Eurasian snow depth in long-term climate reanalyses

Wegmann, Martin; Orsolini, Yvan; Dutra, Emanuel; Bulygina, Olga; Sterin, Alexander; Brönnimann, Stefan

doi:10.5194/tc-11-923-2017

Cited by 40 publications

(32 citation statements)

References 51 publications

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“…Snow depth statistics derived from daily station data are reasonably well reproduced in all three modern reanalyses, which is in agreement with Wegmann et al (2017), who investigated April snow depth in ERAI-L. While snow depth differences between ERAI-L and ERAI-LG are small, ERAI-LG shows slightly higher deviations from the station data than ERAI-L, which might be caused by the higher vegetation in the station surroundings and by an underestimation of snowfall due to instrumentation used at the Russian station network (Rasmussen et al, 2012).…”

Section: Wegmann Et Al: Spring Snow Albedo Feedback Over Northernsupporting

confidence: 84%

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Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

et al. 2018

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Abstract. This study uses daily observations and modern reanalyses in order to evaluate reanalysis products over northern Eurasia regarding the spring snow albedo feedback (SAF) during the period from 2000 to 2013. We used the state-of-the-art reanalyses from ERA-Interim/Land and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) as well as an experimental set-up of ERA-Interim/Land with prescribed short grass as land cover to enhance the comparability with the station data while underlining the caveats of comparing in situ observations with gridded data. Snow depth statistics derived from daily station data are well reproduced in all three reanalyses. However day-to-day albedo variability is notably higher at the stations than for any reanalysis product. The ERAInterim grass set-up shows improved performance when representing albedo variability and generates comparable estimates for the snow albedo in spring. We find that modern reanalyses show a physically consistent representation of SAF, with realistic spatial patterns and area-averaged sensitivity estimates. However, station-based SAF values are significantly higher than in the reanalyses, which is mostly driven by the stronger contrast between snow and snow-free albedo. Switching to grass-only vegetation in ERA-Interim/Land increases the SAF values up to the level of station-based estimates. We found no significant trend in the examined 14-year time series of SAF, but interannual changes of about 0.5 % K −1 in both station-based and reanalysis estimates were derived. This interannual variability is primarily dominated by the variability in the snowmelt sensitivity, which is correctly captured in reanalysis products. Although modern reanalyses perform well for snow variables, efforts should be made to improve the representation of dynamic albedo changes.

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Section: Wegmann Et Al: Spring Snow Albedo Feedback Over Northernsupporting

confidence: 84%

“…Snow depth is used as inferred by reanalyses and, if needed, converted to centimetres. More information about general characteristics of reanalysis products in the Arctic can be found in Lindsay et al (2014), Dufour et al (2016) and Wegmann et al (2017).…”

Section: Reanalysis Datamentioning

confidence: 99%

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

et al. 2018

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“…Atmospheric reanalyses, based on data assimilation and modeling (Saha et al, 2010), can provide important information about the temporal evolution of the atmosphere. Meteorological variables obtained from reanalysis data can be used as inputs for models of snow mass and energy balance which can be applied to describe the behavior of the snowpack over large areas (Brun et 80 al., 2013;Krogh et al, 2015;Wegmann et al, 2017). However, the coarse resolution (cell size: ~10s of km) implies these simulations may have insufficient spatial resolution for characterizing the topographical complexity of mountain areas (Mass et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González¹,

López-Moreno²,

Gascoin³

et al. 2017

Preprint

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We present snow observations and a validated daily gridded snowpack dataset that was 15 simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used 20 meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 ×10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to 25 drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial 30 variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management.

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“…Atmospheric reanalyses, based on data assimilation and modeling (Saha et al, 2010), can provide important information about the temporal evolution of the atmosphere. Meteorological variables obtained from reanalysis data can be used as inputs for models of snow mass and energy balance which can be applied to describe the behavior of the snowpack over large areas (Brun et al, 2013;Krogh et al, 2015;Wegmann et al, 2017). However, the coarse resolution (cell size: around tens of kilometers) implies these simulations may have insufficient spatial resolution for characterizing the topographical complexity of mountain areas (Mass et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González

López‐Moreno

Gascoin

et al. 2018

Earth Syst. Sci. Data

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Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

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Eurasian snow depth in long-term climate reanalyses

Cited by 40 publications

References 51 publications

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

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