scholarly journals Seasonal sea ice prediction based on regional indices

2018 ◽  
Author(s):  
John E. Walsh ◽  
J. Scott Stewart ◽  
Florence Fetterer
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Numerical Models ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Severity Index ◽  
The Arctic ◽  
Linear Quadratic ◽  
Predictive Skill ◽  
Arctic Ice

Abstract. Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. However, the strong negative trend of sea ice coverage in recent decades complicates the evaluation of statistical skill by inflating the correlation of interannual variations of pan-Arctic and regional ice extent. In this study we provide a quantitative evaluation of the contribution of the trend to the predictive skill of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea where the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the trend during the 1990s in most of the Arctic subregions. The Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical predictability out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea’s ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

scholarly journals Benchmark seasonal prediction skill estimates based on regional indices

2019 ◽  
Vol 13 (4) ◽  
pp. 1073-1088 ◽  
Author(s):  
John E. Walsh ◽  
J. Scott Stewart ◽  
Florence Fetterer
Keyword(s):  
Chukchi Sea ◽  
Numerical Models ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Seasonal Prediction ◽  
Severity Index ◽  
Linear Quadratic ◽  
East Siberian Sea ◽  
Arctic Ice ◽  
Regional Forecasts

Abstract. Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistence-based statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime “predictability barrier” in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead time of a month or two.

scholarly journals Exploring the utility of quantitative network design in evaluating Arctic sea-ice thickness sampling strategies

2015 ◽  
Vol 9 (2) ◽  
pp. 1735-1768 ◽  
Author(s):  
T. Kaminski ◽  
F. Kauker ◽  
H. Eicken ◽  
M. Karcher
Keyword(s):  
Sea Ice ◽  
Network Design ◽  
Chukchi Sea ◽  
Software Tool ◽  
Severity Index ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Target Area ◽  
Sea Ice Thickness ◽  
Arctic Sea

Abstract. We present a quantitative network design (QND) study of the Arctic sea ice-ocean system using a software tool that can evaluate hypothetical observational networks in a variational data assimilation system. For a demonstration, we evaluate two idealised flight transects derived from NASA's Operation IceBridge airborne ice surveys in terms of their potential to improve ten-day to five-month sea-ice forecasts. As target regions for the forecasts we select the Chukchi Sea, an area particularly relevant for maritime traffic and offshore resource exploration, as well as two areas related to the Barnett Ice Severity Index (BSI), a standard measure of shipping conditions along the Alaskan coast that is routinely issued by ice services. Our analysis quantifies the benefits of sampling upstream of the target area and of reducing the sampling uncertainty. We demonstrate how observations of sea-ice and snow thickness can constrain ice and snow variables in a target region and quantify the complementarity of combining two flight transects. We further quantify the benefit of improved atmospheric forecasts and a well-calibrated model.

scholarly journals Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies

2015 ◽  
Vol 9 (4) ◽  
pp. 1721-1733 ◽  
Author(s):  
T. Kaminski ◽  
F. Kauker ◽  
H. Eicken ◽  
M. Karcher
Keyword(s):  
Sea Ice ◽  
Network Design ◽  
Chukchi Sea ◽  
Software Tool ◽  
Severity Index ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Target Area ◽  
Sea Ice Thickness ◽  
Arctic Sea

Abstract. We present a quantitative network design (QND) study of the Arctic sea ice–ocean system using a software tool that can evaluate hypothetical observational networks in a variational data assimilation system. For a demonstration, we evaluate two idealised flight transects derived from NASA's Operation IceBridge airborne ice surveys in terms of their potential to improve 10-day to 5-month sea ice forecasts. As target regions for the forecasts we select the Chukchi Sea, an area particularly relevant for maritime traffic and offshore resource exploration, as well as two areas related to the Barnett ice severity index (BSI), a standard measure of shipping conditions along the Alaskan coast that is routinely issued by ice services. Our analysis quantifies the benefits of sampling upstream of the target area and of reducing the sampling uncertainty. We demonstrate how observations of sea ice and snow thickness can constrain ice and snow variables in a target region and quantify the complementarity of combining two flight transects. We further quantify the benefit of improved atmospheric forecasts and a well-calibrated model.

Assessment of relationships between Arctic sea ice and stratification of water column modelled by NEMO version 4.0

2020 ◽  
Author(s):  
Byoung Woong An ◽  
Pil-Hun Chang
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Chukchi Sea ◽  
Numerical Models ◽  
Forecast Accuracy ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
The Impact

<p>The Arctic Ocean is globally important for the weather and climate and has a unique environment. Therefore accurate prediction of the Arctic sea ice remains crucial in most numerical models. It is because small changes within the atmosphere or the ocean can cause major changes in the areal extent and thickness of the sea ice. Such changes, in turn, will have pronounced effects on the ocean and atmosphere through modification of the albedo, the ocean-atmosphere heat and momentum exchanges, and the ocean-ice heat and salt fluxes. The focus of this study is on the impact of such coupling on sea ice and upper ocean properties and the halostad related sea ice variations and inflows from Oceans. To assess the impact of the vertical mixing, we perform a set of sensitivity experiments with a global oceanic configuration at 1/4° resolution based on the version 4.0 of NEMO (Nucleus for European Modelling of the Ocean). In particular we examine the spatio-temporal distributions of Pacific and Eastern Arctic origin waters in the Chukchi Sea using 2016-2018 hydrographic data. Overall, the model agrees well with observations in terms of sea ice extent in spite of inaccurate vertical stratification of the water column. We conclude that beyond seasonal time scale forecast accuracy could be improved by more accurate representation of the structure of water masses.</p>

Sub-Seasonal predictability of Arctic Ocean sea ice conditions: Bering Strait and Ekman-driven Ocean Heat Transport

2021 ◽  
Author(s):  
Jed E. Lenetsky ◽  
Bruno Tremblay ◽  
Charles Brunette ◽  
Gianluca Meneghello
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Heat Transport ◽  
Chukchi Sea ◽  
Linear Trend ◽  
Bering Strait ◽  
Ocean Heat Transport ◽  
Seasonal Predictability ◽  
Sharp Drop ◽  
Predictive Skill

AbstractWe use ocean observations and reanalyses to investigate the sub-seasonal predictability of summer and fall sea ice area (SIA) in the western Arctic Ocean associated with lateral ocean heat transport (OHT) through Bering Strait and vertical OHT along the Alaskan coastline from Ekman divergence and upwelling. Results show predictive skill of spring Bering Strait OHT anomalies in the Chukchi and eastern East Siberian seas for June and July SIA, followed by a sharp drop in predictive skill in August, September, and October and a resurgence of the correlation in November during freeze-up. Fall upwelling of Pacific Waters along the Alaskan coastline - a mechanism that was proposed as a preconditioner for lower sea ice concentration (SIC) in the Beaufort Sea the following summer shows - minimal predictive strength on both local and regional scales for any months of the melt season. A statistical hindcast based on May Bering Strait OHT anomalies explains 74% of July Chukchi Sea SIA variance. Using OHT as a predictor of SIA anomalies in the Chukchi Sea improves hindcasts from the simple linear trend by 32% and predictions from spring sea ice thickness anomalies by 22%. This work highlights the importance of ocean heat anomalies for melt season sea ice prediction and provides observational evidence of sub-seasonal changes in forecast skill observed in model based forecasts of the Chukchi Sea.

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.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Arctic Fog Detection Using Infrared Spectral Measurements

2019 ◽  
Vol 36 (8) ◽  
pp. 1643-1656
Author(s):  
Li Yi ◽  
King-Fai Li ◽  
Xianyao Chen ◽  
Ka-Kit Tung
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Open Water ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Satellite Observations ◽  
The Arctic ◽  
Water Surface Area ◽  
Infrared Spectral ◽  
Fog Detection

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

scholarly journals From sea ice to seals: a moored marine ecosystem observatory in the Arctic

Ocean Science ◽  
2018 ◽  
Vol 14 (6) ◽  
pp. 1423-1433 ◽  
Author(s):  
Claudine Hauri ◽  
Seth Danielson ◽  
Andrew M. P. McDonnell ◽  
Russell R. Hopcroft ◽  
Peter Winsor ◽  
...  
Keyword(s):  
Sea Ice ◽  
Marine Mammals ◽  
Chukchi Sea ◽  
Marine Ecosystem ◽  
Extreme Environment ◽  
The Arctic ◽  
Zooplankton Abundance ◽  
Test Bed ◽  
Ecosystem Monitoring ◽  
Cold Temperatures

Abstract. Although Arctic marine ecosystems are changing rapidly, year-round monitoring is currently very limited and presents multiple challenges unique to this region. The Chukchi Ecosystem Observatory (CEO) described here uses new sensor technologies to meet needs for continuous, high-resolution, and year-round observations across all levels of the ecosystem in the biologically productive and seasonally ice-covered Chukchi Sea off the northwest coast of Alaska. This mooring array records a broad suite of variables that facilitate observations, yielding better understanding of physical, chemical, and biological couplings, phenologies, and the overall state of this Arctic shelf marine ecosystem. While cold temperatures and 8 months of sea ice cover present challenging conditions for the operation of the CEO, this extreme environment also serves as a rigorous test bed for innovative ecosystem monitoring strategies. Here, we present data from the 2015–2016 CEO deployments that provide new perspectives on the seasonal evolution of sea ice, water column structure, and physical properties, annual cycles in nitrate, dissolved oxygen, phytoplankton blooms, and export, zooplankton abundance and vertical migration, the occurrence of Arctic cod, and vocalizations of marine mammals such as bearded seals. These integrated ecosystem observations are being combined with ship-based observations and modeling to produce a time series that documents biological community responses to changing seasonal sea ice and water temperatures while establishing a scientific basis for ecosystem management.

scholarly journals From sea ice to seals: A moored marine ecosystem observatory in the Arctic

2018 ◽  
Author(s):  
Claudine Hauri ◽  
Seth Danielson ◽  
Andrew M. P. McDonnell ◽  
Russell R. Hopcroft ◽  
Peter Winsor ◽  
...  
Keyword(s):  
Sea Ice ◽  
Marine Mammals ◽  
Chukchi Sea ◽  
Marine Ecosystem ◽  
Extreme Environment ◽  
The Arctic ◽  
Zooplankton Abundance ◽  
Test Bed ◽  
Ecosystem Monitoring ◽  
Cold Temperatures

Abstract. Although Arctic marine ecosystems are changing rapidly, year-round monitoring is currently very limited and presents multiple challenges unique to this region. The Chukchi Ecosystem Observatory (CEO) described here uses new sensor technologies to meet needs for continuous, high resolution, and year-round observations across all levels of the ecosystem in the biologically productive and seasonally ice-covered Chukchi Sea off the northwest coast of Alaska. This mooring array records a broad suite of parameters that facilitate observations, yielding better understanding of physical, chemical and biological couplings, phenologies, and the overall state of this Arctic shelf marine ecosystem. While cold temperatures and eight months of sea ice cover present challenging conditions for the operation of the CEO, this extreme environment also serves as a rigorous test bed for innovative ecosystem monitoring strategies. Here, we present data from the 2015–16 CEO deployments that provide new perspectives on the seasonal evolution of sea ice, water column structure and physical properties, annual cycles in nitrate, dissolved oxygen, phytoplankton blooms and export, zooplankton abundance and vertical migration, the occurrence of Arctic cod, and vocalizations of marine mammals such as bearded seals. These integrated ecosystem observations are being combined with ship-based observations and modeling to produce a time-series that documents biological community responses to changing seasonal sea ice and water temperatures while establishing a scientific basis for ecosystem management.

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