Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-191-2024
https://doi.org/10.5194/gmd-17-191-2024
Development and technical paper
 | 
11 Jan 2024
Development and technical paper |  | 11 Jan 2024

Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts

Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp

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Cited articles

Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437, 2021. a
Agbazo, M. N. and Grenier, P.: Characterizing and avoiding physical inconsistency generated by the application of univariate quantile mapping on daily minimum and maximum temperatures over Hudson Bay, Int. J. Climatol., 40, 3868–3884, https://doi.org/10.1002/joc.6432, 2020. a
Bennett, A. R., Hamman, J. J., and Nijssen, B.: MetSim: A Python package for estimation and disaggregation of meteorological data, Journal of Open Source Software, 5, 2042, https://doi.org/10.21105/joss.02042, 2020. a
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R., and Cannon, A. J.: Downscaling Extremes – An Intercomparison of Multiple Statistical Methods for Present Climate, J. Climate, 25, 4366–4388, https://doi.org/10.1175/JCLI-D-11-00408.1, 2012. a, b
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
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