Steric sea-level change estimates from 2005-2015 and 1993-2017

doi: 10.4121/12764933.v3
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doi: 10.4121/12764933
Datacite citation style:
Machado Lima de Camargo, Carolina (2021): Steric sea-level change estimates from 2005-2015 and 1993-2017. Version 3. 4TU.ResearchData. dataset.
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version 3 - 2021-05-07 (latest)
version 2 - 2020-09-24 version 1 - 2020-08-25
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global mean, world (±66˚ latitude),
time coverage
1993-2017, 2005-2015
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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.

v3: Added new file with the preferred trend and uncertainty (output of analysis shown in Figure 7 and Figure 8 of paper)
  • 2020-08-25 first online
  • 2021-05-07 published, posted
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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


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