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Grenoble, 03/04/2013

Snow Grain Size Workshop. Implementation and evaluation of prognostic representations of the optical diameter of snow in the detailed snowpack model SURFEX/ISBA-Crocus. C.M. Carmagnola 1 , S. Morin 1 , M. Lafaysse 1 , F. Domine 2 , G. Picard 3 and L. Arnaud 3.

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Grenoble, 03/04/2013

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  1. Snow Grain Size Workshop Implementation and evaluation of prognostic representations of the optical diameter of snow in the detailed snowpack model SURFEX/ISBA-Crocus C.M. Carmagnola1, S. Morin1, M. Lafaysse1, F. Domine2, G. Picard3 and L. Arnaud3 1 Météo-France - CNRS, CNRM - GAME, CEN, Grenoble, France 2 Takuvik Joint International Laboratory, CNRS and Université Laval, Québec (QC), Canada 3 CNRS - Université Joseph Fourier Grenoble 1, LGGE, Grenoble, France Grenoble, 03/04/2013

  2. The problem ● Making existing snow physical models to be able to simulate accurately metamorphic processes is crucial ● Few snowpack models incorporate an explicit representation of metamorphic processes ● In SURFEX/ISBA-Crocus snow model: - metamorphism implemented in a phenomenological way - semi-empirical shape variables, not measurable easily in the field Our goal ● Implementing optical diameter into Crocus as prognostic variable ● Re-formulating dry metamorphism in terms of rate of change of optical diameter

  3. SURFEX/ISBA-Crocus snow model overview • Numerical model developed in Grenoble (Brun et al., 1989,1992; Vionnet et. al, 2012) • Multi-layer (up to 50), unidimensional • Mass and energy balance of the snowpack • Snow layers characterized by: - thickness - density - temperature - lwc - variables describing snow grains Vionnet et al., 2012

  4. SURFEX/ISBA-Crocus snow model snow grain characteristics ● dendricity (d) -> share of fresh snow crystals -> varies from 1 to 0 (decreasing systematically) ● sphericity (s) -> share of rounded snow crystals -> varies from 0 to 1 ● size (gs) ● dendritic case -> fresh snow crystals still remaining -> layers described by: d and s ● non-dendritic case -> snow ages and d reaches 0 -> layers described by: s and gs

  5. Time step i Time step i+1 Time step i+1 Time step i+1 ρ T G d s gs ρ T G d s gs dopt prognostic variables updating ρ (densification) T,G (heat equation) updating d,s,gs (metamorphism laws) diagnostic variable B92 formulation Why optical diameter? - impacted by snow aging in a more predictable way (compared to d, s, gs) - practical metric for relating microphysical state of the snowpack to radiative properties (albedo) - can be retrieved from remote sensing and easily measured in the field using optical methods - linked to specific surface area (SSA) SURFEX/ISBA-Crocus snow model snow metamorphism C13 formulation

  6. ● Inversion of the formula giving dopt as a function of d, s, gs ● Change of variable through the entire code in order to replace d and gs by dopt and s ● Transition from dendritic to non-dendritic regime: - now, snow enters its non-dendritic state when dopt grows beyond a certain threshold - the higher s value, the easier for dopt to exceed this threshold New metamorphism formulation overview C13 formulation ● Re-formulation of original Crocus snow metamorphism (B92) in terms of: - optical diameter - sphericity (in the future it will be replaced by other measurable quantities, such as anisotropy of kT)

  7. New metamorphism formulation rate equations C13 formulation Dry metamorphism evolution laws of B92 re-written in terms of dopt and s ● Six cases are distinguished, depending on: - G value (weak, middle and strong temp. gradient) - regime (dendritic and non-dendritic snow) ● Wet snow metamorphism also re-formulated in terms of dopt ● Rate equations for s are identical to those of B92 ●dopt is always increasing ● s and dopt are function of G (in K m-1), T (in K) and t (in d)

  8. ● Empirical parameterisation of the rate of decay of SSA ● Based on ET and TG experiments ● Snow age / Time-averaged T / Temp. gradient ● Physically-based model to predict the evolution of dry snow optical diameter ● Simulation of diffusive vapour flux amongst collections of spherical ice crystals ● ρ / T / Temp. gradient / Initial size distrib. Other parameterisations of dopt rate of change Implementing optical diameter as prognostic variable of the code -> allows to incorporate and test easily other parameterisations of dopt rate of change T07 formulation Taillandier et al., 2007 F06 formulation Flanner and Zender, 2006

  9. Crocus metamorphism formulations vs cold room measurements isothermal experiments ● Most rapid snow aging is always produced by the combination of low ρ, high T and large G ● B92 and C13 display a discontinuous derivative when snow enters the non-dendritic state ● G < 15 K m-1: B92 and C13 give the same results

  10. Crocus metamorphism formulations vs cold room measurements temperature gradient experiments ● In B92: SSAmax = 65 m2 kg-1 (when d = s = 1) ● In C13: we can initialize with any SSA value ● G > 15 K m-1: B92 and C13 are different -> in C13 SSA starts decreasing as soon as the non-dendritic state is reached -> in B92 only when gs > 8 10-4 m

  11. Crocus metamorphism formulations vs cold room measurements computing RMSD for SSA (m2 kg-1) Results seem to reveal that F06 fits observational data better than other formulations

  12. Snow dataset used to evaluate metamorphism formulations SSA data acquired with high vertical resolution (about 1 cm) ASSSAP (presented this morning by G. Picard) DUFISSS (presented yesterday by F. Domine) Note: Do you want to know more about these intruments? Come to La Grave tomorrow!

  13. results… Forcing variables for Crocus simulations ● A complete meteorological forcing has to be provided to the model: - air temperature and specific humidity at 2 m above ground - wind speed at 10 m above ground - snowfall and rainfall rates - incoming radiation (divided into short-wave and long-wave) ● Simulations at Summit Camp: - input variables provided by the ERA-Interim reanalysis (Dee et al., 2011) - spatial resolution of about 80 km ● Simulations at Col de Porte: - in situ data filled with SAFRAN local meteorological reanalysis (Durant et al., 1993)

  14. Results: Crocus metamorphism formulations time evolution of density at Col de Porte B92 C13 T07 F06

  15. Results: Crocus metamorphism formulations time evolution of SSA at Col de Porte B92 C13 T07 F06

  16. Results: Crocus metamorphism formulations vs observations snow height and SWE at Summit and Col de Porte Summit Camp, Greenland Col de Porte, France ● Differences between simulations < differences between simulations and observations ● T07 ≠ during melting periods

  17. Results: Crocus metamorphism formulations vs observations density and SSA profiles at Summit, May 10 2011 ● Layered system of hard wind slabs interspersed with faceting RG and SH at the surface ● Density between 200-300 kg m-3 -> different layer thickness (rules of aggregation) ● SSA decresing with depth -> similar profiles, except for T07

  18. Results: Crocus metamorphism formulations vs observations density and SSA profiles at Col de Porte, February 11 2010 ● About 35 cm of recent snow, rounded grains below ● Different simulated snow heights ● Measured snow height is underestimated

  19. Results: Crocus metamorphism formulations vs observations density and SSA profiles at Col de Porte, February 6 2012 ● 5 cm of DF, 15 cm of FC, RG below ● Continuous SSA measurements (ASSSAP profiler) ● T07 gives higher SSA value for recent snow, with vertical shift of ~10 cm

  20. Summit ● B92: RMSD values similar to that for cold room experiments CdP 2009/10 ● C13: ~B92 -> doptintegrated successfully as prognostic variable ● F06: comparable to B92 and C13 CdP 2011/12 ● T07: less accurate (especially for low SSA values) Results: computing RMSD between simulations and observations ● Projection on 1 mm vertical grid ● Simulations stretched vertically in order to match measured snow height ● Same for dopt

  21. Conclusions and perspectives ● Metamorphism in SURFEX/ISBA-Crocus is now described in terms of dopt ● Different parameterisations of the dopt rate of change have been compared to observations ● Model F06 and parametric equations B92 and C13 give similar results Empirical parameterisation T07 less accurate ● Comparing model predictions of surface snow SSA with estimates from remote sensing ● Assimilating remote sensing albedo ● Formulation of metamorphism in terms of dopt will allow to improve several parametric laws (viscosity, mobility index for wind transport, …) which depend on grain characteristics

  22. Thanks for your attention! Looking for a post-doc in 2014…

  23. Legagneux et al., 2004 ● A and B = f(Tmean) ● CdP -> recent snow (high SSA) / Summit -> rounded grains (low SSA)

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