Development of Alternative Methods

For EstimatingDry Deposition Velocity

In CMAQ

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

Dry Deposition Velocity estimation

INTRODUCTION

1 cbad RRRV

Time Series of Dom Avg ResistancesL

og

Sca

le

• 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.)

WESELY

JARVIS

GEM

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

GEM development results1-D Model Results

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)

Diagnostic & Other Parameters

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%)

Land Use Patterns

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%)

ABL Depths at 20 UTC

WES JAR GEM

(Acquire Lidar & other ABL obs)

TRF per hour

WES JAR GEM

(Acquire Stage IV Radar)

Cloud Fraction

WES JAR GEM

(Acquire GOES)

MCIP was modified to generate Dep Vel fieldsusing M3-DryDepfor CMAQ

WES JAR GEM

Dep. Vel. for Ozone at 22 UTC

WES JAR GEM

Dep. Vel. for NO2 at 22 UTC

Domain Averaged Vd for O3

We are still doing analysis of MET fields

Once completed, we willperform CMAQ simulationsby keeping all MET fields identical except Dep Vel

• These Schemes are also being tested in WRF model

• WRF-CMAQ driver is alsoUnder construction