability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.