Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Petty, Alek; Keeney, Nicole; Cabaj, Alex; Kushner, Paul J.; Bagnardi, Marco

doi:10.5194/tc-17-127-2023

Cited by 12 publications

(12 citation statements)

References 73 publications

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“…A. Petty et al (2023) and are consistent with the data posted on Zenodo at https:// zenodo.org/record/7051062.…”

Section: Nesosimsupporting

confidence: 85%

“…A. Petty et al, 2023). NASA ICESat-2 Level 4 Monthly Gridded Sea Ice Thickness data (IS2SITMOGR4) is available at https://nsidc.org/data/is2sitmogr4/versions/2 (A.…”

Section: Discussionmentioning

confidence: 99%

“…A. Petty et al., 2023). First, the measurements from each product are gridded to the 100 × 100 km NESOSIM grid and averaged by day in each grid square.…”

Section: Models and Data Setsmentioning

confidence: 99%

“…A. Petty et al, 2023) is available on Zenodo at https://zenodo.org/ record/7051062 (A. Petty & Cabaj, 2021).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Automated Calibration of a Snow‐On‐Sea‐Ice Model

Cabaj

Kushner

Petty

2023

Earth and Space Science

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Snow on Arctic sea ice has many, contrasting effects on ice thickness and extent. Furthermore, estimates of snow depth on Arctic sea ice are a key input for ice thickness estimates from satellite altimeters such as ICESat‐2. Models such as the NASA Eulerian Snow on Sea Ice Model (NESOSIM) have been recently utilized by the sea ice community to provide time‐varying basin‐wide estimates of snow depth and density on Arctic sea ice. NESOSIM is a two‐snow‐layer model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Two free parameters in NESOSIM, which dictate the bulk effect of wind packing (densification) and blowing snow processes, lack direct observational constraints. We present an indirect calibration of these parameters using a Markov Chain Monte Carlo (MCMC) approach. NESOSIM output is calibrated to observations of snow depth from Operation IceBridge and CRREL‐Dartmouth buoys, and density from historical drifting stations. OIB measurements alone are found to more strictly constrain the blowing snow parameter, and including additional observations yields more physically reasonable density estimates. The MCMC‐calibrated model output is further used to estimate sea ice thickness and uncertainty from model parameter uncertainty using ICESat‐2 freeboard measurements. Despite visible differences in density, the change in ice thickness is minimal. We also find that the model is relatively insensitive to parameter variations, and hence, the snow model uncertainty contribution to ice thickness is small compared to the systematic uncertainty from snow in the current ICESat‐2 thickness product.

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“…A. Petty et al (2023) and are consistent with the data posted on Zenodo at https:// zenodo.org/record/7051062.…”

Section: Nesosimsupporting

confidence: 85%

“…A. Petty et al, 2023). NASA ICESat-2 Level 4 Monthly Gridded Sea Ice Thickness data (IS2SITMOGR4) is available at https://nsidc.org/data/is2sitmogr4/versions/2 (A.…”

Section: Discussionmentioning

confidence: 99%

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Automated Calibration of a Snow‐On‐Sea‐Ice Model

Cabaj

Kushner

Petty

2023

Earth and Space Science

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“…As a result, MYI is more robust and resilient to summer melt than FYI, and thus forms the backbone of the Arctic ice pack through the melt season with the end of winter MYI edge being a prognosticator of the annual minimum sea ice extent (Thomas & Rothrock, 1993). As the Arctic has warmed, the MYI pack has declined in area (Comiso, 2012; Kwok, 2018; Maslanik et al., 2011) and thickness (Kacimi & Kwok, 2022; Kwok et al., 2009; Petty et al., 2023) weakening the backbone of the Arctic ice pack and making it more susceptible to further reductions. MYI loss represents a significant shift in the Arctic environment that has implications for the Arctic ecosystem, the global climate system, industrial and transportation related interests in the north, and most importantly for Inuit who live in the Arctic and rely on the marine environment (Constable et al., 2022; Meredith et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980

Babb,

Galley,

Kirillov

et al. 2023

JGR Oceans

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The loss of multiyear sea ice (MYI) in the Arctic Ocean is a significant change that affects all facets of the Arctic environment. Using a Lagrangian ice age product, we examine MYI loss and quantify the annual MYI area budget from 1980 to 2021 as the balance of export, melt, and replenishment. Overall, MYI area declined at 72,500 km2/yr; however, a majority of the loss occurred during two stepwise reductions that interrupt an otherwise balanced budget and resulted in the northward contraction of the MYI pack. First, in 1989, a change in atmospheric forcing led to a +56% anomaly in MYI export through Fram Strait. The second occurred from 2006 to 2008 with anomalously high melt (+25%) and export (+23%) coupled with low replenishment (−8%). In terms of trends, melt has increased since 1989, particularly in the Beaufort Sea, export has decreased since 2008 due to reduced MYI coverage north of Fram Strait, and replenishment has increased over the full time series due to a negative feedback that promotes seasonal ice survival at higher latitudes exposed by MYI loss. However, retention of older MYI has significantly declined, transitioning the MYI pack toward younger MYI that is less resilient than previously anticipated and could soon elicit another stepwise reduction. We speculate that future MYI loss will be driven by increased melt and reduced replenishment, both of which are enhanced with continued warming and will one day render the Arctic Ocean free of MYI, a change that will coincide with a seasonally ice‐free Arctic Ocean.

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