| 项目编号: | 1724540
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| 项目名称: | Collaborative Research: Improving the Prediction of Sea Ice through Targeted Study of Poorly Parameterized Sea Ice Processes at MOSAiC and Responsive Model Development |
| 作者: | Donald Perovich
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| 承担单位: | Dartmouth College
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| 批准年: | 2017
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| 开始日期: | 2017-10-01
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| 结束日期: | 2021-09-30
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| 资助金额: | 868142
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Polar
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| 英文关键词: | climate model
; cice
; year-long sea ice drift experiment
; career polar researcher
; sea ice cover
; operational sea ice forecasting system
; sea ice
; ice mass
; seasonal ice system
; sea ice mass budget
; sea ice mass balance
; process understanding
; future arctic sea ice loss
; ice-ocean energy exchange
; study
; cesm
; model
; feedback process
; model process representation
; albedo feedback
; model projection
; widely-used los alamos community ice model
; many process
; community earth system model
; model development
; arctic prediction
; earth system prediction
; model parameterization
; modeling activity
; prediction capability
; parameterization development
; sensitivity study
; model prediction
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| 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/88704
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
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. 2017-01-01.
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