Arctic sea ice concentration numerical forecasting and its evaluation

Li Ming; Yang Qinghua; Zhao Jiechen; Sun Xiaoyu; Tian Zhongxiang; Shen Hui; Hao Guanghua; Li Chunhua; Zhang Lin

doi:10.3969/j.issn.0253-4193.2018.11.005

Article Contents

Article Navigation > Haiyang Xuebao > 2018 > 40(11): 46-53

Li Ming, Yang Qinghua, Zhao Jiechen, Sun Xiaoyu, Tian Zhongxiang, Shen Hui, Hao Guanghua, Li Chunhua, Zhang Lin. Arctic sea ice concentration numerical forecasting and its evaluation[J]. Haiyang Xuebao, 2018, 40(11): 46-53. doi: 10.3969/j.issn.0253-4193.2018.11.005

Citation:

Li Ming, Yang Qinghua, Zhao Jiechen, Sun Xiaoyu, Tian Zhongxiang, Shen Hui, Hao Guanghua, Li Chunhua, Zhang Lin. Arctic sea ice concentration numerical forecasting and its evaluation[J]. Haiyang Xuebao, 2018, 40(11): 46-53. doi: 10.3969/j.issn.0253-4193.2018.11.005

Citation:

Li Ming, Yang Qinghua, Zhao Jiechen, Sun Xiaoyu, Tian Zhongxiang, Shen Hui, Hao Guanghua, Li Chunhua, Zhang Lin. Arctic sea ice concentration numerical forecasting and its evaluation[J]. Haiyang Xuebao, 2018, 40(11): 46-53. doi: 10.3969/j.issn.0253-4193.2018.11.005

PDF( 11504 KB)

Arctic sea ice concentration numerical forecasting and its evaluation

doi: 10.3969/j.issn.0253-4193.2018.11.005

1.
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China;National Marine Environmental Forecasting Center, Beijing 100081, China
2.
Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmosphere Sciences, Sun Yat-sen University, Zhuhai 519082, China
3.
National Marine Environmental Forecasting Center, Beijing 100081, China

Received Date: 2018-01-19
Rev Recd Date: 2018-05-17

Abstract

Abstract

In this study, we evaluated the 24-120 h Arctic sea ice concentration forecasts provided by National Marine Environmental Forecasting Center during the 7th Chinese National Arctic Research Expedition (CHINARE 2016). The Arctic sea ice forecast system was based on the MIT general circulation model (MITgcm) ice-ocean coupled model with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data Nudged. We compared the numerical forecast products with the satellite data, reanalysis data and ship-based in situ sea ice concentration observations during CHINARE 2016. It was shown that the Arctic sea ice concentration forecasts were smaller than the satellite data. The mean biases between 24 h, 72 h, 120 h forecasts and satellite data were -2.7%, -3.1% and -3.2%. The numerical sea ice concentration forecasts were better than the climatological means and the inertial forecasts. But the forecast skill was required to improve when the Arctic sea ice had surged rapid melting or freezing. Moreover, the forecast biases were larger compared with ship in situ observations in the marginal ice zone. The mean biases between 24 h, 72 h, 120 h forecasts and ship in situ data were 8.8%, 12.0% and 14.5%.
- sea ice concentration,
- forecast,
- Arctic,
- evaluation

FullText(HTML)

References(21)

References

IPCC. Climate change 2014:Impacts, adaptation, and vulnerability. Part B:Regional aspects[M]. Cambridge:Cambridge University Press, 2014.

Comiso J C, Parkinson C L, Gersten R, et al. Accelerated decline in the Arctic sea ice cover[J]. Geophysical Research Letters, 2008, 35:L01703.

Parkinson C L, Cavalieri D J, Gloersen P, et al. Arctic sea ice extents, areas, and trends, 1978-1996[J]. Journal of Geophysical Research:Oceans, 1999, 104(C9):20837-20856.

Rothrock D A, Yu Y, Maykut G A. Thinning of the Arctic sea-ice cover[J]. Geophysical Research Letters, 1999, 26(23):3469-3472.

Lindsay R, Schweiger A. Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations[J]. The Cryosphere, 2015, 9(1):269-283.

Posey P G, Metzger E J, Wallcraft A J, et al. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems[J]. The Cryosphere, 2015, 9(4):1735-1745.

Smith G C, Roy F, Reszka M, et al. Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System[J]. Quarterly Journal of the Royal Meteorological Society, 2016, 142(695):659-671.

Sakov P, Counillon F, Bertino L, et al. TOPAZ4:an ocean-sea ice data assimilation system for the North Atlantic and Arctic[J]. Ocean Science, 2012, 8(4):633-656.

杨清华, 刘骥平, 张占海, 等. 北极海冰数值预报的初步研究——基于海冰-海洋耦合模式MITgcm的模拟试验[J]. 大气科学, 2011, 35(3):473-482. Yang Qinghua, Liu Jiping, Zhang Zhanhai, et al. A preliminary study of the Arctic sea ice numerical forecasting:coupled sea ice-ocean modelling experiments based on MITgcm[J]. Chinese Journal of Atmospheric Sciences, 2011, 35(3):473-482.

杨清华, 李春花, 邢建勇, 等. 2010年夏季北极海冰数值预报试验[J]. 极地研究, 2012, 24(1):87-94. Yang Qinghua, Li Chunhua, Xing Jianyong, et al. Arctic sea ice forecasting experiments in the summer of 2010[J]. Chinese Journal of Polar Research, 2012, 24(1):87-94.

Yang Qinghua, Liu Jiping, Zhang Zhanhai, et al. Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing:a case study[J]. Acta Oceanologica Sinica, 2014, 33(12):15-23.

赵杰臣, 杨清华, 李明, 等. Nudging资料同化对北极海冰密集度预报的改进[J]. 海洋学报, 2016, 38(5):70-82. Zhao Jiechen, Yang Qinghua, Li Ming, et al. Improving Arctic sea ice concentration forecasts with a Nudging data assimilation method[J]. Haiyang Xuebao, 2016, 38(5):70-82.

Zhang J L, Hibler W D Ⅲ. On an efficient numerical method for modeling sea ice dynamics[J]. Journal of Geophysical Research:Oceans, 1997, 102(C4):8691-8702.

Zhang J, Hibler W D Ⅲ, Steele M, et al. Arctic ice-ocean modeling with and without climate restoring[J]. Journal of Physical Oceanography, 1998, 28(2):191-217.

Spreen G, Kaleschke L, Heygster G. Sea ice remote sensing using AMSR-E 89-GHz channels[J]. Journal of Geophysical Research:Oceans, 2008, 113(C2):C02S03.

Dee D P, Uppala S M, Simmons A J, et al. The ERA-Interim reanalysis:configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656):553-597.

Worby A, Allison I, Dirita V. Technique for making ship-based observations of Antarctic sea ice thickness and characteristics[R]. Research Report No. 14. Hobart:Australia Antarctic Division, 1999.

Hebert D A, Allard R A, Metzger E J, et al. Short-term sea ice forecasting:an assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System[J]. Journal of Geophysical Research:Oceans, 2015, 120(12):8327-8345.

van Woert M L, Zou Chengzhi, Meier W N, et al. Forecast verification of the Polar Ice Prediction System (PIPS) sea ice concentration fields[J]. Journal of Atmospheric and Oceanic Technology, 2004, 21(6):944-957.

Yang Qinghua, Losa S N, Losch M, et al. Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter[J]. Journal of Geophysical Research:Oceans, 2014, 119(10):6680-6692.

Yang Qinghua, Losa S N, Losch M, et al. The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction[J]. Quarterly Journal of the Royal Meteorological Society, 2015, 141(691):2314-2323.

Relative Articles

Supplements(0)

Cited By

Proportional views