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Abstract Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.
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Building on a human-centred, iterative, and agile co-design strategy to facilitate the availability of deep ocean data
Abstract Current information on the status and trends of ocean change is needed to support effective and responsive management, particularly for the deep ocean. Creating consistent, collaborative and actionable mechanisms is a key component of the Deep Ocean Observing Strategy, a program of the United Nations Decade of Ocean Science for Sustainable Development. Here, we share an iterative, agile, and human-centred approach to co-designing datastreams for deep-sea indicators that serves stakeholders, including US National Marine Sanctuaries, presented as a four-phase project roadmap initially focused on the Monterey Bay National Marine Sanctuary, and then generalized to other areas such as the US West Coast, offshore wind development areas, and managed marine spaces globally. Ongoing efforts to provide key physical, biogeochemical, biological, and ecosystem variables for California's Marine Protected Areas are informing this co-design process. We share lessons learned so far and present co-design as a useful tool for (1) assessing the availability of information from deep ecosystems, (2) ensuring interoperability, and (3) providing essential information on the status and trends of indicators. Documenting and sharing this co-design strategy and scalable four-phase roadmap will further the aims of DOOS and other initiatives, including the Deep Ocean Stewardship Initiative and Challenger 150.
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- Award ID(s):
- 2114717
- PAR ID:
- 10370851
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- ICES Journal of Marine Science
- Volume:
- 80
- Issue:
- 2
- ISSN:
- 1054-3139
- Format(s):
- Medium: X Size: p. 347-351
- Size(s):
- p. 347-351
- Sponsoring Org:
- National Science Foundation
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