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。在北极和地球其他地方，都需要适应并计划当前和未来的变化。气候模型表明，随着温室气体浓度的增加，温暖的温度和北极海冰损失将继续。但是，这些模型在变暖和海冰损失的速度上显示出很大的差异。这些模型差异很大程度上来自大气，海冰和海洋之间相互作用的不完美表示。关键互动之一是海冰对阳光的反射。在春季，海冰被雪覆盖，反映了85％的阳光保持表面凉爽。然而，进入夏季，冰融化，阳光的量反映在减少，从而使表面变暖，从而导致更融化。在模型中准确表示此过程至关重要。该项目将结合观察结果和气候模型，以更好地模拟控制阳光和海冰相互作用及其对海冰融化的影响的过程。这种建模工作将导致气候模型中海冰的精致代表，这将改善对海冰损失和全球变暖的预测。本项目将使用多学科漂流观测站的观测来进行北极气候（Mosaic）现场运动的研究（2019年10月至2020年9月2020年），以改善对冰冰的关键参数，以改善冰冰的关键参数，并进行分析，并分析递送返还，馈送返还，供您进行进来，进料往来，进料往来，进料往来，供您进行馈送。气候模型的预测技能。它将专注于建立模型参数化，以更准确地代表冰山反馈过程的可变性。这些方法包括在空间和时间协调的现场观测，单列建模和全球气候建模的集成。我们将合成现场观测值，以产生完整的强迫/测试数据集，并通过单列建模进行迭代，以开发参数化，以改善雪和熔体池塘的空间分布的处理，通过海冰的光传播，以及对夏季降雪和雨水降雨的事件的响应。参数化将纳入开源社区气候模型中，并用于评估海冰可预测性的改进。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

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