Consulta: creatorFacets:"Fiedler, Bjoern"
Registros recuperados: 2
Data/hora: 09/06/2024 05:49:30

Multidisciplinary Observing in the World Ocean's Oxygen Minimum Zone Regions: From Climate to Fish - The VOICE Initiative
Provedor de dados: 5
Autores: Garcon, Veronique; Karstensen, Johannes; Palacz, Artur; Telszewski, Maciej; Aparco Lara, Tony; Breitburg, Denise; Chavez, Francisco; Coelho, Paulo; Cornejo-d'Ottone, Marcela; Santos, Carmen; Fiedler, Bjoern; Gallo, Natalya D.; Gregoire, Marilaure; Gutierrez, Dimitri; Hernandez-ayon, Martin; Isensee, Kirsten; Koslow, Tony; Levin, Lisa; Marsac, Francis; Maske, Helmut; Mbaye, Baye C.; Montes, Ivonne; Naqvi, Wajih; Pearlman, Jay; Pinto, Edwin; Pitcher, Grant; Pizarro, Oscar; Rose, Kenneth; Shenoy, Damodar; Van Der Plas, Anja; Vito, Melo R.; Weng, Kevin.
Multidisciplinary ocean observing activities provide critical ocean information to satisfy ever-changing socioeconomic needs and require coordinated implementation. The upper oxycline (transition between high and low oxygen waters) is fundamentally important for the ecosystem structure and can be a useful proxy for multiple observing objectives connected to eastern boundary systems (EBSs) that neighbor oxygen minimum zones (OMZs). The variability of the oxycline and its impact on the ecosystem (VOICE) initiative demonstrates how societal benefits drive the need for integration and optimization of biological, biogeochemical, and physical components of regional ocean observing related to EBS. In liaison with the Global Ocean Oxygen Network, VOICE creates a...
Tipo: Text
Palavras-chave: Oxygen minimum zones; Oxycline; Ocean observing system; Multidisciplinary; Readiness level; Ecosystem.
Ano: 2019
URL: https://archimer.ifremer.fr/doc/00659/77112/78546.pdf

An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O-2 Data Using Bayesian Neural Networks
Provedor de dados: 5
Autores: Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre.
This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) from temperature, salinity, oxygen, and geolocation data. "CANYON-B" is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. "CONTENT" combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As "dynamic climatologies" they show comparable performance to classical...
Tipo: Text
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf