scholarly journals Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007

2009 ◽  
Vol 114 (C5) ◽  
Author(s):  
P. Rampal ◽  
J. Weiss ◽  
D. Marsan
Keyword(s):  
Sea Ice ◽  
Deformation Rate ◽  
Arctic Sea Ice ◽  
Positive Trend ◽  
Arctic Sea ◽  
The Mean

Outflow of Arctic Ocean Sea Ice into the Greenland and Barents Seas: 1979–2007

2009 ◽  
Vol 22 (9) ◽  
pp. 2438-2457 ◽  
Author(s):  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Sea Level ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Positive Trend ◽  
Fram Strait ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Franz Josef Land ◽  
The Mean

Abstract Twenty-nine years of Arctic sea ice outflow into the Greenland and Barents Seas are summarized. Outflow is computed at three passages: Fram Strait, between Svalbard and Franz Josef Land (S–FJL), and between Franz Josef Land and Severnaya Zemlya (FJL–SZ). Ice drift at the flux gates has been reprocessed using a consistent and updated time series of passive microwave brightness temperature and ice concentration (IC) fields. Over the record, the mean annual area outflow at the Fram Strait is 706(113) × 103 km2; it was highest in 1994/95 (1002 × 103 km2) when the North Atlantic Oscillation (NAO) index was near its 29-yr peak. The strength of the “Transpolar Drift Stream” (TDS) was high during the late 1980s through the mid-1990s. There is no statistically significant trend in the Fram Strait area flux. Even though there is a positive trend in the gradient of cross-strait sea level pressure, the outflow has not increased because of a negative trend in IC. Seasonally, the area outflow during recent summers (in 2005 and 2007) has been higher (> 2σ from the mean) than average, contributing to the decline of summer ice coverage. Without updated ice thickness estimates, the best estimate of mean annual volume flux (between 1991 and 1999) stands at ∼2200 km3 yr−1 (∼0.07 Sv: Sv ≡ 106 m3 s−1). Net annual outflow at the S–FJL passage is 37(39) × 103 km2; the large outflow of multiyear ice in 2002–03, marked by an area and volume outflow of 141 × 103 km2 and ∼300 km3, was unusual over the record. At the FJL–SZ passage, there is a mean annual inflow of 103(93) × 103 km2 of seasonal ice into the Arctic. While the recent pattern of winter Arctic circulation and sea level pressure (SLP) has nearly reverted to its conditions typical of the 1980s, the summer has not. Compared to the 1980s, the recent summer SLP distributions show much lower SLPs (2–3 hPa) over much of the Arctic. Overall, there is a strengthening of the summer TDS. Examination of the exchanges between the Pacific and Atlantic sectors shows a long-term trend that favors the summer advection of sea ice toward the Atlantic associated with a shift in the mean summer circulation patterns.

scholarly journals Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130283 ◽  
Author(s):  
Stephan Juricke ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Stochastic Perturbations ◽  
Arctic Sea ◽  
The Mean ◽  
Mean Climate

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

Response of Northern Hemisphere weather and climate to Arctic sea ice decline: The role of resolution in Polar Amplification Model Intercomparison Project (PAMIP) simulations

2021 ◽  
Author(s):  
Jan Streffing ◽  
Tido Semmler ◽  
Lorenzo Zampieri ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Atmospheric Response ◽  
Model Intercomparison ◽  
Polar Amplification ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Weather And Climate ◽  
Intercomparison Project ◽  
The Mean

<p>The impact of Arctic sea ice decline on the weather and climate in mid-latitudes is still much debated, with observation suggesting a strong and models a much weaker link. In this study, we use the atmospheric model OpenIFS, in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP), to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125km to 39km with 91 vertical levels; in a second step resolution is further increased to 16km with 137 levels in the vertical. We find that neither the mean atmospheric response nor the ensemble convergence toward the mean are strongly impacted by the model resolutions considered here.</p>

An estimate of the mean field of Arctic sea ice motion

1984 ◽  
Vol 89 (C6) ◽  
pp. 10623 ◽  
Author(s):  
R. Colony ◽  
A. S. Thorndike
Keyword(s):  
Sea Ice ◽  
Mean Field ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
The Mean

scholarly journals Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

2021 ◽  
Author(s):  
Robbie Mallett ◽  
Julienne Stroeve ◽  
Michel Tsamados ◽  
Rosemary Willatt ◽  
Thomas Newman ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Field ◽  
The Mean ◽  
Ice Impacts ◽  
Multiyear Ice

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of heat and light, and is of importance for satellite estimates of sea ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

scholarly journals Arctic sea ice in the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 simulations

2012 ◽  
Vol 8 (4) ◽  
pp. 3445-3480
Author(s):  
M. Berger ◽  
J. Brandefelt ◽  
J. Nilsson
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Solar Insolation ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
The Mean ◽  
The Difference ◽  
Mean State ◽  
Paleoclimate Modelling

Abstract. The Arctic sea ice in the mid-Holocene simulations of 11 coupled global circulation models part of the Paleoclimate Modelling Intercomparison Project phase 2 (PMIP2) is analysed in this study. The work includes a comparison of the mid-Holocene simulations to the pre-industrial control simulations for each individual model and also a model-model comparison. The forcing conditions in the mid-Holocene and pre-industrial simulations differ in the atmospheric methane concentration and the latitudinal and monthly distribution of solar insolation (due to differences in the orbital parameters). Other studies have found that the difference in insolation, with increased northern hemisphere summer insolation, explain the major differences between the simulated mid-Holocene and pre-industrial climates. The response of the simulated sea ice extent and thickness to the changes in solar insolation and atmospheric greenhouse gases is investigated. The model-model variation in pre-industrial simulated Arctic sea ice is large, with sea ice area extent ranging from 10.1 to 28.2 (7.01 to 24.6) million km2 in March (September), and the maximum sea ice thickness ranging from 1.5 m to more than 5 m in both September and March. Nevertheless, all models agree on the sign of the difference between mid-Holocene and pre-industrial in both March and September. All models have smaller summer sea ice extent and thinner ice cover in all seasons in the mid-Holocene climate compared to the control (pre-industrial) climate. The reduction in sea ice extent is mostly confined to the sea ice margins, whereas the thinning of the ice occurs over the entire ice cover. In addition, the models also experience an enhanced summer warming north of 60° N. For the central Arctic region, models with thicker ice in the mean state in the control simulation experience the largest change in the mean state between the two climates. Comparison to available Climate Model Intercomparison Project 3 (CMIP3) simulations with the same model version and atmospheric CO2 concentration increased to a doubling has also been performed. The sea ice response in this future scenario is stronger than the response in the mid-Holocene simulation. Again we find that the model with the thickest mean state has the largest response.

scholarly journals Spatial and Temporal Variations in the Extent and Thickness of Arctic Landfast Ice

2019 ◽  
Vol 12 (1) ◽  
pp. 64 ◽  
Author(s):  
Zixuan Li ◽  
Jiechen Zhao ◽  
Jie Su ◽  
Chunhua Li ◽  
Bin Cheng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Coastal Areas ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Maximum Extent ◽  
Arctic Sea ◽  
Landfast Ice ◽  
The Mean ◽  
The Laptev Sea

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976–2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 104 km2/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 104 km2/yr (5.2% per decade). The annual maximum extent reached 2.3 × 106 km2 in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 106 km2, which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.

scholarly journals Using records from submarine, aircraft and satellite to evaluate climate model simulations of Arctic sea ice thickness

2014 ◽  
Vol 8 (2) ◽  
pp. 2179-2212 ◽  
Author(s):  
J. Stroeve ◽  
A. Barrett ◽  
M. Serreze ◽  
A. Schweiger
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Ocean Modeling ◽  
Cmip5 Models ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
The Mean ◽  
Arctic Sea Ice Thickness

Abstract. Arctic sea ice thickness distributions from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 5 are evaluated against observations from submarines, aircraft and satellites. While it's encouraging that the mean thickness distributions from the models are in general agreement with observations, the spatial patterns of sea ice thickness are poorly represented in most models. The poor spatial representation of thickness patterns is associated with a failure of models to represent details of the mean atmospheric circulation pattern that governs the transport and spatial distribution of sea ice. The climate models as a whole also tend to underestimate the rate of ice volume loss from 1979 to 2013, though the multi-model ensemble mean trend remains within the uncertainty of that from the Pan-Arctic Ice Ocean Modeling and Assimilation System. These results raise concerns regarding the ability of CMIP5 models to realistically represent the processes driving the decline of Arctic sea ice and project the timing of when a seasonally ice-free Arctic may be realized.

scholarly journals Bacteria and viruses in Arctic Sea ice

2019 ◽  
Vol 59 (3) ◽  
pp. 373-382
Author(s):  
A. F. Sazhin ◽  
N. D. Romanova ◽  
A. I. Kopylov ◽  
E. A. Zabotkina
Keyword(s):  
Sea Ice ◽  
Vertical Distribution ◽  
Bacterial Abundance ◽  
Mean Value ◽  
Arctic Sea Ice ◽  
Bacterial Cells ◽  
The North ◽  
Viral Particles ◽  
Arctic Sea ◽  
The Mean

We studied vertical distribution of bacteria and viruses in different layers of the Arctic sea ice drilled at the North Pole. The sampled multi-year ice was characterized by uneven vertical distribution of bacterial abundance. This characteristic varied within the range of 8±1.2×103 to 95±2.6×103 cells ml-1. The layers with the maximal bacterial abundance were located in the intermediate and lower layers of the ice cores. Bacterial biomass varied from 0.5 to 5 mg C m-3 with the mean value 1.57±0.2 mg C m-3. The ratio of viral to bacterial abundance varied from 0.6 to 28, with the mean value 12.5. The average total number of phages attached to bacteria was 6.2×103 viral particles ml-1. The number of viral particles located within bacterial cells varied from 2 to 21 particles per a bacterial cell. The frequency of visibly infected bacterial cells (FVIC) calculated for the upper, intermediate and lower layers of the ice was 0.92, 1.23 and 0.8% of the total bacterial abundance, respectively. The overall frequency of infected cells (FIC) calculated for the same layers was 6.3, 8.4 and 0.8% of bacteria numbers, respectively, while the viral-mediated mortality of bacteria (VMB) was 7.1, 9.8 and 6.1 %, respectively. Our data show that during the study period the rate of viral infection of bacterial cells and the viral-mediated mortality of bacterial cells in the multy-year ice of the North Pole were relatively low.

scholarly journals Comparing and contrasting the behaviour of Arctic and Antarctic sea ice over the 35 year period 1979-2013

2015 ◽  
Vol 56 (69) ◽  
pp. 18-28 ◽  
Author(s):  
Ian Simmonds
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Positive Trend ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
The Antarctic

AbstractWe examine the evolution of sea-ice extent (SIE) over both polar regions for 35 years from November 1978 to December 2013, as well as for the global total ice (Arctic plus Antarctic). Our examination confirms the ongoing loss of Arctic sea ice, and we find significant (p˂ 0.001) negative trends in all months, seasons and in the annual mean. The greatest rate of decrease occurs in September, and corresponds to a loss of 3 x 106 km2 over 35 years. The Antarctic shows positive trends in all seasons and for the annual mean (p˂0.01), with summer attaining a reduced significance (p˂0.10). Based on our longer record (which includes the remarkable year 2013) the positive Antarctic ice trends can no longer be considered ‘small’, and the positive trend in the annual mean of (15.29 ± 3.85) x 103 km2 a–1 is almost one-third of the magnitude of the Arctic annual mean decrease. The global annual mean SIE series exhibits a trend of (–35.29 ± 5.75) x 103 km2 a-1 (p<0.01). Finally we offer some thoughts as to why the SIE trends in the Coupled Model Intercomparison Phase 5 (CMIP5) simulations differ from the observed Antarctic increases.

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