1. Forward/Inverse Atmospheric Modelling: Recent Results and Future Plans Martyn Chipperfield, Manuel Gloor University of Leeds NCEO meeting, University of Sheffield, 28 th and 29 st February 2012 Paul Palmer University of Edinburgh

2. Talk Layout Example Leeds forward/inverse model results Edinburgh plans (Paul Palmer) Additional science slides (Manuel Gloor, Leeds) Summary

3. TOMCAT CH 4 forward simulations – Transcom Results Annual mean CH4 comparisons for six emission scenarios against NOAA surface flask data, 1988-2008.

4. Average monthly mean CH4 comparisons for six emission scenarios against NOAA surface flask data, 2000-2006. TOMCAT CH 4 forward simulations – Transcom Results

5. TOMCAT Adjoint Modelling – ALT station sensitivities sensitivity CTL sensitivity INV sensitivity Adjoint transport carried out using an initial value of 1 at Alert, Canada to find the sensitivity (LH plots) This sensitivity is then multiplied by the emission rate for CTL (Centre plots) and INV emission (RH plots) inventories This gives an ‘emission sensitivity’ for the ALT station, indicating the emission regions which are influencing the tracer concentration at the station

6. TransCom CH 4 emissions NH Wetland areas treated individually 1.Alaska and Canada (>60N) (AL_CAN) 2.West Siberian Plain (WSP) 3.Eastern Siberia (>60N) (E_SIB) (Below) Total CH 4 emissions for three TransCom emission inventories in wetland regions

7. TOMCAT 4D-Var inversion results One year inversion carried out (2008), assimilating eight-day mean data from six Arctic stations. Representative results show relatively small changes in NH winter, but large reductions in wetland areas during April – October Wilson et al (in prep), 2012

8. More TOMCAT 4D-Var inversion results (Left) Cost function decrease over three iterations of 4D-Var minimisation, split into contributions from observations (CF_O) and background (CF_B) (Below) Total CH 4 emissions for three wetland areas. TOMCAT inversion has decreased emissions in line with INV and VISIT in Eastern Siberia and Alaska/Canada regions

9. Effect of emission changes There is a marked improvement in RMSE between model and observations at the stations from which data has been assimilated, especially during NH summer months

10. U. Ed. Past NCEO T3 CH4 activities Developed CH4 simulation ready for EO Used EO data to develop wetland emissions [Bloom et al, 2010] Developed EnKF for CH4 source/sink estimation Extensively evaluated model [Fraser et al, 2011] Established links with GOSAT team DATA: SCIAMACHY, GOSAT, SAGE, GRACE, IASI, NOAA/ESRL, TCCON, CONTRAIL, regional aircraft, train-borne data Ensemble Kalman Filter GEOS-Chem transport model + bottom-up emission inventories Core framework

11. U. Ed. Ongoing NCEO T3 CH4 activities TransCom (Patra et al, 2011) and GCP (Kirschke et al, 2011) Flux estimates: improve geographical and sector breakdown. GOSAT collaboration with U. Leicester (Parker et al, 2011) SH NH

12. GEOS-Chem to use GEOS v5 met. data (0.25 o x0.3125 o ) Data: GOSAT, IASI, Sentinel-5P + any new national missions; exploiting correlations with CO Major focus: U. Ed. Future NCEO CH4 activities GEOS-Chem/EnKF JULES/HadGEM2 Compare bottom-up/top-down wetland emissions Help inform JULES development Co-join two models Develop inverse problem (parameter estimation?) EO data

13. What could be focus of research? Could include: GHG flux estimation using inversions (and new measurements programs like AMAZONICA or OCO2?) Land surface observation using remote sensing – e.g. GRACE or NDVI Land surface – climate interaction modelling using JULES ?

14. Seneviratne et al. 2010 Links to Leeds Amazon Work Regions with strong soil- moisture temperature coupling

15. Currently: not much predictive capability e.g. in tropics

16. Amazon river discharge at Obidos (drains nearly 80 % of Amazon basin) ( data from ABA, Brazil govt. hydro- logy measurements, gap filling by Callède et al.) - Overall an upward trend in river discharge - extremes increase – dry season drier, wet season wetter How will land vegetation respond ? Peak Mean Minimum

18. AMAZONICA - biweekly Greenhouse Gas Data Dec. 2009 onwards for next 4 years (L. Gatti, Sao Paulo, M. Gloor, Leeds, H. Rocha, Sao Paulo, J. Miller NOAA, Boulder)

19. Simple Analysis Through Back Trajectories

20. Tabatinga Santarém Rio Branco Alta Floresta In 2010: Amazon weak net carbon source (~0.2PgC yr -1 )

22. What could be focus of research: GHG flux estimation using inversions (and new measurements programs like AMAZONICA or OCO2?) Land surface observation using remote sensing – e.g. GRACE or NDVI Land surface – climate interaction modelling using JULES ? Possibly: Observe and understand ongoing trends of the land-vegetation climate coupled system in those regions which are sensitive

23. Summary Tools: Forward atmospheric CH4/CO2/CO (+ full chem) chemical transport models operational and tested Inverse schemes EnKF and 4D-var operational Plans: Assimilate existing/new satellite datasets (GOSAT, IASI, Sentinel 5-P) Test top down/bottom up emission estimates Close collaboration with JULES team (CEH) Scientifically: Arctic wetlands AMAZONICA / vegetation-atmosphere interactions Others…

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