cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models

Context 

The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, Ouranos is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. 

Climate simulations 

From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016).  This objective selection method identifies a reduced number of simulations that best represent the overall ensemble.  Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble.  A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). 

Reference dataset 

The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. 

As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. 

Coverage 

The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. 

It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. 

Bias-adjustment 

The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. 

Climate indicators 

This dataset is used to in the first versions (up to 1.3) of Ouranos’ Climate Portraits website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022).  The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.

On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".

• Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons.  Ensemble percentiles are computed in order to summarize climate model uncertainty.  In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. 

• Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. 

• Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. 

In versions 2.x of the app, this data will be presented as "CMIP5".

Data availability 

This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.

The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.
https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html 

The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html

Terms of use:  Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project https://pcmdi.llnl.gov/mips/cmip5/citation.html.