Contents
New Analytical Methods
N2 fixation
- Cassar, N., Tang, W., Gabathuler, H., Huang, K. 2018. Method for high frequency underway N2 fixation measurements: Flow-through incubation Acetylene Reduction Assays by Cavity ring down laser Absorption Spectroscopy (FARACAS). Analytical Chemistry, DOI: 10.1021/acs.analchem.7b04977.
- US Patent Application # 62/619,303
- Cassar, N., Bellenger, J.-P., Jackson, R. B., Karr, J., Barnett, B. A. 2012. N2 fixation in real-time by cavity ring-down laser absorption spectroscopy. Oecologia: doi:10.1007/s00442-011-2105-y.
Biological O2 and pCO2
- Cassar et al. A second generation equilibrator inlet mass spectrometer. In prep. (EIMS nXT Instruction Manual)
- Cassar, N., Barnett, B. A., Bender, M. L., Kaiser, J., Hamme, R. C., Tilbrook, B. 2009. Continuous high-frequency dissolved O2/Ar measurements by Equilibrator Inlet Mass Spectrometry (EIMS). Analytical Chemistry 81(5): 1855-1864.
Dissolved Inorganic Carbon (DIC)
- Huang, K., Cassar, N., Jonsson, B., Cai, W.-J., Bender, M. L. 2015. An ultra-high precision, high frequency dissolved inorganic carbon analyzer based on dual isotope dilution and cavity ring-down spectroscopy. Environmental Science & Technology, 49 (14): 8602–8610.
- Huang, K., Cassar, N., Wanninkhof, R., Bender, M. L. 2013. A novel method for high frequency measurements of dissolved inorganic carbon concentration in the surface ocean. Limnology and Oceanography: Methods 11: 572-583.
Carbon Stable & Radio-Isotope Disequilibrium & Fractionation
- Cassar, N., Laws, E. A., Popp, B. N., Bidigare, R. R. 2002. Isotope disequilibrium Experiments: Sources of inorganic carbon for photosynthesis in a strain of Phaeodactylum tricornutum. Limnology and Oceanography 47: 1192-1197.
Matlab Scripts & Functions
- Scripts & functions for satellite data retrievals
- Scripts & functions for O2/Ar -NCP calculations
- GitHub repository
Satellite algorithms
Satellite NCP algorithms
Global Ocean
Genetic Programming NCP (Li & Cassar 2016)
This section provides access to our oceanic satellite NCP estimates for the world’s oceans based on genetic programming and support vector machine. These products are based on:
- Sensor: SeaWiFS and MODISA
- Spatial resolution: 4 and 9 km
- Temporal resolution: daily, 8-day, monthly and climatology
- NCP_metadata.
Further description of the algorithms is provided in:
Li, Z. & Cassar, N. 2016. Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochemical Cycles, .
Data access methods
Access estimates based on specific algorithms:
- Li & Cassar (2016) (SVR) (SeaWiFS) (MODISA)
- Li & Cassar (2016) (GP) (SeaWiFS) (MODISA)
- Some years also available on Zenodo.
Southern Ocean
This section provides access to our oceanic satellite NCP estimates for the Southern Ocean based on self-organizing maps/artificial neural networks.
Further description of the algorithms is provided in:
Chang, C.-H., Johnson, N. C., Cassar, N. 2014. Neural network-based estimates of Southern Ocean net community production from in-situ O2/Ar and satellite observations: A methodological study. Biogeosciences, 11: 3279-3297.
Data access method: Access Southern Ocean NCP estimates here: Chang et al. (2014) : Southern Ocean SOM NCP
Western Antarctic Peninsula
This section provides access to our oceanic satellite NCP estimates for the Western Antarctic Peninsula based on a Bayesian hierarchical model.
Description of the algorithms is provided in:
Li, Z., Cassar, N., Huang, K., Ducklow, H., Schofield, O. 2016. Interannual variability in net community production at the Western Antarctic Peninsula region (1997-2014). Journal of Geophysical Research, .
Data access method: Access WAP NCP estimates here: SeaWiFS and MODISA
Satellite N2 fixation and diazotroph algorithms
This section provides access to our oceanic satellite N2 fixation estimates for the world’s oceans based on random forest and support vector machine.
Further description of the algorithms is provided in:
Tang, W., Li, Z., Cassar, N. 2019. Machine learning estimates of global marine nitrogen fixation. JGR-Biogeosciences, https://doi.org/10.1029/2018JG004828.
Tang, W. & Cassar, N. 2019. Data-driven modeling of the distribution of diazotrophs in the global ocean. Geophysical Research Letters, doi: 10.1029/2019GL084376.
Data access method: Access global marine nitrogen fixation and diazotroph abundance here:
Global marine nitrogen fixation
Observed and modeled diazotroph nifH abundances in the global ocean
Satellite GPP algorithms
This section provides access to our global ocean Gross Primary Production estimates based on machine-learning methods.
Further description of the algorithms is provided in:
Huang, Y., Nicholson, D., Huang, B. Cassar, N. Machine-learning estimates of global marine gross primary production. Global Biogeochemical Cycles, https://doi.org/10.1029/2020GB006718.
Data access method: Access global ocean Gross Primary Production (GPP) here:
Global machine-learning GPP estimates
Data Products
-
-
O2/Ar-NCP observations
-
N2 fixation estimates (in prep.)
-
High-resolution DIC estimates
-
Link to sequencing reads (in prep.)
-
Family recipes
ZE KIF (a collection of family [and others’] recipes)
“Cette cuisine est un monde dont la cheminée est le soleil.” V. Hugo
You must be logged in to post a comment.