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KOSMOS 2023 Helgoland mesocosm study on ocean alkalinity enhancement: sediment trap particle flux data and water column biogeochemistry
Open CC BY V4.0
Dataset
Data for: KOSMOS 2023 Helgoland mesocosm study on ocean alkalinity enhancement: sediment trap particle flux data and water column biogeochemistry
PANGAEA
31/05/2026
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Abstract
The data presented herein originates from a mesocosm study conducted as part of the BMBF CDRmare, Retake project (grant agreement no. 03F0895A), aimed at investigating the ecological ramifications of ocean alkalinity enhancement (OAE). Twelve mesocosms were deployed in Helgoland South Harbor, Germany, and systematically sampled using integrated water samplers over the period spanning from March 12th to April 20th, 2023. Six alkalinity levels under two dilution scenarios were established to differentiate between localized and uniform OAE additions. Alkalinity was increased stepwise to ΔTAmax = 1250 μmol kg-1 (250 μmol TA kg-1 increments) using sodium hydroxide (NaOH) with calcium chloride (CaCl2) to simulate cation release during calcium-based mineral dissolution, causing strong carbonate chemistry perturbations (e.g., pHT > 9.25). The dataset encompasses a spectrum of sediment trap particle flux data, water column biogeochemistry including pigment variables, inorganic nutrients, carbonate chemistry parameters. The study and data set offer insights into impacts of alkalinity enhancement on marine ecosystems and their associated biogeochemistry.
Details
- Title
- Data for: KOSMOS 2023 Helgoland mesocosm study on ocean alkalinity enhancement: sediment trap particle flux data and water column biogeochemistry
- Creators
- Philipp Suessle - Universidade dos AçoresKai Georg Schulz - Southern Cross UniversityJoana Barcelos e Ramos - University of the AzoresNico Manuel Sievers - GEOMAR Helmholtz Centre for Ocean Research KielUlf Riebesell - GEOMAR Helmholtz Centre for Ocean Research Kiel
- Publisher
- PANGAEA
- Grant note
- DAM CDRmare - RETAKE, GEOMAR - 03F0895A / Federal Ministry of Education and Research (https://doi.org/10.13039/501100002347) UI/BD/150890/2021 / Fundação para a Ciência e Tecnologia (https://doi.org/10.13039/501100001871)
- Identifiers
- 991013386652202368
- Academic Unit
- Faculty of Science and Engineering; Science
- Language
- English
- Resource Type
- Dataset