Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios
Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023. All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code simila
Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.
All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:
from fsspec.implementations.zip import ZipFileSystem
import xarray as xr
xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())File Inventory
Products
- pyCIAM_outputs.zarr.zip: Outputs of the pyCIAM model, using the SLIIDERS dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (LocalizeSL and FACTS) and for multiple emissions scenarios and ice sheet models.
- pyCIAM_outputs_case.nc: A NetCDF version of
pyCIAM_outputs, in which the netcdf files are divided up by adaptation “case” to reduce file size. - diaz2016_outputs.zarr.zip: A replication of the results from Diaz 2016 – the model upon which pyCIAM was built, using an identical configuration to that of the original model.
- suboptimal_capital_by_movefactor.zarr.zip: An analysis of the observed present-day allocation of capital compared to a “rational” allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.
Inputs
- ar5-msl-rel-2005-quantiles.zarr.zip: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in Kopp 2014, Bamber 2019, DeConto 2021, IPCC SROCC. The results contained in pyCIAM_outputs.zarr.zip cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.
- diaz2016_inputs_raw.zarr.zip: The coastal inputs used in Diaz 2016, obtained from GitHub and formatted for use in the Python-base
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Files are hosted on the source repository. Click download to access the full dataset.