Rossana Dragani ECMWF 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, 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 AvailabilityPeriod assessed Reanalysis streams (Merged) TCO 3 Apr 1996 – Jun 2011 Apr 1996 – Jun 2011 ERA-Interim MACC JRA-25 Nadir Profile O 3 Jan-Dec 1997 Jan2007-Dec 2008 Jan-Dec 1997 Jan-Dec 2008 ERA-Interim * MACC (Merged) Limb O 3 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 O 3  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 spread Obs - ERA-Int Obs - MACC CCI Sdev JRA-25 Estimated uncertainty (DU) Observation uncertainty (DU)

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

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

(Merged) Limb Profile Ozone (LPO3) CCI LPO3 ERA-Interim MACC CCI LPO3 SDEV Ensemble Spread

Aerosol CCI

Aerosols Name / versionParameterPeriodProvider Acronym AATSR_ADV / 1.42AOD FMI ADV AATSR_ORAC / 2.02AOD2008Uni. Oxford / RAL ORAC AATSR_SU / 4.0AOD2008Uni. Swansea SU AATSR_SU / 4.1AOD Uni. Swansea SU AATSR_SU / 4.2AOD2008Uni. Swansea SU 550nm659nm670nm865nm870nm1610nm1640nm ADV Y Y Y ORAC YY SU Y YY Y MACC YYYY

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 4.0 SU 4.1 SU 4.2 ORAC2.02 ADV1.42 MACC

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 & usage Variable Algorithm/version Sensor PeriodProvider CO2 BESD / SCIAMACHYAug 2002 – Mar 2012 IUP OCFP /4.0GOSATJun 2009 – Jan 2012 Uni. Leicest. SRFP / 2.1GOSATJun 2009 – Sep 2012 SRON CH4 WFMD / 3.3SCIAMACHYJan 2003 – Dec 2011 IUP IMAP / 6.0SCIAMACHYJan 2003 – Apr 2012 SRON SRFP / 2.1GOSATJun 2009 – Sep 2012 SRON OCPR / 4.0GOSATJun 2009 – Dec 2011 Uni. Leicest. VariableDescriptionLabelPeriodProvider CO2/CH4 Forecast (Fc) runMACCJan 2003 – Dec 2012 MACC CO2 Fc run with optimized fluxesMCO2Jan 2003 – Dec 2012 MACC CH4 Fc run with optimized fluxesMCH4Jan 2008 – Dec 2008 MACC MCO2 and MCH4 are Fc runs with optimized fluxes from the flux inversion  The CO 2 fluxes were optimized using only surface observations (no satellite data included).  The CH 4 fluxes were obtained using both SCIAMACHY and surface observations.

CO 2 long-term behaviour Global annual CO 2 change (ppm) BESD NOAA ESRL data Initial Value Mean anomaly (ppm) BESD OCFP SRFP BESD

CCI CO 2 vs. MACC CO 2 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 CO 2 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

CH 4 long-term behaviour Global annual CH 4 change (ppb) IMAPWFMD Initial value 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 CH 4 vs. MACC CH 4  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 O 3 data  Profiles: Retrievals show lower values than the reanalyses. In the region of the O 3 maximum (10hPa), the differences from ERA-Int seem consistent with the reanalysis validation. Further investigation of the region below the O 3 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 : 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  CO 2 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 CH 4 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 CO 2 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 20 th century onwards.  ES from these simulation is used to assess the “area typical” CCI O 3 uncertainties: a: Geographical area t: time i: i th grid point N a : Points in area a