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

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>

Testing for Dynamical Dependence: Application to the Surface Mass Balance Over Antarctica

2020 ◽  
Author(s):  
Quentin Dalaiden ◽  
Stephane Vannitsem ◽  
Hugues Goosse
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Mass Balance ◽  
Information Transfer ◽  
Climate Models ◽  
Weddell Sea ◽  
Surface Mass Balance ◽  
Sea Ice Concentration ◽  
Surface Mass ◽  
Ice Concentration

<p>Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI‐ESM‐P and the CESM1‐CAM5 climate models. The approach used is a novel method allowing for evaluating the rate of information transfer between observables that goes beyond the classical correlation analysis and allows for directional characterization of dependence. It reveals that a large proportion of significant correlations do not lead to dependence. In addition, three coherent results concerning the dependence of SMB emerge from the analysis of both models: (i) The SMB over the Antarctic Plateau is mostly influenced by the surface temperature and sea ice concentration and not by large‐scale circulation changes; (ii) the SMB of the Weddell Sea and the Dronning Maud Land coasts are not influenced significantly by the surface temperature; and (iii) the Weddell Sea coast is not significantly influenced by the sea ice concentration.</p>

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

2018 ◽  
Vol 4 (4) ◽  
pp. 525-537 ◽  
Author(s):  
Zhanpei Fang ◽  
Patrick T. Freeman ◽  
Christopher B. Field ◽  
Katharine J. Mach
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Storm Event ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Reporting Frequency ◽  
Protection Period ◽  
Inuit Community

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.

scholarly journals Arctic sea ice sensitivity to lateral melting representation in a coupled climate model

2021 ◽  
Author(s):  
Madison Smith ◽  
Marika Holland ◽  
Bonnie Light
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Coupled Climate Model ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ice Floes

Abstract. The melting of sea ice floes from the edges (lateral melting) results in open water formation and subsequently increases absorption of solar shortwave energy. However, lateral melt plays a small role in the sea ice mass budget in both hemispheres in most climate models (Keen et al., 2020). This is likely influenced by simple parameterizations of this process in sea ice models that are constrained by limited observations. Here we use a coupled climate model (CESM2.0) to assess the sensitivity of modeled sea ice state to the lateral melt parameterization. The results show that sea ice is sensitive both to the parameters determining the effective lateral melt rate, as well as the nuances in how lateral melting is applied to the ice pack. Increasing the lateral melt rate within the range of reasonable values is largely compensated by decreases in the basal melt rate, but can still result in a significant decrease in sea ice concentration and thickness, particularly in the marginal ice zone. We suggest that it is important to consider the efficiency of melt processes at forming open water, which drives the majority of the ice-albedo feedback. Melt processes are more efficient at forming open water in thinner ice scenarios (as we are likely to see in the future), suggesting the importance of well representing thermodynamic evolution. Revisiting model parameterizations of lateral melting with observations will require finding new ways to represent important physical processes.

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

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.

Influence of Nordic Seas dynamics on the Atlantic Water propagation and its impacts on sea ice concentration.

2021 ◽  
Author(s):  
Sourav Chatterjee ◽  
Roshin P Raj ◽  
Laurent Bertino ◽  
Nuncio Murukesh
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Atlantic Water ◽  
The Arctic ◽  
Deep Water Formation ◽  
Sea Ice Concentration ◽  
Nordic Seas ◽  
Ice Concentration ◽  
Water Formation

<p>Enhanced intrusion of warm and saline Atlantic Water (AW) to the Arctic Ocean (AO) in recent years has drawn wide interest of the scientific community owing to its potential role in ‘Arctic Amplification’. Not only the AW has warmed over the last few decades , but its transfer efficiency have also undergone significant modifications due to changes in atmosphere and ocean dynamics at regional to large scales. The Nordic Seas (NS), in this regard, play a vital role as the major exchange of polar and sub-polar waters takes place in this region. Further, the AW and its significant modification on its way to AO via the Nordic Seas has large scale implications on e.g., deep water formation, air-sea heat fluxes. Previous studies have suggested that a change in the sub-polar gyre dynamics in the North Atlantic controls the AW anomalies that enter the NS and eventually end up in the AO. However, the role of NS dynamics in resulting in the modifications of these AW anomalies are not well studied. Here in this study, we show that the Nordic Seas are not only a passive conduit of AW anomalies but the ocean circulations in the Nordic Seas, particularly the Greenland Sea Gyre (GSG) circulation can significantly change the AW characteristics between the entry and exit point of AW in the NS. Further, it is shown that the change in GSG circulation can modify the AW heat distribution in the Nordic Seas and can potentially influence the sea ice concentration therein. Projected enhanced atmospheric forcing in the NS in a warming Arctic scenario and the warming trend of the AW can amplify the role of NS circulation in AW propagation and its impact on sea ice, freshwater budget and deep water formation.</p>

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

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
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.

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

2012 ◽  
Vol 5 (2) ◽  
pp. 1627-1667 ◽  
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.

scholarly journals VALIDATION OF ASMR2 SEA ICE CONCENTRATION DATA USING MODIS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1741-1746
Author(s):  
K. Cho ◽  
R. Nagao ◽  
K. Naoki
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Passive Microwave ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Free Condition ◽  
Modis Data ◽  
Ice Concentration

<p><strong>Abstract.</strong> Passive microwave radiometer AMSR2 was launched by JAXA in May 2012 on-board GCOM-W satellite. The antenna diameter of AMSR2 is 2.0&amp;thinsp;m which provide highest spatial resolution as a passive microwave radiometer in space. The sea ice concentration images derived from AMSR2 data allow us to monitor the detailed sea ice distributions of whole globe every day. The AMSR bootstrap algorithm developed by Dr. Josefino Comiso is used as the standard algorithm for calculating sea ice concentration from AMSR2 data. Under the contract with JAXA, the authors have been evaluating the performance of the algorithm. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W. Since the spatial resolution of MODIS is much higher than that of AMSR2, under the cloud free condition, the ice concentration corresponds to the size of a pixel of AMSR2 can be calculated much accurately with MODIS data. The procedures of the evaluation are as follows. Firstly, MODIS band 1 reflectance were binarized to discriminate sea ice(1) from open water(0) and sea ice concentration of each pixel size of AMSR2 were calculated. In calculating sea ice concentration from MODIS data, the selection of the threshold level of MODIS band 1 reflectance is critical. Through the detailed evaluation, the authors selected 5% as the optimum threshold level. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10% error under the cloud free condition.</p>

scholarly journals Variability in Antarctic sea ice from 1998-2017

2018 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic ◽  
Melt Duration ◽  
Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

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