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

合作研究：海冰系统中太阳辐射分区的时空变化：利用 MOSAiC 实地活动的观测结果改进气候模型

• 批准号: 2138785
• 负责人: Donald Perovich
• 金额: $ 44.49万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138785&HistoricalAwards=false
• 关键词: Collaborative; Research; Spatiotemporal; variability; solar

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.

改善北极气候预测对社会至关重要。它们需要适应和规划北极和地球其他地区目前和未来的变化。气候模型显示，随着温室气体浓度的增加，气温上升和北极海冰的减少将继续下去。然而，这些模型显示出变暖和海冰损失速度的巨大差异。这些模式的差异主要来自于对大气、海冰和海洋之间相互作用的不完美描述。其中一个关键的相互作用是海冰对阳光的反射。在春天，海冰被雪覆盖，反射85%的阳光，保持表面凉爽。然而，进入夏季，冰融化，反射的阳光减少，使表面变暖，导致更多的融化。在模型中准确地表示这一过程至关重要。该项目将把联合收割机观测和气候模型结合起来，以更好地模拟控制阳光和海冰相互作用的过程及其对海冰融化的影响。这一模拟工作将导致在气候模型中精确地表示海冰，这将改善对海冰损失和全球变暖的预测。（2019年10月至2020年9月）通过分析相关的，以过程为导向的观测，以提高气候模型的预测能力。它将侧重于建立模型参数化，更准确地代表冰-水反馈过程的变化。这些方法包括空间和时间协调的现场观测，单柱模拟和全球气候模拟的集成。我们将综合实地观测，以产生一个完整的强迫/测试数据集和单柱建模，以开发参数化，改善雪和融化池的空间分布，海冰的透光率，以及对夏季降雪和雨等事件的响应。参数化将被纳入一个开源社区气候模型，并用于评估海冰可预测性的改善。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Sea ice heat and mass balance measurements from four autonomous buoys during the MOSAiC drift campaign

MOSAiC 漂流活动期间四个自主浮标的海冰热量和质量平衡测量

• DOI: 10.1525/elementa.2023.00017
• 发表时间: 2023
• 期刊: Elem Sci Anth
• 作者: Perovich, Don;Raphael, Ian;Moore, Ryleigh;Clemens-Sewall, David;Lei, Ruibo;Sledd, Anne;Polashenski, Chris
• 通讯作者: Polashenski, Chris