Constraints on Free Tropospheric Ozone from the Tropospheric Emission Spectrometer (TES)

Dylan JonesUniversity of Toronto

Thomas Walker (University of Toronto)Mark Parrington (University of Edinburgh)Kevin Bowman, John Worden (JPL)Anne Thompson (Penn State)David Tarasick (Environment Canada)Ivanka Stajner (Noblis)Lee Murray (Harvard U.)

•One of four instruments on the NASA Aura spacecraft (launched July 2004)

• Infrared Fourier transform spectrometer (3.3 - 15.4 m) •Nadir footprint = 8 km x 5 km•Orbit repeats every 16 days

Tropospheric Emission Spectrometer (TES)

700 hPa

400 hPa

70-90 hPa

Objective: Assess the impact of assimilating TES data on surface ozone in GEM-MACH

150 - 50 hPa500 - 150 hPa

1000 - 500 hPa

Averaging kernels for retrieval at 30°N, 87°W

Ozone Analysis for Aug 1-15th, 2006

• TES data were assimilated from 1 Jul. to 31 Aug. 2006 into GEOS-Chem using a sequential Kalman filter

• Assimilation of TES data significantly increased ozone abundances across the extratropics

Without assimilation (7 km) With TES assimilation (7 km)

Ozone Analysis Over North America (at 5 km on 15 August 2006)

ppb

Before assimilation After assimilation

• Assimilation of TES data increased O3 across North America by up to 40%

• Large increases in O3 in the eastern Pacific, in the vicinity of a stratospheric intrusion, and across Canada, linked to stratosphere-troposphere exchange

• The summertime O3 maximum over the southeast is more pronounced after assimilation

[Parrington et al., JGR, 2008]

Modelled O3 Over North America along 40°N

NOx O3 without TES assim O3 with TES assim

The upper tropospheric ozone maximum is linked to NOx emissions from lightning, which were 0.068 Tg N for North America (in August), a factor of 4 lower than recommended by Hudman et al. [JGR, 2007] based on comparisons of the model with aircraft data.

[Parrington et al., JGR, 2008]

Comparison with IONS-06 Ozonesondes Over North America

Significant improvement in fee tropospheric O3 (300 - 800 hPa) after assimilation. The bias was reduced from a maximum of -35% to less than 5% (between 300-800 hPa).

Sonde profileBefore assimAfter assim

Mean (August 2006) O3 profile over North America (model sampled at the

ozonesonde observation time and location) Mean Profiles Difference relative to sondes

[Parrington et al., JGR, 2008]

Comparison of TES analysis with assimilation of OMI and MLS

Northern midlatitude in better agreement after assimilation of TES data

GMAO OMI+MLS ozone assimilation

GEOS-Chem with TES assimilation GMAO – GEOS-Chem differences

GEOS-Chem ozone (7-8 km) for Aug. 2006

Over Asia, the biased between GMAO ozone and GEOS-Chem decreased from 6.8 ppb to 1.4 ppb after TES assimilation

[Worden et al., JGR, 2009]

GMAO – G-C with TESGMAO – G-C without TES

Comparison of TES ozone analysis with assimilation of OMI and MLS

GMAO OMI+MLS analysis

TES assimilation

Without TES assimilation

Mean Ozone (Aug. 2006) Along 45°E

[Worden et al., JGR, 2009]

• The O3 distribution in the TES analysis is more consistent with OMI+MLS

• The Middle East ozone maximum is reduced in the TES assimilation, relative to the free running simulation

Impact of TES Assimilation on Surface Ozone (Aug. 2006)

• The model overestimates surface ozone in the east and underestimates it in the west

• Assimilation increases surface O3 by as much as 9 ppb, with the largest increase in western North America

[Parrington et al., GRL, 2009]

• TES-based estimates of background O3 are 20-40 ppb

Before assimilation After assimilation

Surface O3 difference (assim - no assim) AQS and NAPS surface O3 data

Background O3 at the surface after assimBackground O3 at the surface before assim

Location Mean bias before (ppb)

Mean bias with TES

Kelowna, AB -1.81 4.52

Bratt’s Lake, SK 0.99 4.96

Glacier NP, MT -5.61 0.65

Pinnacles NM, CA -6.36 0.19

Theodore Roosevelt NP, ND

-8.39 -4.49

Table Mt., CA 0.64 6.47

Boulder, CO -3.90 -0.37

Dallas, TX 5.14 8.74

Egbert, ON 1.63 4.90

Narragansett, RI 8.21 11.26

Coffeeville, MS 11.76 13.70

Sumatra, FL 16.05 17.66

Evaluation with surface ozone measurements

• Assimilating TES data reduces the model bias in western North America at the sites most sensitive to background ozone

• Large residual bias at sites such as Egbert, Sumatra, and Table Mt. due to the coarse model resolution

Sites sensitive to background O3: TES assimilation reduced the bias

Sites sensitive to local O3 production: bias enhanced

Summary and Future Work

• Assimilation of TES data provides sufficient information to constrain the vertical structure of ozone in the free troposphere

• We are interested in assimilating the TES data to assess their impact on the ozone simulation in GEM-MACH, with a focus on surface ozone forecasts in North America. (We will use MLS data to constrain stratospheric ozone.)

• Since the TES observational coverage is poor, an important issue to examine is the trade-off between vertical resolution and spatio-temporal coverage of free tropospheric ozone observationso We are interested in comparing the impact of TES and OMI data on the

surface ozone fields

TES Assimilation Aug 2006, 5 km

Comparison of the new model with assimilated ozone

v8 GEOS-Chem O3 Aug 2006, 5 km

In the middle troposphere,relative to the TES assimilation, the mean bias between 20º-50ºN in the model decreased from about -7 ppb to 2 ppb with the new lightning NOx source

O3 in v8 of GEOS-Chem with new lightning NOx source(and with biomass burning emissions based on GFED3)

Chemical Data Assimilation Methodology

Sequential sub-optimal Kalman filter:

Model

Kalman Gain Matrix:

Analysis Error Cov. Matrix:

• GEOS-Chem model with detailed nonlinear tropospheric chemistry

• Linearized (LINOZ) O3 chemistry in the stratosphere

•Model transport driven by assimilated meteorological fields (GEOS-4) from the NASA GMAO (at a resolution of 2° x 2.5° or 4° x 5° )•O3 and CO profile retrievals from TES are assimilated from 1 Jul. - 31 Aug. 2006• 6-hour analysis cycle• Assumed initial forecast error of 50% for CO and O3

• Neglected horizontal correlations in forecast and observation error covariance matrices

Observation Operator: