GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

Luojus, Kari; Pulliainen, Jouni; Takala, Matias; Lemmetyinen, Juha; Mortimer, Colleen; Derksen, Chris; Mudryk, Lawrence; Moisander, Mikko; Hiltunen, Mwaba; Smolander, Tuomo; Ikonen, Jaakko; Cohen, Juval; Salminen, Miia; Norberg, Johannes; Veijola, Katriina; Venäläinen, Pinja

doi:10.1038/s41597-021-00939-2

Cited by 72 publications

(45 citation statements)

References 45 publications

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“…The monthly mean values from 1979 to 2019 of several atmospheric variables such as geopotential height, surface air temperature, sea level pressure (SLP), snow fall, and snow depth are studied with a focus on the boreal winter season (i.e., December-January-February). Version 4 of the Northern Hemisphere EASE-Grid 2.0 snow cover (Brodzik and Armstrong 2013) used in this study is obtained from the National Snow and Ice Data Center (http:// nsidc.org/data/) with the original weekly data at a grid cell size of 25 km × 25 km, which has been converted to monthly mean with 1 × 1 spatial resolution, and the newly released GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset for the period of 1979-2018 is also used (Luojus et al, 2021). The AO, East Atlantic/Western Russia (EA/WR), and Polar/Eurasia (POLEUR) teleconnection indices are provided by the Climate Prediction Center (CPC).…”

Section: Data Method and Coupled Model Outputmentioning

confidence: 99%

Diverse Inter-Annual Variations of Winter Siberian High and Link With Eurasian Snow in Observation and BCC-CSM2-MR Coupled Model Simulation

et al. 2021

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An observational study illustrates that three distinct modes of winter Siberian high variability exist in observations at the inter-annual time scale. In this paper, we compare the connection between these diverse Siberian high variation modes with pre-autumn and simultaneous Eurasian snow cover in an observation and BCC-CSM2-MR coupled climate model run under pre-industrial conditions from the CMIP6 project. Our analyses indicate that the inter-annual variation of observed Siberian high modes do have a connection with pre-autumn and simultaneous Eurasian snow cover anomalies, but the BCC-CSM2-MR coupled climate model does not capture the observed diverse Eurasian snow–Siberian high relationships well. The BCC-CSM2-MR coupled climate model can partly reproduce the observed Siberian high variation modes, but fail to capture the spatial distribution and statistics of boreal fall and winter Eurasian snowpack, which is a key facet of simulated diverse Siberian high variability irrespective of the influence of Eurasian snow cover.

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Section: Data Method and Coupled Model Outputmentioning

confidence: 99%

Diverse Inter-Annual Variations of Winter Siberian High and Link With Eurasian Snow in Observation and BCC-CSM2-MR Coupled Model Simulation

et al. 2021

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“…We used the robust sample Mahalanobis distance (RMD) (Leys et al, 2018) to identify spurious SWE-SD data pairs as in Hill et al (2019). The RMD method is based on the traditional Mahalanobis distance (MD) (Mahalanobis, 1930), which is the distance of a point from the mean of a multivariate distribution. It relies on the mean and covariance matrices of the multivariate distribution, which are affected by outliers.…”

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confidence: 99%

Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Vionnet

Mortimer

Brady

et al. 2021

Earth Syst. Sci. Data

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Abstract. In situ measurements of water equivalent of snow cover (SWE) – the vertical depth of water that would be obtained if all the snow cover melted completely – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth (SD) and bulk snow density (defined as the ratio of SWE to SD) are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020, and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro-Québec were included. CanSWE includes over 1 million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734371 (Vionnet et al., 2021).

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“…PF-CLM-EU3km simulated higher SWE across the domain, which is particularly noticeable in north eastern Europe. However, it has been shown that GlobSnow-3 data tends to underestimate SWE in the northern hemisphere (Luojus et al, 2021), so the overestimation in PF-CLM-EU3km may not be as large as this comparison suggests. Overall, the PF-CLM-EU3km northern Europe streamflow performance results agree with previous pan-European studies which showed that most hydrological models perform worse in northeastern Europe, primarily due to forcing data errors and/or a coarse topographic resolution of these models that misrepresent the effects of topography on snow dynamics in these regions (Gudmundsson et al, 2012).…”

Section: Streamflow Evaluationmentioning

confidence: 62%

“…The GlobSnow SWE dataset is developed through a data assimilation approach by combining the ground-based synoptic snow depth stations with satellite passive microwave radiometer data and using the HUT snow emission model (Takala et al, 2011). Compared to previous versions of GlobSnow, Luojus et al (2021) further improved this dataset through bias-correction of monthly SWE data using the snow-course SWE measurements, independent from the snow depth data used in the assimilation. For comparison with model simulated SWE, we interpolated the bias-corrected monthly time series of SWE from 25 km to 3 km resolution using the first-order conservative interpolation method (Jones, 1999).…”

Section: Snow Water Equivalent Datamentioning

confidence: 99%

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model ParFlow-CLM (v3.6.0) over Europe

Naz¹,

Sharples

Ma³

et al. 2022

Preprint

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Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications including water resource management. However, many of the existing global to continental scale hydrological models are applied at coarse resolution and or neglect lateral surface and groundwater flow, thereby not capturing smaller scale hydrologic processes. Applications of high-resolution and more complex models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically-based coupled land surface groundwater model; Parflow-CLM version 3.6.0, over a pan-European model domain at 0.0275° ( 3 km) resolution. The model simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of hydrologic states and fluxes, resulting from a 10 year (1997–2006) model simulation, was performed using in-situ and remote sensing observations including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent and water table depth. Overall, the uncalibrated PF-CLM-EU3km model shows good agreement in simulating river discharge for 176 gauging stations across Europe. Comparison with satellite-based datasets of SM shows that PF-CLM-EU3km performs well in semi-arid and arid regions, but simulates overall higher SM in humid and cold regions. Comparisons with Global Land Evaporation Amsterdam Model(GLEAM) and Global Land Surface Satellite (GLASS) ET datasets show no significant differences, both, across the European domain (on average the difference is -0.09 and 0.30 mm d-1 for GLEAM and GLASS products, respectively), and within regions (R > 0.9). The large-scale high-resolution setup forms a basis for future studies, demonstrating the added value of capturing heterogeneities for improved water and energy flux simulations in physically-based fully distributed hydrologic models over very large model domains. This study also provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting, considering the effects of lateral surface and groundwater flows.

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GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

Cited by 72 publications

References 45 publications

Diverse Inter-Annual Variations of Winter Siberian High and Link With Eurasian Snow in Observation and BCC-CSM2-MR Coupled Model Simulation

Diverse Inter-Annual Variations of Winter Siberian High and Link With Eurasian Snow in Observation and BCC-CSM2-MR Coupled Model Simulation

Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model ParFlow-CLM (v3.6.0) over Europe

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