Snow depth on Arctic sea ice from historical in situ data

Shalina, Elena V.; Sandven, Stein

doi:10.5194/tc-12-1867-2018

Cited by 32 publications

(32 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, snow depth over sea ice is one of the key parameters in sea ice thickness retrieval for all existing satellite datasets. Though progress has been made in reducing the uncertainties in estimating snow depth from space, its uncertainty remains high (Lawrence et al, 2018;Shalina and Sandven, 2018). One major challenge for improving sea ice thickness retrievals is the lack of "truth" validation datasets.…”

Section: Discussionmentioning

confidence: 99%

Multidecadal Arctic sea ice thickness and volume derived from ice age

Liu

Key

Wang³

et al. 2020

The Cryosphere

View full text Add to dashboard Cite

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

show abstract

Section: Discussionmentioning

confidence: 99%

Multidecadal Arctic sea ice thickness and volume derived from ice age

Liu

Key

Wang³

et al. 2020

The Cryosphere

View full text Add to dashboard Cite

show abstract

“…Though progress has been made in reducing the uncertainties in estimating snow depth from space, its uncertainty remains high (Lawrence et al 2018, Shalina et al 2018. One major challenge for improving sea ice thickness retrievals is the lack of "truth" validation data sets.…”

Section: Discussionmentioning

confidence: 99%

Multidecadal Arctic sea ice thickness and volume derived from ice age

Liu¹,

Key²,

Wang³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Arctic sea ice is a key component of the Arctic climate system, which in turn impacts global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000), the Ice, Cloud, and land Elevation Satellite (ICESat, 2003–2008), and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice age-based thickness and volume show good agreement in terms of bias and root mean square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February/March and October/November. Sea ice volume exhibits a decreasing trend of −411 km3/year from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting volume, changes in sea ice thickness from November to May contribute at least 80 %, decreasing to around 50 % in August and September. Changes in sea ice area contribute less than 30 % in all months.

show abstract

“…SS18 combines NP data (as in W 99) with additional snow data from the Soviet airborne expeditions (Sever), to produce spring (March-April-May) snow depth fields. Since the aircraft would land on level FYI, SS18 is not limited to MYI in the central Arctic (as W 99), but includes FYI in the Eurasian seas as well (Shalina and Sandven, 2018). The spatial resolution of the SS18 climatology is 100 × 100km within Arctic basin.…”

Section: Snow Depth Climatologiesmentioning

confidence: 99%

“…The climatology from Shalina and Sandven (2018) provides additional detail in the marginal seas, especially over the Eurasian seas but is limited to spring snow depth estimates. Overall SS18 has lower snow depths in the central Arctic compared with W 99.…”

Section: Snowmodel-mentioning

confidence: 99%

Inter-comparison of snow depth over sea ice from multiple methods

Zhou

Stroeve

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

show abstract

Snow depth on Arctic sea ice from historical in situ data

Cited by 32 publications

References 53 publications

Multidecadal Arctic sea ice thickness and volume derived from ice age

Multidecadal Arctic sea ice thickness and volume derived from ice age

Multidecadal Arctic sea ice thickness and volume derived from ice age

Inter-comparison of snow depth over sea ice from multiple methods

Contact Info

Product

Resources

About