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| Collaborative Research: Improving the Prediction of Sea Ice through Targeted Study of Poorly Parameterized Sea Ice Processes at MOSAiC and Responsive Model Development | |
| 项目编号 | 1724540 |
| Donald Perovich | |
| 项目主持机构 | Dartmouth College |
| 开始日期 | 2017-10-01 |
| 结束日期 | 09/30/2022 |
| 英文摘要 | Climate models project large uncertainty in the rate of future Arctic sea ice loss and its climate impacts. On multi-decadal timescales, this uncertainty is largely due to differences in the strength of feedback processes within the models. For the Arctic system, changes in surface reflection of heat and light (albedo) dominate the feedback process with the most significant departures in the surface heat balance. However, models employ relatively crude representations of many processes that are relevant to surface albedo evolution. To improve model predictions, we need enhanced parameterizations that are informed by the collection and analysis of relevant observations. Better Arctic predictions are particularly critical given the large changes underway and the urgent need to plan for and adapt to changes soon. This project will improve the representation of the sea ice cover and the surface reflection of heat and light (albedo feedback) in climate models through the integration of climate model experiments and field measurements. Sensitivity studies with the Community Earth System Model (CESM) will be used to assess the relative importance of albedo-related parameterization uncertainties. These studies will inform the snow, sea ice, and solar radiation observations conducted as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Measurements will then fuel process understanding and parameterization development within CESM. Experiments with the improved CESM will then be performed to determine the effects of upgraded albedo feedback characterization for Earth system predictions. The focus will be on partitioning and fate of solar energy in a seasonal ice system, the role of snow properties in controlling the sea ice mass balance and radiation budget, and processes affecting ice-ocean energy exchange and ice mass and area changes. These are areas for which process understanding is lacking, model parameterizations are crude, and uncertainties have large effects on model projections. By integrating modeling and observational work, the basic process understanding of controls on Arctic solar energy distribution and sea ice mass budgets will be enhanced. The incorporation of this knowledge in improved model process representation will strengthen prediction capabilities and allow for a deeper understanding of the role of the albedo feedback in projected Arctic change. Model developments will be incorporated into the widely-used Los Alamos Community Ice Model (CICE) and made freely available through standard releases of CESM and CICE. This will improve both climate model and operational sea ice forecasting systems with wide-ranging societal implications. Early career polar researchers will be heavily involved in this project, including opportunities for undergraduates, graduate students and post-doctoral scholars to be involved in both field and modeling activities. A year-long sea ice drift experiment also provides an opportunity to capture the imagination and excitement of students and the public. Educational outreach efforts will include developing a network of middle school science classes with national and international participants. Activities will include classroom visits, hands-on measurement activities, data analysis and hypothesis testing. Additional outreach will occur through interactions with local science museums and educational centers. |
| 资助机构 | US-NSF |
| 项目经费 | $868,142.00 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/212620 |
| 推荐引用方式 GB/T 7714 | Donald Perovich.Collaborative Research: Improving the Prediction of Sea Ice through Targeted Study of Poorly Parameterized Sea Ice Processes at MOSAiC and Responsive Model Development.2017. |
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