Folkert Boersma

Reducing errors in using tropospheric Reducing errors in using tropospheric NONO22 columns observed from space columns observed from space

What is so uncertain about emissions?• quantities• locations• times• trends

Main use of satellite observations: estimating emissions of NOx

But we can see the NOx sources from space

Emissions

EMEP

SCIAMACHY

Blond et al. (2007)

chem= 4-24 hrs

Pros• sensitivity to lower troposphere• improving horizontal resolution• global coverage

Satellite observations

Cons• daytime only• column only• clouds• sensitivity to forward model parameters assumptions

Retrieval method

3-step procedure• obtain slant column along average light path• separate stratospheric and tropospheric contributions • convert tropospheric slant column in vertical column

Retrieval method

In equation:

Ns, Ns,st, Mtr are all error sources

Retrieval method

aerosols

surface pressure

IUP Bremen Dalhousie KNMI/BIRA

Ns,st Ref. sector scaled to SLIMCAT strat.

Ref. Sector Data-assimilation in TM4

Cloud fraction FRESCO <0.2 cloud fraction; only cloud selection, no further correction

GOMECAT FRESCO

Cloud pressure Not used GOMECAT FRESCO

Albedo GOME GOME TOMS/GOME

Profile shape MOZART-2 run for 1997, monthly averages on 2.8 x 2.8 °

GEOS-Chem (2x2.5)

TM4 (3x2)

Temperature correction

No Based on U.S. std. atmosphere

Based on ECMWF T-profiles

Aerosols Based on LOWTRAN

Based on GEOS-Chem

No

‘State-of-science’ van Noije et al., ACP, 6, 2943-2979, 2006

Systematic differences

van Noije et al., ACP, 6, 2943-2979, 2006

Accounting for zonal variability or not?

E. J. Bucsela – NASA GSFC

41.5°N

Stratospheric column

Model information

Reference Sector

Without correction errors up to 11015 molec.cm-2

Stratospheric column

March 1997

Alternative: limb-nadir matching

• Limb observes zonal variability

• Stratospheric column estimate may introduce offsets from limb-technique

Courtesy of E. J. Bucsela – NASA GSFC

Stratospheric column

A. Richter et al.– IUP Bremen

Stratospheric column

In summary

• Reference sector method questionable

• Assimilation & nadir-limb correct known systematic errors

• Assimilation self-consistent; uncertainty ~0.2×1015

• Validation needed

- SAOZ network (sunrise, sunset)

- Brewer direct sun (Cede et al.) in unpolluted areas

Retrieval method

Tropospheric air mass factor Mtr - Computed with radiative transfer model and stored in tables

Mtr = f(xa,b)

xa = a priori tropospheric NO2 prf

b = forward model parameters

- cloud fraction

- cloud pressure

- surface albedo

- aerosols

( - viewing geometry)

Air mass factor

A priori profile

(a) Clear pixel, albedo = 0.02

(b) Clear pixel, albedo = 0.15

(c) Cloudy pixel with fcl = 1.0, pcl = 800 hPa

Air mass factor errors

• Large range in sensitivities between 200 & 1000 hPa, especially in the BL

• Low sensitivity in lower troposphere for dark surfaces

Eskes and Boersma, ACP, 3, 1285-1291, 2003

A priori profile from CTMs

Air mass factor errors

• Shapes reasonably captured by CTMs

• Effect of model assumptions on BL mixing lead to errors <10-15%

• Models are coarse relative to latest retrievals

Martin et al., JGR, 109, D24307, 2004

Effect of choice of CTM on retrieval

Air mass factor errors

MOZART-2 (2°2°)

vs.

WRF-CHEM (0.2°0.2°)

Jun-Aug 2004 SCIAMACHY NO2

MOZART-2 AMF

A. Heckel et al. (IUP Bremen)

Effect of choice of CTM on retrieval

Air mass factor errors

Effect ~10%Jun-Aug 2004 SCIAMACHY NO2

WRF-Chem AMF

A. Heckel et al. (IUP Bremen)

Cloud fraction

Albedo

Cloud pressure

Air mass factor sensitivities

M = wMcl + (1-w)Mcr

Boersma et al., JGR, 109, D04311, 2004

M = M/asf asf

asf = 0.02 (GOME-TOMS)

AMF errors – surface albedo

(%)

M = M/fcl fcl

fcl = 0.05 (FRESCO)

AMF errors – cloud fraction

(%)

M = M/pcl pcl

pcl = 50.0 (FRESCO)

AMF errors – cloud pressure

(%)

• If NO2 present, then also aerosol• Aerosols affect radiative transfer dep. on particle type

Air mass factor errors - aerosols

Martin et al., JGR, 108, 4537, 2003

• Aerosols affect radiative transfer• Cloud fraction sensitive to aerosols ( = +1.0 fcl +0.01)

Air mass factor errors - aerosols

Direct correction

Indirect correction through M=wMcl+(1-w)Mcr

Air mass factor errors – surface pressure

• Surface pressure from CTMs (2° × 3°)• Strong differences with hi-res surface pressures

GOME SCIAMACHY

Schaub et al., ACPD, 2007

Error top-10

1. Cloud fraction errors ~30%

2. Surface albedo ~15% + resolution effect?

3. Vertical profile ~10% + resolution effect?

4. Aerosols ~10%? More research needed

5. Cloud pressure ~5%

6. Surface pressure depends on orography

Is there a recipe for reducing all these errors?

1. Better estimates of forward model parameters

A good example: surface pressures (Schaub et al.)

What should be done:

- a validation/improvement of surface albedo databases

- a validation/improvement of cloud retrievals

- investigate effects aerosols on (cloud) retrievals

- validation vertical profiles

- higher spatial resolution (sfc. albedo, pressure, profile)

Is there a recipe for reducing all these errors?

2. How do we know if better forward model parameters improve retrievals?

We need an extensive, unambiguous and well-accessible validation database

Testbed for retrieval improvements:

- in situ aircraft NO2 (Heland, ICARTT, INTEX)

- surface columns (SAOZ, Brewer, (MAX)DOAS)

- in situ profiles (Schaub/Brunner)

- surface NO2 (regionally)

Is there a recipe for reducing all these errors?

3. Towards a common algorithm/reduced errors?

Difficult!

• Without testbed, verification of improvements is hard

• Improvements for one algorithm may deteriorate other algorithms, depending on retrieval assumptions

• Improved model parameters may work for some regions and some seasons, but not for others

Is there a recipe for reducing all these errors?

3. Towards a common algorithm/reduced errors?

Worth the try!

• Systematic differences can be reduced (emission estimates)

• Requires ‘scientific will’ – enormous task

- Collection of validation set

- Flexible algorithms digesting various model parameters

- Intercomparison leading to recommendations

- Fits purpose ACCENT/TROPOSAT