气候变化领域集成服务门户(CCPortal): Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of arctic sea ice

CCPortal

DOI	10.1175/JCLI-D-20-0469.1
	Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of arctic sea ice
	Zhang Y.-F.; Bushuk M.; Winton M.; Hurlin B.; Yang X.; Delworth T.; Jia L.
发表日期	2021
ISSN	08948755
起始页码	2107
结束页码	2126
卷号	34 期号:6
英文摘要	The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL's next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice-ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (;10%) from up to 3 times larger than this (;30%) in the freerunning model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months. © 2021 American Meteorological Society. All rights reserved.
英文关键词	Arctic; Data assimilation; Sea ice
语种	英语
scopus关键词	Atmospheric temperature; Errors; Forecasting; Sea ice; Submarine geophysics; Surface properties; Surface waters; Correlation analysis; Ensemble adjustment Kalman filter; Generation predictions; Observational errors; Optimum interpolation; Sea ice concentration; Sea surface temperature (SST); Sea surface temperatures; Oceanography; arctic environment; climate modeling; climate prediction; concentration (composition); data assimilation; Kalman filter; satellite data; satellite imagery; sea ice; sea surface temperature
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178678
作者单位	Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; University Corporation for Atmospheric Research, Boulder, CO, United States
推荐引用方式 GB/T 7714	Zhang Y.-F.,Bushuk M.,Winton M.,et al. Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of arctic sea ice[J],2021,34(6).
APA	Zhang Y.-F..,Bushuk M..,Winton M..,Hurlin B..,Yang X..,...&Jia L..(2021).Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of arctic sea ice.Journal of Climate,34(6).
MLA	Zhang Y.-F.,et al."Assimilation of satellite-retrieved sea ice concentration and prospects for september predictions of arctic sea ice".Journal of Climate 34.6(2021).