Sinks Mathew Evans, Daniel Jacob, Bill Bloss, Dwayne Heard, Mike Pilling Sinks are just as important as sources for working out emissions! 1.NO x N 2 O 5 hydrolysis 2.OH Comparison with direct observations

N 2 O 5 hydrolysis ‘Ultimate’ NO x sinks dominated by OH + NO 2 + M  HNO 3 (historically interesting) N 2 O 5 + aerosol  HNO 3 Roughly 50% from each OH+NO 2 dominates in summer N 2 O 5 + aerosol dominates in winter

N 2 O 5 + aerosol Rate defined by  the ‘reaction probability’ Fraction of molecules that hit aerosol surface that react For the stratosphere   0.1 But is this true for the troposphere –Different types of aerosols –Warmer and wetter

Rumblings of discontent Tie et al., [2003] found  N2O5 <0.04 gave a better simulation of NO x concentrations during TOPSE Photochemical box model analyses of observed NO x /HNO 3 ratios in the upper troposphere suggested that  N2O5 is much less than 0.1 [McKeen et al., 1997; Schultz et al., 2000]

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

Parameterization based on best available literature Aerosol typeReaction probability b Reference Sulfate a  =  (RH)  10  T)  = 2.79    RH   RH   RH 3  = 4   (T-294) (T ≥ 282K)  = (T < 282K) [Kane et al., 2001] [Hallquist et al., 2003] c Organic Carbon  = RH  5.2  (RH < 57%)  = 0.03(RH  ≥ 57%) [Thornton et al., 2003] d Black Carbon  = [Sander et al., 2003] Sea-salt  = (RH < 62%)  = 0.03 (RH ≥ 62%) [Sander et al., 2003] e Dust  = 0.01 [Bauer et al., 2004] f

What  s do we get? Much lower than 0.1 Dry low values Higher at the surface

What is the impact on composition? Lower  N 2 O 5 higher N 2 O 5 250% higher NO3 30% higher NO x 7% Higher NO x higher O 3 7% Higher NO x higher OH 8%

Compare with observations Emmons et al. [2000] climatology of NO x 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 Ox (odd oxygen) budget Chemical production increases 7% 3900 Tg O 3 yr -1 to 4180 Tg O 3 yr-1

Compare with observations Global annual mean tropospheric OH 0.99  10 6 cm -3 to 1.08  10 6 cm -3 8% increase. Both values are consistent with the current constraints on global mean OH concentrations based on methyl- chloroform observations: 1.07 ( )  10 6 cm -3 [Krol et al., 1998] 1.16  0.17  10 6 cm -3 [Spivakovsky et al., 2000] 0.94  0.13  10 6 cm -3 [Prinn et al., 2001]

Conclusions Aerosol reaction of N 2 O 5 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 (NH 4 ) 2 SO 4 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

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 × 10 6 cm -3, Modelled mean is 2.3 × 10 6 cm -3 Ratio of 1.56 ± The statistical distribution of the ratio is not normal and so more appropriate metrics such as the median (1.13) or the geometric mean ( ), The model simulates 30% of the linear variability of OH (as defined by the R 2 ). The uncertainty in the observations (13%) suggests that the model systematically overestimates the measured OH concentrations.

Other HO x components

Over a year Smoothed mean OH from model Sampled for the NAMBLEX campaign Sampled for the EASE ‘97 campaign Observed Campaign means

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? All 10 6 cm -3 A Priori OH (Model) Compare Observed OH A Posteri OH Prinn et al. OH NH % ± 0.20 SH % ± 0.20 Global %

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

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

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