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Research Spending & Results

Award Detail

Doing Business As Name:University of New Hampshire
  • Lawrence C Hamilton
  • (603) 862-1859
Award Date:11/13/2017
Estimated Total Award Amount: $ 215,868
Funds Obligated to Date: $ 215,868
  • FY 2018=$215,868
Start Date:01/01/2018
End Date:12/31/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.050
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NSFGEO-NERC Collaborative Research: Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2)
Federal Award ID Number:1748325
DUNS ID:111089470
Parent DUNS ID:001765866
Program Officer:
  • Marc Stieglitz
  • (703) 292-8029

Awardee Location

Awardee Cong. District:01

Primary Place of Performance

Organization Name:University of New Hampshire
Cong. District:01

Abstract at Time of Award

NSFGEO-NERC Collaborative Research: Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2) The shrinking Arctic sea-ice cover has captured the attention of the world. A downward September trend has accelerated over the last decade, with the 10 lowest September sea-ice extents occurring in the last 10 years. An essentially ice-free Arctic during summer is expected by mid-century. Loss of the sea- ice cover has profound consequences for ecosystems and human activities in the Arctic, so there is an urgent need to advance sea-ice predictions in all seasons at both the pan-Arctic and regional scales. A better quantification of the role of oceanic heat and climate variations in the Pacific sector, new observational-based sea-ice products, and network activities will advance understanding of seasonal predictability of Arctic sea ice, the limits of this predictability, and the economic value of forecasts for stakeholders. The network supported by this grant will examine origins and impacts of extreme ocean surface warming in preconditioning the ice cover in the Pacific Arctic for continued major reductions in sea-ice extent and duration. A key finding that emerged from the earlier Sea Ice Prediction Network (SIPN) effort is that predictions of September sea-ice extent tend to have less skill in extreme years that strongly depart from the trend line. The objective of proposed research under Phase 2 of SIPN (SIPN2) is to improve forecast skill through adopting a multi-disciplinary approach that includes modeling, new products, data analysis, scientific networks, and stakeholder engagement. This grant will: Investigate the sensitivity of subseasonal-to-seasonal sea-ice predictability in the Alaska Arctic to variations in oceanic heat and large- scale atmospheric forcing using a dynamical model Community Earth System Model (NCAR CESM) and statistical forecasting tools, focusing on spatial fields in addition to total extent summaries; Assess the accuracy of Sea Ice Outlook (SIO) submissions based on methodology and initialization; Develop new observation-based products for improving sea-ice predictions, including sea-ice thickness, surface roughness, melt ponds, and snow depth; Evaluate the socio-economic value of sea-ice forecasts to stakeholders who manage ship traffic and coastal village resupply in the Alaska Sector, and engage the public in Arctic climate and sea-ice prediction through blog exchanges, accessible SIO reports, bi-monthly webinars, and by making public data sources useful to non-scientists and scientists alike; and Continue and evolve network activities to generate SIO forecasts and reporting for September minima as in SIPN and expand SIPN2 forecasts to include full spatial resolution and emerging ice-anomaly-months (October - November). This work will directly engage stakeholders that create and use sea-ice forecasts in Alaska and lead to improved safety around sea ice. Work under SIPN2 will also track public awareness and perceptions regarding sea ice, helping to raise understanding through accessible reports, discussions, and public data sources useful to non-scientists and scientists alike. Stakeholder engagement during the research process will potentially facilitate rapid research-to-operations implementation of the products of this work.

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