DOI: 10.5281/zenodo.15288817
title: Modified Sea Level Reconstruction Reveals Improved Separation of Climate and Trend Patterns

description: The Modified Sea Level Reconstruction gridded data products, along with the associated analytical results and visualization codes, are the primary data products as a result of  the manuscript entitled "Sea Level Reconstruction Reveals Improved Separation of Regional Climate and Trend Patterns Over the Last Seven Decades."

This repository contains monthly global sea level reconstructions at a spatial resolution of 1° × 1°, spanning the period from January 1950 to January 2022. The dataset was developed using a modified sea level reconstruction framework. Empirical Orthogonal Function (EOF) analysis results based on this 72-year reconstruction are included. The first four EOF modes have been identified as being associated with the long-term sea level trend, the modulated annual cycle, the El Niño–Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO), respectively. (MATLAB-based software for identifying EOF modes associated with climate patterns is provided. The Niño 3.4 and PDO indices used for correlation analysis were downloaded from NOAA (https://psl.noaa.gov/data/timeseries/month/) and JMA (https://ds.data.jma.go.jp/tcc/tcc/products/elnino/), respectively, and are included in the uploaded dataset.)  Validation datasets are also included, featuring comparisons between reconstructed sea level and measurements from satellite altimetry and tide gauge records. Additional analytical outputs—such as reconstructed trend fields with and without ENSO- and PDO-related modes, acceleration fields associated with these climate modes, and other results—are also provided.

The datasets are distributed in standard NetCDF and plain text formats to ensure broad compatibility across platforms. To support data exploration and interpretation, visualization scripts are provided—Matlab_main.m for time series plotting and analysis in MATLAB, and GMT_main.sh for generating spatial field visualizations using GMT (Generic Mapping Tools). Detailed descriptions are included in this README file, with additional notes and annotations embedded within the accompanying scripts.

For comparison, the GMSL (global mean sea level) dataset also includes time series from Church and White (2011, updated; data downloaded from https://research.csiro.au/slrwavescoast/sea-level/), Hamlington et al. (2014), Frederikse et al. (2020), and Dangendorf et al. (2024). The estimates from  Hamlington et al. (2014) and Dangendorf et al. (2024) are derived from gridded data, with spatial coverage adjusted to best align with that used in this study. All yearly GMSL records have been interpolated to mid-year to ensure temporal consistency.

The color palette files (bal.cpt, temp_18.cpt, and temp.cpt) used for visualization were downloaded from the CPT City archive (http://seaviewsensing.com/pub/cpt-city/).

 

The GMSL datasets used for comparison in this study are derived from the following published sources:

Church, J. A. and White, N. J.: Sea-Level Rise from the Late 19th to the Early 21st Century, Surv Geophys, 32, 585–602, https://doi.org/10.1007/s10712-011-9119-1, 2011.

Hamlington, B. D., Leben, R. R., Strassburg, M. W., and Kim, K. ‐Y.: Cyclostationary empirical orthogonal function sea‐level reconstruction, Geosci. Data J., 1, 13–19, https://doi.org/10.1002/gdj3.6, 2014.

Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y.-H.: The causes of sea-level rise since 1900, Nature, 584, 393–397, https://doi.org/10.1038/s41586-020-2591-3, 2020

Dangendorf, S., Sun, Q., Wahl, T., Thompson, P., Mitrovica, J. X., and Hamlington, B.: Probabilistic reconstruction of sea-level changes and their causes since 1900, Earth Syst. Sci. Data, 16, 3471–3494, https://doi.org/10.5194/essd-16-3471-2024, 2024.