scholarly journals Employing Satellite-Derived Sea Ice Concentration to Constrain Upper-Ocean Temperature in a Global Ocean GCM

2008 ◽  
Vol 21 (17) ◽  
pp. 4498-4513 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Sea Ice ◽  
Deep Ocean ◽  
Global Ocean ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ocean Properties

Abstract The quality of Southern Ocean sea ice simulations in a global ocean general circulation model (GCM) depends decisively on the simulated upper-ocean temperature. This is confirmed by assimilating satellite-derived sea ice concentration to constrain the upper-layer temperature of a sea ice–ocean GCM. The resolution of the model’s sea ice component is about 22 km and thus comparable to the pixel resolution of the satellite data. The ocean component is coarse resolution to afford long-term integrations for investigations of the deep-ocean equilibrium response. Besides improving the sea ice simulation considerably, the simulations with constrained upper-ocean temperature yield much more realistic global deep-ocean properties, in particular when combined with glacial freshwater input. Both outcomes are relatively insensitive to the passive-microwave algorithm used to retrieve the ice concentration being assimilated. The sensitivity of the long-term global deep-ocean properties and circulation to the possible freshwater input from ice shelves and to the parameterization of vertical mixing in the Southern Ocean is reevaluated under the new constraint.

Drivers of interannual sea ice variability on the Arctic continental margin north of Svalbard

2020 ◽  
Author(s):  
Øyvind Lundesgaard ◽  
Arild Sundfjord ◽  
Angelika H. H. Renner
Keyword(s):  
Sea Ice ◽  
Continental Margin ◽  
Barents Sea ◽  
Atlantic Water ◽  
Reanalysis Data ◽  
The Arctic ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Sea Ice Concentration ◽  
Ice Concentration

<p>Sea ice concentration along the Arctic continental margin north of Svalbard is in decline, but superimposed on this trend is considerable interannual variability. Many factors impact sea ice in this region, including atmospheric cooling and heating, winds, sea ice advection, and oceanic heat transport associated with the inflow of Atlantic Water, and regional sea ice cover remains difficult to predict. We present observations of upper ocean temperature between 2012 and 2017 from an ocean mooring located on the continental shelf break north of the Barents Sea, together with concurrent time series of atmospheric variables and sea ice concentration, drift, and thickness, derived from satellite and reanalysis data. While the inflow of Atlantic Water undoubtedly plays a key role in maintaining the area north of Svalbard ice-free through much of the year, variations in upper ocean temperature do not explain major interannual sea ice anomalies during the study period. Instead, we find that the magnitude of sea ice advection from the north and east was a major driver of interannual sea ice variability during our study.</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 Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts

2014 ◽  
Vol 7 (6) ◽  
pp. 2613-2638 ◽  
Author(s):  
E. W. Blockley ◽  
M. J. Martin ◽  
A. J. McLaren ◽  
A. G. Ryan ◽  
J. Waters ◽  
...  
Keyword(s):  
Sea Ice ◽  
Forecast Accuracy ◽  
Deep Ocean ◽  
Ocean Model ◽  
Global Ocean ◽  
Surface Boundary ◽  
Observation Window ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Shelf Sea

Abstract. The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf sea regimes using the NEMO (Nucleus for European Modelling of the Ocean) ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7-day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4° resolution. Satellite and in situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved the implementation of a new variational, first guess at appropriate time (FGAT) 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of coordinated ocean-ice reference experiment (CORE) bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2-year reanalysis integrations of the Global FOAM configuration including an assessment of short-range ocean forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlights specific areas upon which to focus future improvements.

scholarly journals Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts

2013 ◽  
Vol 6 (4) ◽  
pp. 6219-6278 ◽  
Author(s):  
E. W. Blockley ◽  
M. J. Martin ◽  
A. J. McLaren ◽  
A. G. Ryan ◽  
J. Waters ◽  
...  
Keyword(s):  
Sea Ice ◽  
Forecast Accuracy ◽  
Deep Ocean ◽  
Ocean Model ◽  
Global Ocean ◽  
Surface Boundary ◽  
Observation Window ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
The North

Abstract. The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf seas regimes using the NEMO ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7 day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4° resolution and at 1/12° resolution in the North Atlantic, Indian Ocean and Mediterranean Sea. Satellite and in-situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved: the implementation of a new variational, first guess at appropriate time 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of CORE bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2 yr reanalysis integrations of the Global FOAM configuration including an assessment of forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlight specific areas upon which to focus future improvements.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 5 (2) ◽  
pp. 311-318 ◽  
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

scholarly journals Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

2020 ◽  
Vol 13 (10) ◽  
pp. 4773-4787
Author(s):  
Eduardo Moreno-Chamarro ◽  
Pablo Ortega ◽  
François Massonnet
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
The Arctic ◽  
Global Ocean ◽  
Ice Thickness ◽  
Model Data ◽  
Atmospheric Variability ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.

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.

Analysis on long-term variability of sea ice albedo and its relationship with sea ice concentration over Antarctica

2016 ◽  
Author(s):  
Minji Seo ◽  
Hyun-cheol Kim ◽  
Noh-hun Seong ◽  
Chaeyoung Kwon ◽  
Honghee Kim ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Concentration
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