Satellite-derived sea-ice export and its impact on Arctic ice mass
balance

Ricker, Robert; Girard-Ardhuin, Fanny; Krumpen, Thomas; Lique, Camille

doi:10.5194/tc-2018-6

Cited by 6 publications

(12 citation statements)

References 33 publications

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“…Consequently, the area export dominantly contributes more than the thinning of ice in controlling the Fram strait's ice volume export. These results are very well consistent with Ricker et al (2018) who, using different satellite products, observed and found the ice drift as the main driver of the annual and interannual ice volume export in Fram Strait.…”

Section: Trends Of Volume Exportsupporting

confidence: 92%

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Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations

et al. 2019

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Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990-2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 10 3 km 2 a − 1 (1990-2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 10 3 km 3 a − 1 (1990-2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.

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Section: Trends Of Volume Exportsupporting

confidence: 92%

“…Smedsrud et al (2017) showed that sea ice export through Fram Strait preconditions early ice retreat and influences summer ice anomalies during some years which are higher after 2004. Following Ricker et al (2018), 54% of winter multiyear ice volume changes can be explained by the variations of ice volume export through the Fram Strait.…”

Section: Introductionmentioning

confidence: 99%

Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations

et al. 2019

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“…Interestingly, the impact of using the wind forcing from an anomalous year 30 (2009/10, stronger transpolar-drift), CICE-climforcing-wind20100ini, is stronger on April ice thickness (-0.13 m) than from the cold atmospheric conditions. This confirms the important role of sea ice dynamics and export through the Fram Strait in controlling the sea ice volume variability (Ricker et al, 2018).…”

Section: Impact Of Initial Conditions and Atmospheric Forcingsupporting

confidence: 67%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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“…Following ERS, Giles et al [] used ESA Envisat (2002–2012) data from 2002–2008 to show a thinning of winter sea ice across the Arctic after the 2007 record minimum September sea ice extent that year. Subsequent studies have used CryoSat‐2 data to suggest that year‐to‐year fluctuations in winter Arctic sea ice thickness and volume since 2010 can be attributed to summer temperatures [Kwok & Cunningham, ; Tilling et al, ], wind‐driven ice convergence [Kwok & Cunningham, ], and ice export through the Fram Strait [Ricker et al, ]. To quantify long‐term trends in Arctic sea ice thickness and investigate the drivers of variability, it is necessary to combine multimission satellite data.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

2019

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Multidecadal observations of sea ice thickness, in addition to those available for extent, are key to understanding long-term variations and trends in the amount of Arctic sea ice. The European Space Agency 's Envisat (2002's Envisat ( -2010 and CryoSat-2 (2010-present) satellite radar altimeter missions provide a continuous 17-year dataset with the potential to estimate sea ice thickness. However, the satellite footprints are not equal in area and so different distributions of floes and leads are sampled by each mission. Here, we compare lead and floe sampling from Envisat and CryoSat-2 to investigate the impact of geometric sampling differences on Arctic sea ice thickness estimates. We find that Envisat preferentially samples wider, thicker sea ice floes, and that floes in less consolidated ice regions are effectively thickened by off-nadir ranging to leads. Consequently, Envisat sea ice thicknesses that are an average of 80 cm higher than CryoSat-2 over first-year ice and 23 cm higher over multiyear ice. By considering the along-track distances between lead and floe measurements, we are able to develop a sea ice thickness correction that is based on Envisat's inability to resolve discrete surfaces relative to CryoSat-2. This is a novel, physically based approach to addressing the bias between the satellites and reduces the average thickness difference to negligible values over first-year and multiyear ice. Finally, we evaluate our new bias-corrected Envisat sea ice thickness product using independent airborne, moored-buoy and submarine data. The European Space Agency's Envisat and CryoSat-2 satellites have the potential to produce a continuous record of Arctic sea ice thickness since 2002, but this is complicated by the fact that the satellites do not sample the sea ice surface in the same way. We find that Envisat is only able to sample larger, thicker sea ice relative to CryoSat-2, because of its poorer resolution. In this paper we account for these differences in sampling to combine Arctic sea ice thickness estimates from two the satellite missions. Applying a sea ice thickness bias correction to Envisat data reduces the ice thickness difference between Envisat and CryoSat-2 from an average of 53.0 to 0.5 cm So far, only empirical solutions have been proposed to reconcile Arctic sea ice freeboard retrievals from Envisat with those from CryoSat-2. For example, Guerreiro et al.[2017] developed a radar freeboard

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Satellite-derived sea-ice export and its impact on Arctic ice mass balance

Cited by 6 publications

References 33 publications

Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations

Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

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