气候变化领域集成服务门户(CCPortal): Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness

CCPortal

DOI	10.5194/tc-12-3419-2018
	Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness
	Blockley E.W.; Andrew Peterson K.
发表日期	2018
ISSN	19940416
卷号	12 期号:11
英文摘要	Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere-ocean-sea-ice models. Although assimilation of sea-ice concentration into these systems is commonplace, sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialization on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season. © Author(s) 2018.
学科领域	air temperature; CryoSat; data assimilation; human activity; ice cover; ice thickness; prediction; sea ice; seasonality; snowmelt; Arctic
语种	英语
scopus关键词	air temperature; CryoSat; data assimilation; human activity; ice cover; ice thickness; prediction; sea ice; seasonality; snowmelt; Arctic
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119041
作者单位	Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
推荐引用方式 GB/T 7714	Blockley E.W.,Andrew Peterson K.. Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness[J],2018,12(11).
APA	Blockley E.W.,&Andrew Peterson K..(2018).Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness.Cryosphere,12(11).
MLA	Blockley E.W.,et al."Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness".Cryosphere 12.11(2018).