Comparison of forestsnow process models Snow MIP 2

Comparison of forest-snow process models (Snow. MIP 2): 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 1

Why, what and how? 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 Snow Model Inter-comparison Project 2 (Snow. MIP 2) 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) IUGG, Perugia, Italy, 10 July 2007: Why, what and how? 2

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

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 4

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 IUGG, Perugia, Italy, 10 July 2007: Model complexity 5

Model complexity Degree day models e. g. Snow-17 S Low complexity e. g. ISBA Snow-physics models e. g. SNOWPACK & CLASS & SNOWCAN Land surface schemes N O W 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 Snow. MIP 2 workshop is appreciated! IUGG, Perugia, Italy, 10 July 2007: Model complexity 6

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

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

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

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

Model uncertainties: forest vs open x 2. 16 x 2. 42 x 1. 59 x 1. 35 IUGG, Perugia, Italy, 10 July 2007: Results x 1. 53 x 1. 17 11

Model uncertainties: forest vs open x 1. 02 x 1. 40 x 1. 05 x 5. 35 x 3. 00 IUGG, Perugia, Italy, 10 July 2007: Results x 1. 37 12

Conclusions 32 models successfully participated in Snow. MIP 2 – 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 IUGG, Perugia, Italy, 10 July 2007: Conclusions 13