scholarly journals Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽  
2011 ◽  
Vol 7 ◽  
pp. 37-40 ◽  
Author(s):  
Takahiro Toyoda ◽  
Toshiyuki Awaji ◽  
Nozomi Sugiura ◽  
Shuhei Masuda ◽  
Hiromichi Igarashi ◽  
...  
1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.


2016 ◽  
Author(s):  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Pedro Elosegui ◽  
Joaquim A. Pla-Resina ◽  
Marcos Portabella

Abstract. We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness


1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.


2019 ◽  
Vol 32 (22) ◽  
pp. 7783-7796 ◽  
Author(s):  
Liisi Jakobson ◽  
Timo Vihma ◽  
Erko Jakobson

Abstract NCEP CFSR reanalysis 6-hourly fields from 1979 to 2015 were used to investigate the relationships of sea ice concentration (SIC), atmospheric stratification, surface roughness, and wind speed at 10-m height (W10) and 850-hPa level (W850). We found that in autumn (September–November), winter (December–February), and spring (March–May) a lower SIC favors less-stable stratification and a higher W10. In autumn, the decrease in SIC is strongest, and SIC has its strongest correlation with the atmospheric stratification, W10, and the ratio of W10 and W850 (WSR). W10 and WSR have increased in autumn, and the negative trends in SIC typically are collocated with positive trends in W10 and WSR. In winter, W850 has negative trends over the Arctic Ocean, which, together with the lack of decrease of SIC in the central Arctic, has prevented W10 from increasing in winter. The winter trends are notably different from those for autumn, but the correlations are fairly similar. In autumn, winter, and spring, the negative correlation between SIC and W10 originated from the reduction of both stratification and aerodynamic surface roughness z0 with a reduction of SIC. The dependence of z0 on SIC is, however, weak in NCEP CFSR. In summer, the ratio of W10 and W850 has increased over large areas. The correlations between SIC and atmospheric variables were stronger on interannual time scales than on subseasonal time scales. The causal relationships are complicated by the two-way interaction between SIC and W10. In most cases, especially in summer, SIC increases after periods of W10 exceeding 5 m s−1.


2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim

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.


2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  

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.


2017 ◽  
Author(s):  
Jun Ono ◽  
Hiroaki Tatebe ◽  
Yoshiki Komuro ◽  
Masato I. Nodzu ◽  
Masayoshi Ishii

Abstract. To assess the skill of predictions of the seasonal-to-interannual detrended sea ice extent in the Arctic Ocean (SIEAO) and to clarify the underlying physical processes, we conducted ensemble hindcasts, started on January 1st, April 1st, July 1st, and October 1st for each year from 1980 to 2011, for lead times of up three years, using the Model for Interdisciplinary Research on Climate (MIROC) version 5 initialized with the observed atmosphere and ocean anomalies and sea ice concentration. Significant skill is found for the winter months: the December SIEAO can be predicted up to 1 year ahead. This skill is attributed to the subsurface ocean heat content originating in the North Atlantic. The subsurface water flows into the Barents Sea from spring to fall and emerges at the surface in winter by vertical mixing, and eventually affects the sea ice variability there. Meanwhile, the September SIEAO predictions are skillful for lead times of up to 3 months, due to the persistence of sea ice in the Beaufort, Chukchi, and East Siberian Seas initialized in July, as suggested by previous studies.


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