期刊
DATA IN BRIEF
卷 41, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.dib.2022.107969
关键词
MARDAO dataset; TAAD dataset; Anomaly; Tropical Atlantic; Climate change
资金
- European Union [817578]
- Brazilian Research Network on Global Climate Change-Rede CLIMA (FINEP) [01.13.0353-00]
- CNPq/FAPESB/CAPES [565054/2010-4, 625 8936/2011, 465634/2014-1]
The article introduces the Tropical Atlantic Ocean Database and Monthly Anomalies of River Discharge on Atlantic Ocean datasets, which consist of anomalies of various physical and biogeochemical parameters in the tropical Atlantic Ocean, as well as anomalies of river runoff in the Atlantic Ocean and its adjacent seas. These datasets provide valuable information for studying climate variations in the tropical Atlantic Ocean.
The Tropical Atlantic Ocean Database and Monthly Anomalies of River Discharge on Atlantic Ocean datasets encompass the monthly anomalies of a variety of physical, biogeochemical parameters from the tropical Atlantic Ocean and the monthly anomalies of river runoff in the Atlantic Ocean and its adjacent seas. The parameters used as the base for the computation of anomalies come from the TROPFLUX, GPCP, ASCAT, SODA, GODAS, DASK, SeaWiFS, OAFLUX, WAVEWATCH III, NOAA/ESRL 20th Century Reanalysis, GLOBAL_REANALYSIS_BIO_001_029, GLOBAL_REANALYSIS_BIO_001_033, OCEANCOLOUR_GLO_ OPTICS_L4_REP_OBSERVATIONS_009_081, OSCAR, SMOS, MODIS-Aqua, CO2_Flux, and GRDC datasets. Several of the anomaly data are redundant, but come from different data sources making comparative studies possible. For ease of use, both datasets are provided in NetCDF format, CF convention. These datasets include 18 files in NetCDF format, which facilitates its handling due to the diversity of freeware tools that exist and are structured in two-, three- and four-dimensional grids. All these anomalies can be useful to oceanographers, meteorologists, ecologists and other researchers for studies of climate variation in the tropical Atlantic Ocean. These datasets are hosted at https://www.seanoe.org/data/00718/82962 and https://data.mendeley.com/datasets/pn5b35vn6s/1. (C) 2022 The Author(s). Published by Elsevier Inc.
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