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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.

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slide1

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

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

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?

model inputs
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

model outputs
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

model complexity
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

slide6

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

alptal switzerland 47 n 8 e
Alptal, Switzerland (47°N, 8°E)

UNCALIBRATED

OPTIONAL CALIBRATION

UNCALIBRATED ‘CONTROL’

IUGG, Perugia, Italy, 10 July 2007: Results

berms saskatchewan canada 53 n 104 w
BERMS, Saskatchewan, Canada (53°N, 104°W)

IUGG, Perugia, Italy, 10 July 2007: Results

fraser colorado usa 39 n 105 w
Fraser, Colorado, USA (39°N, 105°W)

IUGG, Perugia, Italy, 10 July 2007: Results

slide10

Model uncertainties: forest vs open

IUGG, Perugia, Italy, 10 July 2007: Results

slide11

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

slide12

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

conclusions
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