Rossana Dragani

ECMWF

rossana.dragani@ecmwf.int

Comparison of L3 CCI Ozone, Aerosols, and GHG data with

models outputs using the CMF

The Climate Monitoring Facility (CMF)

An interactive interface to visualize and facilitate model-observation confrontation for L3 products with a focus on multi-year variability of statistical averages (monthly/regional means).

The CMF Database includes pre-calculated statistical averages of 100+ distinct variables defined over 32 different geographical regions, 12-18 layers (if applicable), several data streams (various reanalyses and several CCI datasets).

Uncertainties compared with either the spread of an Ensemble of DA runs (if available) – infers the climate variability - or observation residuals from their model equivalent.

CMF usage and disclaimer: It should be used for the applications it was designed for:

• Monitoring – as opposed to assessing – data, i.e. spotting potential issues that need to be investigated further;

• Looking at long-term variability, multi-year homogeneity (jumps, unrealistic changes,…) and consistency with related variables.

To bear in mind:

• Differences in data sampling: Models are defined ‘everywhere’, observations are not;

• Refinements (e.g. AK convolution) are not considered.

Ozone CCI

L3 Ozone data availability

Availability Period assessedReanalysis

streams

(Merged)

TCO3

Apr 1996 – Jun 2011

Apr 1996 – Jun 2011

ERA-Interim

MACC

JRA-25

Nadir

Profile O3

Jan-Dec 1997 Jan2007-Dec 2008

Jan-Dec 1997 Jan-Dec 2008

ERA-Interim*

MACC

(Merged)

Limb O3 Jan 2007-Dec 2008 Jan 2007-Dec 2008

ERA-Interim

MACC

*ERS-2 GOME ozone profiles (RAL, and precursor of CCI NPO3 for 1997) were assimilated from Jan 1996-Dec 2002 the comparisons in 1997 are not independent.

(Merged) Tropical total column O3

Generally good agreement between CCI TCO3 and the European reanalyses. Agreement with ERA-Interim

degrades when reanalyses only constrained by total columns

JRA-25 shows much lower TCO3 than the other datasets.

The observation uncertainty is comparable with its residuals from the two European reanalyses and the ensemble spread.

Ensemble spreadObs - ERA-IntObs - MACC

CCI Sdev

JRA-25

Esti

mate

d u

ncert

ain

ty (

DU

)

Observation uncertainty (DU)

Nadir Profile Ozone (NPO3)

CCI NPO3ERA-InterimMACC

5 hPa

10 hPa

30 hPa

100 hPa

SAGE HALOE

5

10

30

10

0 x

(O

bs –

ER

A-I

nt)

/ E

RA

-In

t (%

)

1997

Nadir Profile Ozone (NPO3)

5 hPa

10 hPa

30 hPa

100 hPa

CCI NPO3 SDEVEnsemble Spread

(Merged) Limb Profile Ozone (LPO3)

2007 2008

CCI LPO3ERA-InterimMACC

CCI LPO3 SDEVEnsemble Spread

Aerosol CCI

Aerosols

Name / version Parameter Period Provider Acronym

AATSR_ADV / 1.42 AOD 2007-2010 FMI ADV

AATSR_ORAC / 2.02 AOD 2008 Uni. Oxford / RAL ORAC

AATSR_SU / 4.0 AOD 2008 Uni. Swansea SU

AATSR_SU / 4.1 AOD 2002-2012 Uni. Swansea SU

AATSR_SU / 4.2 AOD 2008 Uni. Swansea SU

550nm 659nm 670nm 865nm 870nm 1610nm 1640nm

ADV Y Y Y

ORAC Y Y

SU Y Y Y Y

MACC Y Y Y Y

CCI AOD vs. MACC AOD (Oceans, 2008)

Agreement typically within the obs error bars.

659nm

865nm

550nm

1610nm

ADV1.42

SU4.0

ADV1.42

ORAC2.02

MACC

SU4.1

SU4.2

CCI AOD vs. MACC AOD (550 nm, Oceans, 2008)

Assimilation could improve future AOD reanalysis

Preliminary results based on one month of ADV AATSR assimilation by MACC team show

good synergy with MODIS; the AATSR+MODIS AOD analyses have the best fit to

AERONET data compared to the analyses constrained with either MODIS or AATSR.

SU 4.0SU 4.1

SU 4.2

ORAC2.02ADV1.42

MACC

Global

@550nm

Long-term behaviour (SU4.1 & ADV 1.42)

SU4.1

ADV1.42

AOD550

AOD659

AOD865

AOD1610

AOD (550nm) over land and oceans

Land

Global MACC SU4.1 ADV1.42

Oceans

GHG CCI

Data availability & usageVariable Algorithm/version Sensor Period Provider

CO2BESD / 02.00.04 SCIAMACHY Aug 2002 – Mar 2012 IUP

OCFP /4.0 GOSAT Jun 2009 – Jan 2012 Uni. Leicest.

SRFP / 2.1 GOSAT Jun 2009 – Sep 2012 SRON

CH4

WFMD / 3.3 SCIAMACHY Jan 2003 – Dec 2011 IUP

IMAP / 6.0 SCIAMACHY Jan 2003 – Apr 2012 SRON

SRFP / 2.1 GOSAT Jun 2009 – Sep 2012 SRON

OCPR / 4.0 GOSAT Jun 2009 – Dec 2011 Uni. Leicest.

Variable Description Label Period ProviderCO2/CH4 Forecast (Fc) run MACC Jan 2003 – Dec 2012 MACC

CO2 Fc run with optimized fluxes MCO2 Jan 2003 – Dec 2012 MACC

CH4 Fc run with optimized fluxes MCH4 Jan 2008 – Dec 2008 MACC

MCO2 and MCH4 are Fc runs with optimized fluxes from the flux inversion The CO2 fluxes were optimized using only surface observations (no satellite data included).

The CH4 fluxes were obtained using both SCIAMACHY and surface observations.

CO2 long-term behaviour

Global annual CO2 change (ppm)

BESD NOAA ESRL

data

Initial Value 375.43 374.97

2004 1.52 1.81

2005 2.01 2.03

2006 1.77 2.13

2007 1.66 1.77

2008 1.59 2.08

2009 2.74 1.50

2010 1.61 2.29

2011 1.68 1.92

Mean a

nom

aly

(ppm

)

BESD

OCFP

SRFP

BESD

CCI CO2 vs. MACC CO2

BESD OCFP SRFP MCO2

20S-20N

20-60N

20-60S

Good agreement at midlatitudes in the NH

In the tropics and midlatitudes in the SH:

Good agreement between SCIA BESD and GOSAT OCFP, while GOSAT SRFP seems lagged in time.

MCO2 shows a slower CO2 growth with time than in the retrievals. Possible issues:

The CO2 fluxes optimized using only surface observations which are more sparse in the tropics and SH.

Difference in the transport models used in the flux inversion and in the forward calculations likely to be also larger in data sparse regions

CH4 long-term behaviour

Global annual CH4 change (ppb) IMAP WFMD

Initial value 1760.24 1748.98

2004 1.84 1.82

2005 6.18 -0.82

2006 -9.28 1.82

2007 2.50 2.37

2008 -2.80 8.09

2009 11.80 12.04

2010 26.02 0.44

2011 17.82 0.78

There seems to be some differences in the trends and mean evolution between the products (even for the same instrument):Differences are small, possibly not statistically significant when normalized to mean CH4;

Some areas might be too small to be significant;

Yet, the two algorithms give different outcome is there scope for a “merged” algorithm with the best features of the two currently available?

IMAP SRFP

WFMD OCPR

Global

CCI CH4 vs. MACC CH4

Good level of agreement between the four CCI products, particularly in the extra-tropics.

MACC is ~ 100ppb low biased compared with the GHG_CCI, while MCH4 shows a very high level of agreement with the corresponding retrievals.

A sudden change is noticeable in the IMAP SCIAMACHY product (grey lines) at the beginning of 2010 in the tropics and in the NH extra-tropics.

Uncertainties: The SCIA retrievals have much larger uncertainties

than the residuals between the CH4 observations and their MCH4 model equivalent.

In some cases the IMAP retrievals have larger than usual uncertainties.

Increased values in the WFMD product in 2005 following instrumental problems.

Conclusions

• Ozone: TCO3: agreement with ERA-Int higher when the latter constrained by vertically resolved O3 data

Profiles: Retrievals show lower values than the reanalyses. In the region of the O3 maximum (10hPa), the differences from ERA-Int seem consistent with the reanalysis validation. Further investigation of the region below the O3 maximum (30hPa) is needed for NPO3;

L3 uncertainties generally well comparable with O-A residuals and Ensemble Spread.

• Aerosols: Residuals from MACC are within the observation errors. The differences can largely be explained by

the +ve bias in the MODIS data (especially in summer). SU 4.0-4.2: Residuals from MACC increased in the latest versions, but they are consistent with

MACC-Aeronet comparisons and likely due to shortcomings in the sea-salt model. SU4.1 and ADV1.42 retrievals globally show good long-term stability land/ocean differences.

• GHG: Generally good agreement between retrievals and the MACC Fc runs with optimized fluxes CO2 shows about 2ppm mean growth rate (consistent with e.g. NOAA ESRL data).

In the tropics, the SRFP GOSAT product appears lagged compared with the other datasets. The SCIA CH4 datasets show small differences in the long-term variability between algorithms.

A sudden change was seen in the IMAP SCIA product in 2010 (in the tropics and northern midlatitudes).

ADDITIONAL SLIDES

XCO2

BESD OCFP SRFP MCO2

20S-20N

20-60N

20-60S

Good agreement at midlatitudes in the NH

In the tropics and midlatitudes in the SH:

Good agreement between SCIA BESD and GOSAT OCFP, while GOSAT SRFP seems lagged in time.

MCO2 shows a slower CO2 growth with time than in the retrievals. Possible issues:

The CO2 fluxes optimized using only surface observations which are more sparse in the tropics and SH.

Difference in the transport models used in the flux inversion and in the forward calculations likely to be also larger in data sparse regions

Sudden increase in MCO2 end of 2004 and beginning of 2005 significant drought in the Amazonian and Central African regions.

BESD

OCFP

SRFP

An approach consists in generating an ensemble of DA runs:

Members initialised from slightly different, but equally probable initial conditions.

The ensemble spread (ES) used as proxy of the internal climate variability of a given variable (e.g.

Houtekamer and Mitchell, 2001; Evensen, 2003) It can be used to estimate the uncertainties when not available or when available to assess their quality.

Model bias and any other model issues should have similar effects on all members of the ensemble

How can we assess uncertainties with the CMF?

As part of the ERA-CLIM project, ECMWF has run an ensemble of low resolution 4D-Var data assimilation runs from the beginning of the 20th century onwards.

ES from these simulation is used to assess the “area typical” CCI O3 uncertainties:

i

tia

ta N2,,

1 a: Geographical areat: timei: ith grid pointNa: Points in area a