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Published December 11, 2023 | Version 1.2
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Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution

  • 1. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03D-14412 Potsdam, Germany
  • 2. Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Deltares, Delft, the Netherlands
  • 3. Department of River-Coastal Science and Engineering, Tulane University, New Orleans, USA
  • 4. Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA; National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL, USA
  • 5. Deutsches Geodätisches Forschungsinstitut der Technischen Universität München, Arcisstraße 21, 80333, Munich, Germany

Description

Important! There is a new version of this record correcting two errors documented at https://data.isimip.org/issues/55/

Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution" published in Earth System Science Data (ESSD). 

Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the ISIMIP repository.

File Descriptions

HCC_analysis_and_plots.ipynb

This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".

hcc_gesla3_99pctl_surge_2011_2015.nc

Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.

Variables:

  • observed_99pctl_surge_level_anomaly -- 99th percentile of daily maximum surge level anomalies from 2011-2015
  • hcc_99pctl_surge_level_anomaly -- HCC surge level anomalies at the same time steps as observed_99pctl_surge_level_anomaly
  • hcc_counterfactual_99pctl_surge_level_anomaly -- HCC counterfactual surge levels at the same time steps as observed_99pctl_surge_level_anomaly
  • hcc_water_level_monthly – Monthly relative water level from 1900-2015
  • hcc_geocentric_water_level_monthly – Monthly geocentric water level from 1900-2015

hcc_hr_psmsl_water_level_monthly_1900_2015.nc

Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.

Variables:

  • hcc_water_level_monthly – Monthly relative water level from the HCC dataset
  • hr_aligned_water_level_monthly -- Monthly relative water level from the HR dataset, aligned with hcc_water_level_monthly
  • psmsl_aligned_water_level_monthly -- Monthly relative water level from the PSMSL database, aligned with hcc_water_level_monthly

hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc

Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.

Variables:

  • gesla3_water_level_hourly -- Hourly relative water level from the GESLA3 database
  • hcc_water_level_hourly -- Hourly relative water level from the HCC dataset
  • codec_water_level_hourly  -- Hourly relative water level from the CoDEC dataset
  • hr_water_level_monthly  -- Monthly relative water level from the HR dataset

 

hcc_gesla3_water_level_hourly_2011_2015.nc

Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.

Variables:

  • gesla3_water_level_hourly -- Hourly relative water level from the GESLA3 database
  • hcc_water_level_hourly -- Hourly relative water level from the HCC dataset

slr_ds_psmsl_selected.nc

Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.

Variables:

  • psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval
  • hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper -- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval
  • hr_rslr, hr_rslr_lower, hr_rslr_upper --  Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval

reg_mask_xr.nc

Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.

Variables:

reg_mask – Float value, representing the ocean basins

 

Notes

Acknowledgments This research has received funding from the German Federal Ministry Ministry of Education and Research (BMBF) under the research projects QUIDIC (01LP1907A) and ISIAccess (16QK05), the European Union's Horizon 2020 research and innovation programme under agreement No 820712grant, and is based upon work from COST Action CA19139 PROCLIAS (PROcess-based models for CLimate Impact Attribution across Sectors), supported by COST (European Cooperation in Science and Technology; https://www.cost.eu). T.W. acknowledges support by NASA's Sea Level Change Team (award number 80NSSC20K1241) and the National Science Foundation (award numbers 1854896 and 2141461). S.D. acknowledges support by NASA's Sea Level Change Team (award number 80NSSC20K1241) and David and Jane Flowerree for their endowment.

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Additional details

Related works

Is original form of
Dataset: 10.48364/ISIMIP.749905 (DOI)
Is supplement to
Software: 10.5281/zenodo.7771501 (DOI)

References

  • Dangendorf, S., Hay, C., Calafat, F. M., Marcos, M., Piecuch, C. G., Berk, K., and Jensen, J.: Persistent acceleration in global sea-level rise since the 1960s, Nat. Clim. Chang., 9, 705–710, 2019
  • 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, 2020.
  • Oelsmann, J., Marcos, M., Passaro, M., Sanchez, L., Dettmering, D., and Seitz, F.: Vertical land motion reconstruction unveils non-linear effects on relative sea level, in submission, 2023.
  • Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su, J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections, Frontiers in Marine Science, 7, 263, 2020.
  • Woodworth, P. L. and Player, R.: The Permanent Service for Mean Sea Level: An update to the 21stcentury, J. Coast. Res., 19, 287–295, 2003.
  • Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M., and Haigh, I.: Towards a global higher-frequency sea level dataset, Geosci. Data J., 3, 50–59, 2016.
  • Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B. A.: Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis, Nat. Commun., 8, 16075, 2017.