Statistically downscaled climate indices from CMIP6 global climate models (CanDCS-U6)

Statistically downscaled climate indices from CMIP6 global climate models (CanDCS-U6) Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with a new set of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). This dataset is referred to as the Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Statistically downscaled individual model output are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 dataset. The climate indices include 27 climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the indices. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products, including the multi-model ensembles, are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization. 2022-11-22 Environment and Climate Change Canada open-ouvert@tbs-sct.gc.ca Nature and EnvironmentClimateClimate changeModelsModellingStatistical analysis Statistically downscaled climate scenarios and indices from CMIP6 global climate models (FR)HTML https://scenarios-climatiques.canada.ca/?page=CanDCSU6-notes Statistically downscaled climate scenarios and indices from CMIP6 global climate models (EN)HTML https://climate-scenarios.canada.ca/?page=CanDCSU6-notes Download CMIP6 statistically downscaled climate indices (FR)NetCDF https://scenarios-climatiques.canada.ca/?page=CanDCSU6-indices Download CMIP6 statistically downscaled climate indices (EN)NetCDF https://climate-scenarios.canada.ca/?page=CanDCSU6-indices

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with a new set of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). This dataset is referred to as the Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6).

CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios.

Statistically downscaled individual model output are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

A total of 31 climate indices have been calculated using the CanDCS-U6 dataset. The climate indices include 27 climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the indices.

Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale.

Individual model datasets and all related derived products, including the multi-model ensembles, are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

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