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Abstract We construct a linear model of microseism power as a function of sea‐ice concentration and ocean‐wave activity with a seismic station located on northern Ellesmere Island. The influence of wind‐ice‐ocean interactions on microseism has been taken into account. We find the increase in microseism power over the last 32 years reflects the long‐term loss of sea ice and increasing ocean‐wave activity in the Arctic Ocean likely associated with climate change. We further assess model performance to determine a representative region over which sea‐ice concentration and ocean‐wave activity most directly influence the microseism power. The seismological methods developed here suggest that there is the potential to augment or refine observations of sea‐ice conditions obtained from satellites and fromin‐situobservations. Seismological methods may thus help determine properties such as sea‐ice thickness, which are less amenable to conventional observations, under a changing climate, particularly in remote areas like the High Arctic.
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Bayesian Calibration for the Arctic Sea Ice Biomarker IP 25
Abstract Sea ice plays multiple important roles in regulating the global climate. Rapid sea ice loss in the Arctic has been documented over recent decades, yet our understanding of long‐term sea ice variability and its feedbacks remains limited by a lack of quantitative sea ice reconstructions. The sea ice diatom‐derived biomarker has been combined with sterols produced by open‐water phytoplankton in the index as a sea ice proxy to achieve semi‐quantitative reconstructions. Here, we analyze a compilation of over 600 published core‐top measurements of paired with brassicasterol and/or dinosterol across (sub‐)Arctic oceans to calculate a newln() index that correlates nonlinearly with sea ice concentration. Leveraging sediment trap and sea ice observational studies, we develop a spatially varying Bayesian calibration (BaySIC) for ln() to account for its non‐stationary relationship with sea ice concentration and other environmental drivers (e.g., sea surface salinity). The model is fully invertible, allowing probabilistic forward modeling of the ln() index as well as inverse modeling of past sea ice concentration with bi‐directional uncertainty quantification.BaySICfacilitates direct proxy‐model comparisons and palaeoclimate data assimilation, providing the polar proxy constraints currently missing in climate model simulations and enabling, for the first time, fully quantitative Arctic sea ice reconstructions.
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- Award ID(s):
- 2202667
- PAR ID:
- 10623821
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Paleoceanography and Paleoclimatology
- Volume:
- 40
- Issue:
- 3
- ISSN:
- 2572-4517
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2024PA005048
