scholarly journals Landfast ice thickness in the Canadian Arctic Archipelago from Observations and Models

2016 ◽  
Author(s):  
Stephen E. L. Howell ◽  
Frédéric Laliberté ◽  
Ron Kwok ◽  
Chris Derksen ◽  
Joshua King
Keyword(s):  
Canadian Arctic ◽  
Coupled Model ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
Coupled Models ◽  
Ocean Reanalysis ◽  
Landfast Ice ◽  
Thickness Variability ◽  
Arctic Ice

Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than five decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957–2014) were statistically significant at Cambridge Bay (−4.31 ± 1.4 cm decade-1), Eureka (−4.65 ± 1.7 cm decade-1) and Alert (−4.44 ± 1.6 cm decade-1) but not at Resolute. Over the 50+ year record, the ice thinned by ~ 0.24–0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to two magnitudes. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

scholarly journals Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

2016 ◽  
Vol 10 (4) ◽  
pp. 1463-1475 ◽  
Author(s):  
Stephen E. L. Howell ◽  
Frédéric Laliberté ◽  
Ron Kwok ◽  
Chris Derksen ◽  
Joshua King
Keyword(s):  
Canadian Arctic ◽  
Coupled Model ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
Coupled Models ◽  
Ocean Reanalysis ◽  
Landfast Ice ◽  
Thickness Variability ◽  
Arctic Ice

Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than 5 decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957–2014) were statistically significant at Cambridge Bay (−4.31 ± 1.4 cm decade−1), Eureka (−4.65 ± 1.7 cm decade−1) and Alert (−4.44  ± 1.6 cm −1) but not at Resolute. Over the 50+-year record, the ice thinned by  ∼ 0.24–0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to 2 orders of magnitude. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

scholarly journals Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter

2017 ◽  
Vol 34 (9) ◽  
pp. 1985-1999 ◽  
Author(s):  
Xi Liang ◽  
Qinghua Yang ◽  
Lars Nerger ◽  
Svetlana N. Losa ◽  
Biao Zhao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Coupled Model ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Canadian Arctic Archipelago ◽  
Negative Effects ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Ice

AbstractSea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations

2018 ◽  
Vol 12 (4) ◽  
pp. 1233-1247 ◽  
Author(s):  
Xianmin Hu ◽  
Jingfan Sun ◽  
Ting On Chan ◽  
Paul G. Myers
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Horizontal Resolution ◽  
Scale Model ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
High Concentration ◽  
Baffin Bay ◽  
Ice Growth ◽  
Ice Volume

Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest to science, as well as local communities and their economy. In this study, based on the NEMO numerical framework including the LIM2 sea ice module, simulations at both 1∕4 and 1/12∘ horizontal resolution were conducted from 2002 to 2016. The model captures well the general spatial distribution of ice thickness in the CAA region, with very thick sea ice (∼ 4 m and thicker) in the northern CAA, thick sea ice (2.5 to 3 m) in the west-central Parry Channel and M'Clintock Channel, and thin (<2 m) ice (in winter months) on the east side of CAA (e.g., eastern Parry Channel, Baffin Island coast) and in the channels in southern areas. Even though the configurations still have resolution limitations in resolving the exact observation sites, simulated ice thickness compares reasonably (seasonal cycle and amplitudes) with weekly Environment and Climate Change Canada (ECCC) New Ice Thickness Program data at first-year landfast ice sites except at the northern sites with high concentration of old ice. At 1∕4 to 1/12∘ scale, model resolution does not play a significant role in the sea ice simulation except to improve local dynamics because of better coastline representation. Sea ice growth is decomposed into thermodynamic and dynamic (including all non-thermodynamic processes in the model) contributions to study the ice thickness evolution. Relatively smaller thermodynamic contribution to ice growth between December and the following April is found in the thick and very thick ice regions, with larger contributions in the thin ice-covered region. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay while a decline (r2 ≈ 0.6, p < 0.01) is simulated in Parry Channel region. The two main contributors (thermodynamic growth and lateral transport) have high interannual variabilities which largely balance each other, so that maximum ice volume can vary interannually by ±12 % in the northern CAA, ±15 % in Parry Channel, and ±9 % in Baffin Bay. Further quantitative evaluation is required.

Future Projections of Landfast Ice Thickness and Duration in the Canadian Arctic

2006 ◽  
Vol 19 (20) ◽  
pp. 5175-5189 ◽  
Author(s):  
J. A. Dumas ◽  
G. M. Flato ◽  
R. D. Brown
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Canadian Arctic ◽  
Future Climate Change ◽  
Ice Thickness ◽  
Climate Modelling ◽  
Regional Variability ◽  
Landfast Ice ◽  
Break Up

Abstract Projections of future landfast ice thickness and duration were generated for nine sites in the Canadian Arctic and one site on the Labrador coast with a simple downscaling technique that used a one-dimensional sea ice model driven by observationally based forcing and superimposed projected future climate change from the Canadian Centre for Climate Modelling and Analysis global climate model (CGCM2). For the Canadian Arctic sites the downscaling approach indicated a decrease in maximum ice thickness of 30 and 50 cm and a reduction in ice cover duration of 1 and 2 months by 2041–60 and 2081–2100, respectively. In contrast, there is a slight increase in simulated landfast ice thickness and duration at Cartwright in the future due to its sensitivity to snow–ice formation with increased snowfall and to a projected slight cooling over this site (along the Labrador coast) by CGCM2. The magnitude of simulated changes in freeze-up and break-up date was largest for freeze up (e.g., 52 days later at Alert by 2081–2100), and freeze-up date changes exhibited much greater regional variability than break up, which was simulated to be 30 days earlier by 2081–2100 over the Canadian Arctic sites.

scholarly journals Evaluation of Sea-Ice Thickness from four reanalyses in the Antarctic Weddell Sea

2020 ◽  
Author(s):  
Qian Shi ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Jinfei Wang ◽  
François Massonnet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Weddell Sea ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
First Year ◽  
Coupled Models ◽  
Sea Ice Thickness ◽  
The Antarctic

Abstract. Ocean-sea ice coupled models constrained by varied observations provide different ice thickness estimates in the Antarctic. We evaluate contemporary monthly ice thickness from four reanalyses in the Weddell Sea, the German contribution of the Estimating the Circulation and Climate of the Ocean project, Version 2 (GECCO2), the Southern Ocean State Estimate (SOSE), the Nucleus for European Modelling of the Ocean (NEMO) based ocean-ice model (called NEMO-EnKF), and the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), and with reference observations from ICESat-1, Envisat, upward looking sonars and visual ship-based sea-ice observations. Compared with ICESat-1 altimetry and in situ observations, all reanalyses underestimate ice thickness near the coast of the western Weddell Sea, even though ICESat-1 and visual observations may be biased low. GECCO2 and NEMO-EnKF can well reproduce the seasonal variation of first-year ice thickness in the eastern Weddell Sea. In contrast, GIOMAS ice thickness performs best in the central Weddell Sea, while SOSE ice thickness agrees most with the observations in the southern coast of the Weddell Sea. In addition, only NEMO-EnKF can reproduce the seasonal spatial evolution of ice thickness distribution well, characterized by the thick ice shifting from the southwestern and western Weddell Sea in summer to the western and northwestern Weddell Sea in spring. We infer that the thick ice distribution is correlated with its better simulation of northward ice motion in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis for understanding the recent variability of sea-ice volume in the Antarctic.

scholarly journals Thermodynamic and Dynamic Ice Thickness Changes in the Canadian Arctic Archipelago in NEMO-LIM2 Numerical Simulations

2017 ◽  
Author(s):  
Xianmin Hu ◽  
Jingfan Sun ◽  
Ting On Chan ◽  
Paul G. Myers
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Numerical Simulations ◽  
Canadian Arctic ◽  
Horizontal Resolution ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
The West ◽  
Sea Ice Thickness ◽  
Frame Work

Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest. In this study, based on the NEMO numerical frame work including the LIM2 sea ice module, simulations at both 1/4° and 1/12° horizontal resolution were conducted from 2002 to 2016. The model captures well the general spatial distribution of ice thickness in the CAA region, with very thick sea ice (∼&amp;rthinsp;4 m and thicker) in the northern CAA, thick sea ice (2.5 m to 3 m) in the west-central Parry Channel and M'Clintock Channel, and thin (

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