A dataset of Oceanographic and biogeochemical anomalies in the Caribbean Sea.

Authors

DOI:

https://doi.org/10.53805/lads.v2i1.50

Keywords:

Anomaly databases, Physical parameters, Biogeochemical parameters, Surface forcing

Abstract

This article describes six ocean datasets consistent in anomalies of biogeochemical, physical, sea wave, biological, oceanic and chemical parameters (DACS-BGC, DACS-PHY, DACS-WAVE, DACS-BIO, DACS-OCE and DACS-CHEM) in several time scales from 3-hourly to monthly frequencies, either on the sea surface, downward/upward fluxes between the ocean and the atmosphere and the water  column in the Caribbean basin (Gulf of Mexico, Caribbean Sea and Atlantic Ocean) in a geographical domain from latitudes 8 degrees to 35 degrees North and from longitudes 45 degrees to 100 degrees West, obtained, from several satellites, modeling services and observational programs. The datasets were created in NetCDF format conserving their original horizontal resolutions of 1.0, 0.5, 0.26, 0.08333 and 0.04 degrees in gridded structure; only the WAVEWATCH3 dataset has a non-uniform step in latitude and longitude. This internal data structure facilitates its handling due to a wide diversity of existent freeware tools, and it is mainly intended to support researchers to understand the evolution and cycles of physical, biogeochemical, chemical, sea wave, oceanic and biological parameters linked to global climate change.

References

ADLER, R.F. et al. The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present). Journal of Hydrometeorology, American Meteorological Society. 4, 6, 1147–1167, 2003. DOI: https://doi.org/10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2

BEHRINGER, D. W. 3.3 The Global Ocean Data Assimilation System (GODAS) at NCEP. Proceedings of the 11th symposium on integrated observing and assimilation systems for the atmosphere, oceans, and land surface, 2007. Available online at <https://ams.confex.com/ams/pdfpapers/119541.pdf>. Accessed January 17, 2022

BENTAMY, A.; FILLON, D. C. Gridded surface wind fields from Metop/ASCAT measurements. International Journal of Remote Sensing. Vol. 33, Issue 6, pp. 1729–1754, 2011. Informa UK Limited. DOI: https://doi.org/10.1080/01431161.2011.600348

BROWN, O. B.; MINNETT P. J. MODIS Infrared Sea Surface Temperature Algorithm Theoretical Basis Document, Ver 2.0. 1999. Available at <http://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf>. Accessed in 2021-12-12.

CARTON, J. A.; GIESE, B. S. A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA). Monthly Weather Review, American Meteorological Society. 136, 8, 2999–3017, 2008. DOI: https://doi.org/10.1175/2007mwr1978.1

CHAU, T. T.; GEHLEN, M.; CHEVALLIER, F. Global Ocean Surface Carbon Product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008. EU Copernicus Marine Service Information. Issue 3.0., 15 pp, 2020. Available online at <https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-MOB-PUM-015-008.pdf>. Accessed January 7, 2022

DE SANTANA, C. S. et al. Amazon river plume influence on planktonic decapods in the tropical Atlantic. Journal of Marine Systems. 212, 103428, pp 1-14, 2020. DOI: https://doi.org/10.1016/j.jmarsys.2020.103428

EDUARDO L. N. et al. Distribution, vertical migration, and trophic ecology of lanternfishes (Myctophidae) in the Southwestern Tropical Atlantic. Progress in Oceanography. 199, 102695, 2021. ISSN 0079-6611. DOI: https://doi.org/10.1016/j.pocean.2021.102695

FARIAS G.B. et al. Uncoupled changes in phytoplankton biomass and size structure in the western tropical Atlantic. Journal of Marine Systems. 227, 103696, 2022. DOI: https://doi.org/10.1016/j.jmarsys.2021.103696

FORGET, G. et al. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscientific Model Development. Vol. 8, Issue 10, pp. 3071–3104, 2015. Copernicus GmbH. DOI: https://doi.org/10.5194/gmd-8-3071-2015

GELARO, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). In Journal of Climate. Vol. 30, Issue 14, pp. 5419–5454, 2017. American Meteorological Society. DOI: https://doi.org/10.1175/jcli-d-16-0758.1

GILBERT, P. S.; LEE, T. N.; PODESTA, G. P. Transport of anomalous low-salinity waters from the Mississippi River flood of 1993 to the Straits of Florida. Continental Shelf Research. 16, 8, 1065-1085, 1996. DOI: https://doi.org/10.1016/0278-4343(95)00056-9

KALNAY, E. et al. The NCEP/NCAR 40-year reanalysis project. Bulletin of American Meteorological Society. 77, 437-472, 1996. DOI: https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2

KERR, Y. H. et al. Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Transactions on Geoscience and Remote Sensing. 39, 8, 1729-1735, 2001. DOI: https://doi.org/10.1109/36.942551

KIM, Y. H.; HWANG, C.; CHOI, B. J. An assessment of ocean climate reanalysis by the data assimilation system of KIOST from 1947 to 2012. Ocean Modelling. Vol. 91, pp. 1–22, 2015. Elsevier BV. https://doi.org/10.1016/j.ocemod.2015.02.006

LAW-CHUNE, S. et al. WAVERYS: a CMEMS global wave reanalysis during the altimetry period. Ocean Dynamics. Vol. 71, Issue 3, pp. 357–378, 2021. Springer Science and Business Media LLC. DOI: https://doi.org/10.1007/s10236-020-01433-w

LEE, C.; WAKEHAM, S.; ARNOSTI, C. Particulate organic matter in the sea: the composition conundrum. Ambio, 33, 8, 565-575, 2004. DOI: https://doi.org/jstor.org/stable/4315547

LIRA, S.M. et al. New records of the larval forms Cerataspis monstrosa and Amphionides reynaudii (Crustacea: Decapoda) from the western tropical Atlantic. Zootaxa, 4237, 2, zootaxa-4237, 2017. DOI: https://doi.org/10.11646/zootaxa.4237.2.7

LITCHMAN, E. et al. Global biogeochemical impacts of phytoplankton: a trait-based perspective. Journal of Ecology. 103 (6), 1384–1396, 2015. DOI: https://doi.org/10.1111/1365-2745.12438

LIU, L. et al. Stereo observation and inversion of the key parameters of global carbon cycle: project overview and mid-term progressess. Remote Sens. Technol. Appl. 36, 11–24, 2021. DOI: https://doi.org10.11873/j.issn.1004-0323.2021.1.0011

MELO, D. C. M, et al. Genetic diversity and connectivity of Flaccisagitta enflata (Chaetognatha: Sagittidae) in the tropical Atlantic ocean (northeastern Brazil). PLoS ONE. 15, 5, e0231574, 2020. DOI: https://doi.org//10.1371/journal.pone.0231574

MERCATOR OCEAN INTERNATIONAL (MOI). Global Ocean Chlorophyll, PP and PFT (Copernicus-GlobColour) from Satellite Observations: Monthly and Daily Interpolated (Reprocessed from 1997) [Data set]. Mercator Ocean International. 2016. DOI: https://doi.org/10.48670/MOI-00100

MERCATOR OCEAN INTERNATIONAL (MOI) DATABASE. Global ocean biogeochemistry hindcast [Data set]. Mercator Ocean International. 2018. DOI: https://doi.org/10.48670/MOI-00019

MERCATOR OCEAN INTERNATIONAL (MOI) DATABASE. Global Ocean Surface Carbon [Data set]. Mercator Ocean International. 2019a. DOI: https://doi.org/10.48670/MOI-00047

MERCATOR OCEAN INTERNATIONAL (MOI). Global Ocean Waves Reanalysis WAVERYS [Data set]. Mercator Ocean International. 2019b. DOI: https://doi.org/10.48670/MOI-00022

MERCATOR OCEAN INTERNATIONAL (MOI) DATABASE. Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon [Data set]. Mercator Ocean International. 2020. DOI: https://doi.org/10.48670/MOI-00046

MERCATOR OCEAN INTERNATIONAL (MOI). Global ocean low and mid trophic levels biomass content hindcast [Data set]. Mercator Ocean International. 2021. DOI: https://doi.org/10.48670/MOI-00020

NABABAN, B. et al. Chlorophyll variability in the northeastern Gulf of Mexico. International Journal of Remote Sensing. 32, 23, 8373-8391, 2011. DOI: https://doi.org/10.1080/01431161.2010.542192

NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. MODIS-Aqua Ocean Color Data, NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, 2014. DOI: https://doi.org/10.5067/AQUA/MODIS_OC.2014.0

NEUMANN-LEITÃO, S. et al. Zooplankton from a reef system under the influence of the Amazon River plume. Frontiers in Microbiology. 9, 355, 2018. DOI: https://doi.org/10.3389/fmicb.2018.00355

PAULY, D.; CHRISTENSEN, V. Primary production required to sustain global fisheries. Nature. 374, 255–257, 1995. DOI: https://doi.org/10.1038/374255a0

PEREYRA, W. T.; PEARCY, W. G.; CARVEY, F. E. Jr. Sebastodes flavidus, a shelf rockfish feeding on mesopelagic fauna, with consideration of the ecological implications. Journal Fishiries Res. Board Can. 26, 2211–2215, 1969. DOI: http://doi.org/10.1139/f69-205

PERRUCHE, C. Product User Manual for the Global Ocean Biogeochemistry Hindcast. GLOBAL_REANALYSIS_BIO_001_029. Issue 1.1, pp 1-17, 2018. Available online at <https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-029.pdf>. Accessed January 30, 2022.

PIERCE, D. W. Ncview: A NetCDF visual browser Scripps Institution of Oceanography. 2016. Available at <http://meteora.ucsd.edu/pierce/ncview_home_page.html>. Accessed in 2021-07-01.

SAUZEDE, R., RENOSH, P. R., CLAUSTRE, H. Product User Manual or Global Ocean 3D Particulate Organic Carbon and Chlorophyll-a concentration Product MULTIOBS_GLO_BIO_BGC_3D_REP_015_010. Issue 3.0, 2021. Available online at <https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-MOB-PUM-015-010.pdf>. Accessed February 1, 2022

SCHULZWEIDA, U. CDO user’s guide. Hamburg: Climate data operators, version 1.0.1, 2006. Max-Planck-Institute for Meteorology. Available at <https://src.fedoraproject.org/lookaside/pkgs/cdo/cdo.pdf/90a93037089dddf6f8919b9d6c30bff7/cdo.pdf>. Accessed in 2021-01-30.

SHIH, Y. Y. et al. Comparison of Primary Production Using in situ and Satellite-Derived Values at the SEATS Station in the South China Sea. Frontiers in Marine Science. 8, 747763, 2021. DOI: https://doi.org/10.3389/fmars.2021.747763

STEMMANN, L.; BOSS, E. Plankton and particle size and packaging: from determining optical properties to driving the biological pump. Annual Review of Marine Science. 4, 263-290, 2012. DOI: https://doi.org//10.1146/annurev-marine-120710-100853

TAKAHASHI, T., SUTHERLAND, S. C., KOZYR, A. Global ocean surface water partial pressure of CO2 database: Measurements performed during 1957–2016 (version 2016). ORNL/CDIAC-161, NDP-088 (V2015), Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. Dataset, 2017. Document available at <https://odv.awi.de/fileadmin/user_upload/odv/data/LDEO_Database/NDP-088_V2016.pdf>. Accessed in 31 July 2020

TILSTONE, G. H. et al. Comparison of new and primary production models using SeaWiFS data in contrasting hydrographic zones of the northern North Atlantic. Remote Sens. Environ. 156, 473–489, 2015. DOI: https://doi.org/10.1016/j.rse.2014.10.013

TITAUD, O.; CONCHON, A.; LEHODEY, P. PRODUCT USER MANUAL for the Global Ocean Low and Mid Trophic Levels Biomass Content Hindcast Product, (3). 2019. Available at <https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-033.pdf>. Accessed February 21, 2022.

TOLMAN, H. L. User manual and system documentation of WAVEWATCH III TM version 3.14. MMAB Contribution, 276, 220, 2009. Technical note. <https://polar.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf>. Accessed March 27, 2022.

VALSALA, V.; MAKSYUTOV, S. Simulation and assimilation of global ocean pCO2 and air–sea CO2 fluxes using ship observations of surface ocean pCO2 in a simplified biogeochemical offline model. In Tellus B: Chemical and Physical Meteorology. Vol. 62, Issue 5, pp. 821–840, 2010. Informa UK Limited. DOI: https://doi.org/10.1111/j.1600-0889.2010.00495.x

VARONA, H. L. mNC: A tool for Oceanographers and Meteorologists to easily create their NetCDF files using Matlab (1.0), 2021a. Zenodo. DOI: https://doi.org/10.5281/zenodo.5572749

VARONA, H. L. CalcPlotAnomaly: Matlab function set for the calculation and plotting of anomalies (1.01), 2021b. Zenodo. DOI: https://doi.org/10.5281/zenodo.5576889

VARONA, H. L. et al. Database of oceanographic anomalies and atmospheric surface fluxes for the study of climate change in the Brazilian Northeast. Latin American Data in Science. Vol. 2, Issue 1, 2022a. Data in Science Editora ltda. DOI: https://doi.org/10.53805/lads.v2i1.39

VARONA, H. L. et al. Monthly anomaly database of atmospheric and oceanic parameters in the tropical Atlantic Ocean. Data in Brief (p. 107969), 2022b. Elsevier BV. DOI: https://doi.org/10.1016/j.dib.2022.107969

WALTON, C. C. et al. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. Journal of Geophysical Research: Oceans, 103(C12), 27999–28012, 1998. American Geophysical Union (AGU). DOI: https://doi.org/10.1029/98jc02370

WANG O.; FUKUMORI, I.; FENTY I. ECCO Version 4 Release 4 User Guide, 2020. Available online at <https://ecco-group.org/docs/v4r4_user_guide.pdf>. Accessed February 2, 2022

WYSOCKI, L. A. et al. Spatial variability in the coupling of organic carbon, nutrients, and phytoplankton pigments in surface waters and sediments of the Mississippi River plume. Estuarine Coastal and Shelf Science, 69, 1-2, 47–63, 2006. DOI: https://doi.org/10.1016/j.ecss.2006.03.022

YU, L.; JIN, X.; WELLER, R. A. Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, 64pp. 2008. Woods Hole. Massachusetts. Available at <ftp://159.226.119.59/ftp/ds135_OAFLUX-v3-netheat_1_1day_netcdf/OAFlux_TechReport_3rd_release.pdf>. Accessed February 3, 2022.

ZENDER, C. S. Analysis of self-describing gridded geoscience data with NetCDF Operators (NCO). Environmental Modelling & Software. 23, 10–11, 1338–1342, 2008. Elsevier BV. DOI: https://doi.org/10.1016/j.envsoft.2008.03.004

ZHOU, K. et al. Impact of physical and biogeochemical forcing on particle export in the South China Sea. Prog. Oceanogr. 187, 102403, 2020. DOI: https://doi.org//10.1016/j.pocean.2020.102403

ZUO, H. et al. The ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice: a description of the system and assessment. Copernicus GmbH. 2019. DOI: https://doi.org/10.5194/os-2018-154

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Published

21-07-2022

How to Cite

CASALS, R.; VARONA, H. L.; CALZADA, A. E.; LENTINI, C. A. D.; NORIEGA, C.; BORGES, D. M.; LIRA, S. M. A.; SANTANA, C. S. de; ARAUJO, M.; SCHWAMBORN, R.; RODRIGUEZ, A. A dataset of Oceanographic and biogeochemical anomalies in the Caribbean Sea. Latin American Data in Science, [S. l.], v. 2, n. 1, p. 30–53, 2022. DOI: 10.53805/lads.v2i1.50. Disponível em: https://ojs.datainscience.com.br/index.php/lads/article/view/50. Acesso em: 1 mar. 2024.