Impact of Ongoing Climate Change on Arctic Coastal Environments: Implications for Lower Latitude Coasts
Bibliographic record
Abstract
Arctic rivers operate as integrators of northern high latitude regions, where large stocks of soil organic carbon (OC) are currently experiencing rapid warming. Here I show that tracking total OC in the Mackenzie Delta whose upstream catchment is underlain by permafrost soils is now possible using polar-orbiting satellite ocean color observations with acceptable uncertainty. A non-parametric trend analysis that is valid for hydrological data shows a significant increase in dissolved OC (DOC) as well as particulate OC (POC) concentrations in late summer. Uncertainties of the satellite estimates of DOC and POC did not influence our results. These concentration increases are not related to changes in river discharge. Parallel increases of independent long-term (1979-2018) in situ measurements of thaw depth of the active layer, as well as meteorological and hydrological data suggest that these late summer increases can likely be explained by increasing inputs of permafrost OC. This study shows great promise for remote, large-scale detection of catchment-scale thaw impacts from space. Although my presentation is oriented to my recent Arctic research, many of the knowledge and experience are relevant to the Eelgrass Resiliency and Water Quality Monitoring projects in New England waters. I hope that this seminar provides a general idea of my contribution to these projects in the Great Bay and beyond in terms of optics and remote sensing. Presenter Bio Atsushi Matsuoka is a satellite/optical oceanographer at SMSOE/UNH. He received a doctorate in the fields of satellite oceanography and marine bio-optics from Hokkaido University (Japan), and conducted post-doctoral research at Laboratoire d’Océanographie de Villefranche/Université de Paris 6 (France), plus at Takuvik Joint International Laboratory, CNRS-ULaval (Canada). His research activities extend from examining intricate in siturelationships between optical properties and microbes/phytoplankton to monitor much broader scale global climate change from space. His most significant research contributions include establishing fundamental relationships among optical properties and constituents observed in the ocean based on in situ observations, introducing these relationships into a radiative transfer model, and developing algorithms and applying them to satellite data to retrieve and monitor geophysical and biogeochemical variables with reasonable uncertainties. In recent years, his research has focused on examining the impact of ongoing climate change on Arctic coastal environments. With those knowledge and experience, he is now working on Water Quality Monitoring and Eelgrass Resiliency projects in New England waters in collaborations with Piscataqua Region Estuaries Partnership (PREP) team to help establishing better mitigation and adaptation strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".