OMI HCHO columnsJan 2006 Jul 2006

Policy-relevant background (PRB) ozone calculations for the EPA ISA and REA

Zhang, L., D.J. Jacob, N.V. Smith-Downey, D.A. Wood, D. Blewitt, C.C. Carouge, A. van Donkelaar, D.B.A. Jones, L.T. Murray, and Y. Wang, Improved estimate of the policy-relevant background ozone in the United States using the GEOS-Chem global model with 1/2ox2/3o horizontal resolution over North America, Atmos. Environ., 45, 6769-6776, doi:10.1016/j.atmosenv.2011.07.054, 2011

Lin Zhang and Daniel Jacob

Air quality management partner: Joe Pinto (EPA/NCEA)

GEOS-Chem nested simulation for North America

2ox2.5o

0.5o x0.67o

• 0.5o x0.67o resolution for North America, 2o x2.5o global, 48 vertical layers

• Driven by NASA GEOS-5 assimilated meteorological data• Detailed mechanistic representation of ozone-NOx -VOC-aerosol chemistry• 2006 anthropogenic emissions: NEI05 for US, CAC for Canada, BRAVO (scaled)

for Mexico, EMEP for Europe, Streets for East Asia, EDGAR (scaled) for rest of world

• Natural sources: lightning (OTD/LIS), fires (GFED2 monthly), soils (Yienger), stratosphere (Linoz)

• 2006-2008 3-year simulation; focus on 2006 for evaluation

Color scale Indicates topography (surface pressure)

Ozone statistics for the US (CASTNet sites)

+ > 1.5 km

PRB: 27 ± 8

40 ± 7

Natural: 18 ± 6

27 ± 6

Frequency distribution of MDA8 ozonefor March-August 2006

• GEOS-Chem is overall unbiased• PRB is 4 ppb higher than in

previous GEOS-Chem versions• Model shows little interannual

variability for 2006-2008, but neither do observations

Obs: 50 ± 13Model: 52 ± 12

58 ± 957 ± 10

Zhang et al. [2011]

Model “4th highest” MDA8 ozone in 2006

Background may limit or confuse ability of West to achieve a 60 ppb NAAQS; no such issue in East

Annual 4th highest ozone

PRB for annual 4th highest ozone 4th highest PRB value

Zhang et al. [2011]

Model PRB statistics vs. observed ozone in Intermountain West

Comparison of daily MDA8 ozone concentrations for the ensemble of elevated sites (>1.5 km; 11 sites) in Intermountain West for spring and summer 2006

Model underestimates high extrema in spring (> 75 ppbv) - stratosphere Correlation with measurements is poor in summer – lightning, wildfires

1:1 line

max

min

75th50th25th

r = 0.57 r = 0.33

Lin Zhang, Harvard

Model underestimate of stratospheric intrusions in spring

Sample stratospheric intrusion at Pinedale (WY) : model captures timing but not magnitude.

Obs.

model

PRB

Observed vs. simulated MDA8 ozone in spring 2006 at the 11 elevated CASTNet sites, colored by model stratospheric ozone concentration.

Model has correct STE and vertical transport; we attribute problem to the general difficulty of Eulerian models in resolving fine structures.

Lin Zhang, Harvard

observations

stratosphere

Ozone enhancement from lightning in Intermountain West

Model lightning increases ozone concentrations by 7-19 ppbv in summer, comparable to enhancements from US anthropogenic emissions.

This lightning influence in the model is biased high, even though lightning location is constrained by OTD/LIS satellite observations;

focus future model improvement on NOx yield per lightning flash, vertical distribution

Lin Zhang, Harvard

model

PRB

observations

lightning

Sensitivity of model ozoneto wildfire emission inventory

fire reportsmonthly

Maximum fire enhancements of MDA8 ozoneIn GEOS-Chem – August 2006

• Model is highly sensitive to choice of inventory, spatial and temporal resolution

• Influence of fires is mainly limited to fire region; little influence at CASTNet sites in 2006

• Need to examine other years, MOPITT and AIRS CO data for fires

Lin Zhang, Harvard

Observed interannual correlation of ozone with wildfiresmay reflect common variation with temperature

Ozone correlation with T at elevated CASTNet sites (summer 2006) Correlation of T with PBL depth

Subsidence of background air appears to be a major factor driving variability of ozone in the intermountain West

Lin Zhang, Harvard

ObservationsModel

GEOS-5

New IP: Nitrogen Deposition to US National Parks

Motivation:• N deposition at some National

Parks has reached critical load: changes may not be reversible

• Nitrogen wet deposition at Rocky Mountain NP is twice the critical load of 1.5 kg N ha-1 a-1

• Need to understand where the N is coming from and in what form

Approach:• Simulate N deposition with nested

GEOS-Chem model, evaluate with surface data and OMI NO2

• Initial focus on source attribution for RMNP

Outcome:• Contribute to RMNP Nitrogen

Deposition Reduction Plan

Raluca Ellis and Daniel Jacob (Harvard)Air quality management partner: Bret Schichtel (NPS)

NH4+

NO3-

1985-2002 trend in wet deposition fluxes(Lehmann et al., 2005)