Technical documentation: CMIP5 statistically downscaled climate indices
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High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. A range of impact-relevant climate indices are available for download, including indices that represent the counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the lengths of episodes when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.
Multi-model datasets of the statistically downscaled climate indices for historical simulations (1951-2005) and three emission scenarios, RCP2.6, RCP4.5 and RCP8.5 (2006-2100), are available at a 10 x 10 km degree grid resolution. Both multi-model ensembles and individual model output are available for download.
Please see Li et al. (2018)Footnote 1 for further details of the calculation and analysis of the statistically downscaled climate indices dataset.
|Variables||For the full variable list and definitions|
|Geographic area||Canada (land mass only)|
|Spatial resolution||10 x 10 km degree grid resolution|
|Time period||1951 to 2100|
|Temporal resolution||Annual values|
Data and processing
Daily minimum and maximum temperature (Tmin and Tmax, respectively) and precipitation GCM output that were statistically downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure were used to calculate the indices. A total of 24 statistically downscaled GCM output were used.
Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) is a hybrid downscaling algorithm that combines downscaling by BCCA (Bias Correction/Constructed Analogues) (Maurer et al., 2010Footnote 2) with quantile mapping. Details are provided in Werner and Cannon (2016)Footnote 3. The use of Quantile Delta Mapping (Cannon et al., 2015Footnote 4), a change-preserving form of quantile mapping, distinguishes BCCAQv2 from the previous version of the algorithm. For further details on BCCAQv2 and the downscaling target.
Note: The calibration and application of the statistical downscaling method requires historical simulations to be concatenated to a RCP projection. As an artifact, statistically downscaled historical simulations concatenated to the three RCPs are not identical, though differences are generally negligible.
Further, the process of downscaling different variables independently may lead to small numbers of cases of physical inconsistency (i.e., minimum temperatures that occasionally exceed maximum temperatures), although tests indicate that ad hoc “correction” of inconsistent temperatures (e.g., by swapping affected minima and maxima) generally results in negligible differences in calculated climate indices (Li et al., 2018Footnote 1).
Definitions of climate indices
There are a range of climate indices available such as the Expert Team on Climate Change Detection and Indices (ETCCDI) indices. The ETCCDI indices were developed for understanding the past and future changes in climate extremes (e.g., Sillmann et al. 2013a, bFootnote 5 Footnote 6). They are not, however, always relevant for tracking climate impacts at the regional and local scales (Li et al 2018Footnote 1).
To address the needs of different user groups in Canada, agroclimate indices and other indices that were proposed by the Canadian adaptation community through a series of consultations are provided. A list of these indices and their definitions are available. Please see the definition list for the equations of each index made available on CCDS.
Equal model weighting
The different CMIP5 models used for the projections are all considered to give equally likely projections in the sense of 'one model, one vote'. Models with variations in physical parameterization schemes are treated as distinct models.
Model range through the use of ensemble percentiles
As local projections of climate change are uncertain, a measure of the range of model projections is provided (i.e., 5th, 25th, 75th and 95th) in addition to the median response (50th percentile) of the model ensemble. It should again be emphasized that this range does not represent the full uncertainty in the projection. The distribution combines the effects of natural variability and model spread.
Uncertainty in projections
The level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios, as Li et al. (2018)Footnote 1 found that there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. In contrast, uncertainty in projected precipitation changes was found to be large; the models do not fully agree on the sign of changes in most regions at the 2.1°C global warming level, but the models do agree on the sign of changes in most regions at the 4.5°C warming level. Nonetheless, the high uncertainty in precipitation projections should not be a reason for ignoring the projected changes in adaptation planning. The projections do provide credible and usable information. There is qualitative consistency between median projections under different forcing scenarios when indexed to temperature change and evidence that most indices scale with temperature changes. There is also qualitative consistency of the projected changes with the expected thermodynamically induced changes in precipitation extremes.
Given the range of natural climate variability and uncertainties regarding future greenhouse gas emission pathways and climate response, output projected by one climate model should not be used in isolation. Rather, it is good practice to consider a range of projections from multiple climate models (ensembles) and emission scenarios.
While likelihoods are not associated with particular climate change scenarios, the use of a range of scenarios may help convey to users the potential spread across a range of possible emission pathways.
Using global climate model output versus downscaled datasets
It should be noted that 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 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 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.
Multi-model ensembles made available through Environment and Climate Change Canada websites are provided under the Open Government Licence - Canada.
|#||CMIP5 model name||Institution|
|1||BNU-ESM||College of Global Change and Earth System Science, Beijing Normal University|
|2||CCSM4||National Center for Atmospheric Research|
|3||CESM1-CAM5||National Science Foundation, Department of Energy, National Center for Atmospheric Research|
|4||CNRM-CM5||Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique|
|5||CSIRO-Mk3.6.0||Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate Change Centre of Excellence|
|6||CanESM2||Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada|
|7||FGOALS-g2||LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences; and CESS, Tsinghua University|
|8||GFDL-CM3||Geophysical Fluid Dynamics Laboratory|
|9||GFDL-ESM2G||Geophysical Fluid Dynamics Laboratory|
|10||GFDL-ESM2M||Geophysical Fluid Dynamics Laboratory|
|11||HadGEM2-AO||National Institute of Meteorological Research / Korea Meteorological Administration|
|12||HadGEM2-ES||Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais)|
|13||IPSL-CM5A-LR||Institut Pierre-Simon Laplace|
|14||IPSL-CM5A-MR||Institut Pierre-Simon Laplace|
|15||MIROC-ESM||Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies|
|16||MIROC-ESM-CHEM||Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies|
|17||MIROC5||Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology|
|18||MPI-ESM-LR||Max Planck Institute for Meteorology (MPI-M)|
|19||MPI-ESM-MR||Max Planck Institute for Meteorology (MPI-M)|
|20||MRI-CGCM3||Meteorological Research Institute|
|21||NorESM1-M||Norwegian Climate Centre|
|22||NorESM1-ME||Norwegian Climate Centre|
|23||BCC-CSM1-1||Beijing Climate Center, China Meteorological Administration Institution|
|24||BCC-CSM1-1-m||Beijing Climate Center, China Meteorological Administration Institution|