a new dustcycle model that includesdynamicvegetationggsrs/website/...the lpj-dust model lpj-dgvm...
TRANSCRIPT
A new dust cycle model that
includes dynamic vegetation
Sarah Shannon
Dan Lunt & Sandy Harrison
Department of Geographical Sciences, University of Bristol, UK
Talk outline
� Introduction – Why include dynamic vegetation in a dust cycle model ?
� Model description
� Model tuning � Model tuning
� Case study: Can variability in the Barbados dust record be explained by vegetation changes in the Sahel ?
Introduction
� Dust cycle models describe vegetation using
� remote sensing data
� BIOME4/BIOME3 models
� Using BIOME4 it is not possible to test if changes in
the atmospheric dust loading is caused by vegetation the atmospheric dust loading is caused by vegetation
changes or other processes
� To test this we require dynamic vegetation
The LPJ-dust model
LPJ-DGVM
(Sitch et al. 2003)
CRU
precipitation,
temperature
and cloud cover
(monthly)
Calculate monthly dust
source areas
Annual CO2
soil texture
ERA-40 large
scale &
convective
precipitation, low
& medium cloud
amounts (6hr)
0.5 x 0.5
Surface emissions
(Tegen et al. 2002)
TOMCAT
(Chipperfield 2006)
ERA-40 10m
wind speed (6hr)
Soil texture
ERA-40 3D wind
speed,
temperature &
specific humidity
(6hr)
Sub cloud
scavenging & dry
deposition (1hr)
Output dust fields
amounts (6hr)
T42
Calculate monthly dust source areas from LPJ
BIOME mapDominant FPC
• Scheme used to reduce high latitude dust emissions
• Use annual mean FPC, GDD5 and tree height to make BIOME map (Joos et al. 2004)
• Prohibit dust emissions from polar desert
• Calculate monthly bare ground fraction using simulated FPAR, soil moisture and snow
depth. Grasses vary seasonally, shrubs protect surface all year around
Mean data for the year 1988 to 2002 using CRU 2.1 data.
Tuning the LPJ-dust model
� Tune threshold limits for surface
emissions
� Threshold friction velocity
� FPAR limit
� Soil moisture limit
� Snow depth limit
� Use Latin Hypercube sampling to
choose select values choose select values
� Tune sub-cloud scavenging
parameterisation
� Λ is independent of rain droplet
size – Brandt et al. 2002
� Λ is dependent on rain droplet
size, 0.5µm and 2µm - Slinn 1983
• Number of tuning experiments
5 x 4 = 20
+1
21 x 3 = 63
• Run model for 1987 to 1989
Tune to observations� DIRTMAP deposition (excluding loess data)
� Ginoux et al., 2001 deposition
� University of Miami surface concentrations (annual mean data -1989)
� Create a skills score
� Calculate T, the value which minimises the NRMSE
� Error = NRMSEDIRTMAP + NRMSEGINOUX + NRMSEMIAMI
� Best experiment uses Slinn Dp=0.5µm, FPARlim=0.37, SMlim=7.79mm, SDlim=0.01m, TFRSF=0.55
DIRTMAP (blue), Ginoux data (Pink) and
University of Miami data (Red).
Slinn Dp=0.5µm
Slinn Dp=2µm
Fixed scav
Experiment skill
Measured and modelled deposition rates for the un-tuned and tuned model
results
Greenland
Antarctica
North Pacific
South Pacific
North Atlantic
South Atlantic
Arabian sea
DIRTMAP DIRTMAP
Arabian sea
Ginoux Ginoux
North Pacific
North Atlantic
South Pacific
1.French Alps
2. Spain
3.Tel Aviv
4.Taklimakan
Model validation: Simulated and observed surface concentrations
Model validation: Simulated and observed surface concentrations
� Good agreement in the
North Pacific– spring
peak simulated
Model validation: Simulated and observed surface concentrations
� Good agreement in the
North Pacific– spring
peak simulated
� Relatively good
agreement in the north
Atlantic
Model validation: Simulated and observed surface concentrations
� Good agreement in the
North Pacific– spring
peak simulated
� Relatively good
agreement in the north
Atlantic
� Poor agreement at � Poor agreement at
sites effected by
Australian dust
emissions- model
simulates peak Aug-
Dec when LPJ predicts
minimum vegetation
cover
Case study: Can vegetation changes explain the
cause of high dust concentrations at Barbados
during the 1980s?
60
80
gm
3
Barbados 13.17N 59.43W40
Ob
se
rva
tio
ns µ
gm
3
Mahowald et al. 2002
suggested high dust
concentrations during the
1980s were caused by
expansion of the Sahara or
Simulated and measured annual mean surface concentrations at
Barbados. The correlation coefficient for data between 1965 and 1978 is
r=0.82
1965 1970 1975 1980 1985 1990 1995 20000
20
40
Mo
de
l µg
m
1965 1970 1975 1980 1985 1990 1995 20000
20
Ob
se
rva
tio
ns
land use changes.
Southward shift of the Sahara-Sahelian boundary line in 1984
• Vegetation shifts southwards in 1984 relative to 1966
• Emissions in Sahel (Latitudes 10oN -20oN, longitudes 17oW – 40oE)
double from 1.1Mtyr-1 in 1966 to 2.2Mtyr-1 in 1984
Conclusions
� The LPJ-dust model can predict the expansion and contraction of
dust source regions by changes in vegetation cover which was not
possible using the BIOME4 model
� Tuning the LPJ-dust model improved estimates of deposition rates
to the North & South Pacific, North Atlantic and Arabian seato the North & South Pacific, North Atlantic and Arabian sea
� The case study showed there was a southward movement of the
Sahara in 1984 relative to 1966 but this is not sufficient to account
for the high dust concentrations measured at Barbados.
Extra slides
The LPJ-dust model
Calculate monthly dust source areas from LPJ: Step 2
� Calculate bare area using monthly FPAR, soil moisture and snow cover.
� Grass biomes
� Shrub biomes
<−
=otherwise
FPARFPARifFPAR
FPAR
Aveg
0
limlim
1
� Snow cover reduces exposed area
� Abare=Aveg. Asnow.Iθ where Iθ =1 if SOILMOISTURE < SOILMOISTURElim ELSE 0
<−
=otherwise
FPARFPARifFPARAveg
0
limmax1
<−=
otherwise
SNOWDEPTHSNOWDEPTHifSNOWDEPTH
SNOWDEPTH
Asnow
0
limlim
1