scholarly journals Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice

2021 ◽  
Vol 13 (21) ◽  
pp. 4421
Author(s):  
Stefan Kern
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Correlation Length ◽  
Large Scale ◽  
Total Error ◽  
Grid Cell ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Length Scales ◽  
Ice Concentration

The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI-450, provide gridded SIC error estimates in addition to SIC. These error estimates, called total error henceforth, comprise a random, uncorrelated error contribution from retrieval and sensor noise, aka the algorithm standard error, and a locally-to-regionally correlated contribution from gridding and averaging Level-2 SIC into the Level-4 SIC CDRs, aka the representativity error. However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. In addition, larger-scale SIC errors due to, e.g., unaccounted weather influence or mismatch between the actual ice type and the algorithm setup are neither well represented by the total error, nor are their correlation scales known for these CDRs. In this study, I attempt to contribute to filling this knowledge gap by deriving spatial correlation length scales for the total error and the large-scale SIC error for high-concentration pack ice. For every grid cell with >90% SIC, I derive circular one-point correlation maps of 1000 km radius by computing the cross-correlation between the central 31-day time series of the errors and all other 31-day error time series within that circular area (disc) with 1000 km radius. I approximate the observed decrease in the correlation away from the disc’s center with an exponential function that best fits this decrease and thereby obtain the correlation length scale L sought. With this approach, I derive L separately for the total error and the large-scale SIC error for every high-concentration grid cell, and map, present and discuss these for the Arctic and the Southern Ocean for the year 2010 for the above-mentioned products. I find correlation length scales are substantially smaller for the total error, mostly below ~200 km, than the SIC error, ~200 km to ~700 km, in both hemispheres. I observe considerable spatiotemporal variability of the SIC error correlation length scales in both hemispheres and provide first directions to explain these. For SICCI-50km, I present the first evidence of the method’s robustness for other years and time series of L for 2003–2010.

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

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.

scholarly journals On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

2019 ◽  
Vol 13 (2) ◽  
pp. 521-543 ◽  
Author(s):  
Leandro Ponsoni ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Matthieu Chevallier ◽  
David Docquier
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Length Scales ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

scholarly journals Estimation of Degree of Sea Ice Ridging Based on Dual-Polarized C-band SAR Data

2017 ◽  
Author(s):  
Alexandru Gegiuc ◽  
Markku Similä ◽  
Juha Karvonen ◽  
Mikko Lensu ◽  
Marko Mäkynen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Large Scale ◽  
Winter Season ◽  
Texture Features ◽  
Route Optimization ◽  
Sea Ice Concentration ◽  
Essential Information ◽  
Ice Concentration ◽  
Dual Polarized

Abstract. For navigation in Baltic Sea ice during winter season, parameters such as ice edge, ice concentration, ice thickness, ice drift and degree of ridging are usually reported daily in the manually prepared Ice Charts, which provide icebreakers essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times and detailed analysis is not possible for large scale maps (e.g. Arctic Ocean). Here, we propose a method for automatic estimation of degree of ridging density in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and the sea ice concentration information extracted from the Finnish Ice Charts. The SAR images were first segmented and then several texture features were extracted for each
 segment. Using the Random Forest classification, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized Ice Charts. The overall agreement between the ice chart based degree of ice ridging (DIR) and the automated results varied monthly, being 83 %, 63 % and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice riding of the manual Ice Charts and the actual ridge density was good when this issue was studied based on an extensive field campaign data in March 2011.

scholarly journals Sea ice and large-scale atmospheric variability around East Antarctica

2021 ◽  
Author(s):  
◽  
Florence Isaacs
Keyword(s):  
Indian Ocean ◽  
Climate Variability ◽  
Sea Ice ◽  
Large Scale ◽  
Wave Train ◽  
East Antarctica ◽  
Sea Ice Concentration ◽  
Atmospheric Wave ◽  
Ice Concentration ◽  
Dronning Maud Land

<p><b>​​Antarctica’s sea ice cover is an important component in the global climate system. The variability and recent trends of sea ice concentration are, however, not accurately reproduced by models. Evaluating model performance is hampered because the processes that determine sea ice distribution are not yet well understood, particularly in the East Antarctic region. Here I explore the relationships between recent climate variability and sea ice around East Antarctica, the spatial variability in these relationships, and the impacts that these may have on other aspects of the climate and cryosphere. To achieve this, I analysed satellite-derived HadlSST sea ice concentration (SIC) alongside ERA5 atmospheric reanalysis data for the period between 1979-2018.</b></p> <p>I found that variability in sea ice coverage around East Antarctica was affected by El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM), and Zonal Wave 3 (ZW3). Additionally, I found that the influence of each of these modes varied spatially and temporally, and that sea ice variability affected how regional scale climate responded to changes in large-scale circulation. Summer and autumn SIC around Dronning Maud Land between 10°E and 70°E exhibited a statistically significant negative correlation with the Niño 3.4 index. Analysis of ERA5 data suggests that a southward‐propagating atmospheric wave train triggered by SST anomalies in the tropical Pacific extends into Dronning Maud Land and alters sea ice concentration by encouraging meridional airflow. Shifts in meridional flow in Dronning Maud Land affected sea ice thermodynamically, by altering local heat transport and in turn altering sea ice formation and melt. </p> <p>Sea ice around the Western Pacific sector (WPS) of East Antarctica showed a significant association with variability in the IOD and the SAM. The IOD was correlated with SIC in all seasons but summer. The IOD-SIC relationship is likely driven by an IOD-associated atmospheric wave-train which propagates polewards from the tropical Indian Ocean to Wilkes Land, altering regional circulation and in turn affecting SIC through changes to local climate and sea ice transport. The correlation between WPS SIC and the SAM shifts from positive in summer and autumn to negative in winter and spring, and is likely due to the influence of the SAM on katabatic winds and coastal polynyas, which in turn affect SIC. </p> <p>A significant correlation was observed between SIC variability around East Antarctica and precipitation variability across the continent and the near-coastal Southern Ocean. Further analysis showed that SIC affected how continental precipitation responded to large-scale atmospheric circulation, including modes such as ZW3 and the SAM. Specifically, increased southward moisture flux was only associated with increased precipitation in the inland coastal regions of the continent when SIC was anomalously low. These findings suggest that any future decrease in sea ice may result in greater coupling of climate variability with continental precipitation.</p>

scholarly journals Consistent biases in Antarctic sea ice concentration simulated by climate models

2017 ◽  
Author(s):  
Lettie A. Roach ◽  
Samuel M. Dean ◽  
James A. Renwick
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Regional Distribution ◽  
Global Climate Models ◽  
Satellite Observations ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Low Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration

Abstract. The simulation of Antarctic sea ice in global climate models often does not agree with observations. In this study, we examine the compactness of sea ice, as well as the regional distribution of sea ice concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, requiring careful treatment when comparing to models. As a fraction of total sea ice extent, models simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 27 CMIP5 simulations, a result not previously highlighted. Targeted model experiments with a coupled ocean – sea ice model show that over-estimation of low-concentration cover is partially determined by choice of constant floe diameter in the lateral melt scheme. This suggests that current sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime.

scholarly journals Climatic changes of thermal condition in the Kara sea at last 40 years

2019 ◽  
Vol 65 (2) ◽  
pp. 125-147
Author(s):  
I. D. Rostov ◽  
E. V. Dmitrieva ◽  
N. I. Rudykh ◽  
A. A. Vorontsov
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Large Scale ◽  
Concentration Field ◽  
Close Correlation ◽  
Kara Sea ◽  
Warm Period ◽  
Cold Period ◽  
Sea Ice Concentration ◽  
Ice Concentration

The paper discusses air (Ta) and sea surface temperature (SST) year-to-year variability due to warming of the Kara Sea, using the data from regular observations at the meteorological stations Roshydromet (GMS) in 1978–2017, NOAA optimum interpolation and reanalysis data. We use the methods of cluster, correlation analysis and Empirical Orthogonal Functions (EOF). We investigate possible cause and effect relationships of these changes with the variations of the wind field components, climatic indices and the sea ice concentration field. The cluster analysis of the three main EOF components has allowed us to identify four areas on the basis of the nature of changes of the water temperature anomalies field. The climatic changes in these areas, in the coastal and island zones of the Kara Sea have manifested themselves in the steady increase of the annual air temperature at GMS from 0,47–0,77 °C/10 years on the southwest coast to 1,33–1,49 °C/10 years in the north of the sea. This is equivalent to warming from 1,9 to 6,0 °C in the last 40 years. For the open sea the value of the Ta trend is about 1,22 °C/10 years, which corresponds to an increase in the average Ta by 4,9 °C in the last 40 years. This value is approximately 3 times greater than that for all the Northern hemisphere for the same period.Annualy, the maximal trend was observed in November and April mainly and exceeded 2–3 °C/10 years at some of the stations. We identify anomalously warm (2016 and 2012) and anomalously cold (1978, 1979, 1992 and 1998) years: the warmest year was 2012, the coldest — 1979. Positive SST trends were observed over all the sea area during the warm period of year (to 1 °C/10 years). SST increased to 2,4 °C, which is approximately 1,5 times greater than the corresponding SST values for the Northern hemisphere. The maximum SST trend (0,4 °C/10 years) was observed in the northwest and southwest parts of the sea. From June to August the trends of SST exceed the annual ones 1,5–2 times. Interannual SST and Ta variations are characterized by close correlation links. Until approximately 1998–2004 the warming was rather insignificant, and after that the growth rate of Ta and SST increased many fold. Apparently it indicates changes in the mode and the large-scale atmospheric circulation in the early 2000s. We also observed a trend of strengthening of the southern wind during the cold period of the year and the northern one — in the warm period (0,5–0,6 m/s in 40 years). It is shown that there is a close correlation between the Ta increase and the changes in the meridional component of the wind speed during the cold period of the year for all the sea areas. For the warm period it is statistically insignificant both for Ta and SST. For the cold season we observed a contribution of the large-scale mode of atmospheric circulation into the variability of V component of the wind speed. The conribution was expressed through the indeces NAO, SCAND, Pol/EUR, AZOR, ISL and the differences of ISLSIB. For the warm season this contribution is expressed through the NAO, SCAND and AO only. For the warm period we showed statistically significant correlation between the increase in SST, Ta and the processes parametrized by the AMO, EA/WR and AZOR indeces. For the cold period the indeces are AMO, Pol/Eur, SIB and ISL SIB. The interannual variations of the sea ice concentration field are characterized by close correlation with Ta changes both in the annual cycle and during the periods of ice cover formation and evolution (R = –0,7... –0,9). For these periods we showed statistically significant relationships between the first EOF mode fluctuations and two climatic indeces — AMO (R = 0,5) and Pol/Eur (R = 0,4). The relationships between the temporary variability of the sea ice concentration and the wind field characteristics are weaker and statistically significant only for the meridional component of the wind speed (R = –0,4).

Seasonal Relationships between Large-Scale Climate Variability and Antarctic Sea Ice Concentration

2012 ◽  
Vol 25 (16) ◽  
pp. 5451-5469 ◽  
Author(s):  
Graham R. Simpkins ◽  
Laura M. Ciasto ◽  
David. W. J. Thompson ◽  
Matthew H. England
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
Large Scale ◽  
Southern Oscillation ◽  
Principal Component ◽  
Southern Annular Mode ◽  
Enso Cycle ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration

Abstract The observed relationships between anomalous Antarctic sea ice concentration (SIC) and the leading patterns of Southern Hemisphere (SH) large-scale climate variability are examined as a function of season over 1980–2008. Particular emphasis is placed on 1) the interactions between SIC, the southern annular mode (SAM), and El Niño–Southern Oscillation (ENSO); and 2) the contribution of these two leading modes to the 29-yr trends in sea ice. Regression, composite, and principal component analyses highlight a seasonality in SH sea ice–atmosphere interactions, whereby Antarctic sea ice variability exhibits the strongest linkages to the SAM and ENSO during the austral cold season months. As noted in previous work, a dipole in SIC anomalies emerges in relation to the SAM, characterized by centers of action located near the Bellingshausen/Weddell and Amundsen/eastern Ross Seas. The structure and magnitude of this SIC dipole is found to vary considerably as a function of season, consistent with the seasonality of the overlying atmospheric circulation anomalies. Relative to the SAM, the pattern of sea ice anomalies linked to ENSO exhibits a similar seasonality but tends to be weaker in amplitude and more diffuse in structure. The relationships between ENSO and sea ice also exhibit a substantial nonlinear component, highlighting the need to consider both season and phase of the ENSO cycle when diagnosing ENSO–SIC linkages. Trends in SIC over 1980–2008 are not significantly related to trends in either the SAM or ENSO during any season, including austral summer when the trend in the SAM is most pronounced.

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