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Kiran AlapatyUniversity of North Carolina at Chapel Hill
Dev NiyogiNorth Carolina State University
Sarav ArunachalamAndrew HollandKimberly HanisakUniversity of North Carolina at Chapel Hill
Marvin Wesely (Posthumous)Argonne National Laboratory
• Rc sum of several resistance for theSoil-vegetation Continuum.
• One of them is the Stomatal
Resistancefor a gas (Rsg)
• Rsg is proportional to Rsw
• Rsw Plays an important role in Land
surface Modeling.
Relation of Rc to Stomatal Resistance
• Stomatal Resistance:
A key Parameter in Land surface Modeling
• Why ? Stomata Controls Water Vapor Exchange
Stoma (pore) through which CO2 enters for use in Photosynthesis; releases O2 & H2O Depending on the
applications, Rs is modeled using a variety of forcings.
For environmental Applications:
- Wesely scheme
- Jarvis scheme
- Ball–Berry scheme
• JARVIS method is used in many LSMs(traditional in Met Models)
• WESELY method is used many AQMs
• Micro-Met and GCMs use Photosynthesis/CO2 assimilation
)40(
400
1.0
2001
2
ccswis TTGRR
])[][]2[][( 4321 RHFTFWFRFLAI
RR is
Sn
Ss bCHAm
CR
Stomatal Resistance Formulations
WESELY
JARVIS
Ball-Berry (GEM)
• JARVIS & WESELY methods Based on Minimum Stom. Resist.
• Ball – Berry method Based on Photosynthesis approach
(e.g., Farquhar, Collatz, Niyogi et al. ,
Wu et al.)
OBJECTIVES
Introduce and evaluate a Photosynthesis-based Vegetation Model for estimating stomatal resistance in MM5 and deposition velocity in CMAQ
Intercompare results from Jarvis-, Wesely-, GEM (photosynthesis) – type methods
Methodology
Photosynthesis Model Development:• Testing in 1D mode• Integrate GEM, Wesely, and Jarvis within a LSM• Couple Unified LSM (with three schemes) to MM5• Develop 3D model simulations using MM5 • Use Vd estimates from the three schemes in CMAQ
MM5 Simulation Details
Simulation Domain – 36 km grids for TexasAir Quality Study
• 28 Layers• MRF ABL• Noah LSM• Grell • RRTM • FDDA• 5.5 days• 23 Aug 2000 • TDL hourly Data
• Discussion of MM5 / Unified Noah
(with three Rs schemes) model Results
– Model performance statistics with surface observations
– Model diagnostics for the 3 schemes (surface parameters – energy fluxes, temperature, and estimated Rs values,….)
Will Present:
600
650
700
750
800
850
900
0 24 48 72 96 120
Nu
mb
er
of
Ob
se
rva
tio
ns
Simulation Time (h)
Surface Observations used in STATS
290
295
300
305
0 24 48 72 96 120
OBS WES JAR GEM
Tem
pe
ra
ture (
K)
Simulation Time (h)
Time Series for Temp1.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0 24 48 72 96 120
WES JAR GEM
Te
mp
era
ture B
ias
(K
)
Simulation Time (h)
Temperature Bias (Model – Obs)
2
2.5
3
3.5
4
0 24 48 72 96 120
WES JAR GEM
R.M
.S. E
rro
r fo
r T
em
peratu
re (
K)
Simulation Time (h)
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
0 24 48 72 96 120
WES JAR GEM
Mix
ing
Rati
o B
ias (
g k
g-1)
Simulation Time (h)
Mod. Lowest Vs Obs. Surface Level Qv
1
1.5
2
2.5
3
3.5
4
4.5
5
0 24 48 72 96 120
WES JAR GEM
R.M
.S. E
rro
r
for M
ixin
g R
ati
o (
g k
g-1)
Simulation Time (h)
0
500
1000
1500
2000
2500
0 24 48 72 96 120
WES JAR GEM
Dep
th o
f th
e A
BL
(m
AG
L)
Simulation Time (h)
Land Domain Avg. ABL Depths (m)
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
0.004
0 24 48 72 96 120
WES JAR GEM
To
tal P
recip
ita
tio
n R
ate
(cm
h-1)
Simulation Time (h)
Land Domain Avg. TRF (cm/h)
0
0.5
1
1.5
2
0 24 48 72 96 120
WES JAR GEM
Can
op
y C
on
du
cta
nce (
cm
s-1)
Simulation Time (h)
0
50
100
150
200
250
300
350
400
0 24 48 72 96 120
WES JAR GEM
Late
nt
Hea
t F
lux (
W m
-2)
Simulation Time (h)
Canopy Conductance
Sfc. Latent Heat Flux
0
100
200
300
400
0 24 48 72 96 120
WES JAR GEM
Sen
sib
le H
eat
Flu
x (
W m
-2)
Simulation Time (h)
0
50
100
150
200
250
300
350
400
0 24 48 72 96 120
WES JAR GEM
Late
nt
Hea
t F
lux (
W m
-2)
Simulation Time (h)
Sfc. Latent Heat Flux
Sfc. Sensible Heat Flux
0
0.5
1
1.5
2
2.5
3
3.5
4
0 24 48 72 96 120
WES JAR GEM
Can
op
y C
on
du
cta
nc
e (
cm
s-1)
Simulation Time (h)
(LU=2)
Agriculture Land (26%)
0
0.5
1
1.5
2
0 24 48 72 96 120
WES JAR GEM
Ca
no
py
Co
nd
uc
tan
ce (
cm
s-1)
Simulation Time (h)
(LU=3)
RANGE Land (34%)
0
0.5
1
1.5
2
0 24 48 72 96 120
WES JAR GEM
Can
op
y C
on
du
cta
nce (
cm
s-1)
Simulation Time (h)
(LU=5)
Coniferous (14%)
0
0.2
0.4
0.6
0.8
1
0 24 48 72 96 120
WES JAR GEM
Can
op
y C
on
du
cta
nce (
cm
s-1)
Simulation Time (h)
(LU=1)
URBAN Land (0.13%)
We are still doing analysis of MET fields
Once completed, we willperform CMAQ simulationsby keeping all MET fields identical except Dep Vel