Comparison of forest-snow process models (SnowMIP2): uncertainty in estimates of snow water equivalent under forest cano

1. Comparison of forest-snow process models (SnowMIP2): uncertainty in estimates of snow water equivalent under forest canopies Nick Rutter and Richard Essery Centre for Glaciology University of Wales Aberystwyth nick.rutter@aber.ac.uk IUGG, Perugia, Italy, 10 July 2007

2. Current Land Surface Schemes (LSS) in models either neglect or use highly simplified representations of physical processes controlling the accumulation and melt of snow in forests SnowModel Inter-comparison Project 2 (SnowMIP2) Quantify uncertainty in simulations of forest snow processes Range of models of varying complexity (not just LSS) Primarily evaluate the ability of models to estimate SWE 32 models 5 locations: 3 presented herein (Switzerland, Canada, USA) 2 sites per location: forest and clearing (open) Why, what and how? IUGG, Perugia, Italy, 10 July 2007: Why, what and how?

3. Model inputs • Meteorological driving data : • Precipitation rate (rain and snow) • Incoming SW and LW • Air Temperature • Wind Speed • Relative humidity • Site specific data: • Tree height • Effective LAI • Instrument heights • Snow free albedo • Soil composition • Initialisation data: • Soil temperature profile • Soil moisture • Calibration data: • In-situ snow water equivalent (SWE) from Year 1, forest sites IUGG, Perugia, Italy, 10 July 2007: Model inputs

4. Model outputs • Energy fluxes • Radiative, turbulent, conductive, advected and phase changes • Mass fluxes • sublimation, evaporation, transpiration, phase change, infiltration, runoff, unloading, drip • State variables • mass (solid and liquid), temperature IUGG, Perugia, Italy, 10 July 2007: Model outputs

5. Model complexity is traditionally defined using numerous computational and snowpack metrics (multi-metric categorisation): Dimensions, purpose, lines of code, number of layers, process replication e.g. Snow Modellers Internet Platform (Zong-Liang Yang) Snow modelling metrics rarely go beyond presence or absence of canopy / vegetation Really require metrics describing: gap fraction, LAI (PAI), turbulent exchange, LW absorption and emittance Model complexity IUGG, Perugia, Italy, 10 July 2007: Model complexity

6. Model complexity Snow-physics models Degree day models Land surface schemes e.g. SNOWPACK & SNOWCAN e.g. ISBA & CLASS e.g. Snow-17 S N O W Low complexity Medium complexity High complexity PLUS 27 OTHER MODELS • Still need to populate a vertical axis describing canopy representation in models to create a 2-D plot of complexity • Your input at IUGG SnowMIP2 workshop is appreciated! IUGG, Perugia, Italy, 10 July 2007: Model complexity

7. Alptal, Switzerland (47°N, 8°E) UNCALIBRATED OPTIONAL CALIBRATION UNCALIBRATED ‘CONTROL’ IUGG, Perugia, Italy, 10 July 2007: Results

8. BERMS, Saskatchewan, Canada (53°N, 104°W) IUGG, Perugia, Italy, 10 July 2007: Results

9. Fraser, Colorado, USA (39°N, 105°W) IUGG, Perugia, Italy, 10 July 2007: Results

10. Model uncertainties: forest vs open IUGG, Perugia, Italy, 10 July 2007: Results

11. x 1.53 x 2.16 x 1.59 x 2.42 x 1.35 x 1.17 Model uncertainties: forest vs open IUGG, Perugia, Italy, 10 July 2007: Results

12. x 1.05 x 1.02 x 1.40 x 5.35 x 1.37 x 3.00 Model uncertainties: forest vs open IUGG, Perugia, Italy, 10 July 2007: Results

13. 32 models successfully participated in SnowMIP2 – thanks to the hard work and goodwill of the participants Low correlation coefficients suggests good model performance at forest sites do not necessarily mean good model performance at open sites (and vice versa) One year of calibration data in forest models is not necessarily good enough for subsequent years It is easier to model inter-annual variability at open sites than forest Uncertainty still exists whether 1) strength of calibration or 2) canopy complexity makes inter-annual variability of forest snow hard to model Conclusions IUGG, Perugia, Italy, 10 July 2007: Conclusions