DOI: 10.6075/j0tx3c9x
title: Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Float Data Archive
title: Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Float Data Archive

description: To develop a new observing system for carbon, nutrients, and oxygen that complement and expand on the existing observing system for heat and freshwater, the observations team deploys a large array (~200) of profiling floats with biogeochemical sensors throughout the Southern Ocean. This robotic float observing system is complemented by shipboard measurements, instrument and sensor development, and data analysis, including state estimation in conjunction with the modeling program.

Principal responsibility for development and deployment of the observing system are in the hands of the Scripps Institution of Oceanography (Theme 1 Lead Lynne Talley), in partnership with the University of Washington (Co-Lead Steve Riser) and Monterey Bay Aquarium Research Institute (Associate Director Ken Johnson), who together design and build the floats and participate in analysis of the data. Deployment opportunities with international partners are an important component to the SOCCOM observational program.
description: Calibration for BGC sensors follows methods described in: 

Maurer, T.L., Plant, J.N. and K.S Johnson, 2021. Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats. Front. Mar. Sci. 8:683207. https://doi.org/10.3389/fmars.2021.683207

Johnson, K. S., J. N. Plant, L. J. Coletti, H. W. Jannasch, C. M. Sakamoto, S. C. Riser, D. D. Swift, N. L. Williams, E. Boss, N. Haentjens, L. D. Talley, and J. L. Sarmiento, 2017. Biogeochemical sensor performance in the SOCCOM profiling float array. J. Geophys. Res. Oceans, 122, 6416-6436. https://doi.org/10.1002/2017JC012838

Three different methods (LIAR, CANYON, MLR) are used in calculating TALK, DIC, and pCO2. These are referenced below. For a description of uncertainties associated with the derived carbon parameters see Williams, N. L., et al. (2017), Calculating surface ocean pCO2 from biogeochemical Argo floats equipped with pH: An uncertainty analysis, Global Biogeochem. Cycles, 31, 591–604, https://doi.org/10.1002/2016GB005541

LIAR:
Carter, B.R., Williams, N.L., Gray, A.R. and Feely, R.A. (2016), Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr. Methods, 14: 268-277, https://doi.org/10.1002/lom3.10087

Carter, B.R., Feely, R.A., Williams, N.L., Dickson, A.G., Fong, M.B. and Takeshita, Y. (2018), Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate. Limnol. Oceanogr. Methods, 16: 119-131. https://doi.org/10.1002/lom3.10232

CANYON:
Sauzède R, Bittig HC, Claustre H, Pasqueron de Fommervault O, Gattuso J-P, Legendre L and Johnson KS (2017) Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. https://doi.org/10.3389/fmars.2017.00128

Bittig HC, Steinhoff T, Claustre H, Fiedler B, Williams NL, Sauzède R, Körtzinger A and Gattuso J-P (2018) An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. https://doi.org/10.3389/fmars.2018.00328

MLR:
pH, Nitrate, DIC: Williams, N. L., Juranek, L. W., Johnson, K. S., Feely, R. A., Riser, S. C., Talley, L. D., Russell, J. L., Sarmiento, J. L., and Wanninkhof, R. (2016), Empirical algorithms to estimate water column pH in the Southern Ocean, Geophys. Res. Lett., 43, 3415– 3422, https://doi.org/10.1002/2016GL068539