Regional Sea-level Budget from 1993-2016

This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022, submited).

Note: The manuscript (and dataset) has not been peer reviewed yet!!! Use this data with caution! 

PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA
Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, most of the data heavily relies on previous work and data sets by many authors, so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').
PLEASE CHECK THIS CAREFULLY!

This repository contains the following files:

budget_components_ENS.nc
Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). If required the individual data sets used for the ensemble, please contact the author. 

masks.nc
netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired.

dmaps_trend.pkl (and .xlsx)
Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl)

som_trend.pkl (and .xlsx)
Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl)

The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB

Corresponding author: carolina.camargo@nioz.nl