Atmospheric Composition Data Assimilation Richard Engelen

Thanks to Angela Benedetti, Johannes Flemming, Antje Inness, Luke Jones, Johannes Kaiser, Jean-Jacques Morcrette , Anna Agusti-Panareda, Miha Razinger, Martin Suttie, and the many data providers.

Outline Why a talk about atmospheric composition? Observations Modelling The art of assimilating L2 retrievals What’s the problem with aerosol? The link with NWP

What’s the difference? Quality of NWP depends largely on initial conditions whereas atmospheric composition modelling depends on initial state and boundary conditions (e.g., emissions) Chemistry is complicated, non-linear, and not fully known Most processes take place in the boundary layer, which is not well-observed from space In short: we have a relatively poorly constrained system with significant model errors

observations

Satellite observations O3, SCIAMACHY, KNMI/ESA O3, OMI, KNMI/NASA AOD, MODIS, NASA NO2, OMI, KNMI

Satellite observations GOME-2, SACS, BIRA IASI, Univ. of Brussels Atmospheric composition observations traditionally come from UV/VIS measurements. This limits the coverage to day-time only. Infrared is now adding more and more to this spectrum of observations (MOPITT, AIRS, IASI, …)

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

Future (example of ozone)

Importance of adequate observations 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 No LS data

modelling

Chemical data assimilation Based on the 4D-Var scheme of ECMWF’s IFS CO2 , CH4 and aerosols are incorporated in the IFS IFS also carries O3, CO, NO2, SO2 and HCHO IFS is coupled to chemistry transport model providing the chemical production and loss terms Chemistry modules are being built fully into IFS

Importance of emissions: Zonal mean total column CO MOPITT Model run latitude 2003 2007 Assimilation run 2003 2007 Boundary condition problem is easily illustrated on long time scales, but also applies to more synoptic time scales CO emissions are too low and model run loses the information from the initial state. Data assimilation can (partly) correct this problem. [1018 molec/cm2]

MACC CH4 inversion system biased Less biased unbiased Daily time scale Prior fluxes Satellite data Data Assimilation (ECMWF) In-situ data Flux inversion (JRC) Optimized fluxes Yearly time scale

CH4 bias problems Mean increments RMS increments 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.

Chemistry ◄ into stratosphere No transport modelled O3, CO, SO2, NO2, CH4, CO2, aerosol are currently routinely observed from space in near-real-time

NO2 and NOx at model level 10 (4.2 hPa) 2008-06-05, 0z Assimilation of NO2 NO2 and NOx at model level 10 (4.2 hPa) 2008-06-05, 0z Fast diurnal NO2 - NO inter-conversion can not be handled with coupling frequency of 1 hour problems at the day/night boundary Use NOx=NO2+NO field and inter-conversion operator. NO2 NOx Day time: NO2 + hν -> NO + O Unit: 109 kg/kg

NO2/NOx observation operator Use NOx as state variable (NO2 observations) Less spatial variability NOx is not so strongly influenced by solar radiation Chemical development of NOx can be better simulated by coupled system More complex observation operator needed, based on simple photochemical equilibrium between NO2 and NO JNO2 (photolysis freq) depends on: SZA, surf. albedo, O3 concentration, slant O3 column, temperature, clouds JNO2 parameterization based on TM5 routine k(T) rate coefficient of O3 +NO -> NO2 + O2

Does it work? Using the NOx/NO2 observation operator ensures the NO2 fields are drawn towards the satellite data.

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 CO & NO2 assimilation Control (no CO or NO2 assimilation) NO2 assimilation Observations

Assimilating Retrievals

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

How to deal with 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

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 these a priori assumptions. (And we still need to know xa and A in the observation operator calculations).

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 analysis

Issues Total column retrievals come with integrated averaging kernels; some information is lost Profile retrievals with full averaging kernels and retrieval errors become easily 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

IASI & MOPITT combined IASI: LATMOS/ULB MOPITT: NASA Reanalysis Model run MOPITT IASI

Aerosol complications

Aerosols, what’s the problem? 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. This makes it difficult to model the background error covariances properly

4D-Var for aerosols Aerosol prognostic variables include 3 bins for desert dust, 3 bins for sea-salt, hydrophobic and hydrophilic organic matter, hydrophobic and hydrophilic black carbon, and sulphate. The control variable is formulated in terms of the total aerosol mixing ratio Assimilated observations: MODIS Aerosol Optical Depths (AODs) at 550 nm over land and ocean Observation operator converts total aerosol mixing ratio into AOD The observed AODs are spread out over the various aerosol types and bins based on the modelled ratios

Aerosol optical depth for desert dust: monthly Sydney dust storm, 23-09-09 H+72 H+48 Aerosol optical depth for desert dust: monthly average for September 2008 H+24

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 dust sea salt 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

Interaction with NWP

Examples of connection between NWP and composition Meteorological and atmospheric composition data assimilation are closely coupled. Both can benefit from each other. Aerosol direct and indirect effects Trace gas climatologies in radiation models Trace gases in radiance assimilation

Reduction of AIRS/IASI bias correction with realistic CO2 Bias correction using fixed CO2 of 377 ppm, the value prescribed in RTTOV Bias correction using variable CO2 modelled with MACC system Mean bias correction (K) for August 2009 for AIRS channel 175 (699.7 cm-1; maximum temperature sensitivity at ~ 200 hPa) Engelen and Bauer (2011)

Positive effects on temperature analysis Using more realistic CO2 values in radiative transfer model causes changes in temperature analysis. Bias correction of AMSU-A channels is reduced as well.

Thank you.

Ozone hole example

Ozone Satellite Retrievals OMI TC SCIA TC SBUV TC MLS 1 August 2008, 0 – 12 UTC UV-VIS: SBUV, SCIAMACHY, OMI Total Columns at high resolution No observation in polar night Micro Wave Limb sounder (MLS) Profiles in Stratosphere

Instrument - Biases over Antarctica MLS observes in polar night Large area-averaged differences due to different sampling Actual biases are small (2-3%)

Ozone hole predictions with without assimilation Free Running Model Initialized every 15 days MOZ Stratospheric Chemistry scheme IFS Linear Chemistry Scheme (Cariolle) TM5 Climatology ANA Analysis

Ozone hole assimilation with different chemistry schemes MOZ Stratospheric Chemistry scheme IFS Linear Chemistry Scheme (Cariolle) TM5 Climatology MOZ-NRT Assimilation, but without MLS