Modified Sea Level Reconstruction Reveals Improved Separation of Climate and Trend Patterns

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.本研究对应的手稿为《近七十年来海平面重建揭示区域气候与趋势模式的分离度提升》，由此产生的核心数据产品为改进型海平面重建网格化数据集，及其配套分析结果与可视化代码。 本仓库包含1950年1月至2022年1月时段内、空间分辨率为1°×1°的全球月度海平面重建数据。本数据集基于改进的海平面重建框架构建，同时包含基于这72年重建结果的经验正交函数（Empirical Orthogonal Function, EOF）分析结果。前四个EOF模态分别被证实与长期海平面趋势、调制年循环、厄尔尼诺-南方涛动（El Niño–Southern Oscillation, ENSO）以及太平洋年代际振荡（Pacific Decadal Oscillation, PDO）相关。本数据集提供了用于识别与气候模式关联的EOF模态的基于MATLAB的软件。本研究用于相关性分析的Niño 3.4指数与PDO指数分别下载自美国国家海洋和大气管理局（National Oceanic and Atmospheric Administration, NOAA，https://psl.noaa.gov/data/timeseries/month/）与日本气象厅（Japan Meteorological Agency, JMA，https://ds.data.jma.go.jp/tcc/tcc/products/elnino/），且已包含在上传的数据集中。此外本数据集还包含验证数据集，用于对比重建海平面结果与卫星测高及验潮站观测记录。额外的分析输出结果同样提供，包括有无ENSO与PDO关联模态的重建趋势场、与这些气候模式相关的加速度场及其他分析结果。 本数据集采用标准NetCDF与纯文本格式存储，以确保跨平台的广泛兼容性。为支持数据探索与解读，本仓库提供了可视化脚本：用于MATLAB环境下时序绘图与分析的Matlab_main.m，以及基于通用制图工具（Generic Mapping Tools, GMT）生成空间场可视化结果的GMT_main.sh。本README文件包含详细的数据集说明，配套脚本中也嵌入了额外的注释与注解。 为便于对比，本研究使用的全球平均海平面（global mean sea level, GMSL）数据集还包含来自Church与White（2011，更新版，数据下载自https://research.csiro.au/slrwavescoast/sea-level/）、Hamlington等（2014）、Frederikse等（2020）以及Dangendorf等（2024）的时序数据。其中Hamlington等（2014）与Dangendorf等（2024）的估算结果均源自网格化数据，其空间覆盖范围已调整至与本研究使用的范围尽可能匹配。所有年度GMSL记录均被插值至年中时刻，以保证时间一致性。 本研究可视化所用的调色板文件（bal.cpt、temp_18.cpt与temp.cpt）下载自CPT City存档（http://seaviewsensing.com/pub/cpt-city/）。 本研究用于对比的GMSL数据集源自以下已发表文献： 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.