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Comparison of L3 CCI Ozone, Aerosols, and GHG data with models outputs using the CMF

Comparison of L3 CCI Ozone, Aerosols, and GHG data with models outputs using the CMF. Rossana Dragani ECMWF rossana.dragani@ecmwf.int. The Climate Monitoring Facility (CMF).

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Comparison of L3 CCI Ozone, Aerosols, and GHG data with models outputs using the CMF

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  1. Comparison of L3 CCI Ozone, Aerosols, and GHG data with models outputs using the CMF Rossana Dragani ECMWF rossana.dragani@ecmwf.int

  2. 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.

  3. Ozone CCI

  4. L3 Ozone data availability *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.

  5. (Merged) Tropical total column O3 • CCI Sdev • JRA-25 • 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. Estimated uncertainty (DU) • The observation uncertainty is comparable with its residuals from the two European reanalyses and the ensemble spread. Ensemble spread Obs - ERA-Int Obs - MACC Observation uncertainty (DU)

  6. Nadir Profile Ozone (NPO3) 5 5 hPa 10 10 hPa CCI NPO3 ERA-Interim MACC 100 x (Obs – ERA-Int) / ERA-Int (%) 30 30 hPa SAGEHALOE 1997 100 hPa

  7. Nadir Profile Ozone (NPO3) 5 hPa 10 hPa CCI NPO3 SDEV Ensemble Spread 30 hPa 100 hPa

  8. (Merged) Limb Profile Ozone (LPO3) CCI LPO3 ERA-Interim MACC 2007 2008 CCI LPO3 SDEV Ensemble Spread

  9. Aerosol CCI

  10. Aerosols

  11. CCI AOD vs. MACC AOD (Oceans, 2008) ADV1.42 SU4.0 ADV1.42 SU4.1 SU4.2 659nm 550nm ORAC2.02 MACC 1610nm 865nm • Agreement typically within the obs error bars.

  12. CCI AOD vs. MACC AOD (550 nm, Oceans, 2008) SU 4.0 SU 4.1 SU 4.2 Global ORAC2.02 ADV1.42 MACC @550nm • 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.

  13. Long-term behaviour (SU4.1 & ADV 1.42) SU4.1 ADV1.42 AOD550 AOD659 AOD865 AOD1610

  14. AOD (550nm) over land and oceans Global MACC SU4.1 ADV1.42 Land Oceans

  15. GHG CCI

  16. Data availability & usage 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.

  17. CO2 long-term behaviour BESD OCFP SRFP Mean anomaly (ppm) BESD

  18. CCI CO2 vs. MACC CO2 • 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 20-60N BESD OCFP SRFP MCO2 20S-20N 20-60S

  19. CH4 long-term behaviour IMAP SRFP WFMD OCPR Global • 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?

  20. 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.

  21. 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).

  22. ADDITIONAL SLIDES

  23. XCO2 20-60N • 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 BESD OCFP SRFP MCO2 20S-20N 20-60S

  24. How can we assess uncertainties with the CMF? • 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 • 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: a: Geographical area t: time i: ith grid point Na: Points in area a

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