scholarly journals Assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems: Arctic Cap Nowcast/Forecast System (ACNFS) and the Global Ocean Forecast System (GOFS 3.1)

2015 ◽  
Vol 9 (2) ◽  
pp. 2339-2365 ◽  
Author(s):  
P. G. Posey ◽  
E. J. Metzger ◽  
A. J. Wallcraft ◽  
D. A. Hebert ◽  
R. A. Allard ◽  
...  
Keyword(s):  
Sea Ice ◽  
Horizontal Resolution ◽  
Global Ocean ◽  
Forecast System ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Us ◽  
Ice Concentration ◽  
Edge Location ◽  
Ice Edge

Abstract. This study presents the improvement in the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements to both of the Navy's sea ice forecasting systems.

scholarly journals Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

2015 ◽  
Vol 9 (4) ◽  
pp. 1735-1745 ◽  
Author(s):  
P. G. Posey ◽  
E. J. Metzger ◽  
A. J. Wallcraft ◽  
D. A. Hebert ◽  
R. A. Allard ◽  
...  
Keyword(s):  
Sea Ice ◽  
Horizontal Resolution ◽  
Forecast System ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Us ◽  
Ice Concentration ◽  
Edge Location ◽  
High Horizontal Resolution ◽  
Ice Edge

Abstract. This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.

Antarctic sea ice in global ocean reanalyses

2021 ◽  
Author(s):  
Julia Selivanova ◽  
Doroteaciro Iovino
Keyword(s):  
Sea Ice ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Global Ocean ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Ocean Reanalyses ◽  
Ice Concentration ◽  
Ensemble Mean

<p>Ocean reanalyses (ORAs) are used extensively in polar research, hence their realism should be assessed regularly. Here the ORAs performance in the Antarctic region is analyzed with specific emphasis on sea ice concentration and thickness. We used four global ocean-sea ice products: C-GLORSv7, FOAM-GLOSEA5v13, GLORYS2v4, and ORAS5, and their ensemble mean GREP (provided by CMEMS) within the 1993 to 2018 period. All ORAs use the NEMO ocean model in a global eddy-permitting configuration (1/4° horizontal resolution and 75 vertical levels) and are forced by the ECMWF ERA-Interim atmospheric reanalysis.</p><p>Here we examine the ability of ORAs to reproduce sea ice properties in the Southern Ocean taking into account regional characteristics and sea ice types. Seasonal and interannual variability of sea ice concentration (SIC) and sea ice thickness (SIT) is examined in the hemispheric domain and in five sub-regions for three different sea ice classes: pack ice (SIC ≥ 80%), marginal ice zone (MIZ) (15% ≤ SIC < 80%), and sparse ice (0 < SIC <15%).  Modeled sea ice properties are compared to a set of satellite products: NSIDC CDR, Ifremer/CERSAT, and EUMETSAT OSI-SAF for SIC and Envisat and CryoSat-2 for SIT, together with PIOMAS and GIOMAS reanalyses. We revealed shortcomings of reanalysis systems to be improved in the future representation of Antarctic sea ice. Additionally, we focused on the assessment of the GREP ensemble mean product. We found that for certain metrics GREP minimizes the single errors and outperforms individual members. The evidence from this study implies that GREP can be a feasible product for a number of applications.</p>

scholarly journals Modeling the effect of Ross Ice Shelf melting on the Southern Ocean in quasi-equilibrium

2018 ◽  
Vol 12 (9) ◽  
pp. 3033-3044 ◽  
Author(s):  
Xiying Liu
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ross Sea ◽  
Global Ocean ◽  
Freshwater Flux ◽  
Quasi Equilibrium ◽  
Ice Shelf ◽  
Sea Ice Concentration ◽  
Ross Ice Shelf ◽  
Ice Concentration

Abstract. To study the influence of basal melting of the Ross Ice Shelf (BMRIS) on the Southern Ocean (ocean southward of 35∘ S) in quasi-equilibrium, numerical experiments with and without the BMRIS effect were performed using a global ocean–sea ice–ice shelf coupled model. In both experiments, the model started from a state of quasi-equilibrium ocean and was integrated for 500 years forced by CORE (Coordinated Ocean-ice Reference Experiment) normal-year atmospheric fields. The simulation results of the last 100 years were analyzed. The melt rate averaged over the entire Ross Ice Shelf is 0.25 m a−1, which is associated with a freshwater flux of 3.15 mSv (1 mSv = 103 m3 s−1). The extra freshwater flux decreases the salinity in the region from 1500 m depth to the sea floor in the southern Pacific and Indian oceans, with a maximum difference of nearly 0.005 PSU in the Pacific Ocean. Conversely, the effect of concurrent heat flux is mainly confined to the middle depth layer (approximately 1500 to 3000 m). The decreased density due to the BMRIS effect, together with the influence of ocean topography, creates local differences in circulation in the Ross Sea and nearby waters. Through advection by the Antarctic Circumpolar Current, the flux difference from BMRIS gives rise to an increase of sea ice thickness and sea ice concentration in the Ross Sea adjacent to the coast and ocean water to the east. Warm advection and accumulation of warm water associated with differences in local circulation decrease sea ice concentration on the margins of sea ice cover adjacent to open water in the Ross Sea in September. The decreased water density weakens the subpolar cell as well as the lower cell in the global residual meridional overturning circulation (MOC). Moreover, we observe accompanying reduced southward meridional heat transport at most latitudes of the Southern Ocean.

scholarly journals Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea Ice Concentration Climate Data Records

2018 ◽  
Author(s):  
Thomas Lavergne ◽  
Atle Macdonald Sørensen ◽  
Stefan Kern ◽  
Rasmus Tonboe ◽  
Dirk Notz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Grid Point ◽  
Ice Cover ◽  
Quantitative Information ◽  
Horizontal Resolution ◽  
Climate Data ◽  
Time Step ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 GHz or 19 GHz), in their horizontal resolution (25 km or 50 km) and in the time period they cover. We introduce the underlying algorithms and provide an initial evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.

scholarly journals Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Data ◽  
General Circulation ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Uncertainty Estimates ◽  
Ice Concentration ◽  
Data Products

Abstract. Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

scholarly journals Better constraints on the sea-ice state using global sea-ice data assimilation

2012 ◽  
Vol 5 (2) ◽  
pp. 1627-1667 ◽  
Author(s):  
P. Mathiot ◽  
C. König Beatty ◽  
T. Fichefet ◽  
H. Goosse ◽  
F. Massonnet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ice Thickness ◽  
Initial State ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

scholarly journals Comparison of sea-ice extent and ice-edge location estimates from passive microwave and enhanced-resolution scatterometer data

2008 ◽  
Vol 48 ◽  
pp. 65-70 ◽  
Author(s):  
Walter N. Meier ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Resolution Enhancement ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Local Conditions ◽  
Enhanced Resolution ◽  
Edge Location ◽  
Ice Conditions ◽  
Ice Edge

AbstractPassive microwave sea-ice concentration fields provide some of the longest-running and most consistent records of changes in sea ice. Scatterometry-based sea-ice fields are more recently developed data products, but now they provide a record of ice conditions spanning several years. Resolution enhancement techniques applied to scatterometer fields provide much higher effective resolutions (~10 km) than are available from standard scatterometer and passive microwave fields (25–50 km). Here we examine ice-extent fields from both sources and find that there is general agreement between scatterometer data and passive microwave fields, though scatterometer estimates yield substantially lower ice extents during winter. Comparisons with ice-edge locations estimated from AVHRR imagery indicate that enhanced scatterometer data can sometimes provide an improved edge location, but there is substantial variation in the results, depending on the local conditions. A blended product, using both scatterometer and passive microwave data, could yield improved results.

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