Sinks�Mathew Evans, Daniel Jacob,�Bill Bloss, Dwayne Heard, Mike Pilling

• Sinks are just as important as sources for working out emissions!
• NO_x N₂O₅ hydrolysis
• OH Comparison with direct observations

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N₂O₅ hydrolysis

• ‘Ultimate’ NO_x sinks dominated by

OH + NO₂ + M 🡪 HNO₃ (historically interesting)

N₂O₅ + aerosol 🡪 HNO₃

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• Roughly 50% from each

OH+NO₂ dominates in summer

N₂O₅ + aerosol dominates in winter

N₂O₅ + aerosol

• Rate defined by γ the ‘reaction probability’
• Fraction of molecules that hit aerosol surface that react
• For the stratosphere γ ≈ 0.1
• But is this true for the troposphere
• Different types of aerosols
• Warmer and wetter

Rumblings of discontent

• Tie et al., [2003] found γ_N2O5<0.04 gave a better simulation of NO_x concentrations during TOPSE
• Photochemical box model analyses of observed NO_x/HNO₃ ratios in the upper troposphere suggested that γ_N2O5 is much less than 0.1 [McKeen et al., 1997; Schultz et al., 2000]

New literature

• Kane et al., 2001 - Sulfate – RH
• JPL
• Hallquist et al., 2003 - Sulfate - temp
• Tony Cox’s group in Cambridge
• Thornton et al., 2003 - Organics - RH
• Jon Abbatt’s group at U Torontio

Parameterization based on best available literature

What γs do we get?

• Much lower than 0.1
• Dry low values
• Higher at the surface

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What is the impact on composition?�Lower γN₂O₅ � higher N₂O_5� 250%�� higher NO3� 30%�_� higher NO_x � 7% ��Higher NO_x � higher O₃� 7%��Higher NO_x � higher OH� 8%�

Compare with observations

Emmons et al. [2000] climatology of NO_x

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Mass weighted model bias changes from

–14.0 pptv to –7.9 pptv

Mean ratio changes from

0.77 to 0.86

Middle troposphere (3-10km) changes from

0.79 to 0.91

Compare with observations

Logan [1998] Ozonesonde climatology

Mass weighted model bias

-2.9 ppbv to -1.4 ppbv

Mean ratio changes from

0.94 to 0.99.

Ox (odd oxygen) budget

Chemical production increases 7%

3900 Tg O₃ yr^-1 to 4180 Tg O₃ ^yr-1

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Compare with observations

Global annual mean tropospheric OH

0.99×10⁶ cm^-3 to 1.08×10⁶ cm^-3

8% increase.

Both values are consistent with the current constraints on global mean OH concentrations based on methyl-chloroform observations:

1.07 (^+0.09 _-0.17) × 10⁶ cm^-3 [Krol et al., 1998]

1.16 ± 0.17 × 10⁶ cm^-3 [Spivakovsky et al., 2000]

0.94 ± 0.13 × 10⁶ cm^-3 [Prinn et al., 2001]

Conclusions

• Aerosol reaction of N₂O₅ is very important for the atmosphere
• Previous estimates have been too high
• New laboratory data allows a better constraint
• Sorting out old problems although not ‘sexy’ is important

Future improvements

• Assumed (NH₄)₂SO₄
• But model ‘knows’ the degree of neutralization in the aerosol
• There is a inhibiting effect of nitrate on uptake
• Future lab studies – dust?
• Is the ‘cost benefit’ worth improving it?

A ‘cheeky’ bottom-up evaluation of global mean OH�

Global mean OH

How do they calculate global mean OH

• Methyl chloroform made by a few large chemical companies
• Sources are known (nearly)
• Can measure concentrations across the globe
• Then invert to get the sink

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Bottom up approach

• Can directly observe OH
• But lifetime of OH is ~ 1s
• So measurements at one site don’t tell you much about global concentrations
• Is this true?
• Can we get a ‘bottom up’ global OH distribution?

NAMBLEX, EASE ’97, SOAPEX

• OH measured by the FAGE group in chemistry
• Time series of OH
• Can we use this to provide information about global OH
• ‘Couple’ global atmospheric chemistry model and the observations

Observed vs Modelled OH

Mace Head - Ireland

More useful comparison

Measured mean is 1.8 × 10⁶ cm^-3, Modelled mean is 2.3 × 10⁶ cm^-3

Ratio of 1.56 ± 1.62.

The statistical distribution of the ratio is not normal and so more appropriate metrics such as the median (1.13) or the geometric mean (1.13 ^+1.44 _-0.64 ),

The model simulates 30% of the linear variability of OH (as defined by the R²).

The uncertainty in the observations (13%) suggests that the model systematically overestimates the measured OH concentrations.

Other HO_x components

Over a year

Other places

Cape Grim - Australia

So what have we learnt?

• Mace Head we tend to over estimate
• Cape Grim doesn’t seem so bad
• Can we combine this information and the model to get a global number?
• Very Cheeky!

What do we get?

What does this mean

• Very, very lucky!!!!
• The FAGE OH and the MCF inversions seem consistent
• Model transfer seems to work
• Uncertainties suggest it could have gone the other way

Can we do this better?

• Include more data
• Aircraft campaign
• Surface sites
• Ships

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Availability of data

How do we incorporate this?

• Principal components of the GEOS-CHEM tracers
• Redefine the temporal and spatial space in terms of different components
• ‘Optimal estimate’ of global mean OH
• Don’t know if this will work ☺

Component 1

Component 2

Component 3

Component 4

How might we use this?

• Compare OH modelled with OH measured
• For each point workout the fraction of that box represented by each component
• R _{(Box Model / Measured)} = Σ C_strength R_component
• Find the Rs
• Reapply to the model OH field
• Calculate a global OH

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Conclusions

• CTM comparison with OH looks pretty good
• We can use this information to constrain the model OH and this gives a reasonable result
• To take this further requires a bit more thought