Sea Ice

Eicken, Hajo; Mahoney, Andrew R.

doi:10.1016/b978-0-12-396483-0.00013-3

Cited by 19 publications

(2 citation statements)

References 32 publications

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“…The increase in human activity and potential for maritime disasters in the presence of ice (e.g., Barber et al, 2018), as well as the need to better understand potential restructuring of food webs as a result of sea ice changes, call for a new suite of sustained observations targeting biogeochemical variables and the coastal zone where much of human activity is concentrated and risks are most pronounced (Eicken and Mahoney, 2015). Much of the demand for these types of observations can be met through a combination of newly developed autonomous sensing systems (Whitlock, 2018) and community-based observations (Johnson et al, 2015).…”

Section: Sea Icementioning

confidence: 99%

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

et al. 2019

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Rapid Arctic warming drives profound change in the marine environment that have significant socio-economic impacts within the Arctic and beyond, including climate and weather hazards, food security, transportation, infrastructure planning and resource extraction. These concerns drive efforts to understand and predict Arctic environmental change and motivate development of an Arctic Region Component of the Global Ocean Observing System (ARCGOOS) capable of collecting the broad, sustained observations needed to support these endeavors. This paper provides a roadmap for establishing the ARCGOOS. ARCGOOS development must be underpinned by a broadly endorsed framework grounded in high-level policy drivers and the scientific and operational objectives that stem from them. This should be guided by a transparent, internationally accepted governance structure with recognized authority and organizational relationships with the national agencies that ultimately execute network plans. A governance model for ARCGOOS must guide selection of objectives, assess performance and fitness-to-purpose, and advocate for resources. A requirements-based framework for an ARCGOOS begins with the Societal Benefit

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Section: Sea Icementioning

confidence: 99%

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

et al. 2019

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“…After that, the Antarctic sea ice cover did not recover and eight new lowest monthly records appeared during 2016-2020 [13]. The uneven changes in Antarctic sea ice potentially cause significant impacts and disaster risks to the polar climates and ecosystems [14,15]. Moreover, the particular change presents unprecedented challenges in understanding the future variations of Antarctic sea ice and the associated climate change.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Antarctic Sea Ice Cover in CMIP6 Prediction with Comparison to AMSR2 during 2015–2021

Zhang

Chen

et al. 2023

Remote Sensing

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A comprehensive assessment of Antarctic sea ice cover prediction is conducted for twelve CMIP6 models under the scenario of SSP2-4.5, with a comparison to the observed data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) during 2015–2021. In the quantitative evaluation of sea ice extent (SIE) and sea ice area (SIA), most CMIP6 models show reasonable variation and relatively small differences compared to AMSR2. CMCC-CM4-SR5 shows the highest correlation coefficient (0.98 and 0.98) and the lowest RMSD (0.98 × 106 km2 and 1.07 × 106 km2) for SIE and SIA, respectively. In the subregions, the models with the highest correlation coefficient and the lowest RMSD for SIE and SIA are inconsistent. Most models tend to predict smaller SIE and SIA compared to the observational data. GFDL-CM4 and FGOALS-g3 show the smallest mean bias (−4.50 and −1.21 × 105 km2) and the most reasonable interannual agreement of SIE and SIA with AMSR2, respectively. In the assessment of sea ice concentration (SIC), while most models can accurately predict the distribution of large SIC surrounding the Antarctic coastal regions, they tend to underestimate SIC and are unable to replicate the major patterns in the sea ice edge region. GFDL-CM4 and FIO-ESM-2-0 exhibit superior performance, with less bias (less than −5%) and RMSD (less than 23%) for SIC in the Antarctic. GFDL-CM4, FIO-ESM-2-0, and CESM2 exhibit relatively high positive correlation coefficients exceeding 0.60 with the observational data, while few models achieve satisfactory linear trend prediction of SIC. Through the comparison with RMSD, Taylor score (TS) consistently evaluates the Antarctic sea ice cover and proves to be a representative statistical indicator and applicable for its assessment. Based on comprehensive assessments of sea ice cover, CESM2, CMCC-CM4-SR5, FGOALS-g3, FIO-ESM-2-0, and GFDL-CM4 demonstrate more reasonable prediction performance. The assessment findings enhance the understanding of the uncertainties associated with sea ice in the CMIP6 models and highlighting the need for a meticulous selection of the multimodel ensemble.

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Persistent artifacts in the NSIDC ice motion data set and their implications for analysis

Szanyi

Lukovich

Barber

et al. 2016

Geophysical Research Letters

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In this study we evaluate weekly Equal‐Area Scalable Earth Grid (EASE‐Grid) sea ice velocities released by the National Snow and Ice Data Centre (NSIDC). We identify persistent Eulerian and Lagrangian features that arise solely as an artifact of the method used in the incorporation of buoy data. This, in turn, significantly impacts calculation of sea ice motion gradients, including divergence, convergence, and shear. Our numerical experiments and comparison with observations further demonstrate the impact of these artificial features on climatological assessments, including age‐of‐ice studies. In particular, we find that age‐of‐ice studies using this data set significantly underestimate multiyear ice extent by an average of 0.8 × 106 km2 in the month of March.

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Sea Ice

Cited by 19 publications

References 32 publications

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

Assessment of Antarctic Sea Ice Cover in CMIP6 Prediction with Comparison to AMSR2 during 2015–2021

Persistent artifacts in the NSIDC ice motion data set and their implications for analysis

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