Measurements and Modeling of Solar Ultraviolet Radiation and

Photolysis Rates during SCOS97

Laurent Vuilleumier Environmental Energy Technologies Division

Presented at the SCOS97-NARSTOData Analysis Conference

February 14, 2001

Collaborators

Nancy J. Brown, Berkeley Lab

Robert A. Harley, UC Berkeley

Jeffrey T. Bamer , UC Berkeley

Steven D. Reynolds, Envair

James R. Slusser, CSU

David S. Bigelow, CSU

Donald Kolinski, UCAR

Motivations

Numerous sensitivity analysis studies* indicate large ozone (smog) formation sensitivities to NO2, and HCHO photolysis rates.

Monte Carlo study of ozone modeling uncertainties, Hanna et al. (2000, EPRI) report:

Uncertainties in ozone predictions are most strongly correlated with uncertainties in NO2 photolysis rate.

* Falls et al., 1979, Milford et al., 1992, Gao et al., 1995, 1996,Yang et al., 1995, 1996, Vuilleumier et al., 1997, Bergin et al. 1998,Hanna et al., 1998, 2000

Outline

Uncertainty in photolysis rate coefficients

Optical depth variability during SCOS97

Modeling photolysis rate coefficients

Comparison between observations and predictions of NO2 photolysis rate coefficients

Photolysis Reaction Rates

X concentration rate of change due to photolysis reaction i

Species X undergoes photodissociation.

Reaction i: X + h products

dETdt

di

i

),(),,()(]X[]X[

X reac

X absorption cross section

Reaction i quantum yield

Spectral actinic flux

Wavelength

Action spectrum

Reaction rate coefficient

Uncertainties inPhotolysis Reaction Rates

Action SpectrumExperimental uncertainties reduced by better determination of cross sections & quantum yields

Actinic Flux (solar light flux available for photolysis)

Depends on atmospheric optical properties that exhibit spatial and temporal variation

Natural variability & Measurement uncertainty

Important Atmospheric Optical Properties

Optical DepthMeasures light extinction along vertical path.

Ex: constant atmosphere

Single Scattering AlbedoRepresents fraction of extinguished light that is scattered (remaining is absorbed).

low SSA = high absorption

Effect on light intensity is maximum when optical depth is high (extinction) and SSA is low (absorption).

z

dzk

z (a

ltit

ud

e)

Beam intensity

Incoming light beam

Constant atmosphere

Total optical depth(t) obtained by using relationship between irradiance at ground I(t), extraterrestrial irradiance I0, and air mass factor mR(t).

Optical Depth Computation

))()(exp()( 02 ttmIRtI R

m1 mi

mn

ln(Ii)

slope i

m

slope n

mnmim2m1

ln(R2I0)

slope 1

Measurements

Direct irradiance from UV multifilter radiometers:

> Measurement at  = 300, 306, 312, 318, 326, 333 and 368 nm.

> 2 nm nominal full-width half-maximum filters with integrated out-of-band light contamination less than 0.5%.

Data acquired at Riverside and Mt Wilson, CA from 1 July to 1 November 1997.

> Riverside (260 m a.s.l.) characterized by frequent occurrences of severe air pollution episodes.

> Mt Wilson (1725 m a.s.l.) located above much of the urban haze layer.

Optical Depth Variability

After data selection (reject cloudy periods or low signal to noise ratio), 8,232 optical depths obtained at Riverside and 11,261 at Mt Wilson:

Accounting forOptical Depth Variability

PCA attributes 97% and 2% of variability to 1st and 2nd most important components at Riverside, and 89% and 10% at Mt Wilson.Components correspond to light extinction by aerosols and ozone.

aerosols

ozone

Significant variability in atmospheric optical depth due to aerosols.

Is it possible to reproduce it in models?

What are the most significant sources of uncertainty?

Modeling Photolysis Rates

Selected and modified TUV* program from Madronich (NCAR**) for implementation in AQM’s (UAM-IV, UAM-FCM, SAQM).

TUV allows consideration of:> absorption and scattering by aerosols,> absorption and scattering by gases

(O3, O2, NO2, SO2),

> ground albedo,> atmospheric pressure and temperature vertical

profiles.

* Tropospheric Ultraviolet-Visible, ** National Center for Atmospheric Research

Modifications to TUV

Increased modularity to enhance incorporating new science

Improved user interface for facilitating changing input variables

TUV can be called during AQM simulation with selected inputs depending on time and location:> Aerosol characteristics,

> Ozone total optical depths,

> Ground albedo (depends on location only).

Effect of Optical Depth Variability on TUV Predictions

TUV used to predict NO2 photolysis rate (JNO2) for aerosol optical depths observed at times of high and low turbidity.

low turbidity (aer = 0) and

high turbidity (aer = 0.8 at = 340 nm, 95th percent.)

Predictions show differences between 15% and 40%.

Comparison of observedand predicted JNO2

SCOS97 JNO2 measurements (UC Riverside) used to assess correctness of TUV predictions.

> Ground level data measured at Riverside with chemical actinometer on selected days

> Required matching measurements of JNO2, aerosol optical depth and ozone column

> Obtained 121 simultaneous observations and predictions of JNO2 over 14 non-continuous days.

JNO2 Predicted toObserved Ratio

Ratio of predicted to observed JNO2 reveals an average bias of 15 to 30% depending on single scattering albedo.

Daily profile reproduced, including variations due to atmospheric condition changes, resulting in low ratio standard deviation around average (±10%).

JNO2 Daily Profile

Predictions using constant average input (aerosol optical depth and ozone column) only show influence of solar zenith angle.

Predictions using time-varying input correctly predicts variations due to changes in optical depth.

Possible Sources of Bias

Single Scattering Albedo:Uncertainty in SSA can result in:

> Bias in predicted JNO2 (uncertainty in average SSA)

> Random uncertainty in predicted JNO2 (temporal variability of SSA)

Corrections used for JNO2 measurements:Quantum yield factor used for observed JNO2.

> Impurities in carrier gas (N2) have significant influence on quantum yield factor and can lead to bias in observed JNO2 *.

* Dickerson and Stedman (1980) Environ. Science & Technol. 14, 1261-1262

Conclusions

Natural atmospheric variability has significant influence on photolysis rates.

In cloud-free situations, aerosols are responsible for most of the variability.

Aerosol single scattering albedo remains a significant source of uncertainty.

Conclusions (2)

Radiative transfer models can reproduce variability providing good input data are available:

> Challenge at the scale of Air Quality Modeling.

> Synergy between ground-based, air-borne, and satellite-based observation of troposphere may be key to success.

Additional Material

Langley Plot Calibration

If V(t) corresponds toI(t), V0 corresponding to R2I0 is obtained with a Langley plot method(1) applicable at time of low atmospheric turbidity.

is computed with:

)(

)(lnln)( 0

tm

tVVt

R

(1) Slusser et al. (2000) J. Geophys. Res. 105, 4841-4849

Optical Depth Data Selection

Clouds:> High photochemical air

pollution is linked to stagnant high-pressure systems.

> Times when clouds are present are rejected based on broadband visible irradiance.

Low signal:> Events where total minus

diffuse irradiance is low are rejected to reduce electronic noise influence.

Optical Depth Correlations

Correlation between measurements at the seven wavelengths is strong.

Correlation is stronger between measurement at neighboring wavelengths.

Correlation is stronger at Riverside than Mt Wilson.

At Mt Wilson, two groups show stronger correlation: short (300, 306) and long wavelengths (312–368).

Correlation Matrix

Riverside 306 nm 312 nm 318 nm 326 nm 333 nm 368 nm

300 nm 0.99 0.97 0.93 0.94 0.92 0.92

306 nm 0.99 0.95 0.97 0.95 0.95

312 nm 0.97 0.99 0.97 0.97

318 nm 0.99 1.00 0.99

326 nm 1.00 0.99

333 nm 1.00

Mt Wilson 306 nm 312 nm 318 nm 326 nm 333 nm 368 nm

300 nm 0.94 0.85 0.76 0.69 0.62 0.59

306 nm 0.92 0.85 0.82 0.77 0.74

312 nm 0.98 0.96 0.93 0.91

318 nm 0.98 0.96 0.95

326 nm 0.99 0.98

333 nm 1.00

Correlations at Mt Wilson

Correlations at Riverside

Principal Component Analysis

PCA is used to transform a set of correlated variables into a set of uncorrelated variables called components.

The most important components are linked to the physical causes of the observed variability.

The components are found by diagonalizing the correlation matrix.

ab

PC 1

PC 2

Contribution from 1 to PC 1

Wavelength Contributionsto the Components

The wavelength contributions to the components suggest that the first two components correspond to absorption and scattering by aerosols and ozone, respectively.