Machado Lima de Camargo, Carolina (2020): Steric sea-level change estimates from 2005-2015 and 1993-2017. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:b6e5e4bc-d382-4b51-837b-c5cde4980bf3

Dataset

x choose version: version 3 - 2021-05-07 (latest) version 1 - 2020-08-25

• Oceanography

ocean temperature and salinity, steric sea-level change

1993-2017, 2005-2015

CC0

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by Carolina M.L. Camargo

We collected and analyzed 15 publicly available gridded datasets of monthly ocean temperature and salinity published by different research groups worldwide (article, Table 1). The datasets were divided according to their data type: Argo – for datasets that have only data from Argo floats; Multiple in-situ (MiS) – for products that combine several sources of in-situ observations, in addition to Argo data; ocean reanalysis (REA). From the T,S datasets, we computed steric sea-level anomalies (SLA) using the TE0-10 as the equation of state. First, the steric SLA was computed in the native resolution of each dataset. Afterwards, we standardized the varying resolution by remapping all datasets to a 1˚ by 1˚ grid. Next, we selected the data within 66˚N to 66˚S of latitude, and applied a land mask based on ETOPO1 (Amante and Eakins, 2009). Next, we computed a mean dataset for each of the three categories (Argo, MiS, REA) and a total ensemble mean, creating four new steric SLA datasets. Using an area-weighted mean, we computed a global mean steric SLA for each dataset. The trends and respective uncertainties were estimated using the Hector software (Bos et al., 2013). We used 8 different noise-models to obtain the trends: WN, PL, PLWN, GGM, AR(1), AR(5), AR(9), ARFIMA.

• 2020-08-25 first online

• 2020-09-24 published, posted

4TU.ResearchData

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• NWO

NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems, and Utrecht University;
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing