Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer‐EOS ...(AMSR‐E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA‐Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85‐GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18‐, 23‐, and 37‐GHz AMSR‐E data using weather filters. During two ship campaigns, the correlation of ASI, NASA‐Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR‐E period (2002–2006) between ASI and NASA‐Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.
We examine the basinwide trends in sea ice circulation and drift speed and highlight the changes between 1982 and 2009 in connection to regional winds, multiyear sea ice coverage, ice export, and the ...thinning of the ice cover. The polarity of the Arctic Oscillation (AO) is used as a backdrop for summarizing the variance and shifts in decadal drift patterns. The 28‐year circulation fields show a net strengthening of the Beaufort Gyre and the Transpolar Drift, especially during the last decade. The imprint of the arctic dipole anomaly on the mean summer circulation is evident (2001–2009) and enhances summer ice area export at the Fram Strait. Between 2001 and 2009, the large spatially averaged trends in drift speeds (winter: +23.6%/decade, summer: +17.7%/decade) are not explained by the much smaller trends in wind speeds (winter: 1.46%/decade, summer: −3.42%/decade). Notably, positive trends in drift speed are found in regions with reduced multiyear sea ice coverage. Over 90% of the Arctic Ocean has positive trends in drift speed and negative trends in multiyear sea ice coverage. The increased responsiveness of ice drift to geostrophic wind is consistent with a thinner and weaker seasonal ice cover and suggests large‐scale changes in the air‐ice‐ocean momentum balance. The retrieved mean ocean current field from decadal‐scale average ice motion captures a steady drift from Siberia to the Fram Strait, an inflow north of the Bering Strait, and a westward drift along coastal Alaska. This mean current is comparable to geostrophic currents from satellite‐derived dynamic topography.
Key Points
Changes in connection to winds, multiyear ice , export and thinning.
Strengthening of the Beaufort Gyre and the Transpolar Drift.
Positive trends in drift speed are associated with decline in multiyear sea ice.
Fram Strait is the main gateway for sea ice export out of the Arctic Ocean, and therefore observations there give insight into the composition and properties of Arctic sea ice in general and how it ...varies over time. A data set of ground-based and airborne electromagnetic ice thickness measurements collected during summer between 2001 and 2012 is presented here, including long transects well into the southern part of the Transpolar Drift obtained using fixed-wing aircrafts. The primary source of the surveyed sea ice leaving Fram Strait is the Laptev Sea and its age has decreased from 3 to 2 years between 1990 and 2012. The thickness data consistently also show a general thinning of sea ice for the last decade, with a decrease in modal thickness of second year and multiyear ice, and a decrease in mean thickness and fraction of ice thicker than 3 m. Local melting in the strait was investigated in two surveys performed in the downstream direction, showing a decrease in sea ice thickness of 0.19 m degree−1 latitude south of 81° N. Further north variability in ice thickness is more related to differences in age and deformation. The thickness observations were combined with ice area export estimates to calculate summer volume fluxes of sea ice. While satellite data show that monthly ice area export had positive trends since 1980 (10.9 × 103 km2 decade−1), the summer (July and August) ice area export is low with high uncertainties. The average volume export amounts to 16.78 km3. Naturally, the volume flux estimates are limited to the period when airborne thickness surveys are available. Nevertheless, we could show that the combination of satellite data and airborne observations can be used to determine volume fluxes through Fram Strait and as such, can be used to bridge the lack of satellite-based sea ice thickness information in summer.
Fram Strait is the passageway where most drifting sea ice exits the Arctic Ocean into the North Atlantic, and the sea‐ice velocity (SIV) is the most critical parameter for the variability of the sea ...ice area flux through Fram Strait. Sea ice flux estimates through the Fram Strait based on satellite remote sensing exist. However, they show discrepancies, which mainly result from the sparse valid satellite observations and the increased uncertainty of sea ice motion in the Fram Strait. In order to improve the sea ice flux estimates, we develop an improved sea‐ice velocity retrieval algorithm from 89 GHz brightness temperature fields of Advanced Microwave Scanning Radiometer for EOS and AMSR2. The improved retrieval algorithm employs an adjusted size of the template window and performs bilinear interpolation on candidate windows before the matching procedure. Instead of depending on external data sources, we construct the SIV uncertainty fields depending only on the maximum correlation coefficients of the image itself. Compared with two similar Passive Microwave (PM) sea ice velocity datasets, the new sea‐ice velocity has lower bias and root‐mean‐square error with respect to buoy observations. In addition, the data density of the new SIV is 14% and 36% higher than the two reference datasets, respectively. Monthly sea ice area flux estimates through the Fram Strait are acquired with the new daily sea‐ice velocity data and the existed sea‐ice concentration. And it indicates that the improved sea ice area flux estimated by PM sea ice velocity agrees well with the Synthetic‐aperture radar‐derived sea ice area flux.
Plain Language Summary
The sea ice outflux through the Fram Strait is critical for the mass balance of Arctic sea ice cover. However, previous studies tend to underestimate ice flux based on satellite data sets due to the weaker southward ice velocity with respect to buoy measurements. In order to acquire the accurate ice velocity in the Fram Strait to improve the ice flux estimates, we improve a pattern‐tracking sea ice retrieval algorithm derived from 89 GHz brightness temperature in the Fram Strait. Besides, we acquire the uncertainties with spatiotemporal variability of ice velocity based on the pattern features rather than any external data sources. This improved sea ice velocity data set will be helpful to future studies on data assimilation and model improvements.
Key Points
An optimal sea ice motion tracking method based on passive microwave image in the Fram Strait has been investigated
Uncertainty of sea ice velocity has been estimated based on the maximum correlation coefficient of template
Improved sea ice area flux through Fram Strait during winter has been acquired based on improved sea ice velocity data sets
Within a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic‐wide scale is based on passive microwave ...satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N‐ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower‐frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher‐frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong.
Key Points
The uncertainty of snow depth retrievals from satellite microwave radiometers is estimated using field measurements and model simulations
Snow properties have the strongest contribution to the uncertainty of snow depth retrievals compared to the influence of atmosphere and ice
The snow depth retrieval based on 19 and 7 GHz has a lower uncertainty compared to the 37 and 19 GHz one
The role of sea ice in the Earth climate system is still under debate, although it is known to influence albedo, ocean circulation, and atmosphere–ocean heat and gas exchange. Here we present a ...reconstruction of 1950 to 1998 AD sea ice in the Laptev Sea based on the Akademii Nauk ice core (Severnaya Zemlya, Russian Arctic). The chemistry of halogens bromine (Br) and iodine (I) is strongly active and influenced by sea ice dynamics, in terms of physical, chemical and biological process. Bromine reacts on the sea ice surface in autocatalyzing "bromine explosion" events, causing an enrichment of the Br / Na ratio and hence a bromine excess (Brexc) in snow compared to that in seawater. Iodine is suggested to be emitted from algal communities growing under sea ice. The results suggest a connection between Brexc and spring sea ice area, as well as a connection between iodine concentration and summer sea ice area. The correlation coefficients obtained between Brexc and spring sea ice (r = 0.44) as well as between iodine and summer sea ice (r = 0.50) for the Laptev Sea suggest that these two halogens could become good candidates for extended reconstructions of past sea ice changes in the Arctic.
Physical and biogeochemical processes in the Southern Ocean are fundamental for modulating global climate. In this context, a process-based understanding of how Antarctic diatoms control primary ...production and carbon export, and hence global-ocean carbon sequestration, has been identified as a scientific priority. Here we use novel sediment trap observations in combination with a data-assimilative ocean biogeochemistry model (ECCO-Darwin) to understand how environmental conditions trigger diatom ecology in the iron-fertilized southern Scotia Sea. We unravel the role of diatoms assemblage in controlling the biogeochemistry of sinking material escaping from the euphotic zone, and discuss the link between changes in upper-ocean environmental conditions and the composition of settling material exported from the surface to 1,000 m depth from March 2012 to January 2013. The combined analysis of
in situ
observations and model simulation suggests that an anomalous sea-ice episode in early summer 2012–2013 favored (
via
restratification due to sea-ice melt) an early massive bloom of
Corethron pennatum
that rapidly sank to depth. This event drove high biogenic silicon to organic carbon export ratios, while modulating the carbon and nitrogen isotopic signals of sinking organic matter reaching the deep ocean. Our findings highlight the role of diatom ecology in modulating silicon vs. carbon sequestration efficiency, a critical factor for determining the stoichiometric relationship of limiting nutrients in the Southern Ocean.
ABSTRACT
The ice cover of the Arctic peripheral seas bordering the Northern North Atlantic is examined for 1992–2008 using the ARTIST Sea Ice (ASI) algorithm applied to derive the sea ice ...concentration from 85 GHz SSM/I measurements. Our analysis reveals a 2 months longer ice‐free season in the Irminger Sea (IS), and reductions in ice area and extent between 1992–1999 and 2000–2008 by 10–20% during winter and 30–55% in summer. Barents Sea (BS) ice‐cover anomalies (ICA) persist twice as long as ICA in the other regions. Early winter ICA in region IS are correlated to late summer/fall Greenland Sea (GS) ICA. Summertime GS and wintertime IS ICA are correlated to winter Fram Strait ice‐area flux anomalies. The wintertime GS ice‐cover decrease is associated with less Is Odden events. Our analysis suggests a large‐scale, interregional ocean–ice–atmosphere feedback mechanism involving regions BS, Kara (KS) and White/Pechora Sea (WPS). To understand this mechanism the current and preceding general atmospheric circulation, associated variations in Arctic Ocean ice export and oceanic heat advection are needed. However, our results suggest (1) BS ICA could play a key role to predict subsequent KS ICA and (2) anomalous Arctic Ocean ice export into BS could trigger long‐lasting BS ICA.
Enhanced-resolution QuikSCAT/SeaWinds (QS er ) data recently entered the daily ice chart operation of the national ice services. Algorithms have been developed to extract four important sea ice ...parameters from this data over the whole Arctic: sea ice edge, type, concentration, and drift. This paper will summarize the different algorithms with a more detailed presentation of the sea ice concentration (IC) algorithm that has not been previously published. The sea ice edge can be detected to IC as low as 10%. Sea ice types can be roughly separated by a single threshold of 12 dB in the horizontal polarization. The IC algorithm gives reasonable qualitative results, separating into three classes: high, medium, and low ICs. It resolves even some characteristic ice features in the marginal ice zone and dynamic areas like the Fram Strait. However, it is very empirical and quantitatively not reliable. Sea ice drift can be determined with an accuracy of about 2.6 cm/s for a 48-h drift. Operating since 1999, QS is an important global data set for climate research, and two crucial applications how these sea ice products can be used for climate research are presented: the seasonal evolution of the sea ice cover and the export of sea ice volume through Fram Strait.
In this study, we compare colocated near‐coincident X‐, C‐, and L‐band fully polarimetry SAR satellite images with helicopter‐borne ice thickness measurements acquired during the Norwegian Young sea ...ICE 2015 (N‐ICE2015) expedition in the region of the Arctic Ocean north of Svalbard in April 2015. The air‐borne surveys provide near‐coincident snow plus ice thickness, surface roughness data, and photographs. This unique data set allows us to investigate how the different frequencies can complement one another for sea ice studies, but also to raise awareness of limitations. X‐band and L‐band satellite scenes were shown to be a useful complement to the standard SAR frequency for sea ice monitoring (C‐band) for lead ice and newly formed sea ice identification. This may be in part be due to the frequency but also the high spatial resolution of these sensors. We found a relatively low correlation between snow plus ice thickness and surface roughness. Therefore, in our dataset ice thickness cannot directly be observed by SAR which has important implications for operational ice charting based on automatic segmentation.
Key Points
Three different synthetic aperture radar (SAR) frequency bands are compared regarding their response to different sea‐ice characteristics
Newly formed sea ice generate an above average response in L‐band and below average response in C‐band and X‐band copolarization ratio
Surface roughness and snow pack structure can in some cases be a stronger influence on sea ice classification than sea‐ice thickness itself
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