DOI: 10.5281/zenodo.16422789
title: PINS: Bias-adjusted projections of snow cover over the Quebec Province using an ensemble of regional climate models

description: Portraits d'indices de neige au sol / Bias-adjusted projections of snow cover over the Quebec Province using an ensemble of regional climate models.

This publication holds the code that generates the PINS datasets of daily snow water equivalent projections and the related indices. It also holds the daily projections as well as a selection of the indices. The PINS project was first described in Bresson et al. (2024) (Ouranos project report) and is fully analyzed in Bresson et al. (2025) (journal article). This page is only a short summary describing the code and the data.

Context

In the context of climate change, stakeholders and decision-makers are in demand of easily accessible bias-adjusted projections of snow cover and their resulting indices to develop adaptation plans. To meet this need, we produced an ensemble of regional climate projections statistically bias adjusted of snow water equivalent (SWE) over the Quebec province. This bias adjustment required some fine-tuning to operational methods, mainly due to the seasonality in the SWE. We calculated SWE indices of interest for several sectors based on the bias-adjusted SWE. These indices include, but are not limited, the maximum of SWE, duration, start, and end of the snow season.

Dataset composition

Table 1 below shows the members of PINS, the CORDEX-NA CMIP5 RCMs that were selected through an initial screening. The selection was based upon the RCMs ability to simulate the start and end of the snow season when compared to ERA5-Land.

Table 1 : Members of PINS v1.0




Institution
Model
Experiment
Driving model
Resolution


UQAM
CRCM5
RCP4.5
MPI-ESM-LR
0.44°


OURANOS
CRCM5
RCP4.5
CNRM-CM5
0.22°


OURANOS
CRCM5
RCP4.5
GFDL-ESM2M
0.22°


OURANOS
CRCM5
RCP4.5
MPI-ESM-LR
0.22°


Iowa State University
RegCM4
RCP8.5
HadGEM2-ES
0.22°


UCAR
RegCM4
RCP8.5
MPI-ESM-LR
0.22°


NCAR
WRF
RCP8.5
GFDL-ESM2M
0.22°


OURANOS
CRCM5
RCP8.5
CNRM-CM5
0.22°


OURANOS
CRCM5
RCP8.5
GFDL-ESM2M
0.22°


OURANOS
CRCM5
RCP8.5
MPI-ESM-LR
0.22°




The PINS project that produced this dataset aimed to deliver a collection of annual surface snow indices. A selection of the most robust indices is shared alongside the code here.

The first step, however, was to produce bias-adjusted timeseries of surface snow amount (snw, also called "snow water equivalent", SWE).

Table 2 : Description of selected indicators




Short name
Description
Units


no_snw_days
Annual number of days where the surface snow amount is under 4 kg/m²
days


snw_max
Maximum annual surface snow amount
kg m-2 s-1


snw_season_start
First day of the period where surface snow amount is above or equal to 4 kg/m² for 14 consecutive days
day of year


snw_season_end
First day of the period, following the snow season start, where surface snow amount is below 4 kg/m² for 14 consecutive days
day of year


snw_season_length
Number of days between the snow season start and the snow season end
days




All indicators are computed for annual periods going from August to July, except snw_season_end which is computed for the period from December fo November.

Spatial and temporal coverage

The PINS data was produced over a region slightly larger than Québec, over northeastern North America. Daily snow amount data was produced for the whole length of the RCM simulations, so 1950 to 2100 (or 2099 in some cases). Indicators were computed on each year of the period. The reference period for the adjustment was chosen to be 1981-2010.

Reference data

The bias-adjustment was made in reference to ERA5-Land (Muñoz-Sabater et al. 2021) data which was selected out of 4 candidate products (Blended-5, MERRA-2, ERA5 and ERA5-Land) by testing its performance against station observations from the CanSWE dataset (Vionnet et al., 2021). This followed the conclusions of Mudryk et al. (2024).

Methodology

The snow amount presents a strong seasonality which makes conventional bias-adjustment methods harder to apply. Source simulations that did not simulate the beginning and end of the snow season appropriately were rejected from the ensemble. Following Michel et al. (2023), the end of the season was slowed down using an exponential decay as a preliminary step, in order to let the quantile-mapping adjustment do its job.

Data processing tools

The code relies on xscen for the workflow management, xsdba for the bias-adjustment and xclim for the indicators calculations, three open-source python libraries maintained by Ouranos. All are built upon the packages xarray and dask for data handling and parallelization management.

Details of the workflow are given on the github page. 

Note: The code for the no_snw_days indicator is missing from this release, please refer to the github repo for the proper implementation of that diagnostic.

Data availability and download

The daily timeseries and a selection of indicators are made available directly here.

This data with all indicators computed for the project is also available on Ouranos' PAVICS THREDDS server. Indicators computed over ERA5-Land are available in another folder.

References

Bresson, É., Dupuis, É. et Bourgault, P. (2024). PINS - Portrait des indices de neige au sol. Rapport présenté à l'Association des Stations de ski du Québec et au gouvernement du Québec. Ouranos, Montréal, Canada. 34 pages + Annexes 9 pages. URL

Bresson, É., Dupuis, É. & Bourgault, P. (2025). Bias-adjusted projections of snow cover over the Quebec Province using an ensemble of regional climate models (in writing)

Mearns, L.O., et al., 2017: The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder CO, https://doi.org/10.5065/D6SJ1JCH

Michel, A., Aschauer, J., Jonas, T., Gubler, S., Kotlarski, S., & Marty, C. (2023). SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping. Geosci. Model Dev. Discuss., 2023, 1–28. https://doi.org/10.5194/gmd-2022-298

Mudryk, L. R., Mortimer, C., Derksen, C., Elias Chereque, A., & Kushner, P. J. (2024). Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project. EGUsphere, 2024, 1–28. https://doi.org/10.5194/egusphere-2023-3014

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., & Thépaut, J.-N. (2021). ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data, 13(9), 4349–4383. https://doi.org/10.5194/essd-13-4349-2021

Vionnet, V., Mortimer, C., Brady, M., Arnal, L., & Brown, R. (2021). Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020). Earth Syst. Sci. Data, 13(9), 4603–4619. https://doi.org/10.5194/essd-13-4603-2021