cloud processing: past work and issues to address

Cloud Processing: Past Work and Issues to Address

Mary Barth

National Center for Atmospheric ResearchMesoscale and Microscale Meteorology

andAtmospheric Chemistry Divisions

transport

washout and rainout

NO production from lightning

How clouds affect chemical species

high photolysis rates

low photolysis rates

cloud chemistry

ice chemistry

cloud microphysics and chemical species

size

phase

transport


Transport

Cloud Scale: Given good input data, cloud-scale models simulate transport of passive tracers well. For example:

0 50 100 150 200

12

10

8

6

4

2

Data from STERAO-Deep Convection 1996 (Dye et al., 2000)

Transport

Cloud Scale: Skamarock et al. (2000) simulated with a 3-d cloud model the tracer transport well

Transport

Cloud Scale: Chatfield and Crutzen (1984)Dickerson et al. (1987) Scala et al. (1990, 1993)Pickering et al. (1992a,b)Thompson et al. (1994) Wang and Chang (1993a-d)Wang and Crutzen (1995)Hauf et al. (1995)And many others

TransportLarge Scale: A much bigger challenge, particularly for subgrid convection

Mass flux schemesEmmanuel (1991), Feichter and Crutzen (1990), Hack (1994),

Tiedtke (1989), Zhang and McFarlane (1995), and others

Mass flux + convective-scale w + mesoscale effectsDonner et al. (2001)

“superparameterization” 2-d cloud resolving model used as convective transport

parameterizationGrabowski (2001)Khairoutdinov and Randall (2001)

Mass flux + convective-scale w + mesoscale effectsDonner et al. (2001)

“superparameterization”Khairoutdinov and Randall (2001)

CCSM/CRM

CCSM

Obs.

How can we improve convective transport of chemical constituents in large-scale models?

CRMs need to generalize their results

Can we use several CRMs with chemistry in concert to analyze convective transport for all different types of convection? Could the results be brought together to produce general characteristics of tracer transport?

Should superparameterizations include simple cloud chemistry?

Effort to verify model results with observations

washout and rainout


Wet DepositionResolved Clouds:Precipitation rate = Vr qr

where r = air density Vr = fall speed of rain qr = mixing ratio of rain

Wet deposition rate = Vr Cr

where Cr = mixing ratio of species in rain

If Cr is not predicted in the model,usually Henry’s law is used:

Cr = KH R T LWC Cg

where KH = Henry’s law coefficient (mol/L-atm) R (L-atm/mol-K), T (K), LWC (cm3 H2O/cm3 air), Cg = gas-phase mixing ratio

Is the Henry’s law assumption valid?

This assumption depends on the time step and Henry’s law coefficient

HNO3

NH3

H2O2

25 m

20 m

15 m

10 m 5 m

T=10°C

2000

1000

500

r=25 m

r=10 m

T=30°C 20°C 10°C

H2O2

100

1000

1e5 3.5e5

1e4 1e6 1e8 1e10 1e12

Wet Deposition

Large-scale Models

Wabove

Win = qr_prod KH* Cgas

Wbelow = Wabove + Win

W_below_cloud = (Pr – E) KH* Cgas

qr_prod = conversion from CW to rain, Pr = precip. Rate, E= evaporation rate

Giorgio and Chameides (1986)Wetdep = - Cgas where = Q F Tc L

Flux Method 1 (Barth et al., 2000; Roelofs and Lelieveld, 1995)

More physically correct, but still is not great

Wet Deposition

Subgrid-scale Clouds

Wabove

Win = (qr_prod – E) KH* Cgas

Wbelow = Wabove + Win

qr_prod = conversion from CW to rain, E= evaporation rate

Flux Method 2 (work in progress, Hess et al.)

This is even more physically correct, but still needs work:

MOZART, Giorgi and Chameides (1986) wet deposition

MOZART, Flux Method 2 wet deposition

Observations

He

igh

t (km

)

Wet Deposition: Needs

Wet deposition depends on both the precipitation rate and the concentration of the species in the precipitation. Do we get the precipitation rate right? If not, what are we missing? – Small cumulus: giant aerosols that form large

cloud drops that initiate formation of rain– Deep convection: ice processes

Verification of what parameterizations work or not– Comparisons of nitrate wet deposition model vs.

observations (Holland et al.)– Comparisons of wet deposition from global-scale

models with cloud-scale models


cloud chemistry

ice chemistry

Cloud Chemistry

Aqueous-phase Chemical Reactions

Modification of Gas-phase Chemistry because Reactants are Separated

Examples:

SO2

H2O2

S(IV)

H2O2

SO4=

Aqueous Chemistry

Separation of Species

OHNONOHO 22

HO2 + O2-

Simulated Sulfate BudgetSource: Roelofs et al., 2001; Rasch et al., 2000

-100

-80

-60

-40

-20

0

20

40

60

80

100

DJF JJA DJF JJA DJF JJA Annual

Emissions

Gas Chemistry

AqueousChemistryDry Deposition

Wet Deposition

E. N. America Europe SE Asia Global

E. North America Europe SE Asia Global

Pe

rce

nta

ge

of T

ota

l Pro

du

ctio

n o

r D

est

ruct

ion

Ra

te

Rates of S(IV) aqueous reactions

SO3= + O3

HSO3- + O3

HSO3- + H2O2

pH

Rat

e of

Rea

ctio

n (M

s-1)

SO2 = 2 ppbvH2O2 = 1 ppbvO3 = 50 ppbv

Aqueous Sulfur Chemistry Needs

• Representing S(IV) S(VI) conversion seems to be pretty well in hand.

• Minor – Major Improvements:– Importance of representing size and therefore pH

of drops better (more on this later)– Importance of getting the LWC correct– Conversion via other reactions e.g. transition

metal ion chemistry

Does Cloud Chemistry Affect O3 Concentrations?

Lelieveld and Crutzen (1991)

“Clouds thus directly reduce the concentrations of O3, CH2O, NOx and HOx”

Liang and Jacob (1997)

“It is found that the maximum perturbation to O3 from cloud chemistry in the tropics and midlatitudes summer is less than 3%”


Walcek, Yuan, and Stockwell (1997)

“… in-cloud reactions strongly influence local O3 production in polluted areas, but longer-term impacts of clouds on O3 formation would be much smaller due to compensating chemical processes in regions remote from NOx emissions.”

Barth, Hess, and Madronich (2002) find that O3 is depleted via cloud chemistry by a small amount at low pH and by a more significant amount at high pH. Further, the effect of cloud on photolysis rates can contribute to O3 depletion.

Percent Change in Ozone for a Cloud-topped Marine Boundary Layer (z<2 km) near Hawaii (regional

chemistry transport model results)

pH = 4.0 pH = 4.5 pH = 5.0 pH = 5.5

O3 -3.0 -6.1 -10.8 -16.9

Percent Change in Ozone with the Effect of Clouds on Photolysis vs. without the Effect on Photolysis

pH = 4.5No cloud correction on j-values

With cloud correction on j-values

O3 -3.0 -6.1

Spatially Averaged, Diurnally Averaged O3 Production and Loss Rates

gas pH=4.0 pH=4.5 pH=5.0 pH=5.5

NO + HO2 244 162 152 145 142

NO + CH3OO 288 306 312 317 322

O3 + h 4211 4094 3970 3774 3508

O3 + HO2 509 323 286 257 232

O3 + OH 227 212 203 189 171

O3 + O2- 0 402 710 1163 1741

Units are pptv/day


Cloud Chemistry (aqueous chemistry + separation of reactants) may not have a big effect on O3 concentrations by itself, but the sum of the cloud effects (cloud chem., radiation, scavenging, etc.) may perturb O3 “substantially”.

What are the key parameters for calculating cloud chemistry?

Liquid Water Content

The size of the drops(more on this later)

Is the question, “Does cloud chemistry alter O3 concentrations?”, dead?

• NO! Because there are so many things to consider with ozone chemistry.– More accurate depiction of clouds– Many situations where the chemistry is much more

complex, e.g. cloud-topped boundary layers with nearby hydrocarbon and NOx emissions

– Volatile organic compounds participation will then need to be assessed Organic Aqueous Chemistry


cloud microphysics and chemical species

size

phase

Microphysics and Chemistry

Cloud Drop Activation

Collision/CoalescenceCondensation

FreezingRimingMelting

Representing Cloud Physics in Large-scale Models

Represent cloud drops as one reservoir, rain as another reservoir, and ice and snow as separate reservoirs. This is termed bulk-water microphysics.

Cloud water

Water vapor

Ice

Rain Snow

Representing Cloud Physics in Parcel Models


Cloud chemistrySO2 SO4


Represent aerosols, cloud drops, and rain drops using size bins

Parcel model results should be closer to the truth

?

Simulating Size-Varying Cloud Drop Population vs. Cloud Water with a Mean Radius

• Hegg and Larson (1990) condensational growth only

• Roelofs (1993) condensation and collision/coalescence

• Gurciullo and Pandis (1997) condensational growth only

• Kreidenweis et al. (2003) condensational growth only; intercomparison of 7 aerosol parcel models

Sulfate Production from Explicit Models vs. Bulk-Water Models

SO2 (ppbv)

H2O2 (ppbv)

Bulk (ppbv)

Explicit (ppbv)

E/B Reference

2.0 0.5 0.72 0.54 0.8 Hegg and Larson, 1990

0.2 0.5 0.08 0.18 2.1 Hegg and Larson, 1990

4.0 0.5 6.0 7.5 1.25 Roelofs, 1993

1.0 0.5 2.4 4.1 1.7 Roelofs, 1993

4.0 1.0 9.4 10.1 1.07 Roelofs, 1993

10.0 0.5 0.62 0.81 1.29 Gurciullo and Pandis, 97

0.25 0.5 0.20 0.24 1.21 Gurciullo and Pandis, 97

0.2 0.5 .145 .172 1.18 Kreidenweis et al., 2003

mol/L

Why is there more sulfate production with explicit microphysics?

pH varies across the droplet spectrum

Observations have shown that the chemical composition varies with the size of the cloud drop

Noone et al. (1988), Ogren et al. (1989, 1992)Munger et al. (1989), Collett et al. (1993, 1994)

Percent Change in Ozone for a Cloud-topped Marine Boundary Layer (z<2 km) near Hawaii (regional

chemistry transport model results)

pH = 4.0 pH = 4.5 pH = 5.0 pH = 5.5

O3 -3.0 -6.1 -10.8 -16.9

Is there an important effect of drop size (pH variability) on cloud photochemistry?




FreezingRimingMelting


?

?

?

drop liquidin amount t Constituen

particlefrozen in amount t Constituen Factor Retention

Snow Accreting Cloud Water (riming)

Retention Factor Reference

SO2 0.25 Iribarne et al., 1983

0.012 + 0.0058 TLamb and Blumenstein, 1987

0.25 + 0.012 T Iribarne et al., 1990

0.62 (ventilated) Iribarne et al., 1990

0.34 to 0.83 Iribarne and Barrie, 1995

0.02 Voisin et al., 2000

T = 0 – T (ºC)

H2O2 1.0Iribarne and Pyshov, 1990

0.07 to 0.56 Snider et al., 1992

0.01 to 0.36 Snider and Huang, 1998

Test the Importance of Retaining Gas Species in Frozen Hydrometeors

Convective Cloud Model coupled with Gas and Aqueous Phase Chemistry

Simulate a storm that was observed in northeastern Colorado (Dye et al., 2000) Evaluate how well the model represents observed

convection Evaluate passive tracer transport Skamarock et al. (2000)

Convective Cloud Simulation

Hydrometeor Mixing Ratios

Barth et al. (2001)

SO2 and H2O2 in outflow region

Barth et al. (2000)

Yin et al. (2002) used a 2-d axisymmetric cloud model to investigate retention during riming and adsorption.

Crutzen and Lawrence (2000) found the mixing ratio of trace gases with KH = 103, 104, 105 M/atm reduced in the middle to upper troposphere by 20%, 60%, 90% from global model calculations.

KH

0102

103

104

105

M/atm


Size of drops is important to cloud chemistry

Phase of cloud is important to cloud chemistry (generally aqueous chemistry does not happen in ice) and scavenging (wet deposition).

Ice chemistry

HNO3 iceNO3

- + h NO2

Heterogeneous Chlorine chemistry, e.g.:HCl + ClONO2 Cl2 + HNO3

Other chemistry?


high photolysis rates

low photolysis rates

Radiative Effects on Photolysis Rates

Matthijsen et al. (1998)

ACE-1 observations and modeling

O3 + h O('D) + O2 photodissociation rate

jO3jO3

jO3 jO3

Radiative Effects on OH concentration

Matthijsen et al. (1998)

ACE-1 observations and modeling

+ observed OH modeled OH

Monte Carlo simulations to calculate photodissociation rates in the presence of cumulonimbus. Brasseur, A.-L. et al. (2002)

J-CH2O

Radiative Effects on Photolysis Rates

Online calculations of photodissociation rates this allows consistent calculation of j-values with environmental conditions, e.g. clouds and aerosols

Landgraf and Crutzen (1998)Blan and Prather (2002)Tie et al., in preparation

Landgraf and Crutzen (1998)

Cloud

Cloud

Issues regarding the Radiative Effect of Clouds

• How important are ensembles of cumulus clouds which produce lots of scattering (multiple reflections) to the sides of clouds?

• How important are absorbing aerosols within the cloud?

NO production from lightning


Lightning-produced NOx global estimates

REFERENCE(type of estimate)

P(NO)(1016 molecules

J-1)

P(NO)(1025

molecules flash-1)

F(102 flashes s-1)

G(NO)(Tg (N) year-1)

Lawrence et al. (1995) (review)

- 2.3 (1 – 7) 1 ( 0.7 – 1.5) 2 (1 – 8)

Price et al. (1997) (obs)

10 - 0.7 – 1 12.2 (5 – 20)

Price et al. (1997) (theor.)

10 - - 13.2 (5 – 25)

Wang et al. (1998) (lab)

- 3.1 0.3 – 1 2.5 – 8.3

Nesbitt et al. (2000) (satellite)

- 0.87 – 6.2 0.57 0.9

Skamarock et al. (2003) (obs/mdl)

- 2.6 0.001 – 0.009* -

Skamarock et al. (2003)

Skamarock et al. (2003)Defer et al. (in review)

Allen and Pickering (2003)

OTD data

Mass Flux estimate

Convec. Precip. estimate

Cloud Height estimate

Allen and Pickering (2003)

Calculations of Effect of Lightning on ChemistryHauglustaine et al. (2001)

Issues on Parameterizing Lightning-Produced NOx

• Knowing the number of thunderstorms occurring on the earth over a year

• Parameterizations based on characteristics of individual storms: Deep convection comes in several different flavors

• Verification of the parameterizations with observations

Some Issues to Address

Vertical TransportCoupling CRMs and global modelsSuperparameterizationsVerification

LightningCoupling small scale to global scale modelsVerification

MicrophysicsVerification of cloud volume, precipitation, phase, LWC

Coupling the climate, cloud, aerosol, chemistry system

cloud processing: past work and issues to address

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