1. D IAGNOSTICS AND E NHANCEMENTS TO THE N OAH LSM S NOW M ODEL By: Ben Livneh With contributions from: Youlong Xia, Kenneth E. Mitchell, Michael B. Ek, and Dennis P. Lettenmaier 1

2. Presentation Overview Motivation for the project. Model background Problem description Nature of model extensions Off-line testing and results Conclusions 2

3. Motivation Snow acts as a natural reservoir/buffer, to redistribute winter precipitation later into spring and summer when it is needed, important for water-supply, wildlife, etc… Snow cover has a significant impact on numerical weather prediction; higher reflectance, altered surface thermal regime. Source www.usgcrp.com Source www.waterencyclopedia.com Source www.wunderground.com www.wunderground.com ≈ Seasonal Reservoir Man-made Reservoir

4. Background – NOAH LSM NOAH LSM: (NCEP, OSU, Air Force, Hydrologic research lab) Land scheme for suite of weather and atmospheric models (NOAA), most versions of WRF. Partitioning of radiation (H, LE) Characterizes soil moisture, snow cover, etc… Focus: snow cover very important to both land and atmosphere.

5. Problem Negative in modeled snow water equivalent (SWE) bias noted in Noah LSM. Important for coupled modeling (partitioning radiative inputs, fluxes) as well as hydrology (soil moisture anomalies and streamflow timing). Prompted offline testing of various model components 5 Observed SWE (mm) Noah SWE (mm) Mitchell et al, 2004

6. Snow model structure (‘Control’: Noah v.2.7.1) Single-layer snow model. Linearized energy balance computation (computes skin temperature). No liquid water retention Uses a satellite-based spatially varying maximum snow albedo α MAX-satellite (Robinson and Kukla, 1985), constant value. Turbulent exchange embodied in a surface exchange coefficient: CH Partial snow coverage: T 1, W MAX α MAX, ρ, CH Areal Depletion Snow cover frac. SWE/W MAX 6 Snowpack Energy Balance = Q n + Q h + Q e + Q g + Q a

7. Model Extensions 1. Evaluation of a snow albedo decay scheme that captures the varying characteristics of the snowpack, currently not addressed in the control model; 2. Implementation of an algorithm that accounts for liquid water storage and refreeze within the pore space of the snowpack; a process that is not represented in the control. In addition, we report more limited sensitivity on: (a)Sensitivity of fractional snow coverage thresholds as they interact with albedo; (b)An alternative stability correction to the turbulent exchange physics. 7

8. Snow Albedo Decay Scheme 8

9. Snow Albedo CONTROL model considers a fixed maximum snow albedo value, based on the conversion of scene brightness (satellite imagery). Does not account for seasonal variability, changes to the snow surface, etc.. Control Noah α MAX-satellite converted from DMSP satellite imagery (1979) 9 DMSP Satellite image, 1979 Defense Meteorological Satellite Program (DMSP)

10. Snow Albedo Decay Scheme Snow albedo decay phenomenon confirmed by numerous studies (e.g Warren et al., 1980). Rate is determined by character of snow surface (metamorphism, recrystalization, debris, etc…). One method to quantify decay, via age of snow surface (equation). Seasonal decay rate (lower left) is faster during melt season: Shift between seasonal curves determined by snow surface temperature. Albedo decay scheme; based on Corps of Engineers, 1956; CA Accumulation Season Melt Season α MAX ≈ 0.85; t: days since last snowfall; A,B: constants. Equation: 10 Warren, S. G. and W. J. Wiscombe, 1980: A model for the spectral albedo of snow, II, Snow containing atmosphenc aerosols, J. Atmos. Sci., 37, 2734-2745. U.S. Army Corps Of Engineers, 1956: Summary report of the snow investigations, "Snow Hydrology", U. S. Army Engineer Division, North Pacific, 210 Custom House, Portland, Oregon 97209.

11. Snow Albedo – Additional Considerations NCEP desire to preserve spatial variability of satellite α MAX value for decay scheme. Hence an average value was used (midpoint between α MAX- satellite and 0.85: weighting factor C = 0.5) Required constraining lower bound of albedo (α ≥ 40%) A: Accumulation season curve (α MAX = 0.85) B: Melt season curve (α MAX = 0.85) C: Accumulation season curve (C = 0.5) D: Melt season curve (C = 0.5) E: α MAX-satellite = constant (e.g. 52%) Lower bound 11

12. Liquid Water Refreeze 12

13. Liquid Water Refreeze Important physical aspect (prevents snowmelt from going directly to runoff). Requires extending the snowpack energy balance, utilizing a melt energy (cold content) approach: Q m = Q n + Q h + Q e + Q g + Q a - ΔCC [W/m 2 ] Where: Q m = Energy available for melt; Q n = Net radiation flux Q h = Sensible heat flux Q e = Latent heat flux Q g = Ground flux Q a = Advected heat flux ΔCC = Change in internal energy of the snow pack (cold content) → *New Term: Allows for monitoring of thermal inertia between successive time steps; necessary for controlling melt and refreeze processes; involves new variable: T pack 13 *Key difference in new energy balance

14. Liquid Water Storage Irreducible saturation, S wi, the maximum amount of liquid water a snowpack can hold, beyond which it will transmit water. Tested several quantities (3.5 % SWE, 3 % total snowdepth, 4% of snowpack p.v.) 4% of snow p.v. selected. Where: SWE = snow water equivalent; d snow = snow depth, φ = porosity, ρ = density * Based on observations, Denoth et al., 2003 14

15. Additional Sensitivities 15

16. Additional Sensitivities 1.Adjust areal depletion threshold for SWE; forest vs. non-forest. *Related to recent collaborative work at the University of Arizona. * Wang Z, Zeng X (2009) Evaluation of snow albedo in land models for weather and climate studies. Journal of Applied Meteorology and Climatology: In Press W max thresholds in control model: 0.04 m, 0.02 m (forest, non-forest) In a *recent paper (based on comparison with albedo measurements) proposes: 0.20 m, 0.01 m (forest, non-forest) This work combined with the great disparity in satellite based albedo formed the basis for sensitivity testing. 16

17. Additional Sensitivities 2.Stability correction for turbulent exchange scheme to prevent excessive sublimation from occurring during stable conditions. Extension of Slater et al. (2007) correction to momentum transfer (when Ri B > 0) CH = CH*(1-Ri B /2) CM = CM*(1-Ri B /2) Ri B : dimensionless form of bulk richardson number CH,CM: exchange coefficients for heat and momentum transfer, respectively. 17

18. Model Evaluation Strategy 18

19. Model evaluation strategy Model performance evaluated through comparisons of off-line model simulations with observations. 3 SNOTEL sites were chosen (western U.S.) to provide a reasonable cross section of snow types (continental, maritime, and intermediate) to represent regions where snow melt provides significant streamflow contributions (hydrology) Large-scale model performance was evaluated using spatial plots of cumulative snow covered days (SCE) as compared to satellite data over CONUS domain at 1/8⁰. Duration of snow cover plays a vital role in partitioning atmospheric inputs (coupled model applications) 19

20. Results 20

21. Albedo Decay Improvements in SWE peak timing and magnitude at SNOTEL sites Large-scale performance not consistent; generally improved at higher- elevation sites, however, highly correlated with satellite-based max snow albedo value (shown on next slide) α MAX = 0.85 Comparison between albedo decay scheme (C=0.5) and control model 21 α MAX → C=0.5 α MAX-satellite

22. Albedo Decay Correlation between (a) albedo decay model performance vs. ‘control’, (b) α MAX- satellite, and (c) vegetation type. For areas of high α MAX-satellite : albedo decay (C=0.5) case often falls below α MAX-satellite, yielding fewer snow covered days. Relationship between areal depletion, α MAX and vegetation discussed later. (a) (b)(c) Depiction of Noah forest vegetation classes 22

23. Melt Refreeze Improved quantity and duration of SWE at SNOTEL sites. Large-scale improvements nearly ubiquitous, however control out-performs the melt-refreeze version for some low albedo cases. Altered energy balance and melt decision structure produce a slightly higher skin temperature in these cases, which can yield fewer snow covered days. W.C. = 4% p.v. W.C. = 3.5% SWE 23 Comparison between melt/refreeze model and control model

24. Fractional Snow Coverage Objective: test alternate W max values which accentuate the difference between forest and grasslands (including albedo-decay) to obtain a reasonable match with satellite values. This was done by computing an average snow covered albedo for each grid-cell over the study period and comparing them with the CONTROL satellite-based snow albedo 24

25. Fractional Snow Coverage Time-integrated plots less continuous than satellite value. Wang and Zeng (2009) W max values together with albedo decay (C=0.5) yield a reasonable match. 25

26. Turbulent Exchange – Stability Correction Reduction in sublimation over portions of the domain. However, high elevation sites (SNOTEL) results are inconclusive. Further refinements appear to be in order. Stability correction was considered as part of the ‘control’. 26 Comparison between stability corrected model and uncorrected model

27. Optimal Model Performance Inclusion of albedo decay (C=0.5), liquid water refreeze (W.C. = 4% p.v.) and adjusted W max values. Additionally, provide for separate albedos during partial snow coverage 27 Comparison between suggested model extensions and control model

28. Conclusions Negative SWE bias was addressed to improve model processes, evaluated at point-scale and large-scale. Point scale testing revealed that albedo decay and melt-refreeze processes improve the quantity and timing of SWE (serve to improve streamflow predictions in snowmelt dominated basins) Large-scale evaluation generally showed improvements in duration of snow cover; adjustment of areal depletion thresholds also provided for reasonable snow covered albedo estimates over the CONUS domain. 28

29. Acknowledgements Youlong Xia, Kenneth E. Mitchell, Michael B. Ek (NCEP) John Schaake (NWS) Ming Pan (Princeton) CPPA funding 29

30. T HANK Y OU 30 Livneh, B., Y. Xia, K.E. Mitchell, M.B. Ek, and D.P. Lettenmaier, 2010: Noah LSM Snow Model Diagnostics and Enhancements, J. of Hydrometeorology, in press

31. New Snowpack Energy Balance Solution Procedure Q m = Q n + Q h + Q e + Q g + Q a - ΔCC Procedure: 1.Guess *snowpack temperature (e.g. 273.15⁰K) for current time step and compute fluxes based on this temperature; 2.If residual Q m > 0 (energy surplus): melt an equivalent depth of water. First fill liquid water capacity within the pack; if melt water exceeds storage capacity, send to runoff. 3.If residual Q m < 0 (energy defecit): refreeze pack water, then cool pack with remaining energy deficit, if any. * Snowpack temperature is a new variable 31