Biological production in the Indian Ocean upwelling zones - Part 2: Data based estimates of variable compensation depth for ocean carbon models via cyclo-stationary Bayesian Inversion. (September 2020)
- Record Type:
- Journal Article
- Title:
- Biological production in the Indian Ocean upwelling zones - Part 2: Data based estimates of variable compensation depth for ocean carbon models via cyclo-stationary Bayesian Inversion. (September 2020)
- Main Title:
- Biological production in the Indian Ocean upwelling zones - Part 2: Data based estimates of variable compensation depth for ocean carbon models via cyclo-stationary Bayesian Inversion.
- Authors:
- Sreeush, Mohanan Geethalekshmi
Valsala, Vinu
Santanu, Halder
Pentakota, Sreenivas
Prasad, K.V.S.R.
Naidu, C.V.
Murtugudde, Raghu - Abstract:
- Abstract: This study attempts to resolve the complex interior ocean biophysical interactions in an ocean biogeochemistry model via a cyclo-stationary Bayesian approach using surface ocean partial pressure of carbon dioxide (pCO2 ) and upper ocean inventories of phosphate as observational constraints with a special focus on the Indian Ocean. A seasonal cycle in community compensation depth (Z c ), a key parameter involved in the estimation of biological production in ecosystem models is been retrieved without any prior information. Z c typically is assumed to be a constant in ecosystem models but in reality it undergoes a seasonal cycle as evidenced by observations and model simulations. To retrieve the seasonality in compensation depth via inversion, the Indian Ocean is divided into 8 key regions and Z c is optimized for each climatological month in each region. The data-based inversions with surface ocean pCO2 and upper ocean phosphate as observational constraints retrieve a seasonal cycle in Z c consistent with what is identified by the biological parameterization in our earlier study (Sreeush et al., 2018). When implemented in the model, the data-based estimation of Z c significantly reduce the RMSE of CO2 flux and pCO2 over major parts of the Indian Ocean as compared to that of a process-based estimation of Z c from Sreeush et al. (2018). The results here demonstrate that surface ocean pCO2 data, as compared to upper ocean phosphate, offers a stronger observationalAbstract: This study attempts to resolve the complex interior ocean biophysical interactions in an ocean biogeochemistry model via a cyclo-stationary Bayesian approach using surface ocean partial pressure of carbon dioxide (pCO2 ) and upper ocean inventories of phosphate as observational constraints with a special focus on the Indian Ocean. A seasonal cycle in community compensation depth (Z c ), a key parameter involved in the estimation of biological production in ecosystem models is been retrieved without any prior information. Z c typically is assumed to be a constant in ecosystem models but in reality it undergoes a seasonal cycle as evidenced by observations and model simulations. To retrieve the seasonality in compensation depth via inversion, the Indian Ocean is divided into 8 key regions and Z c is optimized for each climatological month in each region. The data-based inversions with surface ocean pCO2 and upper ocean phosphate as observational constraints retrieve a seasonal cycle in Z c consistent with what is identified by the biological parameterization in our earlier study (Sreeush et al., 2018). When implemented in the model, the data-based estimation of Z c significantly reduce the RMSE of CO2 flux and pCO2 over major parts of the Indian Ocean as compared to that of a process-based estimation of Z c from Sreeush et al. (2018). The results here demonstrate that surface ocean pCO2 data, as compared to upper ocean phosphate, offers a stronger observational constraint on the estimation of biology in upwelling regions. Surface ocean pCO2 is an integrated response to the solubility and biological pumps and it is apparent that the constraint imposed by pCO2 is able to cascade through the system to improve estimates of the community compensation depth and translate to reduced biases in various other biogeochemical variables. … (more)
- Is Part Of:
- Deep sea research. Volume 179(2020)
- Journal:
- Deep sea research
- Issue:
- Volume 179(2020)
- Issue Display:
- Volume 179, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 179
- Issue:
- 2020
- Issue Sort Value:
- 2020-0179-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-09
- Subjects:
- Indian ocean carbon cycle -- Varying compensation depth -- Cyclo-stationary Bayesian inversion -- Upwelling zones -- Export and new production
Oceanography -- Periodicals
Ocean bottom -- Periodicals
Marine biology -- Periodicals
551.46 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09670645 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.dsr2.2019.07.007 ↗
- Languages:
- English
- ISSNs:
- 0967-0645
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3540.955503
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14773.xml