The detailed snowpack scheme Crocus and its implementation in SURFEX v7

Vionnet, Vincent; Brun, E.; Morin, Samuel; Boone, Aaron; Faroux, Stéphanie; Moigne, Patrick Le; Martin, E.; Willemet, Jean-Marie

doi:10.5194/gmdd-4-2365-2011

Cited by 73 publications

(130 citation statements)

References 61 publications

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“…Although our implementation is simple and further work to improve the parameterization is required, it demonstrates the importance of including additional snow processes in sea ice models for climate applications (Vionnet et al, 2012;Nandan et al, 2017;Liston et al, 2018). Such model development can also benefit recent Arctic wide snow products 20 that rely on satellite observations (Maas et al, 2013;Guerreiro et al, 2016;Lawrence et al, 2018) or reanalysis precipitation fields on drifting sea ice (Kwok and Cunningham, 2008).…”

Section: Cs2 Observations Is Limited and The Sea Ice Is Thin During Wmentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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Section: Cs2 Observations Is Limited and The Sea Ice Is Thin During Wmentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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“…The successive components of the Crocus model have been described by Vionnet et al (2012). In this subsection, we only 205 list them in order to point out those that we modified to include water stable isotopes and water vapor transfer.…”

Section: Model Structurementioning

confidence: 99%

“…It describes dry snow metamorphism (evolution of the snow microstructure) driven by temperature and temperature gradients, using semi-empirical variables and laws. It has been used for ice-sheet conditions in polar regions, both Greenland and Antarctica (Brun et al, 2011;Lefebre et al, 2003;Fréville 85 et al, 2013;Libois et al, 2014Libois et al, , 2015 where it gives realistic predictions of density and snow type profiles (Brun et al, 1992;Vionnet et al, 2012), snow temperature profile (Brun et al, 2011 and snow specific surface area and permeability (Carmagnola et al, 2014;Domine et al, 2013). It has been recently optimized for application to conditions prevailing at Dome C, Antarctica (Libois et al, 2014) to account for specific conditions such as high snow density values at the surface and low precipitation amounts.…”

Section: ;mentioning

confidence: 99%

“…In the model, the profile of temperature evolves with time as a function of the surface temperature and of the energy fluxes at the surface and at the bottom interface (ice or soil). To correctly compute energy balance, the model integrates albedo calculation, deduced from surface microstructure and impurity content (Brun et al, 1992, Vionnet et al, 2012.…”

Section: Model Structurementioning

confidence: 99%

“…Each layer is characterized by its density, heat content, and by parameters describing snow 200 microstructure (sphericity and specific surface area) (Vionnet et al, 2012, Carmagnola et al, 2014). In the model, the profile of temperature evolves with time as a function of the surface temperature and of the energy fluxes at the surface and at the bottom interface (ice or soil).…”

Section: Model Structurementioning

confidence: 99%

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Numerical experiments on isotopic diffusion in polar snow and firn using a multi-layer energy balance model

Touzeau

Landais

Morin

et al. 2017

Preprint

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Abstract.To evaluate the impact of vapor diffusion onto isotopic composition variations in the snow pits and then in ice cores, we introduced water isotopes in the detailed snowpack model Crocus. The isotopes routine is run with a 1 s resolution. At each step and for each snow layer, 1) the initial isotopic composition of vapor is taken at equilibrium with solid phase, 2) kinetic 15 fractionation is applied during transport, and 3) condensation is realized.We study the different effects of temperature gradient, compaction, wind compaction and precipitation on the final vertical isotopic profiles. We also run complete simulations of vapor and isotopic diffusion at GRIP, Greenland and at Dome C, Antarctica over periods of 1 or 10 years. The vapor diffusion tends to smooth the original seasonal signal, with an attenuation of 9.5% of the original signal over 10 years at GRIP. This is smaller than the observed attenuation in ice cores, 20 indicating that the model underestimates attenuation due to diffusion or that other processes, such as ventilation, also contribute to the observed attenuation. At Dome C, the attenuation is stronger (14 %), probably because of the lower accumulation and stronger δ 18 O gradients.Because vapor diffusion is not the only process responsible of the signal attenuation, it would be useful to implement in the model ventilation of the snowpack and exchanges with the atmosphere to evaluate their contribution. 25Geosci. Model Dev. Discuss., https://doi

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White water: Fifty years of snow research in WRR and the outlook for the future

Sturm

2015

Water Resources Research

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Over the past 50 years, 239 papers related to snow have been published in Water ResourcesResearch (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

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The detailed snowpack scheme Crocus and its implementation in SURFEX v7

Cited by 73 publications

References 61 publications

New insight from CryoSat-2 sea ice thickness for sea ice modelling

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Numerical experiments on isotopic diffusion in polar snow and firn using a multi-layer energy balance model

White water: Fifty years of snow research in WRR and the outlook for the future

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