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: 2138786
• 负责人: Melinda Webster
• 金额: $ 30.48万
• 依托单位: University of Alaska Fairbanks Campus
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138786&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.

改进北极气候预测对社会至关重要。他们需要适应北极和地球其他地区当前和未来的变化并为其制定计划。气候模型显示，随着温室气体浓度的增加，气温变暖和北极海冰消失将继续下去。然而，这些模型显示变暖和海冰消失的速度存在很大差异。这些模型的差异很大程度上来自于大气、海冰和海洋之间相互作用的不完美表示。关键的相互作用之一是海冰反射阳光。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，反射的阳光量减少，地表变暖，导致更多的融化。在模型中准确地表示这一过程至关重要。该项目将结合观测和气候模型，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。这项建模工作将在气候模型中完善海冰的表示，从而改善对海冰消失和全球变暖的预测。该项目将利用北极气候研究多学科漂流观测站（MOSAiC）实地活动（2019年10月至2020年9月）的观测结果，通过分析相关的、面向过程的观测结果来改进模拟海冰物理和反馈的关键参数化，以增强 气候模型的预测能力。它将专注于构建模型参数化，以更准确地表示冰反照率反馈过程的可变性。这些方法包括空间和时间协调的现场观测、单柱建模和全球气候建模的整合。我们将综合现场观测结果，生成完整的强迫/测试数据集，并通过单列建模进行迭代，以开发参数化，以改进对雪和融水池空间分布、海冰透光率以及对夏季降雪和雨雪事件等事件的响应的处理。参数化将被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 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Relationships between summertime surface albedo and melt pond fraction in the central Arctic Ocean: The aggregate scale of albedo obtained on the MOSAiC floe

• DOI: 10.1525/elementa.2023.00001
• 发表时间: 2023
• 期刊: Elem Sci Anth
• 作者: R. Calmer;G. de Boer;Jonathan Hamilton;Dale A. Lawrence;M. Webster;N. Wright;M. Shupe;C. Cox;J. Cassano

Preconditioning of Summer Melt Ponds From Winter Sea Ice Surface Temperature

• DOI: 10.1029/2022gl101493
• 发表时间: 2023-02
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Linda Thielke;Niels Fuchs;G. Spreen;B. Tremblay;G. Birnbaum;M. Huntemann;N. Hutter;P. Itkin;Arttu Jutila;M. Webster

Temporal evolution of under-ice meltwater layers and false bottoms and their impact on summer Arctic sea ice mass balance

• DOI: 10.1525/elementa.2022.00035
• 发表时间: 2023
• 期刊: Elementa: Science of the Anthropocene
• 作者: E. Salganik;C. Katlein;B. Lange;I. Matero;R. Lei;A. Fong;S. Fons;D. Divine;M. Oggier;G. Castellani;Deborah Bozzato;E. J. Chamberlain;C. Hoppe;O. Müller;J. Gardner;A. Rinke;P. Pereira;Adam Ulfsbo;C. Marsay;M. Webster;S. Maus;K. Høyland;M. Granskog