scholarly journals Understanding the Forecast Skill of Rapid Arctic Sea Ice Loss on Subseasonal Timescales

2021 ◽  
pp. 1-35
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Forecast Error ◽  
Ocean Basin ◽  
Arctic Sea Ice ◽  
Forecast Skill ◽  
Central Arctic Ocean ◽  
Sea Ice Extent ◽  
Marginal Seas ◽  
Forecast Models

Abstract Predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) timescales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability—in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas, rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on non-extreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and non-extreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events; and that while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization.

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>

scholarly journals Large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen E. L. Howell ◽  
Mike Brady ◽  
Alexander S. Komarov
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
Hudson Bay ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Narrow Channels ◽  
New Era ◽  
The North ◽  
Arctic Sea

Abstract. As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent 5 spaceborne SAR sensors, that together routinely cover the pan-Arctic sea ice domain. Here, we utilized over 60,000 SAR images from Sentinel-1AB (S1) and RCM to generate large-scale sea ice motion (SIM) estimates over the pan-Arctic domain from March to December, 2020. On average, 4.5 million SIM vectors from S1 and RCM were automatically detected per week for 2020 and when combined (S1+RCM) they facilitated the generation of 7-day, 25 km SIM products across the pan-Arctic domain. S1+RCM SIM provided more coverage in Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and over the North Pole compared to SIM from S1 alone. S1+RCM SIM was able to be resolved within the narrow channels and inlets across the pan-Arctic alleviating the main limitation of coarser resolution sensors. S1+RCM SIM provided larger ice speeds with a mean difference (MD) of 1.3 km/day compared to the National Snow and Ice Data Center (NSIDC) SIM product and a MD of 0.76 km/day compared to Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) SIM product. S1+RCM was also able to better resolve SIM in the marginal ice zone compared to the NSIDC and OSA-SAF SIM products. Overall, our results demonstrate that combining SIM from multiple spaceborne SAR satellites allows for large-scale SIM to be routinely generated across the pan-Arctic domain.

Insights for the Future of Arctic Sea Ice from the CMIP6 lig127k Experiment

2020 ◽  
Author(s):  
Esther C. Brady ◽  
Bette L. Otto-Bliesner ◽  
Masa Kageyama ◽  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Positive Temperature ◽  
Strong Negative Correlation ◽  
Central Arctic Ocean ◽  
Sea Ice Extent ◽  
Temperature Anomalies ◽  
Arctic Sea

<p>New to CMIP6 is the Tier 1 lig127k experiment, designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models and following a common experimental protocol. We present a multi-model ensemble of 17 climate models, all of which (except for two) have also completed the CMIP6 DECK experiments, looking at the lig127k Arctic’s responses across models and the relationships with each model’s Equilibrium Climate Sensitivity (ECS), preindustrial sea ice thickness and 127ka temperature anomalies.</p><p>Boreal insolation anomalies at 127 ka enhance the seasonal cycle of Arctic sea ice, though with notable differences among the models. The consensus from the lig127k sea ice distributions is a reduced minimum (August-September) summer sea ice extent in the Arctic as compared to the piControl simulations. Sea ice remains above 15% concentrations over the central Arctic Ocean in all but one of the lig127k simulations. More than half of the models simulate a retreat of the Arctic minimum ice edge similar to the average of the last 2 decades. The lig127k minimum Arctic sea ice area anomalies show a strong negative correlation with the Arctic (60-90°N) annual surface temperature anomalies but only a weak correlation with the corresponding June-July-August (JJA) temperature anomalies. Memory in the ocean and cryosphere provide feedbacks to maintain larger positive temperature anomalies, December-January-February (DJF) and annually, in the Arctic than in JJA. The models contributing to the lig127k ensemble have an ECS varying from 2.1 to 5.3°C. There is a notable relationship between the ECS and simulation of lig127k minimum Arctic sea ice area.  With very limited Arctic sea ice proxies for 127 ka, and with evolving interpretation of the relationships of these proxies with sea ice coverage, it is still difficult to rule out the high or low values of ECS from the proxy data.</p>

scholarly journals Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

2016 ◽  
Vol 29 (4) ◽  
pp. 1529-1543 ◽  
Author(s):  
Lei Wang ◽  
Xiaojun Yuan ◽  
Mingfang Ting ◽  
Cuihua Li
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Forecast Skill ◽  
The Arctic ◽  
Var Model ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Vector Autoregressive ◽  
Arctic Sea ◽  
Ice Concentration

Abstract Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

Benthic investigations at the Arctic long-term deep-sea observatory HAUSGARTEN

2020 ◽  
Author(s):  
Christiane Hasemann ◽  
Ingo Schewe ◽  
Thomas Soltwedel
Keyword(s):  
Sea Ice ◽  
Deep Sea ◽  
Large Scale ◽  
Environmental Changes ◽  
Central Arctic Ocean ◽  
Sea Ice Extent ◽  
Marine Research ◽  
Research Activities ◽  
The Impact

<p>The past decades have seen remarkable changes in key arctic variables, including a decrease in sea-ice extent and sea-ice thickness, changes in temperature and salinity of arctic waters, and associated shifts in nutrient distributions. To detect and track the impact of large-scale environmental changes in the transition zone between the northern North Atlantic and the central Arctic Ocean, the Alfred Wegener Institute for Polar and Marine Research (AWI) established in 1999 about 150 km west of Svalbard the deep-sea long-term observatory HAUSGARTEN, which constitutes the first, and until now only open-ocean long-term station in a polar region. 21 permanent sampling sites along a depth transect between 1000 – 5500 m, and along a latitudinal transect following the 2500 m water depth isobath are revisited yearly. The central HAUSGARTEN station serves as an experimental area for biological short- and long-term experiments at the deep seafloor, simulating various scenarios in changing environmental settings. Multidisciplinary research activities at HAUSGARTEN comprise biochemical analyses to estimate the input of organic matter from phytodetritus sedimentation and activities and biomasses of the small sediment-inhabiting biota as well as assessments of distribution patterns of benthic organisms (covering size classes from bacteria to meiofauna as well as megafauna).The past decades have seen remarkable changes in key arctic variables, including a decrease in sea-ice extent and sea-ice thickness, changes in temperature and salinity of arctic waters, and associated shifts in nutrient distributions. To detect and track the impact of large-scale environmental changes in the transition zone between the northern North Atlantic and the central Arctic Ocean, the Alfred Wegener Institute for Polar and Marine Research (AWI) established in 1999 about 150 km west of Svalbard the deep-sea long-term observatory HAUSGARTEN, which constitutes the first, and until now only open-ocean long-term station in a polar region. 21 permanent sampling sites along a depth transect between 1000 – 5500 m, and along a latitudinal transect following the 2500 m water depth isobath are revisited yearly. The central HAUSGARTEN station serves as an experimental area for biological short- and long-term experiments at the deep seafloor, simulating various scenarios in changing environmental settings. Multidisciplinary research activities at HAUSGARTEN comprise biochemical analyses to estimate the input of organic matter from phytodetritus sedimentation and activities and biomasses of the small sediment-inhabiting biota as well as assessments of distribution patterns of benthic organisms (covering size classes from bacteria to meiofauna as well as megafauna).</p>

Assessing the forecast skill of Arctic sea ice extent in the GloSea4 seasonal prediction system

2014 ◽  
Vol 44 (1-2) ◽  
pp. 147-162 ◽  
Author(s):  
K. Andrew Peterson ◽  
A. Arribas ◽  
H. T. Hewitt ◽  
A. B. Keen ◽  
D. J. Lea ◽  
...  
Keyword(s):  
Sea Ice ◽  
Seasonal Prediction ◽  
Arctic Sea Ice ◽  
Forecast Skill ◽  
Prediction System ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

scholarly journals Anomalies of Sea-ice transports in the Arctic

2006 ◽  
Vol 44 ◽  
pp. 310-316 ◽  
Author(s):  
Torge Martin ◽  
Thomas Martin
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Central Arctic Ocean ◽  
Marginal Seas ◽  
Ice Volume ◽  
The North ◽  
Arctic Sea

AbstractIn the Arctic, Sea-ice motion and ice export are prominent processes and good indicators of Arctic climate System variability. Sea-ice drift is Simulated using a dynamic–thermodynamic Sea-ice model, validated with retrievals from SsM/I Satellite observations. Both datasets agree well in reproducing the main Arctic drift patterns. In order to Study inner Arctic transports and ice volume anomalies, the Arctic Ocean is Split by ten boundaries, Separating the central Arctic Ocean from the Nordic and marginal Seas. It is found that the already dominant Sea-ice export through Fram Strait has increased at the expense of export through the Barents Sea in the most recent years investigated. Furthermore, ice export from the Eurasian marginal Seas increased Slightly, followed by greater ice production during the winter. In contrast to this, the Sea-ice volume moved within the Beaufort Gyre distinctly decreased. In total, the ice volume in the central Arctic decreased during the 40 year period covered by this Study. The changes in the ice volume correspond to two wind-driven circulation regimes of the Arctic Sea-ice motion, which recur approximately every 11 years. For the volume anomalies we derived a correlation of –0.59 to the North Atlantic Oscillation (NAO) index, lagging the NAO by 2 years.

scholarly journals Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Pacific Side ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

Sign in / Sign up

Export Citation Format

Share Document