Arctic sea ice conditions in spring 2009–2013 prior to melt

Richter‐Menge, Jacqueline A.; Farrell, S. L.

doi:10.1002/2013gl058011

Cited by 45 publications

(55 citation statements)

References 20 publications

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“…The median feature height across the 0-400 km percentile bins shows higher variability than the CA region. The 95th percentile results from 0 to 300 km are lowest in 2013, which may be due, in part, to the thin level ice sampled in the Chukchi Sea north of Point Hope in 2013 (Richter-Menge and Farrell, 2013). The feature heights also tend to increase (across most percentile ranges) at distances greater than 700 km away, which is likely due to the import of MYI from the CA into the northern Beaufort Sea.…”

Section: Central Arcticmentioning

confidence: 95%

Characterizing Arctic sea ice topography using high-resolution IceBridge data

et al. 2016

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Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes.The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

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Section: Central Arcticmentioning

confidence: 95%

Characterizing Arctic sea ice topography using high-resolution IceBridge data

et al. 2016

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“…-BGEP, Beaufort Gyre Exploration Project: this data set is comprised of a set of three or four (depending on the year) bottom-anchored moorings with top-mounted ULS instruments located in the Beaufort Sea (Woods Hole Oceanographic Institute; Krishfield et al, 2014 Kurtz et al, 2012Richter-Menge and Farrell, 2013). The IceBridge mission was initialized after the end of operations of the ICESat-1 satellite in order to partially continue the time series of sea ice and ice sheet observations until the launch of ICESat-2.…”

Section: Datamentioning

confidence: 99%

Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations

2015

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Abstract. Sea ice thickness is a fundamental climate state variable that provides an integrated measure of changes in the high-latitude energy balance. However, observations of mean ice thickness have been sparse in time and space, making the construction of observation-based time series difficult. Moreover, different groups use a variety of methods and processing procedures to measure ice thickness, and each observational source likely has different and poorly characterized measurement and sampling errors. Observational sources used in this study include upward-looking sonars mounted on submarines or moorings, electromagnetic sensors on helicopters or aircraft, and lidar or radar altimeters on airplanes or satellites. Here we use a curve-fitting approach to determine the large-scale spatial and temporal variability of the ice thickness as well as the mean differences between the observation systems, using over 3000 estimates of the ice thickness. The thickness estimates are measured over spatial scales of approximately 50 km or time scales of 1 month, and the primary time period analyzed is 2000-2012 when the modern mix of observations is available. Good agreement is found between five of the systems, within 0.15 m, while systematic differences of up to 0.5 m are found for three others compared to the five. The trend in annual mean ice thickness over the Arctic Basin is −0.58 ± 0.07 m decade −1 over the period 2000-2012. Applying our method to the period 1975-2012 for the central Arctic Basin where we have sufficient data (the SCICEX box), we find that the annual mean ice thickness has decreased from 3.59 m in 1975 to 1.25 m in 2012, a 65 % reduction. This is nearly double the 36 % decline reported by an earlier study. These results provide additional direct observational evidence of substantial sea ice losses found in model analyses.

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“…Meanwhile, the trends continue for the recent loss of the oldest and thicker ice types, which accelerated starting in 2005 (Maslanik et al 2011). 5.14d; Richter-Menge and Farrell 2013). 5.14d; Richter-Menge and Farrell 2013).…”

Section: E Carbon Dioxide and Methane-l Bruhwiler E Jmentioning

confidence: 99%