Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 – CORRIGENDUM

2020 ◽  
Vol 66 (260) ◽  
pp. 1079-1079
Author(s):  
Longjiang Mu ◽  
Xi Liang ◽  
Qinghua Yang ◽  
Jiping Liu ◽  
Fei Zheng
Keyword(s):  
Sea Ice ◽  
Prediction System ◽  
Ocean Prediction ◽  
Arctic Ice

Development and application of NMEFC Arctic Ice-Ocean Prediction System (ArcIOPS) of CHINA

2020 ◽  
Author(s):  
Xi Liang ◽  
Fu Zhao ◽  
Chunhua Li ◽  
Lin Zhang
Keyword(s):  
Sea Ice ◽  
Early Stage ◽  
The Arctic ◽  
Ice Thickness ◽  
Prediction System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Ocean Prediction ◽  
Arctic Ice

<p>NMEFC provides sea ice services for the CHINARE since 2010, the products in the early stage (before 2017) include satellite-retrieved and numerical forecasts of sea ice concentration. Based on MITgcm and ensemble Kalman Filter data assimilation scheme,  the Arctic Ice-Ocean Prediction System (ArcIOPS v1.0), was established in 2017. ArcIOPS v1.0 assimilates available satellite-retrieved sea ice concentration and thickness data. Sea ice thickness forecasting products from ArcIOPS v1.0 are provided to the CHINARE8, and are believed to have played an important role in the successful passage of R/V XUELONG through the Central Arctic for the first time during the summer of 2017. In 2019, ArcIOPS v1.0 was upgraded to the latest version (ArcIOPS v1.1), which assimilates satellite-retrieved sea ice concentration, sea ice thickness, as well as sea surface temperature (SST) data in ice free areas. Comparison between outputs of the latest version of ArcIOPS and that of its previous version shows that the latest version has a substantial improvement on sea ice concentration forecasts. In the future, with more and more kinds of observations to be assimilated, the high-resolution version of ArcIOPS will be put into operational running and benefit Chinese scientific and commercial activities in the Arctic Ocean.</p>

scholarly journals Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017

2019 ◽  
Vol 65 (253) ◽  
pp. 813-821 ◽  
Author(s):  
Longjiang Mu ◽  
Xi Liang ◽  
Qinghua Yang ◽  
Jiping Liu ◽  
Fei Zheng
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ocean Prediction ◽  
Arctic Ice

AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system.

A New Arctic Ice-Ocean Prediction System

1999 ◽  
Author(s):  
Albert Semtner ◽  
Wieslaw Maslowski ◽  
Yuxia Zhang
Keyword(s):  
Prediction System ◽  
Ocean Prediction ◽  
Arctic Ice

scholarly journals Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

2015 ◽  
Vol 142 (695) ◽  
pp. 659-671 ◽  
Author(s):  
Gregory C. Smith ◽  
François Roy ◽  
Mateusz Reszka ◽  
Dorina Surcel Colan ◽  
Zhongjie He ◽  
...  
Keyword(s):  
Sea Ice ◽  
Prediction System ◽  
Forecast Verification ◽  
Ocean Prediction

Assimilation of SMOS sea ice thickness in the regional ice prediction system

2021 ◽  
Vol 42 (12) ◽  
pp. 4583-4606
Author(s):  
Mukesh Gupta ◽  
Alain Caya ◽  
Mark Buehner
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Prediction System ◽  
Sea Ice Thickness

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

scholarly journals Temperature and sea ice hindcast skill of the MiKlip decadal prediction system in the Arctic

2018 ◽  
Author(s):  
Daniel Senftleben ◽  
Veronika Eyring ◽  
Axel Lauer ◽  
Mattia Righi
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Prediction System ◽  
Decadal Prediction

scholarly journals Correlation and trend studies of the sea-ice cover and surface temperatures in the Arctic

2002 ◽  
Vol 34 ◽  
pp. 420-428 ◽  
Author(s):  
Josefino C. Comiso
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Ice Cover ◽  
The Arctic ◽  
Temperature Anomalies ◽  
Ice Concentration ◽  
Arctic Ice ◽  
Highly Correlated ◽  
Surface Ice ◽  
Perennial Ice

AbstractCo-registered and continuous satellite data of sea-ice concentrations and surface ice temperatures from 1981 to 2000 are analyzed to evaluate relationships between these two critical climate parameters and what they reveal in tandem about the changing Arctic environment. During the 19 year period, the Arctic ice extent and actual ice area are shown to be declining at a rate of –2.0±0.3% dec –1 and 3.1 ±0.4% dec–1, respectively, while the surface ice temperature has been increasing at 0.4 ±0.2 K dec–1, where dec is decade. The extent and area of the perennial ice cover, estimated from summer minimum values, have been declining at a much faster rate of –6.7±2.4% dec–1 and –8.3±2.4% dec–1, respectively, while the surface ice temperature has been increasing at 0.9 ±0.6K dec–1. This unusual rate of decline is accompanied by a very variable summer ice cover in the 1990s compared to the 1980s, suggesting increases in the fraction of the relatively thin second-year, and hence a thinning in the perennial, ice cover during the last two decades. Yearly anomaly maps show that the ice-concentration anomalies are predominantly positive in the 1980s and negative in the 1990s, while surface temperature anomalies were mainly negative in the 1980s and positive in the 1990s. The yearly ice-concentration and surface temperature anomalies are highly correlated, indicating a strong link especially in the seasonal region and around the periphery of the perennial ice cover. The surface temperature anomalies also reveal the spatial scope of each warming (or cooling) phenomenon that usually extends beyond the boundaries of the sea-ice cover.

Sign in / Sign up

Export Citation Format

Share Document