1. 1 Grenoble, 03/04/2013 Implementation and evaluation of prognostic representations of the optical diameter of snow in the detailed snowpack model SURFEX/ISBA-Crocus Snow Grain Size Workshop C.M. Carmagnola 1, S. Morin 1, M. Lafaysse 1, F. Domine 2, G. Picard 3 and L. Arnaud 3 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

2. 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 Our goal ● In SURFEX/ISBA-Crocus snow model: - metamorphism implemented in a phenomenological way - semi-empirical shape variables, not measurable easily in the field ● Re-formulating dry metamorphism in terms of rate of change of optical diameter ● Implementing optical diameter into Crocus as prognostic variable

3. 3 SURFEX/ISBA-Crocus snow model overview Vionnet et al., 2012 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

4. 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 (g s ) ● 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 g s

5. 5 dsgsdsgs ρTGρTG Time step i prognostic variables Why optical diameter? - impacted by snow aging in a more predictable way (compared to d, s, g s ) - 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) C13 formulation B92 formulation Time step i+1 ρTGρTG updating ρ (densification) T,G (heat equation) Time step i+1 dsgsdsgs updating d,s,g s (metamorphism laws) Time step i+1 d opt diagnostic variable SURFEX/ISBA-Crocus snow model snow metamorphism

6. 6 New metamorphism formulation overview ● 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 k T ) ● Transition from dendritic to non-dendritic regime: - now, snow enters its non-dendritic state when d opt grows beyond a certain threshold - the higher s value, the easier for d opt to exceed this threshold C13 formulation ● Inversion of the formula giving d opt as a function of d, s, g s ● Change of variable through the entire code in order to replace d and g s by d opt and s

7. 7 New metamorphism formulation rate equations C13 formulation ● 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 d opt Dry metamorphism evolution laws of B92 re-written in terms of d opt and s Dendritic snowNon-dendritic snow G < 5 K m -1 5 < G < 15 K m -1 G > 15 K m -1 ● Rate equations for s are identical to those of B92 ● d opt is always increasing ● s and d opt are function of G (in K m -1 ), T (in K) and t (in d)

8. 8 Other parameterisations of d opt rate of change T07 formulationF06 formulation Implementing optical diameter as prognostic variable of the code -> allows to incorporate and test easily other parameterisations of d opt rate of change ● Empirical parameterisation of the rate of decay of SSA ● Based on ET and TG experiments ● Snow age / Time-averaged T / Temp. gradient Taillandier et al., 2007Flanner and Zender, 2006 ● 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.

9. 9 ● 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 Crocus metamorphism formulations vs cold room measurements isothermal experiments

10. 10 ● In B92: SSA max = 65 m 2 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 g s > 8 10 -4 m Crocus metamorphism formulations vs cold room measurements temperature gradient experiments

11. 11 Results seem to reveal that F06 fits observational data better than other formulations Crocus metamorphism formulations vs cold room measurements computing RMSD for SSA (m 2 kg -1 ) Cold room experimentConditionsB92C13T07F06 Flin et al., 2004 G = 0 K m -1 T mean = -2 °C 3.63.74.62.5 Schleef and Loewe, 2013 G = 0 K m -1 T mean = -20 °C 4.2 10.83.8 Taillandier et al., 2007 G = 33 K m -1 T mean = -10 °C 10.64.25.13.7 Calonne, 2011 G = 43 K m -1 T mean = -4 °C 2.42.11.50.6

12. 12 Snow dataset used to evaluate metamorphism formulations SSA data acquired with high vertical resolution (about 1 cm) DUFISSS (presented yesterday by F. Domine) ASSSAP (presented this morning by G. Picard) Note: Do you want to know more about these intruments? Come to La Grave tomorrow! Field campaignPeriodMeasurements Summit Camp, Greenland (3210 m a.s.l.) May and June 2011 32 daily snow stratigraphic profiles Col de Porte, France (1325 m a.s.l.) 2009/2010 winter season 14 weekly snow stratigraphic profiles Col de Porte, France (1325 m a.s.l.) 2011/2012 winter season 16 weekly snow stratigraphic profiles

13. 13 Forcing variables for Crocus simulations ● 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) results… ● 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)

14. 14 Results: Crocus metamorphism formulations time evolution of density at Col de Porte T07F06 B92C13

15. 15 Results: Crocus metamorphism formulations time evolution of SSA at Col de Porte T07F06 B92C13

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

17. 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. 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. 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. 20 ● Projection on 1 mm vertical grid ● Simulations stretched vertically in order to match measured snow height ● Same for d opt Results: computing RMSD between simulations and observations CdP 2011/12 CdP 2009/10 Summit ● B92: RMSD values similar to that for cold room experiments ● C13: ~B92 -> d opt integrated successfully as prognostic variable ● F06: comparable to B92 and C13 ● T07: less accurate (especially for low SSA values)

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

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

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