Reduced sea ice protection period increases storm exposure in Kivalina, Alaska

On Arctic coasts, erosion is limited by the presence of nearshore sea ice, which creates a protective barrier from storms. In Kivalina, an Alaskan Inupiaq Inuit community, decreasing seasonal sea ice extent and a lengthening of the open-water season may be resulting in fall storms that (1) generate higher, longer, and more destructive waves and (2) cause damage later in the year, resulting in increased flooding and erosion. We assess trends in the duration of nearshore sea ice and their relationship with storm occurrence over the period 1979–2015 in Kivalina. Analysis of passive microwave sea ice concentration data indicates that the open-water season has increased by 5.6 ± 1.2 days/decade over the last 37 years, with moderate evidence that it is extending further into the fall than into the spring. This is correlated with an increased reporting frequency of high-damage storms; 80% of reported storms since 1970 occurred in the last 15 years. Each high-damage storm event occurred during the open-water season for that year. Our findings support Kivalina villagers’ assertions that climate change increases storm exposure and associated damages from flooding and erosion.

Download Full-text

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

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1627-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 1627-1667 ◽

Cited By ~ 1

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.

Download Full-text

Polynya area and frequency in the Weddell Sea in CMIP6 climate models

10.5194/egusphere-egu2020-19812 ◽

2020 ◽

Author(s):

Martin Mohrmann ◽

Céline Heuzé ◽

Sebastiaan Swart

Keyword(s):

Sea Ice ◽

Deep Water ◽

Climate Models ◽

Numerical Models ◽

Open Water ◽

Weddell Sea ◽

Threshold Method ◽

Concentration Data ◽

Sea Ice Concentration ◽

Ice Concentration

<p>The presence of polynyas has a large effect on air-sea fluxes and deep water production, therefore impacting climate-relevant properties such as heat and carbon exchange between the atmosphere and ocean interior. One of the key areas of deep water formation is in the Weddell Sea, where much attention has recently been placed in the reoccurance of the open ocean Maud Rise polynya. In this study, two methods are presented to track the number, area and spatial distribution of polynyas with a focus on the Weddell Sea. The analysis is applied to a set of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) models and to satellite sea ice concentration data. The first approach is a sea ice threshold method applied to the CMIP6 sea ice data at the original model grid. Open water areas surrounded by sea ice are classified as polynyas. Without requiring any remapping or interpolation, this method preserves the area information of all grid cells and is well suited to compute the combined area of the polynyas in the Weddell Sea. The second approach makes use of an image analysis technique to outline areas with low sea ice concentration. This method is preferable for counting the absolute number of polynyas and obtaining statistical information about their position. Satellite sea ice concentration is used as a reference to compare the performance of the models representing polynya area and to indicate model biases in the location of polynyas. All analyzed CMIP6 models show coastal polynyas, while only about half of the models regularly form open water polynyas. The resolution (about one degree for most models) sets a limit for the number of the polynyas in the numerical models.</p>

Download Full-text

Perspectives on future sea ice and navigability in the Arctic

10.5194/tc-2021-35 ◽

2021 ◽

Author(s):

Jinlei Chen ◽

Shichang Kang ◽

Wentao Du ◽

Junming Guo ◽

Min Xu ◽

...

Keyword(s):

Sea Ice ◽

Coupled Model ◽

Open Water ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Northwest Passage ◽

Ice Concentration ◽

Transportation Accessibility

Abstract. The retreat of sea ice is very significant in the Arctic under global warming. It is projected to continue and have great impacts on navigation. In this investigation, decadal changes in sea ice parameters were evaluated by multimodel from the Coupled Model Intercomparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes within the Arctic transportation accessibility model. The sea ice extent is expected to decrease along the SSP5-8.5 scenario with a high possibility under current emissions and climate change. The decadal decreasing rate will increase in March but decrease in September until 2060 when the oldest ice completely disappears and sea ice changes reach an irreversible tipping point. The sea ice thickness will decrease and transit in parts of the Arctic and will decline overall by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline with decreasing decadal rates, while the decrease in volume will be higher in March than in September. Open water ships will be able to cross the Northeast Passage and Northwest Passage in August–October 2045–2055, with a maximum navigable area in September. The opportunistic crossing time for polar class 6 (PC6) ships will advance to October–December in 2021–2030, while the maximum navigable area will be seen in October. In addition, the Central Passage will also open for PC6 ships during September–October in 2021–2030.

Download Full-text

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

Geoscientific Model Development ◽

10.5194/gmd-5-1501-2012 ◽

2012 ◽

Vol 5 (6) ◽

pp. 1501-1515 ◽

Cited By ~ 16

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) 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 use of synthetic data (i.e. model-generated data), and secondly, through the assimilation of real 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.

Download Full-text

Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Remote Sensing ◽

10.3390/rs13061139 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1139

Author(s):

David Llaveria ◽

Juan Francesc Munoz-Martin ◽

Christoph Herbert ◽

Miriam Pablos ◽

Hyuk Park ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Cost Effective ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Neural Network Approach ◽

Ice Concentration ◽

L Band ◽

Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

Download Full-text

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Remote Sensing ◽

10.3390/rs13122283 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2283

Author(s):

Hyangsun Han ◽

Sungjae Lee ◽

Hyun-Cheol Kim ◽

Miae Kim

Keyword(s):

Random Forest ◽

Sea Ice ◽

Arctic Ocean ◽

Open Water ◽

The Arctic ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Air Temperatures ◽

The Pacific ◽

Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

Download Full-text

Changes of the Arctic marginal ice zone during the satellite era

The Cryosphere ◽

10.5194/tc-14-1971-2020 ◽

2020 ◽

Vol 14 (6) ◽

pp. 1971-1984 ◽

Cited By ~ 1

Author(s):

Rebecca J. Rolph ◽

Daniel L. Feltham ◽

David Schröder

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Satellite Observations ◽

The Arctic ◽

Upper And Lower Bounds ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Marginal Ice Zone ◽

Definition Of

Abstract. Many studies have shown a decrease in Arctic sea ice extent. It does not logically follow, however, that the extent of the marginal ice zone (MIZ), here defined as the area of the ocean with ice concentrations from 15 % to 80 %, is also changing. Changes in the MIZ extent has implications for the level of atmospheric and ocean heat and gas exchange in the area of partially ice-covered ocean and for the extent of habitat for organisms that rely on the MIZ, from primary producers like sea ice algae to seals and birds. Here, we present, for the first time, an analysis of satellite observations of pan-Arctic averaged MIZ extent. We find no trend in the MIZ extent over the last 40 years from observations. Our results indicate that the constancy of the MIZ extent is the result of an observed increase in width of the MIZ being compensated for by a decrease in the perimeter of the MIZ as it moves further north. We present simulations from a coupled sea ice–ocean mixed layer model using a prognostic floe size distribution, which we find is consistent with, but poorly constrained by, existing satellite observations of pan-Arctic MIZ extent. We provide seasonal upper and lower bounds on MIZ extent based on the four satellite-derived sea ice concentration datasets used. We find a large and significant increase (>50 %) in the August and September MIZ fraction (MIZ extent divided by sea ice extent) for the Bootstrap and OSI-450 observational datasets, which can be attributed to the reduction in total sea ice extent. Given the results of this study, we suggest that references to “rapid changes” in the MIZ should remain cautious and provide a specific and clear definition of both the MIZ itself and also the property of the MIZ that is changing.

Download Full-text

Sea Ice Concentration Analyses for the Baltic Sea and Their Impact on Numerical Weather Prediction

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2376.1 ◽

2006 ◽

Vol 45 (7) ◽

pp. 982-994 ◽

Cited By ~ 15

Author(s):

Matthias Drusch

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Baltic Sea ◽

Latent Heat ◽

Latent Heat Flux ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

The Baltic

Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset exhibits less spatial and temporal variability during the winter of 2003/04. Because of the coarse resolution of the NCEP dataset, ice extent is generally larger than in the SMHI analysis. Mean sea ice concentrations derived from both datasets are in reasonable agreement during the ice-growing and ice-melting periods in January and April, respectively. For February and March, during which the sea ice extent is largest, mean sea ice concentrations are lower in the NCEP dataset relative to the SMHI product. Ten-day weather forecasts based on the NCEP sea ice concentrations and the SMHI dataset have been performed, and they were compared on the local, regional, and continental scales. Turbulent surface fluxes have been analyzed based on 24-h forecasts. The differences in sea ice extent during the ice-growing period in January cause mean differences of up to 30 W m−2 for sensible heat flux and 20 W m−2 for latent heat flux in parts of the Gulf of Bothnia and the Gulf of Finland. The comparison between spatially aggregated fluxes yields differences of up to 36 and 20 W m−2 for sensible and latent heat flux, respectively. The differences in turbulent fluxes result in different planetary boundary height and structure. Even the forecast cloud cover changes by up to 40% locally.

Download Full-text

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

The Cryosphere ◽

10.5194/tc-10-761-2016 ◽

2016 ◽

Vol 10 (2) ◽

pp. 761-774 ◽

Cited By ~ 11

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.

Download Full-text