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Measurements and Modeling of Solar Ultraviolet Radiation and Photolysis Rates during SCOS97

Measurements and Modeling of Solar Ultraviolet Radiation and Photolysis Rates during SCOS97. Laurent Vuilleumier Environmental Energy Technologies Division Presented at the SCOS97-NARSTO Data Analysis Conference February 14, 2001. Collaborators. Nancy J. Brown, Berkeley Lab

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Measurements and Modeling of Solar Ultraviolet Radiation and Photolysis Rates during SCOS97

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  1. 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

  2. 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

  3. 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

  4. 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

  5. Photolysis Reaction Rates Species X undergoes photodissociation. Reaction i: X + hn products X absorption cross section Reaction i quantum yield Wavelength Spectral actinic flux Action spectrum X concentration rate of change due to photolysis reaction i Reaction rate coefficient

  6. Uncertainties inPhotolysis Reaction Rates • Action Spectrum Experimental 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

  7. Optical Depth Measures light extinction along vertical path. Ex: constant atmosphere Single Scattering Albedo Represents 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). Beam intensity Incoming light beam t z (altitude) Constant atmosphere t Important Atmospheric Optical Properties

  8. m1 mi mn ln(Ii) ln(R2I0) slope ti slope tn slope t1 m1 m2 mi mn m Optical Depth Computation Total optical deptht (t)obtained by using relationship between irradiance at ground I(t), extraterrestrial irradiance I0, and air mass factor mR(t).

  9. Measurements • Direct irradiance from UV multifilter radiometers: • Measurement at l = 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.

  10. 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:

  11. aerosols ozone 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.

  12. 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?

  13. 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

  14. 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).

  15. 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 (taer = 0) and high turbidity (taer = 0.8 atl = 340 nm, 95th percent.) • Predictions show differences between 15% and 40%.

  16. 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.

  17. 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%).

  18. 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.

  19. 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

  20. 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.

  21. 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.

  22. Additional Material

  23. Langley Plot Calibration • IfV(t) corresponds toI(t), V0corresponding toR2I0is obtained with a Langley plot method(1) applicable at time of low atmospheric turbidity. • tis computed with: (1) Slusser et al. (2000) J. Geophys. Res.105, 4841-4849

  24. 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.

  25. 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).

  26. Correlation Matrix

  27. Correlations at Mt Wilson

  28. Correlations at Riverside

  29. 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. tl2 a b PC 2 PC 1 tl1 Contribution from l1 to PC 1

  30. 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.

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