Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

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Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model. Jennifer A. Logan, Inna Megretskaia, Lin Zhang, and the GMI, TES, and MLS teams Harvard University NASA/Goddard JPL TES meeting, Feb. 24, 2009.

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Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model. Jennifer A. Logan, Inna Megretskaia, Lin Zhang, and the GMI, TES, and MLS teams Harvard University NASA/Goddard JPL. TES meeting, Feb. 24, 2009. The Global Modeling Initiative (GMI) ‘Combo’ Model. - PowerPoint PPT Presentation

Transcript of Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Page 1: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Jennifer A. Logan, Inna Megretskaia, Lin Zhang, and the GMI, TES, and MLS teams

Harvard UniversityNASA/Goddard

JPL

TES meeting, Feb. 24, 2009.

Page 2: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

The Global Modeling Initiative (GMI) ‘Combo’ Model

Combo = tropospheric + stratospheric chemical mechanism GEOS-4 meteorological fields 2° x 2.5° resolution Aura4 simulation, 2004-2007 Model output is available to the community for Aura science Output saved along satellite track at overpass times

GFED2 biomass burning emissions for 2004-2007 MEGAN inventory for biogenics Lightning NOx – regional scaling to average OTD/LIS lightning

spatial patterns (Allen and Pickering).

GEOS-Chem similar, without stratospheric chemistry.

Page 3: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Satellite data

TES V003 Validated with ozonesondes by Lin Zhang, similar high bias to

V002, 3-10 ppb. Filters applied to remove “C-shaped” ozone profiles (Lin

Zhang) Omit data with cloud optical depth >2 for pressure <750 hPa TES AKs and prior applied to model output Uniform prior used for 30°N-30°S

MLS data V2.2 MLS AKs applied to model (makes very little difference)

Page 4: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Outline

Use TES CO to evaluate model performance in lower troposphere to gain insight into reasons for discrepancies with MLS in the upper troposphere

Can the model match the interannual variability in

tropical CO and ozone, and if not, why not?

Page 5: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

GMI model compared to CO at NOAA/GMD surface sites in the tropics, 2005-2006

DataModel

Page 6: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

GMI model and MOZAIC aircraft data for CO in the tropics

DataModel – 2005Model - 2006

Cairo Abidjan Delhi Caracas

Locations with enough aircraft data for model evaluation

Page 7: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, July-Nov. 2005SH biomass burning season.

TES model model-TES

Too much export in easterlies in lower trop., convection N. of equator – similar problems with MOPITT comparisons (Junhua Liu, GEOS-Chem runs)

Fire emissions toolow over Africa

Jul

Aug.

Sep

Oct.

Nov.

Page 8: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, July-Nov. 2005SH biomass burning season.

TES model model-TES

Too much export in easterlies in lower trop., convection N. of equator – similar problems with MOPITT comparisons (Junhua Liu, GEOS-Chem runs)

Fire emissions toolow over Africa

Jul

Aug.

Sep

Oct.

Nov.

Page 9: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~150 hPa, July-Nov. 2005

MLS model model-MLS

Model maximum from convection is one month late, implying not enough convection in October.

Model is biased low everywhere, except where is it too high in equatorial band – same problem in LT

Observed max. over India is missing in model.Jul

Aug.

Sep

Oct.

Nov.

Page 10: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Time series over region of Asian maximum in UT CO

The UT maximum at 150 hPa in June-August is too late in the model. Same problem at 215 hPa.

Papers on high CO seen by MLS over the Himalayas, and effect of Asian monsoon: Li et al., 2005, Fu et al., Randel et al., Park et al. 2008, 2009

2005 2006 2007

Page 11: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, July-Nov. 2006

Similar features to 2005:

too much export in equatorial easterlies

too low BB emissions over Africa

too high CO near Andes(this appeared with switch to MEGAN biogenic emissions)

Huge difference in BB emissions from Indonesia(Logan et al. 2008, Nassar et al. 2009)

TES model model-TES

Jul

Aug.

Sep

Oct.

Nov.

Page 12: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO over South America in LT (TES) and UT (MLS)

Good match with TES in LT in 2005-2006,GFED too high in 2007

Model w/AK suggests lower CO in Aug. and Sept. 2005 – caused by lack of sensitivity.GFED CO emissions

Bench warm-up

Model lower than MLS in UT, peaks one month late in 2005 and 2006. Suggest a problem with timing of convection, since LT looks good.

2005 2006 2007

TESModel w. AKModel w/out AK

Page 13: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

S. America - CO from MOPITT and TES in the lower trop.

2005 2006 2007

MOPITT data also show 2006 had lowest CO over S. America in BB season.

Page 14: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO over Southern Africa in LT and UT

GFED emissions too low over S. Africa, but timing looks OK.

UT max. is a month too late over S. Africa also.

GFED emissions similar each year

GFED CO emissions

2005 2006 2007

Page 15: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, Dec. 2005-April 2006NH biomass burning season.

High CO near the Andes

Largest differences related to BB emissions in N. Africa in March – April.

TES model model-TES

Dec.

Jan

Feb.

Mar.

Apr.

Page 16: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, Dec. 2005-April 2006

MLS model model-MLS

Dec.

Jan

Feb.

Mar.

Apr.

Page 17: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO in the tropics at ~700 hPa, Dec. 2006-April 2007

BB emissions from N. Africa appear to be too high, or transport out of source region too strong.

BB CO is transported south and west

Dec.

Jan

Feb.

Mar.

Apr.

Page 18: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO time series over Equatorial Africa

GFED CO emissions (N. Africa)

Model CO decreases a month too soon in LT, implying emissions in February are too low.

Model UT maximum in Feb.-April similar to timing in MLS data. But since CO decreases too soon in the LT, caution is needed in interpreting the MLS comparison.

x

2005 2006 2007

Page 19: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO time series over Indonesia

2005 2006 2007

See Nassar et al. (2009) for detailed discussion of Indonesia in late 2005 and 2006 (El Nino).

Page 20: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

CO tape recorder, 10ºN- 10ºS

The GMI Combo model looks pretty good. Interannual variability driven by CO fire emissions, especially from Indonesia. Interannual variability in emissions in NH fire season apparent (Jan.-April).

Update of Schoeberl et al. (GRL, 2006), see also Combo model study of Duncan et al. (JGR, 2007), with GCM met. fields.

Means for 2005 subtracted from time series

MLS

GMI Combo

CO from Indonesian fires

Page 21: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Issues with V003 ozone data (and V002)

Some retrievals had “C-shaped” profiles, identified in V002 by Helen Worden, in validation with IONS data over N. America. Test devised to remove them.

The original C-test removed some valid looking profiles in the tropics over e.g., North Africa.

Lin Zhang devised a better test, based on validation of V003 data.

See example to left.

Page 22: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Ozone in the tropics, July-November, 2005

TES Model - TESmodel

The problem is confined to Atlantic sector.

Outflow to Indian Ocean is OK

The worst model agreement globally is in the S. Atlantic in Sept.-Nov. (sonde data shows the same)

Jul

Aug.

Sep

Oct.

Nov.

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Ozone in the tropics, July-November, 2006

TES Model - TESmodel

Discrepancies aremuch smaller in 2006.

TES is lower in 2006, and model is higher.

Jul

Aug.

Sep

Oct.

Nov.

Page 24: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

2005 2006 2007

July-November, interannual variability in TES data

Jul

Aug.

Sep

Oct.

Nov.

Page 25: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Ozone in Oct 2006, 6º-14ºS

AfricaS. Amer.

TES vertical resolution ~6 km! GMI

Problem is in LT not UT. Ozone too low over Africa, so outflow from Africa does not supply S. Atlantic with enough ozone in easterlies

GMI Ascension Island sondes, 8°S

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Ozone over South America, Nov. 2004-Dec. 2008

Sept. in red

In the model, ozone is related to lightning NOx(but not so simple)

LIS data show more lightning in 2006 than 2005.

TES data and OMI/MLS data show higher ozone in the South Atlantic region in 2005 and 2007.

Independent data from OMI/MLS confirms the IAV in the TES ozone data.

200 hPa

500 hPa

Lightning NOx

Sauvage et al., Martin et al. – lightning NOx is main source of ozone in the tropics

Page 27: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Ozone over Southern Africa, Nov. 2004-Dec. 2008

Lightning NOx

Oct.

Page 28: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Ozone in the tropics, Dec. 2005-April 2006

Discrepancies smaller than in Sept. – Nov.

Dec.

Jan

Feb.

Mar.

Apr.

Page 29: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

North Africa

Page 30: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Indonesia

For a detailed analysis of this region using GEOS-Chem, and the effects of the El Nino in late 2006 (and the huge fires in Borneo), see Nassar et al. (2009).

Page 31: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Tropospheric ozone column from TES and OMI/MLS

TES column (integrated profile)Schoeberl product (uses trajectories to fill in MLS)Ziemke/Chandra product

OMI/MLS products:OMI total O3 column - MLS strat. ozone

OMI scans, so has better global cover than MLS.

Variability in TES and OMI/MLS products is essentially the same.

Page 32: Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

Conclusions

Interpretation of MLS CO in upper trop. requires careful analysis of CO data in the lower trop., as errors in LT propagate to the UT.

Over S. America, S. Africa GEOS-4 max. convection appears to be a month too late, but hard to tell for N. Africa as errors in LT CO. • Need to look at convective mass fluxes in model

TES reveals interannual variability in tropical ozone, but model has problems matching this in S. Atlantic, likely due to lightning NOx. • See poster by Junhua Liu for analysis of model

meteorology and NOx in 2005/2006 TES and OMI/MLS products show similar variability