5:15 PM - 6:45 PM
[AOS13-P10] Preliminary study on incorporating observation-derived data into ocean simulations for understanding ocean biophysical interactions at the submesoscale
Keywords:ocean physics, submesoscale, numerical simulations, biogeochemistry
There is an increasing number of studies highlighting the importance of submesoscale processes in determining ocean biogeochemistry. Numerical simulations of coupled ocean physics-biogeochemistry models have shown differences in estimates of primary production and carbon fixation between coarse (O(105) m) and eddy-resolving resolution (O(103) m) simulations of 14% and 34% respectively (Couespel et al. 2021, 2024). There is also emerging observational evidence of spatial variability in plankton communities on scales of a few kilometers (Mangolte et al. 2023, Gray et al. 2023). Simulations utilizing high-resolution observations of ocean physics and biogeochemistry are required to accurately model current and future carbon fixation and primary production in the ocean. In our team, we are aiming to forecast the effects of climate change on ocean ecosystems by utilizing both high-resolution ocean simulations and observations.
As a first step towards this goal, we consider approaches for inputting statistical reanalysis data, derived from observations, into simulation experiments as boundary conditions in an ocean physics model. We use the ocean simulator MITgcm, which incorporates an ocean physics model using hydrostatic primitive equations for an implicit free surface, with a bi-linear equation of state (Marshall et al. 1997). As a test case, we focus on a square basin in the North Atlantic, covering latitude and longitude ranges of 25°N to 55°N and 55°W to 25°W respectively. We discuss the effect of different boundary conditions on numerical stability, and compare these conditions for coarse (O(105) m) and eddy-resolving resolutions (O(103) m). We also compare results with our previous closed basin experiments (Stewart et al. 2023) and discuss potential effects of the introduction of the input of boundary data on ocean biogeochemistry. We plan to extend the introduction of reanalysis data through boundary conditions in these experiments to coupled ocean physics biogeochemistry model experiments. We will review current data availability for biogeochemical parameters and discuss potential approaches for incorporating such data into simulations.
Acknowledgements
This work used computational resources of supercomputer Fugaku provided by the RIKEN Center for Computational Science through the HPCI System Research Project (Project ID: hp230382).
References
Couespel, D. et al. Biogeosciences (2021): 18.14, pp. 4321–4349. doi: 10.5194/bg-18-4321-2021.
Couespel, D. et al. Geophysical Research Letters. (2024). https://hal.science/hal-04396517 (Preprint).
Gray, P.C., et al. bioRxiv (2023): 2023-09. doi:10.1101/2023.09.25.559383.
Mangolte, I., et al. Biogeosciences (2023): 20. pp. 3273-3299. doi: 10.5194/bg-20-3273-2023.
Marshall, J. et al. Journal of Geophysical Research Oceans (1997): 102.C3, pp. 5733–5752. doi: 10.1029/96JC02776.
Stewart, H. et al. EGU General Assembly (2023): EGU23-11212. doi: 10.5194/egusphere-egu23-11212.
As a first step towards this goal, we consider approaches for inputting statistical reanalysis data, derived from observations, into simulation experiments as boundary conditions in an ocean physics model. We use the ocean simulator MITgcm, which incorporates an ocean physics model using hydrostatic primitive equations for an implicit free surface, with a bi-linear equation of state (Marshall et al. 1997). As a test case, we focus on a square basin in the North Atlantic, covering latitude and longitude ranges of 25°N to 55°N and 55°W to 25°W respectively. We discuss the effect of different boundary conditions on numerical stability, and compare these conditions for coarse (O(105) m) and eddy-resolving resolutions (O(103) m). We also compare results with our previous closed basin experiments (Stewart et al. 2023) and discuss potential effects of the introduction of the input of boundary data on ocean biogeochemistry. We plan to extend the introduction of reanalysis data through boundary conditions in these experiments to coupled ocean physics biogeochemistry model experiments. We will review current data availability for biogeochemical parameters and discuss potential approaches for incorporating such data into simulations.
Acknowledgements
This work used computational resources of supercomputer Fugaku provided by the RIKEN Center for Computational Science through the HPCI System Research Project (Project ID: hp230382).
References
Couespel, D. et al. Biogeosciences (2021): 18.14, pp. 4321–4349. doi: 10.5194/bg-18-4321-2021.
Couespel, D. et al. Geophysical Research Letters. (2024). https://hal.science/hal-04396517 (Preprint).
Gray, P.C., et al. bioRxiv (2023): 2023-09. doi:10.1101/2023.09.25.559383.
Mangolte, I., et al. Biogeosciences (2023): 20. pp. 3273-3299. doi: 10.5194/bg-20-3273-2023.
Marshall, J. et al. Journal of Geophysical Research Oceans (1997): 102.C3, pp. 5733–5752. doi: 10.1029/96JC02776.
Stewart, H. et al. EGU General Assembly (2023): EGU23-11212. doi: 10.5194/egusphere-egu23-11212.
