nick rutter and richard essery
DESCRIPTION
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 [email protected]. IUGG, Perugia, Italy, 10 July 2007. - PowerPoint PPT PresentationTRANSCRIPT
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
IUGG, Perugia, Italy, 10 July 2007
2
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 (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)
IUGG, Perugia, Italy, 10 July 2007: Why, what and how?
3
Model inputs
IUGG, Perugia, Italy, 10 July 2007: 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
4
Model outputs
IUGG, Perugia, Italy, 10 July 2007: 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
5
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
IUGG, Perugia, Italy, 10 July 2007: Model complexity
6
Model complexity
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!
High complexityLow complexity
e.g. Snow-17e.g. ISBA
& CLASS
e.g. SNOWPACK
& SNOWCAN
Medium complexity
Degree day models
Land surface schemes
Snow-physics models
PLUS 27 OTHER MODELS
S N O W
IUGG, Perugia, Italy, 10 July 2007: Model complexity
7
Alptal, Switzerland (47°N, 8°E)
IUGG, Perugia, Italy, 10 July 2007: Results
OPTIONAL CALIBRATION UNCALIBRATED
UNCALIBRATED ‘CONTROL’
8IUGG, Perugia, Italy, 10 July 2007: Results
BERMS, Saskatchewan, Canada (53°N, 104°W)
9IUGG, Perugia, Italy, 10 July 2007: Results
Fraser, Colorado, USA (39°N, 105°W)
10
Model uncertainties: forest vs open
IUGG, Perugia, Italy, 10 July 2007: Results
11
Model uncertainties: forest vs open
IUGG, Perugia, Italy, 10 July 2007: Results
x 2.16
x 2.42 x 1.35
x 1.59
x 1.17
x 1.53
12
Model uncertainties: forest vs open
IUGG, Perugia, Italy, 10 July 2007: Results
x 1.02
x 5.35
x 3.00
x 1.40
x 1.37
x 1.05
13
Conclusions
IUGG, Perugia, Italy, 10 July 2007: 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