Network models for ponding on sea ice

2020 ◽  
Author(s):  
Michael Coughlan ◽  
Ian Hewitt ◽  
Sam Howison ◽  
Andrew Wells
Keyword(s):  
Sea Ice ◽  
Network Models ◽  
Pond Water ◽  
Arctic Sea Ice ◽  
Water Levels ◽  
Catchment Basin ◽  
Sea Ice Extent ◽  
Albedo Feedback ◽  
Catchment Areas ◽  
The Individual

<p>Arctic sea ice forms a thin but significant layer at the ocean surface, mediating key climate feedbacks. During summer, surface melting produces considerable volumes of water, which collect on the ice surface in ponds. These ponds have long been suggested as a contributing factor to the discrepancy between observed and predicted sea ice extent. When viewed at large scales ponds have a complicated, approximately fractal geometry and vary in area from tens to thousands of square meters. Increases in pond depth and area lead to further increases in heat absorption and overall melting, contributing to the ice-albedo feedback.</p><p>Previous modelling work has focussed either on the physics of individual ponds or on the statistical behaviour of systems of ponds. We present a physically-based network model for systems of ponds which accounts for both the individual and collective behaviour of ponds. Each pond initially occupies a distinct catchment basin and evolves according to a mass-conserving differential equation representing the melting dynamics for bare and water-covered ice. Ponds can later connect together to form a network with fluxes of water between catchment areas, constrained by the ice topography and pond water levels.</p><p>We use the model to explore how the evolution of pond area and hence melting depends on the governing parameters, and to explore how the connections between ponds develop over the melt season. Comparisons with observations are made to demonstrate the ways in which the model qualitatively replicates properties of pond systems, including fractal dimension of pond areas and two distinct regimes of pond complexity that are observed during their development cycle. Different perimeter-area relationships exist for ponds in the two regimes. The model replicates these relationships and exhibits a percolation transition around the transition between these regimes, a facet of pond behaviour suggested by previous studies. Our results reinforce the findings of these studies on percolation thresholds in pond systems and further allow us to constrain pond coverage at this threshold - an important quantity in measuring the scale and effects of the ice-albedo feedback.</p>

A network model for ponding on sea ice

2021 ◽  
Author(s):  
Michael Coughlan
Keyword(s):  
Network Model ◽  
Percolation Theory ◽  
Pond Water ◽  
Water Levels ◽  
Catchment Basin ◽  
Stage Of Development ◽  
Development Cycle ◽  
Catchment Areas ◽  
Physically Based ◽  
The Individual

<p>I present a physically-based network model for systems of ponds which accounts for both the individual and collective behaviour of ponds, and allows us to investigate the behaviour of both. Each pond initially occupies a distinct catchment basin and evolves according to a mass-conserving differential equation representing the melting dynamics for bare and water-covered ice. Ponds can later connect together to form a network with fluxes of water between catchment areas, constrained by the ice topography and pond water levels.<span> </span></p><p>I use the model to explore how the evolution of pond area and hence melting depends on the governing parameters, and to explore how the connections between ponds develop over the melt season. Comparisons with observations are made to demonstrate the ways in which the model qualitatively replicates properties of pond systems, including fractal dimension of pond areas and two distinct regimes of pond complexity that are observed during their development cycle. The network structure, and tools from percolation theory also allows us to probe how the connectivity of pond systems affect the system at each stage of development.</p>

scholarly journals Arctic sea ice variability and trends, 1979–2010

2012 ◽  
Vol 6 (2) ◽  
pp. 957-979 ◽  
Author(s):  
D. J. Cavalieri ◽  
C. L. Parkinson
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Arctic Sea Ice ◽  
Negative Trend ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Regions ◽  
Arctic Sea ◽  
The Individual ◽  
The Bering Sea

Abstract. Analyses of 32 yr (1979–2010) of Arctic sea ice extents and areas derived from satellite passive microwave radiometers are presented for the Northern Hemisphere as a whole and for nine Arctic regions. There is an overall negative yearly trend of −51.5 ± 4.1 × 103 km2 yr−1 (−4.1 ± 0.3% decade−1) in sea ice extent for the hemisphere. The sea ice extent trends for the individual Arctic regions are all negative except for the Bering Sea: −3.9 ± 1.1 × 103 km2 yr−1 (−8.7 ± 2.5% decade−1) for the Seas of Okhotsk and Japan, +0.3 ± 0.8 × 103 km2 yr−1 (+1.2 ± 2.7% decade−1) for the Bering Sea, −4.4 ± 0.7 × 103 km2 yr−1 (−5.1 ± 0.9% decade−1) for Hudson Bay, −7.6 ± 1.6 × 103 km2 yr−1 (−8.5 ± 1.8% decade−1) for Baffin Bay/Labrador Sea, −0.5 ± 0.3 × 103 km2 yr−1 (−5.9 ± 3.5% decade−1) for the Gulf of St. Lawrence, −6.5 ± 1.1 × 103 km2 yr−1 (−8.6 ± 1.5% decade−1) for the Greenland Sea, −13.5 ± 2.3 × 103 km2 yr−1 (−9.2 ± 1.6% decade−1) for the Kara and Barents Seas, −14.6 ± 2.3 × 103 km2 yr−1 (−2.1 ± 0.3% decade−1) for the Arctic Ocean, and −0.9 ± 0.4 × 103 km2 yr−1 (−1.3 ± 0.5% decade−1) for the Canadian Archipelago. Similarly, the yearly trends for sea ice areas are all negative except for the Bering Sea. On a seasonal basis for both sea ice extents and areas, the largest negative trend is observed for summer with the next largest negative trend being for autumn.

scholarly journals Arctic climate: changes in sea ice extent outweigh changes in snow cover

2018 ◽  
Vol 12 (10) ◽  
pp. 3373-3382 ◽  
Author(s):  
Aaron Letterly ◽  
Jeffrey Key ◽  
Yinghui Liu
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Positive Trend ◽  
Summer Solstice ◽  
Sea Ice Extent ◽  
Solar Absorption ◽  
Albedo Feedback

Abstract. Recent declines in Arctic sea ice and snow extent have led to an increase in the absorption of solar energy at the surface, resulting in additional surface heating and a further decline in snow and ice. Using 34 years of satellite data, 1982–2015, we found that the positive trend in solar absorption over the Arctic Ocean is more than double that over Arctic land, and the magnitude of the ice–albedo feedback is four times that of the snow–albedo feedback in summer. The timing of the high-to-low albedo transition has shifted closer to the greater insolation of the summer solstice over ocean, but further away from the summer solstice over land. Therefore, decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant radiative feedback mechanism over the last few decades.

scholarly journals Arctic Climate: Changes in Sea Ice Extent Outweigh Changes in Snow Cover

2018 ◽  
Author(s):  
Aaron Letterly ◽  
Jeffrey Key ◽  
Yinghui Liu
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Summer Solstice ◽  
Sea Ice Extent ◽  
Solar Absorption ◽  
Albedo Feedback

Abstract. Recent declines in Arctic sea ice and snow extent have led to an increase in the absorption of solar energy at the surface, resulting in additional surface heating and a further decline in snow and ice. Using 34 years of satellite data, 1982–2015, we found that the trend of solar absorption over the Arctic Ocean is more than double that over Arctic land, and the magnitude of the ice-albedo feedback is four times that of the snow-albedo feedback in summer. The time of the high-to-low albedo transition each year is moving toward the high sun of the summer solstice over ocean but moving away from the summer solstice over land. Therefore, decreasing sea ice cover, not changes in terrestrial snow cover, have been the dominant radiative feedback mechanism over the last few decades and may play an even bigger role in future Arctic climate change.

scholarly journals Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Pacific Side ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

scholarly journals Sources of multi-decadal variability in Arctic sea ice extent

2012 ◽  
Vol 7 (3) ◽  
pp. 034011 ◽  
Author(s):  
J J Day ◽  
J C Hargreaves ◽  
J D Annan ◽  
A Abe-Ouchi
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Arctic Sea Ice ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

scholarly journals Sea-ice induced growth decline in Arctic shrubs

2017 ◽  
Vol 13 (8) ◽  
pp. 20170122 ◽  
Author(s):  
Mads Forchhammer
Keyword(s):  
Sea Ice ◽  
Ring Width ◽  
Plant Productivity ◽  
Climatic Conditions ◽  
Arctic Sea Ice ◽  
Annual Growth ◽  
The Arctic ◽  
Growth Time ◽  
Sea Ice Extent ◽  
Negative Effect

Measures of increased tundra plant productivity have been associated with the accelerating retreat of the Arctic sea-ice. Emerging studies document opposite effects, advocating for a more complex relationship between the shrinking sea-ice and terrestrial plant productivity. I introduce an autoregressive plant growth model integrating effects of biological and climatic conditions for analysing individual ring-width growth time series. Using 128 specimens of Salix arctica , S. glauca and Betula nana sampled across Greenland to Svalbard, an overall negative effect of the retreating June sea-ice extent was found on the annual growth. The negative effect of the retreating June sea-ice was observed for younger individuals with large annual growth allocations and with little or no trade-off between previous and current year's growth.

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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