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References

This page collects the main literature and methodological references used in the ClimateTools documentation.

Bias Correction

  • Themeßl, M. J., Gobiet, A., and Leuprecht, A. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Theoretical and Applied Climatology, 2012.
  • Piani, C., Haerter, J. O., and Coppola, E. Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 2010.
  • Roy, P., Rondeau-Genesse, G., Jalbert, J., and Fournier, E. Climate scenarios of extreme precipitation using a combination of parametric and non-parametric bias correction methods in the province of Québec. Canadian Water Resources Journal, 2023. DOI: 10.1080/07011784.2023.2220682.

These references motivate the quantile-mapping workflows exposed through qqmap and related functions.

The Roy et al. (2023) paper is the methodological reference for biascorrect_extremes, including the QQM-GPD framing, external GEV parameter workflow, and transition between bulk and tail correction.

Time Variability Correction

  • Shao, Y., Bishop, C. H., Hobeichi, S., Nishant, N., Abramowitz, G., and Sherwood, S. Time Variability Correction of CMIP6 Climate Change Projections. Journal of Advances in Modeling Earth Systems, 2024. DOI: 10.1029/2023MS003640.

This is the methodological reference for tvc, fit_tvc, and apply_tvc.

Climate Indicators

The grouped index interface in ClimateTools overlaps conceptually with ETCCDI and xclim-style climate-indicator workflows, especially for threshold counts, spell-duration indices, and percentile-based indices.

  • Bourgault, P., Huard, D., Smith, T. J., Logan, T., Aoun, A., Lavoie, J., et al. xclim: xarray-based climate data analytics. Journal of Open Source Software, 2023. DOI: 10.21105/joss.05415.

This is the primary upstream reference for the xclim-style indicator framework that informs ClimateTools' grouped climate-index documentation and parity-oriented test coverage.

Ensembles and Robustness

ClimateTools now includes a first xclim-inspired ensemble layer for summary statistics, percentile aggregation, deterministic subset selection, and robustness diagnostics.

  • Bourgault, P., Huard, D., Smith, T. J., Logan, T., Aoun, A., Lavoie, J., et al. xclim: xarray-based climate data analytics. Journal of Open Source Software, 2023. DOI: 10.21105/joss.05415.
  • Cannon, A. J. Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices. Journal of Climate, 2015. DOI: 10.1175/JCLI-D-14-00636.1.
  • Katsavounidis, I., Jay Kuo, C.-C., and Zhang, Z. A new initialization technique for generalized Lloyd iteration. IEEE Signal Processing Letters, 1994.
  • Tebaldi, C., Arblaster, J. M., and Knutti, R. Mapping model agreement on future climate projections. Geophysical Research Letters, 2011. DOI: 10.1029/2011GL049863.
  • Knutti, R., and Sedlacek, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 2013. DOI: 10.1038/nclimate1716.
  • Intergovernmental Panel on Climate Change (IPCC). Atlas. In Climate Change 2021: The Physical Science Basis, Cambridge University Press, 2023. DOI: 10.1017/9781009157896.021.

These references motivate the xclim-compatible ensemble APIs in ClimateTools: ensemble_mean_std_max_min, ensemble_percentiles, make_criteria, kkz_reduce_ensemble, robustness_fractions, robustness_categories, and robustness_coefficient.

Regridding and Scenario Construction

The ClimateTools documentation uses workflow-oriented documentation patterns inspired by the broader climate-services ecosystem, including xclim’s separation of tutorials, workflow guides, and API reference.