Articles | Volume 17, issue 5
https://doi.org/10.5194/os-17-1437-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-1437-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sea surface salinity short-term variability in the tropics
Center for Marine Science, University of North Carolina Wilmington,
Wilmington, 28403–5928, USA
Susannah Brodnitz
Center for Marine Science, University of North Carolina Wilmington,
Wilmington, 28403–5928, USA
Related authors
Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka
EGUsphere, https://doi.org/10.5194/egusphere-2025-643, https://doi.org/10.5194/egusphere-2025-643, 2025
This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
Short summary
Short summary
We develop a machine learning methods to detect and classify how much sea ice was present around our research vessel. We used a navigation radar common on many merchant vessels attached to a screen capture device. The captured images were classified using a convolutional neural network and the resulting classification were found to be in good agreement with direct observations and satellite-based products.
Kyla Drushka, Elizabeth Westbrook, Frederick M. Bingham, Peter Gaube, Suzanne Dickinson, Severine Fournier, Viviane Menezes, Sidharth Misra, Jaynice Pérez Valentín, Edwin J. Rainville, Julian J. Schanze, Carlyn Schmidgall, Andrey Shcherbina, Michael Steele, Jim Thomson, and Seth Zippel
Earth Syst. Sci. Data, 16, 4209–4242, https://doi.org/10.5194/essd-16-4209-2024, https://doi.org/10.5194/essd-16-4209-2024, 2024
Short summary
Short summary
The NASA SASSIE mission aims to understand the role of salinity in modifying sea ice formation in early autumn. The 2022 SASSIE campaign collected measurements of upper-ocean properties, including stratification (layering of the ocean) and air–sea fluxes in the Beaufort Sea. These data are presented here and made publicly available on the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC), along with code to manipulate the data and generate the figures presented herein.
Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka
EGUsphere, https://doi.org/10.5194/egusphere-2025-643, https://doi.org/10.5194/egusphere-2025-643, 2025
This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
Short summary
Short summary
We develop a machine learning methods to detect and classify how much sea ice was present around our research vessel. We used a navigation radar common on many merchant vessels attached to a screen capture device. The captured images were classified using a convolutional neural network and the resulting classification were found to be in good agreement with direct observations and satellite-based products.
Kyla Drushka, Elizabeth Westbrook, Frederick M. Bingham, Peter Gaube, Suzanne Dickinson, Severine Fournier, Viviane Menezes, Sidharth Misra, Jaynice Pérez Valentín, Edwin J. Rainville, Julian J. Schanze, Carlyn Schmidgall, Andrey Shcherbina, Michael Steele, Jim Thomson, and Seth Zippel
Earth Syst. Sci. Data, 16, 4209–4242, https://doi.org/10.5194/essd-16-4209-2024, https://doi.org/10.5194/essd-16-4209-2024, 2024
Short summary
Short summary
The NASA SASSIE mission aims to understand the role of salinity in modifying sea ice formation in early autumn. The 2022 SASSIE campaign collected measurements of upper-ocean properties, including stratification (layering of the ocean) and air–sea fluxes in the Beaufort Sea. These data are presented here and made publicly available on the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC), along with code to manipulate the data and generate the figures presented herein.
Cited articles
Abe, H. and Ebuchi, N.: Evaluation of sea-surface salinity observed by
Aquarius, J. Geophys. Res.-Oceans, 119, 8109–8121,
https://doi.org/10.1002/2014JC010094, 2014.
Akhil, V. P., Durand, F., Lengaigne, M., Vialard, J., Keerthi, M. G.,
Gopalakrishna, V. V., Deltel, C., Papa, F., and de Boyer Montégut, C.: A
modeling study of the processes of surface salinity seasonal cycle in the
Bay of Bengal, J. Geophys. Res.-Oceans, 119, 3926–3947,
https://doi.org/10.1002/2013JC009632, 2014.
Akhil, V. P., Vialard, J., Lengaigne, M., Keerthi, M. G., Boutin, J.,
Vergely, J. L., and Papa, F.: Bay of Bengal Sea surface salinity variability
using a decade of improved SMOS re-processing, Remote Sens.
Environ., 248, 111964, https://doi.org/10.1016/j.rse.2020.111964, 2020.
Alory, G., Maes, C., Delcroix, T., Reul, N., and Illig, S. C. C.: Seasonal
dynamics of sea surface salinity off Panama: The far Eastern Pacific Fresh
Pool, J. Geophys. Res., 117, C04028, https://doi.org/10.1029/2011JC007802,
2012.
Bao, S., Wang, H., Zhang, R., Yan, H., and Chen, J.: Comparison of
Satellite-Derived Sea Surface Salinity Products from SMOS, Aquarius, and
SMAP, J. Geophys. Res.-Oceans, 124, 1932–1944,
https://doi.org/10.1029/2019jc014937, 2019.
Bingham, F. M.: Subfootprint Variability of Sea Surface Salinity Observed
during the SPURS-1 and SPURS-2 Field Campaigns, Remote Sensing, 11, 2689,
https://doi.org/10.3390/rs11222689, 2019.
Bingham, F. M. and Li, Z.: Spatial Scales of Sea Surface Salinity
Subfootprint Variability in the SPURS Regions, Remote Sensing, 12, 3996,
https://doi.org/10.3390/rs12233996, 2020.
Bingham, F. M., Howden, S. D., and Koblinsky, C. J.: Sea surface salinity
measurements in the historical database, J. Geophys. Res.-Oceans, 107, 8019, https://doi.org/10.1029/2000JC000767, 2002.
Bingham, F. M., Foltz, G. R., and McPhaden, M. J.: Characteristics of the
Seasonal Cycle of Surface Layer Salinity in the Global Ocean, Ocean Sci.,
8, 915–929, https://doi.org/10.5194/os-8-915-2012, 2012.
Bingham, F. M., Brodnitz, S., and Yu, L.: Sea Surface Salinity Seasonal
Variability in the Tropics from Satellites, Gridded In Situ Products and
Mooring Observations, Remote Sensing, 13, 110, https://doi.org/10.3390/rs13010110, 2021a.
Bingham, F. M., Brodnitz, S., Fournier, S., Ulfsax, K., Hayashi, A., and
Zhang, H.: Sea Surface Salinity Subfootprint Variability from a Global
High-resolution Model, Remote Sensing, submitted, 2021b.
Bonjean, F. and Lagerloef, G. S.: Diagnostic Model and Analysis of the
Surface Currents in the Tropical Pacific Ocean, J. Phys.
Ocean., 32, 2938–2954, 2002.
Boutin, J., Chao, Y., Asher, W. E., Delcroix, T., Drucker, R., Drushka, K.,
Kolodziejczyk, N., Lee, T., Reul, N., and Reverdin, G.: Satellite and in
situ salinity: understanding near-surface stratification and subfootprint
variability, B. Am. Meteorol. Soc., 97, 1391–1407,
https://doi.org/10.1175/BAMS-D-15-00032.1, 2016.
Chao, Y., Farrara, J. D., Schumann, G., Andreadis, K. M., and Moller, D.:
Sea surface salinity variability in response to the Congo river discharge,
Cont. Shelf Res., 99, 35–45, https://doi.org/10.1016/j.csr.2015.03.005, 2015.
Dinnat, E. P., Le Vine, D. M., Boutin, J., Meissner, T., and Lagerloef, G.:
Remote Sensing of Sea Surface Salinity: Comparison of Satellite and in situ
Observations and Impact of Retrieval Parameters, Remote Sensing, 11, 750,
https://doi.org/10.3390/rs11070750, 2019.
Drushka, K., Asher, W. E., Sprintall, J., Gille, S. T., and Hoang, C.:
Global patterns of submesoscale surface salinity variability, J. Geophys. Res.-Oceans 49, 1669–1685, https://doi.org/10.1175/JPO-D-19-0018.1, 2019.
ESR: OSCAR third degree resolution ocean surface currents, NASA Physical
Oceanography DAAC [data set], https://doi.org/10.5067/OSCAR-03D01, 2009.
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution
fields of global runoff combining observed river discharge and simulated
water balances, Global Biogeochem. Cy., 16, 15-1–15-10, https://doi.org/10.1029/1999GB001254, 2002.
Feng, Y., Menemenlis, D., Xue, H., Zhang, H., Carroll, D., Du, Y., and Wu, H.: Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions, Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, 2021.
Foltz, G. R., Brandt, P., Richter, I., Rodríguez-Fonseca, B.,
Hernandez, F., Dengler, M., Rodrigues, R. R., Schmidt, J. O., Yu, L.,
Lefevre, N., Da Cunha, L. C., McPhaden, M. J., Araujo, M., Karstensen, J.,
Hahn, J., Martín-Rey, M., Patricola, C. M., Poli, P., Zuidema, P.,
Hummels, R., Perez, R. C., Hatje, V., Lübbecke, J. F., Polo, I.,
Lumpkin, R., Bourlès, B., Asuquo, F. E., Lehodey, P., Conchon, A.,
Chang, P., Dandin, P., Schmid, C., Sutton, A., Giordani, H., Xue, Y., Illig,
S., Losada, T., Grodsky, S. A., Gasparin, F., Lee, T., Mohino, E., Nobre,
P., Wanninkhof, R., Keenlyside, N., Garcon, V., Sánchez-Gómez, E.,
Nnamchi, H. C., Drévillon, M., Storto, A., Remy, E., Lazar, A., Speich,
S., Goes, M., Dorrington, T., Johns, W. E., Moum, J. N., Robinson, C.,
Perruche, C., de Souza, R. B., Gaye, A. T., López-Parages, J., Monerie,
P. A., Castellanos, P., Benson, N. U., Hounkonnou, M. N., Duhá, J. T.,
Laxenaire, R., and Reul, N.: The Tropical Atlantic Observing System,
Frontiers in Marine Science, 6, 206, https://doi.org/10.3389/fmars.2019.00206, 2019.
Freitag, H. P., McPhaden, M. J., and Connell, K. J.: Comparison of ATLAS and
T-FLEX Mooring Data, Pacific Marine Environmental Laboratory, Seattle, WA, NOAA technical memorandum OAR PMEL, 14, https://doi.org/10.25923/h4vn-a328, 2018.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118,
6704–6716, https://doi.org/10.1002/2013JC009067, 2013.
Grodsky, S. A., Carton, J. A., and Bryan, F. O.: A curious local surface
salinity maximum in the northwestern tropical Atlantic, J. Geophys. Res.-Oceans, 119, 484–495, https://doi.org/10.1002/2013JC009450, 2014.
Kao, H.-Y., Lagerloef, G., Lee, T., Melnichenko, O., and Hacker, P.: Aquarius Salinity Validation Analysis; Data Version 5.0, Aquarius/SAC-D, Seattle, 45, AQ-014-PS-0016. 28 February 2018.
Document accessed 03 May 2018, https://doi.org/10.5067/DOCUM-AQR02.
Kao, H.-Y., Lagerloef, G. S., Lee, T., Melnichenko, O., Meissner, T., and
Hacker, P.: Assessment of Aquarius Sea Surface Salinity, Remote Sensing, 10,
1341, https://doi.org/10.3390/rs10091341, 2018b.
Lagerloef, G. S., Colomb, F. R., Le Vine, D. M., Wentz, F., Yueh, S., Ruf,
C., Lilly, J., Gunn, J., Chao, Y., deCharon, A., Feldman, G., and Swift, C.:
The Aquarius/SAC-D Mission: Designed to Meet the Salinity Remote-sensing
Challenge, Oceanography, 20, 68–81, 2008.
McPhaden, M. J., Busalacchi, A. J., Cheney, R., Donguy, J.-R., Gage, K. S.,
Halpern, D., Ji, M., Julian, P., Meyers, G., Mitchum, G. T., Niiler, P. P.,
Picaut, J., Reynolds, R. W., Smith, N., and Takeuchi, K.: The Tropical
Ocean-Global Atmosphere observing system: A decade of progress,
J. Geophys. Res., 103, 14169–14240, https://doi.org/10.1029/97JC02906, 1998.
McPhaden, M. J., Meyers, G., Ando, K., Masumoto, Y., Murty, V. S. N.,
Ravichandran, M., Syamsudin, F., Vialard, J., Yu, L., and Yu, W.: RAMA: The
Research Moored Array for African–Asian–Australian Monsoon Analysis and
Prediction*, B. Am. Meteorol. Soc., 90, 459–480,
https://doi.org/10.1175/2008BAMS2608.1, 2009.
McPhaden, M. J., Busalacchi, A. J., and Anderson, D. L. T.: A TOGA
Retrospective, Oceanography, 23, 86–103, https://doi.org/10.5670/oceanog.2010.26, 2010.
Meissner, T., Wentz, F., and Le Vine, D.: The salinity retrieval algorithms
for the NASA Aquarius version 5 and SMAP version 3 releases, Remote Sensing,
10, 1121, https://doi.org/10.3390/rs10071121, 2018.
Melnichenko, O., Hacker, P., Maximenko, N., Lagerloef, G., and Potemra, J.:
Spatial Optimal Interpolation of Aquarius Sea Surface Salinity: Algorithms
and Implementation in the North Atlantic, J. Atmos. Ocean.
Tech., 31, 1583–1600, https://doi.org/10.1175/JTECH-D-13-00241.1, 2014.
Melnichenko, O., Hacker, P., Maximenko, N., Lagerloef, G., and Potemra, J.:
Optimum interpolation analysis of Aquarius sea surface salinity, J. Geophys. Res.-Oceans, 121, 602–616, https://doi.org/10.1002/2015JC011343, 2016.
Melnichenko, O., Hacker, P., Bingham, F. M., and Lee, T.: Patterns of SSS
Variability in the Eastern Tropical Pacific: Intraseasonal to Interannual
Timescales from Seven Years of NASA Satellite Data, Oceanography, 32, 20–29,
https://doi.org/10.5670/oceanog.2019.208, 2019.
Millero, F. J.: What is PSU?, Oceanography, 6, 67, 1993.
NASA: ECCO Data Portal, NASA [data set], available at: https://data.nas.nasa.gov/ecco/data.php, last access: 2 January 2021.
NOAA: Data Display and Delivery, NOAA [data set], available at: https://www.pmel.noaa.gov/tao/drupal/disdel/, last access: 2 January 2021.
Olmedo, E., Martínez, J., Turiel, A., Ballabrera-Poy, J., and
Portabella, M.: Debiased non-Bayesian retrieval: A novel approach to SMOS
Sea Surface Salinity, Remote Sens. Environ., 193, 103–126,
https://doi.org/10.1016/j.rse.2017.02.023, 2017.
Olmedo, E., González-Haro, C., Hoareau, N., Umbert, M., González-Gambau, V., Martínez, J., Gabarró, C., and Turiel, A.: Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center, Earth Syst. Sci. Data, 13, 857–888, https://doi.org/10.5194/essd-13-857-2021, 2021.
Qin, S., Wang, H., Zhu, J., Wan, L., Zhang, Y., and Wang, H.: Validation and
correction of sea surface salinity retrieval from SMAP, Acta Oceanol.
Sin., 39, 148–158, https://doi.org/10.1007/s13131-020-1533-0, 2020.
Reul, N., Grodsky, S. A., Arias, M., Boutin, J., Catany, R., Chapron, B.,
D'Amico, F., Dinnat, E., Donlon, C., Fore, A., Fournier, S., Guimbard, S.,
Hasson, A., Kolodziejczyk, N., Lagerloef, G., Lee, T., Le Vine, D. M.,
Lindstrom, E., Maes, C., Mecklenburg, S., Meissner, T., Olmedo, E., Sabia,
R., Tenerelli, J., Thouvenin-Masson, C., Turiel, A., Vergely, J. L.,
Vinogradova, N., Wentz, F., and Yueh, S.: Sea surface salinity estimates
from spaceborne L-band radiometers: An overview of the first decade of
observation (2010–2019), Remote Sens. Environ., 242, 111769,
https://doi.org/10.1016/j.rse.2020.111769, 2020.
Reverdin, G., Salvador, J., Font, J., and Lumpkin, R.: Surface Salinity in
the North Atlantic subtropical gyre: During the STRASSE/SPURS Summer 2012
Cruise, Oceanography, 28, 114–123, https://doi.org/10.5670/oceanog.2015.09, 2015.
Su, Z., Wang, J., Klein, P., Thompson, A. F., and Menemenlis, D.: Ocean
submesoscales as a key component of the global heat budget, Nat. Commun., 9, 775, https://doi.org/10.1038/s41467-018-02983-w, 2018.
Supply, A., Boutin, J., Vergely, J. L., Martin, N., Hasson, A., Reverdin,
G., Mallet, C., and Viltard, N.: Precipitation Estimates from SMOS
Sea-Surface Salinity, Q. J. Roy. Meteor. Soc.,
144, 103–119, https://doi.org/10.1002/qj.3110, 2018.
Tang, W., Yueh, S. H., Fore, A. G., Hayashi, A., Lee, T., and Lagerloef, G.:
Uncertainty of Aquarius sea surface salinity retrieved under rainy
conditions and its implication on the water cycle study, J. Geophys. Res.-Oceans, 119, 4821–4839, https://doi.org/10.1002/2014JC009834, 2014.
Tang, W., Fore, A., Yueh, S., Lee, T., Hayashi, A., Sanchez-Franks, A.,
Martinez, J., King, B., and Baranowski, D.: Validating SMAP SSS with in situ
measurements, Remote Sens. Environ., 200, 326–340,
https://doi.org/10.1016/j.rse.2017.08.021, 2017.
Thompson, E., J., Asher, W. E., Jessup, A. T., and Drushka, K.:
High-Resolution Rain Maps from an X-band Marine Radar and Their Use in
Understanding Ocean Freshening, Oceanography, 32, 58–65, https://doi.org/10.5670/oceanog.2019.213,
2019.
Vergely, J.-L. and Boutin, J.: SMOS OS Level 3 Algorithm Theoretical Basis
Document (v300), ACRI-ST, 25, 2017, https://www.catds.fr/content/download/78841/1005020/file/ATBD_L3OS_v1.0.pdf, Last accessed 1 October 2021.
Vinogradova, N., Lee, T., Boutin, J., Drushka, K., Fournier, S., Sabia, R.,
Stammer, D., Bayler, E., Reul, N., Gordon, A., Melnichenko, O., Li, L.,
Hackert, E., Martin, M., Kolodziejczyk, N., Hasson, A., Brown, S., Misra,
S., and Lindstrom, E.: Satellite Salinity Observing System: Recent
Discoveries and the Way Forward, Frontiers in Marine Science, 6, 243,
https://doi.org/10.3389/fmars.2019.00243, 2019.
Short summary
Satellite measurements of sea surface salinity (SSS) are compared with measurements in the ocean to verify the quality of the satellite data. SSS satellites measure average values over a footprint with size of ~100 km, whereas ocean values are usually taken at a single point in space and time. Using SSS data from a network of buoys across the global tropics, we estimate the size of the mismatch between satellite and in situ measurements to better understand the error structure of the satellite.
Satellite measurements of sea surface salinity (SSS) are compared with measurements in the ocean...