Modelling and Assimilation of Atmospheric Composition (II )

1. Modelling and Assimilation of Atmospheric Composition (II) Antje Inness Contributions from: Johannes Flemming, Angela Benedetti, Richard Engelen, Johannes Kaiser & other members of the MACC team Environmental Monitoring

2. Outline • Introduction • Data assimilation for atmospheric composition (AC) - Challenges - Assimilating retrievals • Reactive gases data assimilation • Aerosol data assimilation • Greenhouse gases data assimilation • Concluding remarks Environmental Monitoring

3. Introduction • Motivation given in Johannes Flemming’s talk • Since June 2010, ECMWF’s amended convention includes objectives to “develop, and operate on a regular basis, global models and data-assimilation systems for the dynamics, thermodynamics and compositionof the Earth’s fluid envelope and interacting parts of the Earth-system”. • We are providing near-real-time analyses and forecasts of atmospheric composition as well as reanalyses of past years (2003- …) in the MACC project for: • Global reactive gases (O3, CO, NO2, SO2, HCHO, …..) • Aerosols • Greenhouse gases (CO2, CH4) • Not principally different from meteorological DA but several new challenges. Environmental Monitoring

4. Challenges for Atmospheric Composition • Quality of NWP depends predominantly on initial state • AC modelling depends on initial state (lifetime) and surface fluxes • CTMs have larger biases than NWP models • Only a few species (out of 100+) can be observed • More complex and expensive, e.g. atmospheric chemistry, aerosol physics • Most processes take place in boundary layer, which is not well observed from space • AC Satellite retrievals • Little or no vertical information from satellite observations • Fixed overpass times and day light conditions only (UV-VIS) • Retrieval errors can be large • AC in-situ observations • Sparse (in particular profiles) • Limited or unknown spatial representativeness

5. Importance of emissions: Zonal mean total column CO MOPITT Stand alone run 2003 2007 Assimilation run 2003 2007 • CO emissions are too low and model run loses the information from the initial state. Data assimilation can (partly) correct this problem. • Initial conditions alone are not sufficient (more about emissions in Johannes Flemming’s talk) [1018 molec/cm2]

6. Issues with Observations • Limited representativeness, small scales not resolved by satellites • Very heterogeneous observational data sets (sometimes poor data quality and availability) • Poor near-real time availability • Not all components are well-constrained by observations • Total or partial columns retrieved from radiation measurements. Weak or no signal from boundary layer. • Global coverage in a few days (LEO); often limited to cloud free conditions; fixed overpass time -> no daily maximum/cycle. Environmental Monitoring

7. NRT data coverage for reactive gases Ozone SCIA SBUV/2 NOAA-17 SBUV/2 NOAA-18 CO MOPITT IASI OMI MLS SO2 NO2 OMI GOME-2 OMI SCIA GOME-2

8. No LS data Limb-sounding ozone data assimilated in 2003 (MIPAS) and 2006-2008 (MLS) These data, especially MLS, are clearly beneficial OMI data are used from July 2007 Importance of adequate observations

9. Assimilating retrievals Take profile retrieval xr as measurement y: With a-priori xa , error covariance matrix Sr and averaging kernel A: If we assimilate xr with covariance Sr , we mix in both the a priori profile and the a priori covariance matrix, which is likely to be inconsistent with the model background of the assimilation system. Sy: observation error covariance matrix Sa: prior error covariance matrix K: weighting function Environmental Monitoring

10. Effect of a priori on retrieval product MOPITT CO shows changes from one version to another. Part of these changes are caused by changing prior information. V4 V4-V3 Jan 2003 V3

11. Using the Averaging Kernel We can make use of the averaging kernel A in the observation operator by using the following: We remove the influence of the a priori profile if we use the averaging kernel to sample the model profile according to the assumptions made in the retrieval. However, the a priori error assumptions are still in there and we assume everything is linear within the bounds of the a priori assumptions. (And we still need to know xa and A in the observation operator calculations). Environmental Monitoring

12. Example MOPITT CO Averaging Kernels day night From: Deeter et al. (2003) JGR • Diurnal variations of Tsurf affect retrieval over land. • CO near surface more detectable during day, AKs shift downwards • Diurnal variability of AKs largest over e.g. deserts, smallest over sea • If AKs are not used this can introduce an artificial diurnal CO cycle in the analysis Environmental Monitoring

13. Issues Total column retrievals come with integrated averaging kernels; some information is lost Profile retrievals with full averaging kernels and retrieval errors can become difficult to handle Not all retrieval methods allow the estimation of an averaging kernel; e.g., neural networks Not all data providers use the same definition of averaging kernel in their data files Many different versions of the observation operator needed to deal with all variations We use: • Reactive gases: Profiles, columns with and without averaging kernels • Aerosols: Columns without averaging kernels • Greenhouse gases: Radiances and columns with averaging kernels

14. Reactive gases Data assimilation Environmental Monitoring

15. Setup for the reactive gases • CTM (e.g. MOZART, TM5) coupled to IFS to provide initial fields and chemical tendencies. Also now under development C-IFS: Chemistry integrated in IFS • IFS GRG species: O3, CO, NO2, SO2, HCHO • More species available from CTM output (and in C-IFS) • Background errors calculated with: • NMC method (CO, NOx, HCHO) • Analysis ensemble method (O3) • Prescribed profile (SO2) • Difficulties assimilating species with short lifetimes (e.g. NO2): NOx used control variable and NO2-NOx interconversion operator (Angela Benedetti’s lecture) • Chemistry included in outer loop (ifstraj) not in minimisation • Advantages from chemical coupling Environmental Monitoring

16. ECMWF 4D-VAR Data Assimilation Scheme Assimilation of Reactive Gases transport + “chemistry” advection only transport + “chemistry”

17. Benefit of chemical coupling • Background NOx levels determine O3 production/loss • Assimilation of NO2 has an impact on ozone field (through chemical feedbacks in the CTM) • Assimilation of NO2 can improve O3 field Validation with MOZAIC ozone data Control (no CO or NO2 assim, only O3 assim) MOZAIC observation CO & NO2 assim NO2 assim

18. Chemical coupling HCHO over China and Eastern US • CO, O3, NO2 data were assimilated in the MACC reanalysis • HCHO fields were changed in MACC reanalysis compared to control run even though no HCHO data were assimilated. • Chemical interactions • Possible reason:OH is increased in reanalysis which increases CH4 oxidation and hence HCHO production.

19. Bias correction:VarBC for reactive gases • Variational bias correction scheme at ECMWF (Dick Dee’s talk) has been extended to retrievals of O3, CO, NOx, SO2, HCHO, CH4, AOD. • Bias models (e.g., bias = a0 + a1 * SOE): • - O3: Use solar elevation and global constant as predictor • - CO, AER: Use global constant as predictor • SBUV and MLS are used as anchor (i.e. not bias corrected) for O3, IASI as anchor for CO Environmental Monitoring

20. VarBC for O3: Obstat monitoring plots SCIAMACHY stdv CTRL dep= obs-ana • O3 bias in underlying model • Assimilation of SCIAMACHY O3 with SBUV/2 data used as anchor • VarBC takes out the bias so that analysis has to do less work NO VARBC VARBC Environmental Monitoring

21. VarBC: A new instrument comes in SCIA SCIAMACHY data come in and VarBC spins up Data change. VarBC adjusts, departures are unchanged OMI VarBC adjusts OMI Environmental Monitoring

22. MACC Reanalysis: Carbon Monoxide [1018 molec/cm2] Biomass burning GFEDV3 and GFASv1.0 South America 2010 2003 1997 2010 Africa S. America Indonesia J. Kaiser Indonesian fires 2006 Assimilation of CO from MOPITT and IASI leads to CO reanalysis field that captures interannual variability well.

23. MACC Reanalysis: Seasonal mean CO [ppbv]

24. Aerosol DATA assimilation Environmental Monitoring

25. 4D-Var assimilation system for aerosols Aerosol assimilation is difficult because: • There are numerous unknowns (depending on the aerosol model) and very little observations to constrain them • The concentrations vary hugely with for instance strong plumes of desert dust in areas with very little background aerosol, which makes it difficult to estimate the background error covariance matrix Environmental Monitoring

26. The aerosol prediction system Forward model Analysis • 12 aerosol-related prognostic variables: • * 3 bins of sea-salt (0.03 –0.5 – 0.9 – 20 µm) • * 3 bins of dust (0.03 –0.55 – 0.9 – 20 µm) • * Black carbon (hydrophilic and –phobic) • * Organic carbon (hydrophilic and –phobic) • * SO2 -> SO4 • Physical processes include: • emission sources (some of which updated in NRT, i.e.fires, J. Flemming’s talk), • horizontal and vertical advection by dynamics, • vertical advection by vertical diffusion and convection • aerosol specific parameterizations for • dry deposition, sedimentation, wet deposition • by large-scale and convective precipitation, • and hygroscopicity (SS, OM, BC, SU) Control variable used to be formulated in terms of the total aerosol mixing ratio. Now, aerosol control variables are thefine mode (<1 µm diameter) and coarse mode aerosol mixing ratio. Analysis increments are repartitioned into the species according to their fractional contribution to the fine/coarse mode mixing ratio. Background error statistics have been computed using forecasts errors as in the NMC method (48h-24h forecast differences). Assimilated observations are the 550nm MODIS Aerosol Optical Depths (AODs) over land and ocean and the fine mode AODs over ocean. Observation errors are prescribed fixed values. Global bias corrections are applied to both total and fine mode AOD using the variational bias estimation scheme (VARBC) implemented operationally at ECMWF. Improvements of dual mode control variable are especially seen in fine mode AOD

27. Fine/ coarse aerosol mixing ratio as control variable • Problem: • Several aerosol species and bins, but we do not want to include all of them in the control vector. • Solution: • Use fine/ coarse mode aerosol mixing ratios (originally total aerosol mixing ratio) as control variables. • Increments in fine/ coarse mode mixing ratios have to be distributed into increments in the species/bins mixing ratios. • Caveats: • The relative contribution to the fine/ coarse mixing ratios of single species/bins is kept CONSTANT over the assimilation window, and increments are distributed accordingly. As a result, perturbations from “heavier” species contribute more to the perturbation in fine/ coarse mixing ratios, even if their contribution to the total aerosol optical depth might be smaller! • The aerosol mass needs to be conserved in the trajectory run! This is not physically possible if aerosol source/sink processes are on. • Ok over assimilation window (12h) as most aerosol removal processes act over a longer timescale (24h). Environmental Monitoring

28. Example for wrong aerosol attribution Eruption of the Nabro volcano put a lot of fine ash into the stratosphere. This was observed by AERONET stations and the MODIS instrument. sulphate biomass ICIPE-Mbita - AERONET sea salt dust The MACC aerosol model does not contain stratospheric aerosol yet, so the observed AOD was wrongly attributed to the available aerosol types. MACC AOD analysis AERONET total AOD AERONET fine mode AOD

29. Fine and coarse mode control variable Single CV TOTAL AOD Analysis Bias again AERONET Single CV Fine Mode AOD Dual CV TOTAL AOD Dual CV Fine Mode AOD 24-h Forecast Bias against AERONET

30. Fine and coarse mode control variables - verification Fine and coarse mode control variable: Verification • Close to dust source (i.e. La Laguna) the fine mode AOD is improved greatly but still overestimated • Away from the dust source (i.e. Tudor Hill) the relative magnitude of total and fine mode is well captured • using the dual control variable formulation (and the extra MODIS observations!) • Ongoing model improvements to correct for the large amount of fine mode dust aerosols will help • considerably

31. MACC Reanalysis activities – contribution to BAMS State of Climate 2010 • Good agreement between MACC AOD • climatology and (independent) MISR • observations • Known problems with underestimation • of dust, but biomass burning 2010 • anomalies are picked up by the • MACC system (Russian, Siberian and • South American lively fire activity and • and low fire activity in Indonesia) • East Asia pollution signal also evident A. Benedetti, J. W. Kaiser, and J.-J. Morcrette. [Global Climate] Aerosols [in "State of the Climate in 2010"]. BAMS, 92(6):S65–S67, 2011.

32. GreenHOUSE GAS assimilation Environmental Monitoring

33. MACC CH4and CO2 inversion system biased Daily time scale biased Improved prior fluxes Satellite data Data Assimilation Yearly time scale In-situ data Flux inversion Optimized fluxes Less biased unbiased Forward run Boundary conditions

34. CH4 bias problems Mean increments RMS increments • Assimilation of SCIAMACHY CH4 • It is clear where the work is done – over land. But, a systematic bias over land only, because either the model is biased or the observations, is not tolerated in a 4D-Var. • Bias correction is not straightforward, because we know we have errors in the surface fluxes, which are not easy to correct in a 12-hour 4D-Var. • Potential solution is currently topic of research: Correct surface fluxes offline and then bias correct satellite data in 4D-Var

35. Need for bias correction • Timeseries of CH4 • Observations only used over land. • Impact of assimilating SCIAMACHY CH4 can be seen over Australia. • Less SCIAMACHY data over New Zealand. Problem not seen there. Australia New Zealand Work is in progress to bias correct the prior fluxes that go into the 4D-Var system, so that satellite data can then be bias corrected in 4D-Var.

36. CO2 from 2003-2007 GEMS reanalysis 2003 2004 2005 2006

37. Concluding remarks • IFS has been extended to include fields of atmospheric composition: Reactive gases, greenhouse gases, aerosols • We mainly assimilate retrievals at present (apart from CO2) • Extra challenges for DA of atmospheric composition compared to NWP (but also extra benefits through chemical coupling and potential impact on NWP) • Still work in progress (C-IFS, AER dual control variable, GHG assimilation, revision of background errors, volcanic SO2, …) • We are providing near-real time analyses and forecasts of atmospheric composition as well as reanalyses (2003-2010 and beyond) for reactive gases, greenhouse gases, and aerosols as part of the MACC project Environmental Monitoring

38. More information about environmental monitoring activities at ECMWF and how to access the data can be found on: http://www.gmes-atmosphere.euFor questions contact:info@gmes-atmosphere.eu