Historical gridded snow water equivalent over the Northern Hemisphere from remote sensing and land surface models

Datasets of daily and monthly snow water equivalent (SWE) over the Northern Hemisphere (excluding Greenland) were constructed using a multi-dataset approach for the time period 1981-2020.


The data are on a regular 0.5-degree grid, with a threshold maximum of 2000mm. 


The general methodology for the creation of these datasets follows that of Mudryk et al. (2015).


Supplemental Information


Monthly snow cover fraction (SCF) and monthly snow water equivalent (SWE) are calculated using daily SWE data taken from the following four sources over the 35-year period from 1981 to 2016.


 Four sources of daily SWE data:


 1. The Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) (Global Modeling and Assimilation Office, 2016 [Ref 1]; Gelaro et al., 2017 [Ref 2] is a National Aeronautics and Space Administration (NASA) atmospheric reanalysis product generated with the Goddard Earth Observing System Model, Version 5.2.0 (GEOS-5), atmospheric general circulation model and atmospheric data assimilation system (ADAS).


 2. The temperature index model described by Brown et al. (2003) [Ref 3] reconstructs daily SWE using 6-hourly temperature field and 12-hourly precipitation field inputs from ERA-Interim reanalysis. This simplified index model includes most of the temperature-dependent processes included in the snow component of numerical land surface schemes (e.g. partitioning of precipitation into solid and liquid fractions, melt from rain-on-snow events, specification of new snowfall density, snow aging, and snowmelt).


 3. The physical snowpack model Crocus simulates daily SWE using meteorology from ERA-Interim (Brun et al., 2013) [Ref 4].


 4. The European Space Agency GlobSnow product (Version 2; www.globsnow.info, last access: 20 September 2016) is a gridded product derived through a combination of satellite passive microwave data, forward snow emission model simulations, and climate station observations for non-alpine regions of the Northern Hemisphere (Takala et al., 2011) [Ref 5].


 The GlobSnow product is partially masked over mountainous regions, defined as regions with a slope of 2 degrees or larger. SWE was replaced in grid cells which contain mountains with a blend of the GlobSnow SWE data (if any) and the mean value from the other three data sources. The weighting for the blend was determined by the fraction of the grid cell area which is mountainous. For grid cells with no mountainous terrain, unaltered GlobSnow data are used. As the fraction of mountainous terrain increases, the weight applied to the GlobSnow data is linearly reduced, reaching zero for grid cells containing only mountainous terrain.


 For a given dataset of daily SWE, the data was interpolated to a regular 0.25 degree grid over Canada.


 Monthly SCF is produced by applying a 4mm threshold to each of the four daily SWE fields to produce a daily binary snow cover field; this daily field is averaged over each month to produce a monthly snow cover fraction and the four data sets are averaged together.


 Monthly SWE is produced by averaging the regridded daily SWE fields from each source over the given month.


 Annual maximum SWE fields are calculated as the maximum value of daily SWE attained at each grid location over a given snow season.


 Note: grid cell values for SCF, SWE and SWEmax have been weighted by the fraction of land surface within each grid cell (excluding ocean, lakes and glaciers/ice caps). For example, a grid cell containing 50% land and 50% ocean which is fully snow-covered for the month will have a listed SCF value of 50%. 


 Ref 1. Global Modeling and Assimilation Office: MERRA-2tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GESDISC), https://doi.org/10.5067/AP1B0BA5PD2K, last access: 11 April 2017b.


 Ref 2. Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A.,Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella,S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.,Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka,G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D.,Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2),J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.


 Ref 3. Brown, R., Brasnett, B., and Robinson, D.: Gridded North American monthly snow depth and snow water equivalent for GCM evaluation, Atmos.-Ocean, 41, 1–14, 2003.


 Ref 4. Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette,R., Karbou, F., and Morin, S.: Simulation of Northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses, J. Hydrometeorol., 14,203–219, https://doi.org/10.1175/JHM-D-12-012.1, 2013.


 Ref 5. Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemme-tyinen, J., Kärnä, J.-P., and Koskinen, J.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Proc. Spie., 115, 3517–3529,https://doi.org/10.1016/j.rse.2011.08.014, 2011.

Organization: Environment and Climate Change Canada | Environnement et Changement climatique Canada