Reports from AT2 Task Group 2 - CiteSeerX

Reports from AT2 Task Group 2

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Tropospheric Sounding from Space

Assimilation of Tropospheric Species into a Chemistry Transport Model A contribution to ACCENT-TROPOSAT-2, Task Group 2 J.-L Attié1, P. Ricaud1, V.-H. Peuch2, D. Cariolle3, D. Edwards4, L. El Amraoui2, B. Barret1, S. Pradier2, N. Bousserez1, S. Massart3 and A. Piacentini3 1

Laboratoire d'Aérologie UMR CNRS 5560, Université de Toulouse, France 2 Météo-France, CNRM, Toulouse, France 3 Cerfacs, Toulouse, France, 4 National Center for Atmospheric research, Boulder, USA

Summary This proposal aims at assimilating chemical satellite data from present and future instruments into the MOCAGE chemistry-transport model. The goal is to construct assimilated fields of CO and O3 by using data from MOPITT, IASI (launch in 2006) in the troposphere and Odin in the lower stratosphere. The assimilation methods used are the Kalman filter and the 3-DFGAT implemented in the PALM software developed by the CERFACS. The assimilation results (CO and O3) will be compared to an independent set of data (aircraft or satellite data). This study aims to test the different types of assimilation methods, with respect to different chemical schemes, applied on different species from various satellite instruments. Also it will provide a tool capable of testing various configurations for evaluating a new concept of Air quality and pollution satellite instrument. Introduction Carbon monoxide (CO) with its lifetime between a few weeks to a few months is considered as a very good tracer of pollution. It is produced by incomplete combustion and wild fires are one of the major source of CO. Moreover, CO by reacting with hydroxyl radical OH produces ozone (O3) which is a major pollutant into the troposphere. Therefore, CO is a precursor of O3 in the troposphere. Stratospheric O3 has been measured for a long time from satellites and will complement tropospheric chemistry observations to get information about UTLS via assimilation. Since 2000, IR-nadir MOPITT (Measurements Of Pollution Into The Troposphere) instrument on board Terra satellite is capable of measuring CO into the troposphere with between one and two levels of information (see Drummond and Mand, 1996, Edwards et al., 2003, Deeter et al., 2003). And since 2002, the limb SMR (Sub-Millimeter Radiometer) on board Odin platform measures numerous chemical species (among them CO and O3) in the stratosphere [Murtagh et al., 2002; Ricaud et al., 2005]. We present the first results of assimilation obtained with the global model MOCAGE CTM using these satellite data. The assimilation methods used are in one hand an extended Kalman filter [Khattatov et al., 2000] for the tropospheric CO and in other hand the 3DFGAT method for the stratospheric O3. After describing the CTM model, we show results of MOPITT CO assimilation during ITOP2004 (Intercontinental Transport Of Pollution) campaign and an example of SMR O3 assimilation into the stratosphere. These results are compared to independent measurements.


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The MOCAGE CTM MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle) is the forecast global 3-D Chemistry Transport Model (CTM) developed at the Centre National de Recherches Météorologiques of Météo-France, dedicated to the numerical simulation of the interactions between dynamical, physical and chemical processes in the troposphere and lower stratosphere [Peuch et al., 1999]. It is a semi-Lagrangian gridpoint model which can run with up to four levels of nested domains (two-way nesting), from the resolution of 2 ° (global) down to 0.2 ° over areas of special interest. The vertical grid is composed of 47 hybrid (H, P) levels from the surface to 5 hPa, with typical resolutions of 40 to 400 m in the boundary layer (7 levels) and about 800 m near the tropopause and in the lower stratosphere. ARPEGE [Courtier et al., 1991] meteorological analyses (horizontal wind and temperature) are used to initialize and constrain the model every 6 hours. The vertical velocity is calculated from these horizontal components by imposing mass conservation for each vertical atmospheric column. Convective processes are simulated with the scheme of Bechtold et al. [2001], and turbulent diffusion is calculated with the scheme of Louis [1979]. MOCAGE takes into account the chemical scheme RACMOBUS which is the combination of stratospheric and tropospheric chemical schemes, REPROBUS [Lefèvre et al., 1994] and RACM [Stockwell et al., 1997]. RACMOBUS includes 119 species, among which 89 are prognostic variables, and it considers 372 chemical reactions. For more information, see Michou and Peuch, 2002 and Josse, 2004. In the present study, the time step for the evolution of chemical constituents due to physical and chemical processes is 15 minutes while dynamical changes are calculated every hour. Assimilation of chemistry satellite data into MOCAGE We present an example of MOPITT CO assimilation into MOCAGE CTM during ITOP campaign. ITOP experiment took place between US and Europe during summer 2004 (from July 15th to August 8th) and one of its main goal was to analyse the pollution export from US to Europe. During this period intense wild fires (biomass burning) spread off over Alaska producing large amounts of CO that crossed the American continent before reaching Europe. However, because of the variability of fires, and probably the inadequate emission inventory, models had difficulties to reproduce CO fields. To correct these effects, the assimilation technique is probably a good answer. For example, the assimilation of MOPITT CO gave the results shown in Figure 1 for the case of 15th July. The assimilated CO is fairly similar to daytime MOPITT CO retrievals at 500 hPa. This underlines the successful behavior of the assimilation scheme. Then, we compared these results to an independent set of in-situ measurements (aircraft data from MOZAIC programme). Figure 2 shows a fairly good agreement between average of aircraft data and assimilated values during the ITOP period. Concerning the aircraft data, we took about 200 profiles (take-offs and landings) during the ITOP campaign. One can notice that the agreement is much better over US than over Europe at lowermost levels (between surface to about 800 hPa). This discrepancy is probably due to: 1 An inadequate emission inventory over Europe leading to a large difference with satellite data (annual inventory from EDGAR) 2

A vertical diffusion scheme inadequate for this period

3

Poor information from MOPITT CO at lowermost levels

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Figure 1. Top: Daytime MOPITT CO at 500 hPa (from 15th to 18th of July) superimposed by horizontal wind fields from ARPEGE analysis. Bottom: Assimilated CO obtained with MOCAGE CTM at 12 UT on 17th July at 500 hPa.

Figure 2. Comparison between assimilated CO and in situ CO over US (left) and Europe (right). The horizontal error bars represent the standard deviation relative to the data averaging during the ITOP period.

Assimilation of stratospheric SMR O3 The stratospheric O3 profiles from SMR onboard the Odin satellite have been successfully assimilated into MOCAGE-PALM system. The assimilation technique is a 3DFGAT (hybrid between 3DVAR and 4DVAR). Figure 3 displays an example of the total O3 column of the background field used for the assimilation (top), TOMS (middle) and the assimilated field (bottom). The assimilated total column of ozone is calculated by integrating the assimilated profiles along the observation altitude and by adding the climatology (from the model) where


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the observations are missed. The same method is applied to calculate the background filed. This example is focused on 10th December 2002. The comparison between the assimilated field and TOMS show a general good agreement, however the assimilation tends to reduce the O3 concentrations in the northern-middle latitudes.

Figure 3. O3 field from model without using assimilation (top), TOMS O3 for the same period (middle) and assimilation of SMR O3 into the MOCAGE model (bottom)

Future outlook Concerning the CO assimilation in the troposphere, we are looking at new monthly emission inventories to be used in MOCAGE in order to evaluate its impact in the assimilation results. Moreover, we have implemented two different assimilation schemes into the MOCAGE model allowing us to have 3-D fields of various chemical species into the troposphere and the stratosphere. The next step is to couple the assimilation of CO from both MOPITT and SMR measurements into the MOCAGE CTM. To do so, we plan to use the PALM coupler software (developed by Cerfacs) to make this coupled assimilation. The results should provide CO 3-D fields with a better vertical resolution including the troposphere and the UTLS. References Bechtold et al, A mass flux convection scheme for regional and global models, Quart. J. Roy. Meteor. Soc., 127, 869-886, 2001 Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F., Rochas, M., The ARPEGE project at Météo-France. In Workshop on numerical methods in atmospheric models. Vol. 2, 193-231, Reading, UK. ECMWF, 1991 Drummond, J. R., and G. S. Mand, The Measurements of Pollution in the Troposphere (MOPITT) instrument: Overall performance and calibration requirements. J. Atmos. Oceanic Technol., 13, 314-320, 1996 Edwards et al, 2003: Tropospheric ozone over the tropical atlantic: the satellite perspective. J. Geophys. Res., 108, 4237-4251 Deeter et al, 2003: Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument. J. Geophys. Res., 108(D14), 4399 Josse B., Simon P. and V.-H. Peuch, Radon global simulations with the multiscale chemistry and transport model MOCAGE, Tellus, 56B, 339-356, 2004

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Khattatov et al, Assimilation of satellite observations of long-lived chemical species in global chemistry transport models. J. Geophys. Res., 105, 29,135-29,144, 2000 Lefèvre, F., G.P. Brasseur, I. Folkins, A.K. Smith, P. Simon, The 1991-1992 stratospheric winter: threedimensional simulations, J. Geophys. Res., 99, 8183-8195, 1994. Louis, J.-F., A parametric model of vertical eddy-fluxes in the atmosphere, Bound. Layer. Meteor., 17, 187-202, 1979 Michou, M. and V.-H. Peuch, Echanges en surface dans le modèle chimie-transport multi-échelles MOCAGE, Rev. Sci. Eau, 15, 173-204, 2002. Murtagh D, Frisk U, Merino F, et al. An overview of the Odin atmospheric mission. Canadian Journal of physics 80 (4): 309-319 Apr. 2002 Peuch et al, MOCAGE: Modèle de Chimie Atmosphérique à Grande Echelle. Actes des Ateliers de Modélisation de l'Atmosphère, Météo-France, Centre National de Recherches Météorologiques, 33-36, 1999. Ricaud et al. Polar vortex evolution during the 2002 Antarctic major warming as observed by the Odin satellite J. Geophys. Res. 110 (D5): Art. No. D05302 MAR 11 2005 Stockwell, W. R., F. Kirchner, M. Khun and S. Seefeld, A new mechanism for regional atmospheric chemistry modelling, J. Geophys. Res., 102, 25847-25879, 1997 Pradier S., J.L. Attié, M. Chong, J. Escobar, V-H. Peuch, J-F Lamarque,B. Kattatov, D. Edwards, 2005 : Evaluation of 2001 springtime CO transport over West Africa using MOPITT CO measurements assimilated in a global chemistry transport model. Submitted to Tellus.

Recent publications from AT2 work Pradier S., J.L. Attié, M. Chong, J. Escobar, V-H. Peuch, J-F Lamarque,B. Kattatov, D. Edwards, 2005 : Evaluation of 2001 springtime CO transport over West Africa using MOPITT CO measurements assimilated in a global chemistry transport model. Submitted to Tellus.


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Integrating Chemical Modelling and Satellite Observations for Monitoring Tropospheric Chemistry and Air Quality A contribution to ACCENT-TROPOSAT-2, Task Group 2 Matthias Beekmann1, Nadège Blond1,3, Alma Hodzic2, Igor Konovalov1,4, Gilles Bergametti1, Hélène Chepfer2, Gilles Foret1, Laurent Menut2 and Robert Vautard2 1

Laboratoire Inter-Universitaire des Systèmes Atmosphériques, Faculté des Sciences et Technologie, Université Paris-XII, 61 avenue du Général de Gaulle, 94010 Crétéil Cedex, France 2 Institut Pierre-Simon Laplace, LMD/Ecole Polytechnique 91128 Palaiseau CEDEX, France 3 KNMI, De Bilt, The Netherlands 4 Institute of Applied Physics, Russian Academy of Sciences, Niznhy Novgorod, Russia

Summary The proposed contribution of the IPSL / LISA group aims at using existing and future satellite observations to improve our knowledge of processes constraining the chemical composition of the troposphere and to improve regional scale air quality modelling, with a focus on Europe. Theses objectives are mainly related to work in task group 2, synergistic use of models and observations. In the period 2004 to 2005, our work focussed on: the use of GOME and SCIAMACHY tropospheric NO2 measurements for regional air quality model evaluation (1) and for inverse modelling of NOx emissions (2), and the use of Polder aerosol optical depths for model evaluation (3). Introduction Satellite observations can help to improve pollution modelling in several ways: (1) through better model evaluation, (2) by giving constraints on pollutant emissions, (3) through use in data assimilation to improve pollution forecast. In our contribution, we will highlight several results already obtained in these directions and will give perspectives for future use of satellite data. Scientific activities and major results Regional chemistry-transport model evaluation with tropospheric NO2 columns from GOME and SCIAMACHY The multi-scale CHIMERE transport and chemistry model (http://euler.lmd.polytechnique.fr/chimere) has been built to simulate photo-oxidant and particulate matter pollution with emphasis over Europe. Computer efficient design allows for long term simulations for assessment of emission control strategies. The model is also used in an operational manner for two days in advance air quality forecast in the frame of the PREVAIR system at INERIS, France (http://www.prevair.org). 1

We have compared both tropospheric NO2 columns derived from GOME (by IUP, Univ. Bremen) and SCHIAMACHY (by KNMI) to columns simulated with the CHIMERE model. The comparison of GOME derived (with 320 × 40 km2 horizontal resolution) and simulated NO2 columns over Europe (including the European part of Russia) for summer (June to August) 1997 and 2001 does not show any significant bias [Konovalov et al., 2005]. Spatial correlation coefficients between simulated and observed monthly means are high both for western Europe (0.91) and eastern Europe (0.77). Indeed large NO2 tropospheric column values appear in particular in a region spanned by England, Benelux, and the Rhine-Ruhr area, and more generally over large agglomerations, both in observations and simulations. A

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statistical analysis shows that random errors (in a spatial sense) are of multiplicative nature and of the order of 30 % (at 1 level).

Figure 1.

Comparison between GOME and CHIMERE derived tropospheric NO2 columns; average for June to August 1997; the GOME values have been provided by IUP, University of Bremen [A. Richter, H. Nüß and J.P. Burrows].

In addition, tropospheric NO2 columns (with 60 × 30 km2 horizontal resolution) have been derived from SCIAMACHY by KNMI taking into account the averaging kernels associated with the observations. The comparison with CHIMERE simulated NO2 columns for the year 2003 again does not show any significant bias and large spatial correlation (0.87) [Blond et al., 2005]. However, seasonal differences appear: during winter time CHIMERE NO2 columns are larger than observed one, during summer time, they are lower. This possibly points to uncertainties in the loss processes included in the model’s chemical mechanism, homogeneous


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during summer and heterogeneous during winter. Comparisons for specific days show that transport patterns of NO2 plumes are in general well reproduced by CHIMERE. 2

Inverse modelling of NOx emissions from GOME and SCIAMACHY derived tropospheric NO2 columns The recent important developments in satellite measurements of the composition of the lower atmosphere open a challenging perspective to use such measurements as an independent source of information on sources and sinks of atmospheric pollutants. A particular challenge is to retrieve a spatial structure of emissions with a high resolution that would match a spatial resolution of satellite measurements, which currently is of order of several tenths of kilometres. Because of a much lower spatial resolution of global models, such a task necessitates the implication of regional scale chemistry transport models, in our case CHIMERE with a 0.5 ° horizontal resolution over Europe. The study is based on a novel original method which not only enables a computationally efficient optimisation of a large number (1800 in our case) of model parameters corresponding to the seasonally averaged NOx emissions with an account of transport, but diminishes the amount of a priori assumptions via a combination of satellite and ground based measurements. The key features of our method are (i) the use of SCIAMACHY in conjunction to GOME derived NO2 columns (provided by IUP, University of Bremen) to create a data set with a spatial resolution coherent with the CTM (0.5 °), (ii) the replacement of a CTM by a set of linear regressions describing the relationships between tropospheric NO2 columns and NOx emissions with sufficient accuracy, (iii) the utilisation of measurements of near-ground NO2 concentrations for estimation of a priori unknown ratio of total uncertainties of modelled and measured NO2 columns to the uncertainty of a priori “down-to-top” estimates of emissions, and (iv) the evaluation of uncertainties of both a priori and a posteriori emissions by means of a special Monte-Carlo experiment which is based on random sampling of errors of both NO2 columns and surface concentrations. Using CHIMERE CTM, we applied our method to NO2 columns derived from GOME measurements. GOME data for 2001 were used because surface EMEP measurements were available for the same year. Preliminary results suggest that the inverse modelling procedure leads to substantial reduction of the uncertainty of a priori data for NOx emissions in Western Europe. It is demonstrated also that the use of a posteriori NOx emissions yields a better agreement between the modelled and measured near-ground concentrations of NO2. 3

The aerosol optical depths as seen from POLDER and the CHIMERE regional chemistry-transport model During the first half of August 2003 a severe heat wave hit Western and Central Europe. The persistent anticyclonic conditions characterized by exceptionally high temperatures, were favorable to the development of a large-scale photochemical pollution episode. The stagnation of the air mass also conduced to the accumulation of the primary emitted particulate matter (PM) and the development of secondary aerosols. Furthermore, in conjunction with the dry, hot weather conditions, the Southern part of Europe was hit by important forest fires that generated a huge amount of primary particles. Modelling such a wide pollution episode over Europe is a challenging problem because models have to deal with an exceptional environment for which their parameterizations are not necessarily appropriate. The use of satellite measurements to assess models performances in simulating the aerosol spatial distribution at regional scale offers a great advantage due to their wide spatial coverage. We have compared the aerosol optical thicknesses (AOTs) measured at 865 nm by the Polarization and Directionality of the Earth's Reflectances (POLDER) sensor and those obtained from the simulation by the CHIMERE chemistry-transport model. Simulated and POLDER AOTs generally agree within a factor 2 and with correlations in the 0.4-0.6 range. However some important aerosol features are missed by the simulation using

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standard European emission inventories, especially on August 5th over the Netherlands. Additional simulations including emissions and high-altitude transport of smoke from wildfires that occurred in Portugal indicate that these processes could dominate the signal (see Figure 2). This study also highlights the difficulties of comparing simulated and POLDER-derived AOTs due to large uncertainties in both cases. Observed AOT values are significantly lower than the simulated ones (30-50 %). The simultaneous comparison with the ground-based Sun photometer Aerosol Robotic Network (AERONET) measurements suggests, for the European sites considered here, an underestimation of POLDER-derived aerosol levels with a factor between 1 and 2. AERONET AOTs compare better with simulations (no particular bias) than POLDER AOTs do. This work is described in the article Hodzic et al. [2005], accepted for ACP. Perspectives Our perspectives for the next year are to * continue the work on inverse modelling of pollutant emissions using satellite data, * continue the evaluation of aerosol simulations with the CHIMERE model by using aerosol optical depths from various passive sounders, but also from active sounders (CALIPSO), and * prepare the use of free tropospheric ozone measurements from IASI in conjunction with regional air quality modelling.


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(a) POLDER-2 image (composite Red-Green-Bleu) over Europe on 5th August (courtesy of LOA and CNES/NASDA). (b) Advection of smoke particles coming from forest fires in Portugal, over northern Europe on 5th August as simulated by CHIMERE model between 3000 and 4000 meters. AERONET stations used in this study are also indicated (including 1: Avignon, 2: Dunkerque, 3: El Arenosillo, 4: Evora, 5: Fontainebleau, 6: Hamburg, 7: Helgoland, 8: IMC Oristano, 9: Ispra, 10: Lille, 11: Oostende, 12: Palaiseau, 13: Palencia, 14: Toulon and 15: Toulouse.

References (these are also recent publications from AT2 work) A. Hodzic, R. Vautard, H. Chepfer, P. Goloub, L. Menut, P. Chazette, J.L. Deuzé, A. Apituley, P. Couvert, Evolution of aerosol optical thickness over Europe during the August 2003 heat wave as seen from CHIMERE model simulations and Polder data, accepted for Atmposheric Physics and Chemistry. I.B Konovalov, M. Beekmann, R. Vautard, J.P. Burrows, A. Richter, H. NÜß, and N. Elansky, 2005, Comparison and evaluation of modelled and GOME measurement derived tropospheric NO2 Columns over Western and Eastern Europe, Atmposheric Physics and Chemistry, 5, 169-190. N. Blond, K.F. Boersma, H. Eskes, R. van der A, M. van Roozendaal, I. DE Smidt, G. Bergametti, R. Vautard (2005), Confrontation of SCHIAMACHY nitrogen dioxide observations, in-situ measurements, and outputs of an air quality model, J. Geophys. Res., in press .

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Derivation of Tropospheric Composition from Satellites using a 3-D CTM A contribution to ACCENT-TROPOSAT-2, Task Group 2 M.P. Chipperfield and L.N. Gunn Institute for Atmospheric Science, University of Leeds

The aim of this work is to derive quantitative estimates of species in the troposphere from GOME. The SLIMCAT 3-D CTM has been integrated from 1991 onwards using ECMWF ERA-40 winds. The model run assimilated HALOE CH4 (and other data) to constrain the longlived correlations. The model was sampled at the correct local time for GOME on each day (from 1995 on). In a first step these model profiles have been read into GOMETRAN and used to constrain the stratospheric part of the retrieval. Ongoing work is looking at the sensitivity of the retrieved columns to this stratospheric profile and to the assumed shape of the tropospheric profile. Future work will look at other aspects of the retrieval including clouds and aerosols.


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Validation and Further Development of an Interactively Coupled Climate-Chemistry Model for Detection, Attribution and Prediction of Changes in the UTLS A contribution to ACCENT-TROPOSAT-2, Task Group 2 Martin Dameris and Christian Kurz Institut für Physik der Atmosphäre, DLR-Oberpfaffenhofen, D – 82230 Wessling, Germany

Summary A new version of the coupled chemistry-climate model (CCM) ECHAM5/MECCA has been developed and employed for first scientific investigations. The model system can be used in a so-called “nudged” version, i.e. can be adjusted towards observations, e.g. ECMWF analyses. Therefore, it is suitable to support campaign activities, in particular compare observations of specific episodes (situations) with respective model results. Data collected during the two phases of the TROCCINOX campaign including those derived from SCIAMACHY/ENVISAT have been used to evaluate the model system and to demonstrate its abilities. Introduction During the TROCCINOX field campaign in Brazil (early 2004 and 2005) aircraft measurements of trace gases (e.g. NO, CO, ozone) in the upper (sub-) tropical troposphere were performed. These data were used to test different parameterisations of lightning produced nitrogen oxides emissions in the CCM ECHAM5/MECCA. Additionally, the model results have been compared with tropospheric NO2 columns retrieved from SCIAMACHY/ENVISAT. The satellite observations allow comparison within a more widespread area around the aircraft operation region. Scientific activities, results and highlights Figure 1 shows tropospheric NO2 columns derived from SCIAMACHY on March 3rd, 2004. On this day, very strong tropical thunderstorms were observed within the campaign region. Furthermore, satellite observations of flashes (see Figure 2) show a strong lightning activity over central South America. Though no overpass in the region of the aircraft measurement is available, a strong signal of enhanced tropospheric NO2 columns can be observed over Paraguay, which is very likely an effect of strong lightning NO emissions. Unlike on most previous days, the standard lightning parameterisation in the model (based on convective cloud top height; Price and Rind, 1992) was not able to reproduce this strong increase of thunderstorm activity. Using an alternative parameterisation [Grewe et al., 2001], based on convective updraft mass flux it was possible to simulate high flash rates within the region of interest. The resulting high NO mixing ratios as simulated by ECHAM5/MECCA are in very good accordance with the aircraft measurements. Figure 3 shows simulated tropospheric NO2 columns derived from ECHAM5/MECCA for forenoon of this day (March 3rd) using the new lightning NO parameterisation. The enhanced values over Paraguay can be seen clearly. Absolute values are lower in the model results as they represent mean values over a model grid box.

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Figure 1.


Tropospheric NO2 columns (in molec/cm2) as measured by SCIAMACHY/ENVISAT on March 3rd, 2004. Figure provided by Andreas Richter, IUP/IFE-UB.

LIS

Figure 2. Flash activity observed by the Lightning Imaging Sensor (LIS) on March 3rd, 2004. Figure obtained from the Global Hydrology Resource Centre (GHRC).


Figure 3.

Tropospheric NO2 columns (in molec/cm2) as simulated by ECHAM5/MECCA for March 3rd, 2004.

Figure 4. As Figure 1, but monthly mean for March 2004.

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Figure 5. As Figure 3, but monthly mean 2004.

Despite the good agreement between observed and modelled tropospheric NO2 columns on March 3rd, 2004, in general the SCIAMACHY data do not show strong signals from lightning NO emissions. This is because the ENVISAT overpass takes place at forenoon whereas lightning activity is strongest at late afternoon. This effect can be seen in monthly mean data. Figure 4 shows SCIAMACHY tropospheric NO2 columns for March 2004. Strong signals from anthropogenic emissions (e.g. Santiago, Buenos Aires, Sao Paulo) can be identified. These areas of enhanced tropospheric NO2 values are also present in the ECHAM5/MECCA model result, which is shown in Figure 5. Again, absolute numbers are lower as they are mean values over a model grid box. Neither in the SCIAMACHY nor in the ECHAM5/MECCA monthly mean data strong signals from lightning produced nitrogen oxides can be found. Though SCIAMACHY data are not the best available observations when investigating lightning NO emissions, they are valuable for model evaluation in regions with strong, time independent emissions like North America, Europe or South East Asia. Future outlook The inter-comparison of observations and model results derived from ECHAM5/MECCA will be continued, especially the new data of the 2005 campaign will be used.. Additionally, a free running (i.e. without using the nudging technique) version of the model system will be used to establish multi-year simulations to estimate long-term changes and trends. References Grewe, V., D. Brunner, M. Dameris, J.L. Grenfell, R. Hein, D. Shindell, and J. Staehelin, 2001. Origin and variability of upper tropospheric nitrogen oxides and ozone at northern mid-latitudes, Atmos. Environ.,35, 3421-3433. Price, C. and D. Rind, 1992. A simple lightning parameterisation for calculating global lightning distributions, J. Geophys. Res., 97, 9919-9933.


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Derivation of Tropospheric NO2 by Synergistic Use of Satellite Observations and Chemical Transport Modelling A contribution to ACCENT-TROPOSAT-2, Task Group 2 Thilo Erbertseder1, Julian Meyer-Arnek1, Pieter J. M.Valks2 and Frank Baier1 1

Deutsches Fernerkundungsdatenzentrum Institut für Methodik der Fernerkundung Deutsches Zentrum für Luft und Raumfahrt, DLR, Oberpfaffenhofen, Germany 2

Summary When deriving the NO2 content of the troposphere from satellite borne spectrometers using DOAS two key problems arise: the quantification of the stratospheric contribution and the calculation of the tropospheric air mass factor (hereafter: AMF). This work aims at combining satellite observations and chemical transport modelling to overcome these problems and to gain accurate tropospheric NO2 columns. To take into account the stratospheric (zonal) variability we derive the stratospheric NO2 column at the instrument’s overpass time by means of a stratospheric CTM (ROSE/DLR) driven by meteorological wind- and temperature fields. To avoid a bias, the resulting stratospheric NO2 analysis is scaled to “clean” observation conditions at a reference sector. The stratospheric NO2 slant column density (hereafter: SCD) is derived by applying a geometric AMF. The tropospheric SCD is then derived by simply subtracting the stratospheric SCD from the total SCD derived from SCIAMACHY observations. In order to consider the variability of tropospheric NO2 and to determine a profile dependent tropospheric AMF, the air quality model EURAD-CTM is applied. The tropospheric AMF is derived by weighting the height dependent air mass factors with the relative NO2 concentrations. This allows computing the air mass factor as a function the forecasted NO2 profile shape. In addition, surface albedo, temperature, cloud fraction and cloud top height are considered. Surface reflectivity and the presence of clouds are taken form satellite observations. In an accompanying study we investigated the stratospheric NO2 content using data assimilation and two different MIPAS observational data sets. Introduction The high spectral resolution of the space-borne spectrometers SCIAMACHY and GOME enables to derive column densities of NO2 [Burrows et al., 1998; Bovensmann et al., 1999]. The core of the retrieval is a DOAS (Differential Optical Absorption Spectroscopy) (Platt et al. 1994) fitting technique that involves a multi-linear regression of measured optical densities against a number of reference spectra. For the NO2 retrieval a fitting window between 425 and 450 nm is used. Several methods to derive the nitrogen dioxide content in the troposphere are found in the literature. Two key problems are the quantification of the stratospheric contribution and the calculation of the tropospheric air mass factor [Richter and Burrows, 2002]. Velders et al. [2001] apply the so-called tropospheric excess method [Richter and Burrows, 2002] and image processing technique [Leue et al., 2001] to GOME data in order to derive tropospheric NO2 columnar contents. The tropospheric excess method allows one to separate tropospheric and stratospheric contributions to the total NO2 content under the assumption of a relatively stable

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(both in time and space) NO2 distribution over the free Pacific Ocean. A constraint is that stratospheric (zonal) variability is neglected. To overcome this problem we derive the stratospheric NO2 column at the instruments overpass time by means of the stratospheric CTM DLR/ROSE [Thomas et al., 2003; Baier et al., 2005] driven by UK Met Office meteorological wind- and temperature fields [Swinbank and O’Neill, 1994]. Using a geometric air mass factor the stratospheric NO2 SCD can be extracted from the NO2 analyses. A similar approach was developed by Eskes et al. [2003] using a CTM including data assimilation. Further, we consider the variability of NO2 tropospheric air mass factors. Following Martin et al. [2002] we use a tropospheric chemical transport model to determine the air mass factors as a function of the NO2 profile shape. Our method uses EURAD analyses/forecasts to derive the NO2 profile shape for each satellite observation [e.g. Jakobs et al., 1995; Jakobs et al., 2002]. Accounting for cloudiness, cloud top height, surface albedo we derive the tropospheric air mass factor by weighting the height-dependent air mass factors by the NO2 profile shape. An error estimate for the resulting tropospheric NO2 column is determined [Boersma, 2004]. Scientific activities Derivation of tropospheric NO2 SCD In order to consider the variability of the stratosphere we derive the stratospheric NO2 column by means of the stratospheric CTM DLR/ROSE driven by UKMO meteorological wind- and temperature fields. The original version is described in detail in Rose and Brasseur (1989). For the currently used version significant improvements were recently made [Baier et al. 2005]. The model covers all relevant gas-phase stratospheric chemical processes [Sander et a.l, 2000]. Heterogeneous processes on polar-stratospheric clouds and on sulphuric aerosols are also included in the model. The model’s tropopause is defined by a potential vorticity of 1.6 pvu or a potential temperature of 380 K for the tropics. To account for the daily cycle of NO2 and the nitrogen family in general, the stratospheric analysis is calculated for exactly the overpass time of the satellite instrument. Using a geometric air mass factor for the stratosphere, the stratospheric NO2 SCD is extracted from the NO2 analyses. To avoid a bias, the analyses are scaled to “clean conditions” by means of a reference sector approach. In order to gain the tropospheric NO2 slant column the stratospheric slant column is subtracted from the DOAS total slant column retrieval. The latter is taken from the ESA standard products; currently SCIAMACHY Version 5.0x and GOME GDP Version 4.0. For further details on the total slant column retrieval the reader is referred to DLR [2002] and DLR et al. [2004]. The error is calculated from the standard deviation of the NO2 result and the corresponding uncertainty of the ROSE NO2 results of about 10 %. A possible negative bias due to instrumental features is not included in the error budget. The absolute value of the bias for the stratospheric part remains below 10%. A change of the models tropopause level from 1.6 to 3 pvu does not have a significant impact on calculated tropospheric NO2 columns. The simulations reveal a mean difference of about ± 2 × 1013 molecules cm-2 and an increase of tropospheric levels around 1 × 1013 molecules cm-2 in the area with highest NO2 values which is well below the uncertainty of the calculated tropospheric NO2 column.


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1015 molecules/cm² Figure 1.

Mean tropospheric NO2 SCD as derived from ENVISAT SCIAMACHY for April 2005. White colour indicates data gaps due to cloudiness (>20 %) or snow coverage.

Derivation of tropospheric NO2 vertical column The algorithm uses EURAD analyses/forecasts to derive the NO2 profile shape for each satellite observation. This information is applied to calculate a tropospheric air mass factor. Therefore, height dependent air mass factors are weighted by the NO2 concentrations:

AMF =

AMFi ci ci

AMFi denotes the height dependent air mass factor for level i and ci the NO2 concentration of level i. The height dependent air mass factors are taken from a pre-calculated sensor specific look-up-table. To gain the concentrations of a certain level the EURAD NO2 volume mixing ratios are integrated to partial column taken into account the temperature variability. In a final step, one can derive the tropospheric NO2 column using the tropospheric slant column and the air mass factor for all pixels with a cloud fraction lower than 20 %. Since even the smallest cloud fraction has an effect on the air mass factor, cloud properties need to be taken into account. The air mass factor calculation further considers surface albedo and surface height. The surface albedo is taken from the climatology of Koelemeijer et al. [2001].

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The resulting tropospheric NO2 vertical column does not depend on the NO2 column of the model analysis/forecast, but on the profile shape. While EURAD is best validated in the PBL, satellite observations are not very sensitive to the PBL. Since the observations have a good sensitivity in the upper troposphere, we expect the method to ideally combine both data sources. A main constraint of the method is, as for all approaches using solar backscatter measurements, the cloudiness.

Chemical NO2 analysis using MIPAS observations MIPAS observations are assimilated using a modified version of the chemistry-transport model ROSE/DLR [Baier et al., 2005] to derive consistent global chemical analysis of the stratosphere. Due to different retrieval schemes available MIPAS data sets differ considerably in quality and coverage. In this work we investigate the sensitivity of the data assimilation scheme to two different sample data sets with emphasis on NOx. ENVISAT/MIPAS baseline observations of H2O, O3, HNO3, CH4, N2O and NO2, covering October to November 2003, are considered. Sequential assimilation is performed using an optimum interpolation scheme with error propagation. It is shown that all assimilated model species benefit significantly from observations. Results are analyzed using observation minus first-guess error (OMF) statistics and are additionally compared to UARS/HALOE data. Optimized assimilation parameters are derived using X2 diagnostics. Two different MIPAS data products are examined: the ESA operational product [Carli et al., 2003] and the IMK (Institute for Meteorology Karlsruhe) scientific product [Stiller et al., 2002]. The two data sets differ in coverage and type of chemical species observed. Due to these differences, assimilation results differ also considerably. For example, regions with increased stratospheric H2O concentrations near the tropical tropopause are only present when IMK data is applied. While both data sets are found to be well suited for global assimilation experiments, to study chemistry and dynamics of the middle atmosphere, the IMK data set proves as the more reliable and consistent product. Improvements to model results without assimilation of MIPAS data were quantified by comparisons to HALOE observations. Results show an r.m.s. error reduction of up to 30 % for the assimilated species. The positive influence of data assimilation is also found in the analysis error which significantly decreases where observations are available. At the end of October and again in early November 2003 observed NOx concentrations increased rapidly in the mesosphere and upper stratosphere due to several solar proton events. This is both evident in MIPAS/IMK and MIPAS/ESA NO2 results in terms of increasing analysis errors. Nevertheless, assimilation of NOx species proves valuable, as shown by comparisons to HALOE [Baier et al., 2005]. Scientific results and highlights The method combines the strengths of space borne spectrometers like SCIMACHY or GOME and chemical transport modelling to gain accurate regional NO2 tropospheric columns for Europe. The retrieval takes into account both, stratospheric and tropospheric NO2 variability, cloud properties, surface height and albedo. The tropospheric AMF is derived considering height dependent AMFs weighted by the forecasted NO2 profile shape. Temperature dependency is taken into account, too. The resulting product is currently part of the GMES-PROMOTE service on air quality monitoring (http://wdc.dlr.de/data_products/SERVICES/SCIANRT/tropo_no2.html). In order to examine the stratospheric NO2 modelling we applied a sequential data assimilation scheme to derive an improved analysis of the chemical state from different MIPAS observational data sets for October and November 2003. Using the chemistry-transport model ROSE/DLR, results clearly improve in all cases considered when assimilating the MIPAS


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standard species H2O, O3, HNO3, CH4, N2O and NO2. Significant differences, however, due to different coverage and quality of input data (MIPAS/ESA and IMK) are evident. In order to strengthen the consistency of the analysis, results of an X2 diagnostics were employed to improve the initial background and representativeness errors. Due to strong scatter with altitude and latitude the corrections are found especially significant for MIPAS/ESA data. Improvements to model results without assimilation of MIPAS data were quantified by comparisons to HALOE observations. Results show an r.m.s. error reduction of up to 30% for the assimilated species. The positive influence of data assimilation is also found in the analysis error which significantly decreases where observations are available Future outlook Major tasks will be validation and refinement of the error estimate for the resulting tropospheric NO2 column. So far, two key problems of tropospheric NO2 retrieval are addressed: the quantification of the stratospheric contribution and the calculation of the tropospheric AMF. Two further major error sources are clouds and aerosols. Within the next project period we will also focus on clouds. The link to aerosol variability as analysed by EURAD [Schell et al., 2001] could be included in the future. The significant influence of aerosol type, aerosol optical thickness and vertical aerosol distribution on the AMF in DOAS sulphur dioxide retrievals was recently quantified by Thomas et al. (2005). References Baier, F; Erbertseder, T; Morgenstern, O; Bittner, M and Brasseur, G: Assimilation of MIPAS data into a threedimensional chemical-transport model, submitted to Q. J. R. Met. Soc, 2005 Boersma, K. F., H. J. Eskes, and E. J. Brinksma, Error Analysis for Tropospheric NO2 Retrieval from Space, J. Geophys. Res., 109, D04311, 2004. Bovensmann, H., J. P. Burrows, M. Buchwitz, J. Frerick, S. Noël, V. V. Rozanov, K. V. Chance, and A. P. H. Goede: SCIAMACHY: Mission objectives and measurement modes, J. Atmos. Sci., 56, 127-150, 1999 Burrows, J. P., Weber, M., Buchwitz, M., Rozanov, V., Ladstätter-Weißenmayer, A., Richter, A. Debeek, R., Hoogen, R., Bramstedt, K., Eichmann, K. -U., Eisinger, M., and Perner, D.: The Global Ozone Monitoring Experiment (GOME): Mission concept and first results, J. Atmos. Sciences, 56, 2, 151-175, 1998 Carli, B., Alpaslan, D., Carlotti, M., Castelli, E., Ceccherini, S., Dinelli, B. M.,Dudhia, A., Flaud, J. M., Hoepfner, M., Jay, V., Magnani, L., Oelhaf, H., Payne, V., Piccolo, C., Prosperi, M., Raspollini, P., Ridolfi, M., Remedios, J. and Spang, R., First results from MIPAS/ENVISAT with operational Level 2 code. Adv. Space Res., 33, 7, 1012-1019, doi:10.1016/S0273-1177(03)00584-2, 2003 DLR, BIRA, SAO and ESA: Algorithm Theoretical Basis Document for GOME Total Column Densities of Ozone and Nitrogen Dioxide UPAS/GDOAS: GDP 4.0, Doc.No.: ERSE-DTEX-EOPG-TN-04-0007, 2004 DLR: ENVISAT SCIAMACHY Level 1b to 2 Off-line Processing Input / Output Data Definition Doc.No.: ENVID-DLR-SCI-2200-4, 2002 Eskes, H. J. et al., GOME assimilated and validated Ozone and NO2 fields for Scientific Users and Model Validation, Final, European Commission, Fifth Framework Programme, Environment and Sustainable Development, 1998-2002, April 2003. Jakobs, H.J., H. Feldmann, H. Hass, M. Memmesheimer: The use of nested models for air pollution studies: an application of the EURAD model to a SANA episode. J. Appl. Meteor., 34, No. 6, 1301-1319, 1995. Jakobs, H.J., E. Friese, M. Memmesheimer, A. Ebel: A real-time forecast system for air pollution concentrations. Proc. EUROTRAC Symposium 2002, 2002. Koelemeijer, R. B. A., P. Stammes, J. W. Hovenier, and J. F. de Haan, Global distributions of effective cloud fraction and cloud top pressure derived from oxygen A band spectra measured by the Global Ozone Monitoring Experiment: comparison to ISCCP data, J. Geophys. Res., 107(D12), 4151, doi: 10.1029/2001JD000840, 2002. Leue, C., M. Wenig, T. Wagner, O. Klimm, U. Platt and B. Jaehne, Quantitative analysis of NOx emissions from GOME satellite image sequences, J. Geophys. Res., 106, 5493-5505, 2001.

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Martin, R.V., K.Chance, D. J. Jacob, T. P. Kurosu, R. J. D. Spurr, E. Bucsela, J. F. Gleason, P. I. Palmer, I. Bey, A. M. Fiore, Q. Li, R. M. Yantosca, and R. B. A. Koelemeijer, An improved Retrieval of Tropospheric Nitrogen Dioxide from GOME, , J. Geophys. Res., 107, D20, 4437, 10.1029/2001JD001027, 2002. Platt, U., Differential optical absorption spectroscopy (DOAS), Air monitoring by Spectroscopic Techniques (M. Sigrist, ed.), John Wiley & Sons, 1994, pp. 27–84. Richter, A., and J.P. Burrows, Retrieval of Tropospheric NO2 from GOME Measurements, Adv. Space Res. 29, 1673-1683, 2002 Rose, K. and Brasseur, G.: A three-dimensional model of chemically active trace species in the middle atmosphere during disturbed winter conditions. J. Geophys. Res. 94, 16 387–16 403, 1989 Sander, S. P., Ravishankara, A. R., Friedl, R. R., De-More, W. B., Golden, D. M., Kolb, C. E., Kurylo, M. J., Molina, M. J., Hampson, R. F., Huie, R. E. and Moortgat, G. K.: Chemical kinetics and photochemical data for use in stratospheric modelling. Eval. 13, JPL Publ., 00–3., 2000 Schell, B., I.J. Ackermann, H. Hass, F.S. Binkowski and A. Ebel: Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res., 106, D22, 28.275-28.293, 2001. Stiller, G. P., von Clarmann, T., Funke, B., Glatthor, N., Hase, F., Hpfner, M. and A. Linden, Sensitivity of trace gas abundances retrievals from infrared limb emission spectra to simplifying approximations in radiative transfer modelling. J.Quant. Spectr. & Rad. Transfer, 72, 249-280 2002 Swinbank, R. and A. O'Neill, A stratosphere-troposphere data assimilation system. Mon. Weather Rev., 122, 686702, 1994 Thomas, W; Baier, F; Erbertseder, T; Kästner, M: The Algeria severe weather event of November 1999 and its impact on ozone and NO2 distributions, Tellus B, 55, No. 5, pp. 993-1006, November 2003 Thomas, W., Erbertseder, T., Ruppert, T., van Roozendael, M., Verdebout, J., Balis, D., Meleti, C. and C. Zerefos, On the Retrieval of Volcanic Sulfur Dioxide Emissions from GOME Backscatter Measurements, J. Atm. Chem., 50 (DOI:10.1007/s10874-005-5079-5, 295-320, 2005 Velders, G. J. M., C. Granier, R. W. Portmann, K. Pfeilsticker, M. Wenig, T. Wagner, U. Platt, A. Richter, and J. P. Burrows, Global tropospheric NO2 column distributions: Comparing three-dimensional model calculations with GOME measurements, J. Geophys. Res., 106, 12,643-12,660, 2001.

Recent publications from AT2 work Baier, F; Erbertseder, T; Morgenstern, O; Bittner, M and Brasseur, G: Assimilation of MIPAS data into a threedimensional chemical-transport model, submitted to Q. J. R. Met. Soc, (2005) Thomas, W., Erbertseder, T., Ruppert, T., van Roozendael, M., Verdebout, J., Balis, D., Meleti, C. and C. Zerefos, On the Retrieval of Volcanic Sulfur Dioxide Emissions from GOME Backscatter Measurements, J. Atmos. Chem., 50 (DOI:10.1007/s10874-005-5079-5) (2005) 295-320


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4-Dimensional Variational Assimilation of Satellite Data into a Chemistry Transport Model A contribution to ACCENT-TROPOSAT-2, Task Group 2 Hendrik Elbern and Achim Strunk Rhenish Institute for Environmental Research at the University of Cologne (RIU) Aachener Strasse 201-209, 50931 Köln, Germany

Introduction While satellite data of chemical constituents are mainly attributed to height levels aloft of the tropopause, first useful retrievals of a very limited number of species are meanwhile available for the troposphere. Likewise, variational assimilation of satellite data in the troposphere is also a novel approach. Presented here are assimilation results of ozone profile data and NO2 tropospheric columns. Space time data assimilation algorithms aim to upgrade level 2 satellite retrievals, scattered in space and time to level 3 products, giving a comprehensive and continuous picture of the troposphere, commonly presented synoptically on a regular grid at the user´s disposal. As they combine models with satellite data, this approach is designed to provide key contributions to the following items of Task Group 2 *

development of methods for using satellite data from the troposphere as part of model validation strategy; * use the combination of model results, satellite observations, ground based and airborne measurements in a synergistic way to improve our knowledge about individual tropospheric processes, such as: source attribution and impact assessment of gaseous and particulate pollutants; cloud occurrence and the hydrological cycle; and * use model results to help bridge the gap between point measurements and the satellite view footprint for evaluating satellite retrievals. The 4D-var method is one of the very few techniques to attain a real synergistic use of observation sets, combined from heterogeneous instrumentation. This work will build on present experiences with tropospheric NO2 columns and artificial neuronal network based ozone profile retrievals. The scheduled work includes combinations of satellite data with in situ observations, identifying the important boundary layer portion of the tropospheric columns. While it could be demonstrated that surface data assimilation is beneficial for the predictive skill, satellite data assimilation will be evaluated for an added performance increment of air quality forecasts. Scientific activities The basic 4D-var data assimilation set-up, on which the satellite extensions rest, are described in Elbern and Schmidt, (1999 and 2001) Two satellite data sources were considered in the recent year: 1. assimilation of neural network retrieved ozone profiles 2. tropospheric NO2 column assimilation, and 3. first attempts to assimilate aerosol data obtained from space. Tropospheric ozone profiles are difficult to obtain, with two to three possible data points. Ozone retrievals gained from GOME data with the Müller et al. (2003) NNORSY neural network based method were taken for assimilation, where profiles with one kilometre vertical

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resolution are given by interpolation. From a technical viewpoint of data assimilation, the profile assimilation of ozone requests special attention to the definition of the radii of influences, to be assigned to the different height levels in the tropopause. For optimisation, a suite of experiments have been conducted to achieve a proper radius. The assimilation of NO2 tropospheric columns required a special construction of the observation operator, mapping from the scalar column information to the grid column of the model. The relevant weightings have been introduced and constructed. Presently, a transition to the averaging kernel approach is under way. Aerosol data assimilation with observations from space is a new activity which is based on a collaboration in the EC’s 5th framework program ASSET and AERO-SAM, a collaboration with the German remote sensing data centre (DFD-DLR). Scientific results and highlights Assimilation of tropospheric ozone profiles is especially useful to obtain proper lateral inflow background concentrations. Figure 1 displays profiles for six grid points, where the large error margins reflect the difficulty of tropospheric ozone retrievals. Despite these uncertainties, the EURAD model’s first guess run exhibits strong bias in the upper troposphere, without significant increments to account for the enhanced stratospheric concentration levels. By means of the assimilation procedure, initial values several hours prior to the late morning satellite overpasses could be found, where the bias is removed and the assimilation result demonstrates an appreciable skill. The step function of the model profiles is due to the comparably low upper tropospheric resolution. The conceptual flexibility of the variational technique must be invoked, where data are ingested, which do not have a direct correspondence to a model parameter. This is for example the case where not profiles but tropospheric column densities are given, as for NO2 in terms of molecules per cm2. The model correspondence (operator H in the cost function) can then be calculated, along with its adjoint, and included in the algorithm. Observing some technical details of preconditioning (see Elbern and Schmidt, 2001), the optimisation procedure of the assimilation adapts the model column to the retrieval in consistency with the model. Figure 2 gives an example of the assimilation of NO2 tropospheric columns obtained from IFE, University Bremen. Spain and Tunisia are areas with discrepancies between retrievals and model, prior to assimilation. The final analysis then finds the differences largely removed. The graphics also exhibit that real value can be expected from data void or poor regions, which, in most cases, coincide with rural areas without a chance of getting dense in situ network. The related radii of influence, however, can be expected to be equal or larger than the GOME footprint. The example presented in Figure 2 features a cyclone over the British/Irish isles, which cover the uplift of central European polluted air. This is a systematic co-occurrence with synoptic scale clouds, which warns to use cloud slicing techniques without further individual study of the case.


Figure 1.

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Assimilation of tropospheric ozone profile data as retrieved by the NNORSY algorithm (Müller et al., 2003) for 3.8.1998 about 10-11 local time over 6 European grid-points. + NNORSY ozone retrievals with error bars; black dash-dotted line: first guess (no assimilation); blue line: analysis after assimilation. The step function reflects the applied vertical resolution of the model.

Future outlook Future work will step towards operationalisation in a pre-routine context, to serve GMES needs as expressed in the objectives of the related European projects PROMOTE and GEMS. The forecast error covariance matrix design is a perennial challenge in this respect. A further item is the start to develop algorithms for assimilation of tropospheric aerosol data, retrieved from space by AATSR and SCIAMACHY.

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Figure 2. Assimilation of NO2 tropospheric columns at 3.8.1997 (top panel). Simulation without assimilation for 10:30 UTC (top right panel), and post assimilation forecast for the same time and started at 06:00 UTC, Units are number of molecules/cm2

References Elbern, H., H. Schmidt, Ozone episode analysis by four-dimensional variational chemistry data assimilation, J. Geophys. Res. 106, D4, 3569-3590, 2001. Müller, M. D., A. K. Kaifel, M. Weber, S. Tellmann, J. P. Burrows, and D.Loyola (2003), Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)), J. Geophys. Res., 108(D16), 4497, doi:10.1029/2002JD002784. Richter, A., and J.P. Burrows, Tropospheric NO2 from GOME measurements, Adv. Space Res., 2001

Recent publications from AT2 work Elbern, H., A. Strunk; Tracer Analyses by Mesoscale Field Experiments with 4D-var Data Assimilation, AFO2000 final publication, editor GSF,in press, 2004.


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Scientific Interpretation of SCIAMACHY CO, CO2 and CH4 Measurements A contribution to ACCENT-TROPOSAT-2, Task Group 2 S. Houweling1,2, M. Krol1, J.-F. Meirink3, I. Aben1 1

SRON National Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands 2 IMAU Institute for Marine and Atmospheric research Utrecht, Princetonplein 5, 3584 CC, the Netherlands 3 KNMI Royal Netherlands Meteorology Institute, PO Box 201, 3730 AE De Bilt, the Netherlands

Summary Of the three gases that are the subjects of investigation in this project currently only the SCIAMACHY retrieval of CH4 is of sufficient quality to allow scientific interpretation. In this report results are presented for CH4, showing realistic large scale and regional scale variations that largely agree with results of atmospheric modelling. Of particular scientific interest are discrepancies between model and measurements in the tropics hinting at a significantly underestimated source. The next step is to quantify the sources and sinks of CH4 on the basis of the SCIAMACHY measurements using inverse modelling and to verify the inferred estimates using independent measurements. For this purpose a 4D-VAR system has been developed and thoroughly tested using synthetic simulations. With respect to CO2 and CO most efforts are still focused on validation and improvement of retrieval methods. Recent CO2 results emphasize that a correction for aerosol scattering is needed to meet the accuracy requirements. Introduction The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument, which was launched on board the ENVISAT satellite on March 1 2002, measures in the near-infrared (NIR) global concentrations distributions of a.o. CH4, CO2 and CO. These gases play an important role in tropospheric chemistry and climate change. A good knowledge of the global distributions of these gases is important to understand the present day chemical composition and to predict changes in the near future. The aim of this project is to investigate what can be learned from the SCIAMACHY measurements. This will be done in a comparison with atmospheric transport models to identify shortcomings in our understanding of source and sink processes, which will be quantified using the inverse modelling technique. In a later stage of the project the aim is to combine the constraints that come from atmospheric remote sensing, with that of surface measurements networks and data from other satellites that measure surface properties such as FPAR (photosynthetic activity), fire counts and burned area (biomass burning), as well as emerging techniques to measure wetland area. In this early stage of the project, however, the data of the NIR channels of SCIAMACHY are still under validation (see also Gloudemans et al, this report). Because of this, part the activities within this project have shifted towards validation support. Besides this, inverse modelling software has been developed and tested using synthetic (model-calculated) data. Recently, however, the CH4 retrieval has improved substantially reaching a level of accuracy that allowed a preliminary interpretation. This report is organized as follows: Firstly, we will show results of verification of CO2, and the implications for CO2 retrieval. Secondly, we will briefly discuss the recent advances in

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measuring CH4. Thirdly, an overview is given of algorithm development for inverse modelling, followed by conclusions and planned activities for the next period. Results Interpretation of CO2 measurements SCIAMACHY CO2 retrievals show a much larger variation than anticipated from models. Retrieval results show a variability up to ~15 %, while the variability of model-calculated total columns of CO2 remains within only 2.5 %. Regions of enhanced variability are typically located over deserts. Further investigation has shown that this is explained by perturbation of the optical path by aerosol scattering. Figure 1 shows a clear relationship between SCIAMACHY-retrieved CO2 and dust aerosols as measured by TOMS. Monthly averaged colocations of SCIAMACHY and TOMS are plotted for the months July and October, which more or less span the seasonal variation in dust storm activity over the Sahara. Radiative transfer calculations that account for multiple scattering in the dust aerosol layer can explain the size and the sign of the observed errors. Previous studies [O’Brien et al., 2002; Dufour et al., 2003] mainly highlighted shortening of the light path due to aerosols by elevation of the average level at which sun-light is reflected towards the satellite. Our multiple scattering calculations, however, show that if the surface albedo is sufficiently high, the influence of aerosol-scattered photons that have reached the surface dominates, on average increasing the optical path.

Figure 1.

Colocated measurements of SCIAMACHY CO2 (a and c) and TOMS AI (b and d) for July 2003(a and b) and October 2003 (c and d)

Extrapolation to other parts of the world (see Figure 2) shows that the influence of aerosols on the CO2 retrieval is by far largest over the Sahara. However, significant errors of around 1 % are predicted for many other parts of the world. Note that the regime of CO2 column enhancement (= path-length extension) is the dominant regime over the continents. Only when the surface albedo decreases towards 0.1 or lower a reduction of path-length is found. Generally, these results indicate that aerosol correction is needed to reach the accuracy that is required for a meaningful interpretation.


Figure 2.

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Model calculated annually averaged error in retrieved continental CO2 concentrations caused by aerosol scattering

Interpretation of CH4 measurements The initial approach was to retrieve CH4 from SCIAMACHY channel 8. Since this channel suffers from complications with the calibration, CH4 retrieval from channel 6 has become an attractive alternative. The approach by Frankenberg et al. (2005) was to use CH4 lines in channel 6 and to divide the retrieved CH4 column by the CO2 column as retrieved from a nearby spectral window. The advantage of this approach is that it eliminates systematic errors that are common to CO2 and CH4. The disadvantage is that CH4 columns depend on CO2. However, the variation in the CH4 column is somewhat larger than that of CO2 allowing a large part of the variability in CH4/CO2 to be attributed to CH4. The right panel of Figure 3 shows the global distribution of CH4 for August to November as retrieved using this approach. Note that the coverage is extended to parts of the ocean despite the fact that the ocean albedo in the NIR is too low to obtain a sufficient signal to noise ratio. This is explained by the fact that accurate retrieval of CH4/CO2 is still feasible over scenes with low elevation stratiform cloud cover. The right panel of Figure 3 shows the corresponding simulations of CH4 using the atmospheric Tracer Model 3 (TM3). The comparison highlights a striking resemblance between model and measurements at large scales (North-South gradient) as well as regional scales (e.g. over South East Asia). The most pronounced differences are found in the tropics, where the model significantly underestimates the measurements. It has been hypothesized that sources over tropical rainforests have been substantially underestimated. Tropical wetlands seem to be the most plausible candidate, although the period of August to November coincides with the dry season which is in fact the most difficult time of year to explain highly underestimated CH4 emissions. Further research is needed to resolve this issue. Preparation for inverse modelling Source and sink inversions on the basis of satellite data pose methodological requirements that differ from conventional inversions using measurements from ground-based networks of monitoring stations. Of particular importance is the large number of data and the scale and resolution at which sources can be resolved, tremendously increasing the size of the problem. This calls for highly computationally efficient methods. 4D-VAR is a widely used method for solving large data assimilations problems that is now also being applied to source and sink inversions. First synthetic simulations using this method confirm that this is a promising approach.

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Figure 3.


Comparison of SCIAMACHY-retrieved and TM3 model calculated column-averaged methane concentrations for August to November 2003

The main disadvantage is that it is computationally expensive to calculate the uncertainties of the inversion-derived source and sink estimates. However, sensitivity tests can still provide an indication of the robustness of the estimates. Alternative methods that might overcome this problem are being tested. Meanwhile the 4D-VAR method will be used for the first CH4 inversions using SCIAMACHY data. Conclusions and outlook The scientific interpretation of SCIAMACHY has experienced a delayed start because of several factors complicating the retrieval. CO2 retrieval will require further adjustments in the approach, in particular, to account for aerosol scattering. For CO, like for CO2, retrieval issues will need to be solved first, although at the moment the prospects are somewhat better than for CO2. Of course, CO has the advantage that the atmospheric concentrations gradients are much larger than those of CO2. A promising product exists for CH4, and first tentative conclusions can be drawn. Further interpretation of SCIAMACHY CH4 data will receive high priority. References Dufour, E. and F.-M. Breon, 2003, Spaceborne estimate of atmospheric CO2 column using the differential absorption method: Error analysis, Appl. Optics, 42, 3595-3609. Frankenberg, C. et al., 2005, Assessing methane emissions from global space borne observations, Science. O'Brien, D.~M. and P.~J. Rayner, 2002, Global observations of the carbon budget, 2. CO2 column from differential absorption of reflected sunlight in the 1.61 $\mu$m band of CO2, J. Geophys. Res., 107, doi:10.1029/2001JD000617.

Recent publications from AT2 work Frankenberg, C. et al., 2005, Assessing methane emissions from global space borne observations, Science. Houweling, S., W. Hartmann, I. Aben, H. Schrijver, J. Skidmore, G.-J. Roelofs, and F.-M. Breon, 2005, Evidence of systematic errors in SCIAMACHY-observed CO2 due to aerosols, Atmos. Chem. Phys. Disc., 5, 3313-3340. Meirink, J.F., Eskes, H.J. and Goede, A.P.H., Observing System Simulation Experiments for inverse modelling of methane emissions from SCIAMACHY observations, Atmos. Chem. Phys. Disc. (submitted) 2005


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Assimilating Remote Sensing Derived Air Quality Parameters into a CTM: A Study of Data Suitability and Assimilation Techniques A contribution to ACCENT-TROPOSAT-2, Task Group 2 Martin Hvidberg, and Jørgen Brandt Research Inst., Dept. of Atmospheric Environment, Ministry of Environment. PO Box 358, DK-4000 Roskilde, Denmark

Summary The National Environmental Research Institute (NERI) Department of Atmospheric Environment (ATMI) runs and continuously develops an air pollution forecast system: Thor. Thor includes a Chemical Transport Model (CTM): The Danish Eulerian Hemispheric Model (DEHM). To further improve the prognostic capabilities and accuracy of the Thor system, work is in preparation to implement data assimilation of satellite derived air quality parameters into the Danish Eulerian Hemispheric Model. Introduction The objectives of this work are to investigate the availability of remote sensing derived air quality parameters, to implement data assimilation of these into the CTM and to research which data and which data assimilation techniques better benefits the performance of the forecasts Scientific activities Considerations on which parameters would be best to include in the CTM. NO2, CO, O3 and PM seem to be those most relevant to the air pollution forecast module. CO2 and H2O would be good candidates as control parameters. Further data requirements have been prioritized. 4-D (X,Y,Z,t) data would be optimal, though 3D (X,Y,t) total column data seems to be considerably more available. Priority will be given to collecting many data points, in time and space. This is believed to strengthen the statistical analysis of the performance gains of the forecast model. Secondary priority is given to collecting data sets representing high temporal data density, optimally samples every 6 hours, in 3-D, or less realistic 4-D. Data assimilation techniques and their possibilities have been considered. There have been made experimental implementations for data assimilation of information from surface measuring station, so far focus have been on Optimal Interpolation (OI) and Kalman Filtering. Scientific results and highlights The project in question has been defined as purely a research project, meaning that it is not part of the operational forecast produced every 6 hours. This ensures that data delivered to and used by this project are not included in the operational data base, but is kept on the research platform only. By this specification of the project we can easily meet the data destitution and confidentiality specifications in ACCENT, and hopefully then get access to more sample data. Future outlook Next tasks are closely associated with the investigations of availability of data. We will try to collect sample data, representing the air quality parameters mentioned above. Priority will be, firstly, NO2, CO and O3 and secondly PM, CO2 and H2O. It is the intention to also investigate the data assimilation techniques 3D-Var and 4D-Var.

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Validation of GEM-Chemistry Modelling and Data Assimilation System: A High Resolution Study A contribution to ACCENT-TROPOSAT-2, Task Group 2 Jacek W. Kaminski Centre for Earth and Space Science York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3 www.MAQNet.ca

Summary The Multiscale Air Quality Network (MAQNet) is funded by the Canadian Foundation for Climate and Atmospheric Science to carry out fundamental research in the area of air quality and tropospheric chemistry. The main focus of this research is the development and validation of a chemical weather and data assimilation system. Introduction The primary objective of this study is to perform model validation and assimilation of satellite data using a GEM chemistry modelling system developed by MAQNet and Meteorological Service of Canada. The chemical weather model is based on the Canadian operational weather prediction model, the Global Environmental Multiscale (GEM) model. The meteorological 4-D Var has been operational since March 2005. It includes: 1) a simplified planetary boundary layer; 2) a Kain-Fritsch moist convective parameterization; and, 3) a sub-gridscale orographic gravity wave drag. We are currently developing the TLM and adjoint models for the stratospheric and tropospheric chemistry. Also, we have started preparatory task to assimilate ozone and NO2 profiles from OSIRIS (ODIN satellite) and ACE (SCISAT-1) instruments.


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Synergistic Use of Satellite Data, Ground-based Observations, Back-trajectory Analysis and Global CTM Results for Studies of Tropospheric Trace Gases and Aerosols over the Mediterranean A contribution to ACCENT-TROPOSAT-2, Task Group 2 Maria Kanakidou, Eirini Dermitzaki, Maria Sfakianaki, Kostas Tsigaridis, Nikos Mihalopoulos, Giorgos Kouvarakis and Georgia Tzirita Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece and

John Burrows, Annette Ladstätter-Weißenmeyer, Andreas Richter, Folkard Wittrock and Wolfang von Hoyningen-Huene Institute of Environmental Physics and Remote Sensing, IUP, University of Bremen, Germany

Summary We have investigated the spring O3 variability over Crete in the East Mediterranean based on GOME observations as well as ground based measurements and chemical box model simulations. This variability is linked to the general circulation of air masses and particularly the location of the subtropical front. The tropospheric column of ozone in the area is estimated to vary between 20 and 60 DU. Enhancement of the tropospheric O3 columns of 4-12 DU have been observed and can not be attributed to the built up of O3 during the transport of air mass in the area. Large amounts of tropospheric O3 should be produced before being transported towards the Mediterranean region. Finally GOME is shown to be able to detect significant enhancements in NO2 tropospheric columns during summer 2000 when intensive fire events affect the Mediterranean. This information is not apparent when the monthly composite tropospheric NO2 columns are studied. Objectives The evaluation of the built up and climate impact of ozone and aerosols over the Mediterranean is the overall focus of the work. For this purpose semi-continuous ground based observations of ozone, its precursor molecules and aerosols are combined with satellite data, the output of a chemical box model and a three dimensional global chemistry/transport model. The project aims the synergistic use of satellite data, ground based observations over the Mediterranean, back trajectory analysis, box and global 3-D chemistry transport model results to evaluate the impact of distinct pollution sources (like urban pollution, biomass burning fires, etc.) on oxidant and aerosol levels in the Mediterranean, the consequences on regional climate and the seasonal and inter-annual variations. Priority is given to the use of the European satellite products like GOME (Global Ozone Monitoring Experiment) and SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY). The ground based observations consist of gas phase and aerosol data as well as auxiliary measurements on Crete. The chemical box model and 3-d chemistry transport model are those developed at the ECPL-UoC in the frame of international collaborations.

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The specific objectives for this period were to investigate o the major reasons of variations of tropospheric O3 in the eastern Mediterranean during spring (with focus on the year 1999); and o the impact of European pollution on NO2 and HCHO tropospheric columns in the area. And to initiate a study of the impact of the large fire events over Greece on NO2 and HCHO concentrations in July 2000, together with the aerosol occurrence over the eastern Mediterranean. Introduction The high emissions of nitrogen oxides, carbon monoxide as well as of non methane volatile organic compounds over central Europe and the Balkans influence Mediterranean when air masses originate from N/NW Europe. Enhancements of nitrogen dioxide (NO2) and formaldehyde (HCHO) caused by intense photochemical activity can be observed by the satellite based Global Ozone Monitoring Experiment (GOME). In addition to the transport of pollutants, intensive biogenic emissions of organics from the areas surrounding the Mediterranean Sea are mixed with anthropogenic emissions of nitrogen oxides and organics under the intensive photochemistry conditions that prevail over the area, providing an environment that strongly favours the regional built up of pollutants. When the Mediterranean region is influenced by air masses from the south, tropospheric NO2 and HCHO column amounts are expected to be very low and near the detection limit of satellite instruments like GOME. Scientific results and highlights 1. Spring variability of O3 During spring and fall frequent changes in the wind sectors are occurring in the East Mediterranean with a significant contribution of south winds to the air mass origin over the area. This first year of the study, we have evaluated the O3 variability in the East Mediterranean in spring and the contribution of various emission sources leading to the increase of tropospheric NO2 and HCHO columns from GOME compared to clean air situations in the eastern Mediterranean. In May 1999, the Photochemical Activity and Solar Ultraviolet Radiation (PAUR II) experiment took place over the S.E. Mediterranean [Zerefos et al., 2002] with ground based measurements by Systeme d’Analyse par Observation Zenithale (SAOZ) [Goutail et al. 1999] as well as ozone profile measurements by ozone-sondes [Thompson et al. 2003] and LIDAR [Simeonov et al., 1997] and surface O3 observations [Kouvarakis et al., 2001].


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O3 – stratospheric column evaluation and variability The observed total O3 columns vary between 260 and 400 DU correlated with the changes in air circulation patterns. Indeed, such variation in total O3 columns over areas like the one studied here that located at the boundary between the subtropics and mid-latitudes has been attributed to changes in the location of the subtropical front [Hudson et al., 2003]. Thus, tropical regime alters with mid-latitude regime over Crete during May 1999. Since as documented by Hudson et al. [2003] each regime has a unique tropopause height, we are expecting that the tropopause height over the area is also changing. This is indeed happening and the tropopause height calculated with PV3.5 and depicted in Figure 1 is anti-correlated with the total vertical column of ozone. 400

Correlation VC O3 - TP (PV=3.5) 380 GOME SAOZ am SAOZ pm TOMS

VC O3 [DU]

360 340 320 300 280 260 10

12

14

16

18

20

Tropopause Height [km]

Figure 1. Anti-correlation between the total column of O3 over Crete as derived by the satellite and ground based instruments and the tropopause height as calculated by PV3.5.

Variability of tropospheric ozone content and chemical built up of O3 To evaluate the tropospheric O3 column, the LIDAR [Simeonov et al., 1999] and ozone-sonde [Thompson et al., 2003] measurements over Crete have been used and in addition the Tropospheric Excess Method (TEM) has been applied to the GOME data. Thus, the tropospheric column of ozone in the area is estimated to vary between 20 and 60 DU. The LIDAR measurements [Simeonov et al., 1999; Calpini et al., 1997] carried out during the PAUR II experiment in early morning and late afternoon allowed the estimation of an observed build-up of O3, most probably photochemical, that varies between 0.4 DU h-1 (on May 15th, 1999) and 0.9 DU h-1 (May 14th, 1999). This observed built up of O3 has been confronted to the observations by GOME. For this purpose, the evolution of the air composition as observed by GOME has been followed along an air mass trajectory. The selection of the air mass trajectory was done among all 3dimensional 5-day back-trajectories arriving over Crete at 2, 6 and 8 km and calculated by the HYSPLIT model [Hybrid Single-Particle Lagrangian Integrated Trajectory Model; Draxler and Hess, 1998]. In the case that the Mediterranean region is influenced by air masses from central Europe, the Balkans and Black Sea, pollution leads to an increase of almost a factor of 1.5 in the tropospheric NO2 column and a factor of 1.2 in the tropospheric HCHO column and this increase is detected by GOME. A chemical box model has been used to simulate O3 photochemical production in the troposphere [Poisson et al., 2001; Vrekoussis et al., 2004]. The model has been forced to reproduce the observed by GOME evolution of NO2 and HCHO columns and computes the net photochemical O3 production in the troposphere over the period of the studied events (3 to 5

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days). The thus calculated O3 production varies between 1 and 2 DU per day. However this amount can not explain the earlier mentioned observed enhancement of tropospheric O3 columns of 4-12 DU. Therefore, large amounts of tropospheric O3 should be produced before being transported towards the Mediterranean region. 2. Impact of summer fires on NOx and O3 levels Every summer important areas of forests and grassland are burning in the Mediterranean and emit into the atmosphere large amounts of precursors of tropospheric ozone (O3) and aerosols. Such changes in the atmospheric composition are studied by coupling satellite observations with ground based measurements and atmospheric chemistry/transport simulations for July 2000 when large areas burnt over Greece (Fig. 2).

Figure 2.

METEOSAT images showing fires over Greece in July 2000: left panel the Samos fire (8th July), right panel the Peloponissons fire (13th July).

GOME NO2 observations show a significant built up of NO2 tropospheric column over north and central Greece during the whole month of July 2000 as depicted in Fig. 3 where the enhancement of NO2 from the fires over Peloponissos is clearly shown (left panel). Dilution of NO2 high concentrations is observed after the extinction of major fires (right panel). Note the low NO2 columns over Crete, southern Greece. These features are not apparent when the monthly composite tropospheric NO2 columns are studied.

Figure 3. NO2 columns observed over Greece by GOME – composite picture for July 13th to 16th (fires over Peloponissos and Samos), and for July 18th to 28th, 2000.


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During this intensive fire period in July 2000, tropospheric O3 columns over Crete vary between 25 for clean air masses and 53 DU for polluted situations depending on the air mass origin, the lowest values calculated for W or S air masses origins on the 16th and the 30th of July respectively. These differences are significant even when considering the large uncertainties involved in the calculations (smaller than 6 DU) and will be further analysed during the next year of the project in conjunction with chemistry/transport modelling. Future outlook For the next year we plan to continue our study on O3, HCHO and NO2 analysis for the summer 2000 that was an intensive summer in terms of forest fires over Greece to define the fingerprint of these fires and their environmental impact. For this, we will examine satellite observations of NO2, HCHO and CO in relationship to CTM model predictions, box model calculations and in-situ observations. In addition we will initiate the inter-annual variability studies for trace gases like NO2 and HCHO and for aerosols over the Mediterranean. We will also continue the comparison of aerosol data from different satellite instruments and different resolution over the European area (latitude. 30 °S to 60 °N; long. 10 °W to 44 °E), focusing over eastern Mediterranean Sea and in summer 2003. For most of these projects, we will be making increasing use of the satellite based GOME and SCIAMACHY data to analyse the tropospheric amount of the trace gases O3 and its precursors NO2 and HCHO as well as CO from MOPITT measurements and aerosols from SEAWIFS and MERIS. Acknowledgments This work has been supported by a German-Greek bilateral collaboration project. We thank Dr. A. M. Thompson for communication of the raw ozone-sonde data. We acknowledge the use of the available on the web HYSPLIT 4 model (http://www.arl.noaa.gov/ready/ hysplit4.html). M. Sfakianaki acknowledges support by AT2 on a student exchange project and the Heraklitos PhD grant. References Zerefos et al., JGR, 107, 8134, doi:10.1029/2000JD000134, 2002 Goutail et al. J. Atmos. Chem., 32, 1-34, 1999 Thompson et al. J. Geophys. Res., 108, 8238, doi: 10.1029/2001JD000967, 2003 Simeonov et al., Proc., 19th ILRC, NASA editor, 399-402, 1998 Calpini et al., Chimia, 51, 700-704, 1997 Kouvarakis et al., J. Geophys.Res., 107, 8137, 10.1029/ 2000JD000081, 2002 Hudson et al., J. Atmos. Science, 60, 1669-1677, 2003 Poisson et al., Chemosphere, Global Change Science, 3, 353-366, 2001 Vrekoussis et al., Atmos. Chem. Phys., 4, 169–182, 2004

Recent publications from AT2 work A. Ladstätter-Weißenmayer, M. Kanakidou, J. P. Burrows, G. Tzirita, Pollution events over the Mediterranean region documented by GOME (Global Ozone Monitoring Experiment), ground based and sonde measurements and backtrajectory calculations, in Proc. Quadrennial Ozone Symp., Kos, 1-8 June, 2004. E.V. Dermitzaki, M. Kanakidou, A. Ladstaetter-Weissenmayer, J.P. Burrows, Biomass burning impact on ozone budget over the East Mediterranean during summer 2000, in Proc. Quadrennial Ozone Symp., Kos, 1-8 June, 2004. A. Ladstätter-Weißenmayer, M. Kanakidou, E. V. Dermitzaki, A. Richter, J. P. Burrows and G. Tzirita, Pollution events over the East Mediterranean: Synergistic use of GOME, ground based and sonde observations and models, paper in preparation, 2005.

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Evaluation of the Hydrological Cycle in Off and Online Models using Satellite Measurements A contribution to ACCENT-TROPOSAT-2, Task Group 2 Rüdiger Lang1, Ahilleas N. Maurellis2, Jürgen Fischer3, and Mark G. Lawrence1 1

Max-Planck-Institute for Chemistry, Department of Airchemistry, NWG Joh. J. Becherweg 27, 55128 Mainz, Germany 2 SRON National Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands 3 Institute for Space Research, Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany

Summary In this project a multi-sensor based 3-D water-vapour product for global model evaluation will be developed using satellite remote sensing data from instruments situated on ESA's ERS-2 and ENVISAT and from the NASA Terra and Aqua satellite platforms. The main instruments on these platforms capable of providing water vapour (WV) over various surface types and for different parts of the atmosphere are GOME (ERS-2, Metop), SCIAMACHY, MERIS (all ENVISAT), MWR (ERS-2 and ENVISAT) and AMSU among others (Terra and Aqua). Introduction The objectives of the first year of the project have been the evaluation of model output employing GOME data and the identification of data gaps within the ERS-2 and ENVISAT period, spatially and temporally, with respect to what is required for the model evaluation studies. Following the introduction of the first project product – the GOME Water vapour climatology version 1.0 (GWCv1) 1995 to 2003 – the request for data from GWCv1 became soon quite substantial. As a shift of priorities in this first year of the project we therefore decided to establish a homepage where users can look at the data and download specific data fields relevant to their needs. This task was originally planned for year 3 of the project after a first merged data set (including other sources than GOME) has been provided to the users community. The now established project home page (http://www.mpch-mainz.mpg.de/~saphire/gome_igam/) provides GWCv1 data, i.e. GOME data only as well as a merged product of GOME and SSM/I data, relevant for the tropical regions. Furthermore we identified the high latitudes and polar regions as the most important data gaps of the current data base. We therefore started a project together with the Institute of Environmental Physics at the University of Bremen and the group of Georg Heygster to exploit a new AMSU retrieval algorithm providing total water vapour column (WVC) data in exactly these regions with extremely low WVC. We consider this as a higher priority task than implementing high resolution data from MWR or MERIS or extending the data set by employing SCIAMACHY and GOME-2 data at the current state. However, the latter implementations will be the subject of the forthcoming years of the project. Most effort during the first year of the project has been put in the validation of GWCv1 using a wide set of evaluation data from independent observations. One of the most important databases used is the Max-Planck-Institute for Chemistry Operational Sondes data set (MCOS), which has been established in parallel to this project and comprises operational sonde data from all available WMO stations between 1980 and 2004. Additional validation sources are data from the Special Sensor Microwave Imager (SSM/I) and the European


245

Centre’s for Medium-range Weather-Forecast (ECMWF) re-analysis data set (ERA-40). Sensitivity studies have been performed using ERA-40 data on the impact of the GOME cloud-screening (GWCv1 delivers only data for cloud-free cases) on the water vapour trend values and the differences in trends between 10:30 LT satellite measurements and diurnal averaged values. Comparisons between the Chemistry Transport Model (CTM) MATCH and the General Circulation Models ECHAM5 and GWCv1 have been carried out and already led to two publications in peer-reviewed journals (see reference list). Presentations on global water vapour trends from GWCv1 have been given on a number of occasions among which the ESA ERS-2/Envisat symposium held in September 2004 in Salzburg, the EGU meeting in Vienna 2005 and the AGU meeting 2005 in New Orleans, Louisiana (see publications list). The GWCv1 home page A home page has been launched recently at http://www.mpch-mainz.mpg.de/~saphire/gome_igam/ (see Figure 1) where users of the GWCv1 database are invited to look at monthly mean WVC data from GOME between August 1995 and March 2003 globally or at selected geolocations. The data can be requested via an easy ordering procedure in ASCII format and will be sent by email.

Figure 1.

Screenshot of the GOME Water vapour Climatology (GWC) version 1 web page at http://www.mpch-mainz.mpg.de/~saphire/gome_igam/. Monthly mean fields of WVC between August 1995 and March 2003 are currently available at the web site, as well as time series for specific geolocations.

246


Currently only single monthly mean fields can be ordered as data, but we plan to make available the whole database in netcdf format as soon as additional validation tasks have been performed. The whole dataset in netcdf is currently only available per request from the host institute (MPI for Chemistry). Additional fields like the total retrieval error and a merged product of SSM/I and GOME data, for which GOME data gaps have been filled with SSM/I data (i.e. predominantly in the tropics over sea), are also provided. Differences between both datasets are small in the extra tropics, supporting the good quality of the original GOME data in these regions. Users who want to compare with in situ measurements might be interested in the provided possibility to view time series at specific geolocations within the time period of GWCv1. Validation of GWCv1 Quite a number of detailed validation studies using independent datasets have already been carried out evaluating the quality of GWCv1 absolute values in WVC, as well as the derived trends from the period between August 1995 and August 2002 (In 2002 the current ERA-40 reanalysis data ends. Radio-sonde evaluation studies are available until March 2003). For all details concerning our validation efforts we refer the reader to the forthcoming validation paper, which is currently in preparation. Here we can only provide a brief overview of the most important results. Figure 2 shows a comparison of the total mean WVC distribution over the whole time period between August 1995 and 2002 and the corresponding trends in % for GWCv1, a merged product of MCOS and SSM/I, as well as the ERA-40 data set. Note that ERA-40 and SSM/I data are not truly independent since SSM/I is assimilated in ERA-40 and SSM/I data is forced by observations of SST, which also provide a strong forcing for the ERA-40 water vapor distribution. Trend patterns are therefore naturally quite similar for both of the latter distributions. Most of the significant anomaly patterns are also seen in the independent GWCv1 data set including the strong increase in WVC over Europe and Scandinavia, which is currently subject to an interesting debate [Phillipona and Dürr, 2004, Wild et al., 2005]. The trend calculations for all products presented here are performed with a robust fitting procedure giving less weight to outliers. The stronger scattering in the GWCv1 data trend analysis is due to the fact that GWC data contains temporal gaps in the monthly averaged data due to cloudscreening. Sensitivity studies with ERA-40 data (not shown here) have shown that the local time 10:30 measured values show similar trends than those derived from diurnal averages, however, trend values might be significantly different locally and also on regional averages between all-sky and clear-sky cases. Since GWCv1 provides data for the clear-sky case only this has to be taken into account when using the data.


Figure 2.

247

Global mean distribution of WVC averaged over the whole period between August 1995 and August 2002 (left panels). The right panel shows the WVC trend over the same period in %. The first row shows results from the GWCv1 database using GOME data only, followed by a merged product of operational radio-sonde data over land (the MCOS dataset) and SSM/I over sea (second row). The third row shows corresponding results from the ERA-40 database.

Figure 3 shows two representative examples of collocated radio-sonde GWCv1 measurements, demonstrating the high quality of GWCv1 data even for situations with very strong dynamic in the seasonal cycle, like in the given situation of Hong Kong. We compared results from more than 60 stations following the selection by Lazante et al., 2003, employing high quality sonde data for global temperature trend evaluations (Figure 4). The statistic reveals a correlation between sonde and GWCv1 data of better than 75 % with an error of less than 30 % and a mean regression coefficient of 0.97 ± 0.02 (see Table 1). The data from GWCv1 and the Sondes reveal negative global trends in WVC of -0.072 ± 0.16 and -0.082 ± 0.15 and nearly no trend in the ERA-40 data (+0.009 ± 0.093), however with very large errors due to the small statistics.

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As mentioned before we refer the difference in trends between sonde and GWCv1 on the one side and ERA-40 data on the other side to the fact that GWCv1 is provided for the clear-sky case only (and so are, consequently, the collocated sonde measurements), whereas ERA-40 data provided here represents the all-sky case. This difference therefore remains subject to further validation studies and to more adequate comparisons with ERA-40 data in the collocated situation.

Figure 3.

Monthly mean distribution of WVC from GWCv1 (pink shaded curve; the shading represents the statistical 2-sigma distribution for the monthly mean) between August 1995 and March 2003 are compared to corresponding radio-sondes means from MCOS (blue curves) and ERA-40 (green, no shading) at San Diego (upper panels) and at Hong Kong (lower panels). Additionally the difference between the corresponding residuals (second row) and trends and anomalies (third row) are given. The daily means are given in the right upper panels together with the corresponding scatter plot of collocated measurements. Monthly means are calculated only for collocated measurements.


Figure 5.

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All radio-sonde stations used evaluating the GWCv1 data from the MCOS validation set following a selection by Lazante et al., 2003, employing high quality sondes for temperature trend studies.

Table 1. Mean statistics for all correlations between GWCv1, MCOS and ERA-40 results for all stations in Figure 5. Sea R2 Sondes vs GOME ERA40 vs GOME ERA40 vs Sondes R2 error Sondes vs GOME ERA40 vs GOME ERA40 vs Sondes Bcoeff Sondes vs GOME ERA40 vs GOME ERA40 vs Sondes Bcoeff error Sondes vs GOME ERA40 vs GOME ERA40 vs Sondes Absolute Trend Differences Trendsondes-TrendGOME TrendECMWF-TrendGOME Trendsondes-TrendECMWF Min/Max Differences maxSondes-minSondes maxGOME-minGOME maxECMWF-minECMWF

Coast

Land

0.58375 0.54721 0.56619

0.78616 0.73893 0.76203

Sea/Coast Land/Coast All combined All combined (quality checked) 0.80373 0.73719 0.79082 0.75151 0.77108 0.77757 0.69254 0.74919 0.71084 0.7263 0.80449 0.71465 0.77331 0.73398 0.74914

0.42366 0.48141 0.37512

0.28829 0.34473 0.28286

0.25357 0.27766 0.25994

0.32104 0.3778 0.30518

0.27907 0.32692 0.27677

0.30652 0.35625 0.29545

0.28882 0.34241 0.28231

0.95434 1.0781 1.1297

0.99828 1.0869 1.0904

0.93503 0.96811 1.0733

0.98765 1.0848 1.0999

0.98148 1.0553 1.0859

0.97633 1.0597 1.0942

0.97775 1.0597 1.0932

0.025572 0.04873 0.04873

0.022539 0.045677 0.045677

0.019615 0.036036 0.036036

0.023273 0.046416 0.046416

0.021762 0.043116 0.043116

0.022485 0.044182 0.044182

0.022122 0.044346 0.044346

0.034059 0.028002 0.018595

0.025624 0.02086 0.021772

0.022996 0.019041 0.015477

0.027665 0.022588 0.021003

0.024926 0.020377 0.0201

0.02666 0.021825 0.019814

0.024912 0.020974 0.019307

4.7617 5.2648 2.0113

5.083 4.5797 2.3556

4.7592 4.4839 2.2945

5.0053 4.7454 2.2723

4.7617 4.5542 2.3394

4.9523 4.6891 2.2771

4.9478 4.6577 2.2666

Model evaluation using GWCv1 data MATCH (CTM) We used data from the GWCv1 database to evaluate the WVC distributions in the chemical transport model MATCH. MATCH is driven by basic meteorological fields like temperature, pressure and wind vectors from National Center of Environmental Prediction (NCEP) or ECMWF re-analysis data (NRA or ERA respectively). MATCH employs latent heat fluxes from RA-data but calculates the hydrological cycle online. We identified significant better performance of MATCH WVC distribution model when MATCH is driven by ERA instead of NRA fields. We further showed in two recent publications that MATCH WVC residual patterns with respect to GWCv1 WVC are closely related to residual patterns for a comparison between modelled precipitation fields and those observed by the Global Precipitation Climatology Project (GPCP) [Lang and Lawrence, 2005a]. One of the reason for this close

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correlation could be identified as a too low efficiency of evaporating falling convective precipitation leading to a too dry mid-troposphere in the model (when compared to MCOS data) and a generally too low total WVC when compared to GWCv1 [Lang and Lawrence, 2005b]. An increased efficiency of evaporation of falling precipitate improved the situation significantly and produces comparable WV residence times (WVC over precipitation rates) between 4 and 10 days as have been observed from combining GPCP and GWCv1 data. ECHAM5 (GCM) We finally present preliminary results from the GCM ECHAM5 developed at the MPI for Meteorology in Hamburg. Runs have been performed on a T63 spatial resolution with 39 vertical layers for the period between August 1995 and 2002. Figure 6 provides the same distributions and trend analysis of WVC as shown earlier in Figure 2. but for the ECHAM-5 results.

Figure 6.

Same as Figure 2 but for model results from the GCM ECHAM5.

ECHAM5 shows globally and in hemispheric averages stronger (increasing) trends than ERA40, the radio-sondes and GOME data. Regional differences can be quite large, like the opposite sign of trends above the Sahara, the stronger trends in North America and a less pronounced increase in WV over Europe and Scandinavia. The interpretation of these results will be the subject of forthcoming studies during the next years of this AT2 project together with the implementation of additional WVC products from sensors like SCIAMACHY, MERIS, MWR and AMSU. References Lanzante, J. R., Klein, S. A., and Seidel, D. J., Temporal Homogenization of Monthly Radiosonde Temperature Data. Part I: Methodology, J. Climate, 16 (2003), 224– 240 Phillipona, R., and Dürr, B., Greenhouse forcing outweights decreasing solar radiation driving rapid temperature rise over land, Geophys. Res. Lett., 31(L22208) (2004), doi:10.1029/2004GL020937 Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. G., Forgan, B., Kallis, A., Russak, V., and Tsvetkov, A., From Dimming to Brightening: Decadal Changes in Solar Radiation at Earth’s Surface, Science, 308 (2005), 847 – 850

Recent publications from AT2 work Lang, R., M. G. Lawrence, R. v. Kuhlmann, S. Metzger, S. Casadio, and A. N. Maurellis, The GOME water vapour record for climate and chemistry transport model evaluation, Proc. ESA ENVISAT and ERS-1 Symp., Salzburg, Austria, 6 to 10 of September (2004). Lang, R., and M. G. Lawrence, Evaluation of the hydrological cycle of MATCH driven by NCEP reanalysis data: Comparison with GOME water vapor measurements, Atmospheric Chemistry and Physics, 5 (2005a) 877 – 908 Lang, R., and M. G. Lawrence, Improvement of the vertical humidity distribution in the chemistry-transport model MATCH through increased evaporation of convective precipitation, Geophys. Res. Lett., in press (2005b)


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Aerosol Data Assimilation with the Unified EMEP model A contribution to ACCENT-TROPOSAT-2, Task Group 2 Maarten van Loon1, Jan Eiof Jonson1, Hanne Heiberg1 and Svetlana Tsyro1 1

Norwegian Meteorological Institute, Section Air pollution, P.O. Box 43 Blindern 0313 Oslo, Norway

Introduction Concerning the use of satellite data in combination with data assimilation, the intended contribution from our group to AT2 is highly connected to the EU project GEMS. Unfortunately, its start has been delayed and is now foreseen in the second half of 2005. This means that there has been no progress on the implementation of a data assimilation scheme around the EMEP model. With respect to further model development, a number of activities have been carried out and are reported briefly below. Scientific activities For application of data assimilation using aerosol optical depths it is important that all relevant aerosol sources and processes are included in the model. In the last year a start has been made with testing and implementing various parameterizations for wind blown dust from agricultural areas within Europe. A first approach is described in [Van Loon et al., 2005] as part of modelling work on base cations. This study revealed also the need for further refinement of the parameterization of sea salt, which is now ongoing. Scientific highlights Though only a first attempt, the above mentioned study on base cations [Van Loon et al., 2005] led to quite promising results, with satisfactory correlations between measured and modelled values of wet deposited Calcium. In particular at higher latitudes wind blown dust from agricultural areas contributed significantly to the results. This is illustrated by Figure 1 where the percent contribution from wind blown dust from agricultural areas to the total deposition of Calcium is shown.

Figure 1.

Estimated percent contribution from wind-blown dust from agricultural areas to the total deposition of Calcium. Figure reproduced from Van Loon et al. [2005].

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From Figure 1 it can be seen that in large parts of Europe wind blown dust from agriculture contributes by more than 50 % to the total calcium deposition. In southern Europe this contribution is lower due to the relatively high contribution from the Sahara this area. Although the average contribution from wind blown dust to ground level PM concentrations in air is low, episodically high contributions occur. Part of the dust is within the size range that it is ‘seen’ by satellite based instruments. Future outlook Provided that the GEMS project will start, we will build a data assimilation system around the Unified EMEP model for use with satellite data later in 2005 and 2006. At the same time further model development will continue. With respect to aerosols, further development will be directed towards closing the mass balance: further speciation of the aerosol and inclusion of additional sources of aerosols and formation processes. Wind blown dust is such an additional source and its parameterization will be subject of future work. This will include both source strengths, chemical composition as size distribution. References Van Loon, M., Tarrasón and Posch, M., 2005, Modelling Base Cations in Europe. EMEP Note 2/2005, Norwegian Meteorological Institue, Oslo, Norway.


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The Synergistic Use of Satellite and High-altitude Aircraft Data for the Study of Atmospheric Chemistry, Microphysics, and Transport A contribution to ACCENT-TROPOSAT-2, Task Group 2 A. Robert MacKenzie1 1

On behalf of the Geophysica science community Environmental Science Department, Lancaster University Lancaster, LA1 4YQ, UK

Introduction The Geophysica aircraft is the only European research aircraft capable of reaching the lower stratosphere at all latitudes and in all seasons. It can cover, therefore, the altitude region where the error on satellite retrievals of trace constituents increases dramatically. The Geophysica is equipped to carry a wide range of in-situ and remote-sensing instruments. Some of the remotesensing instruments are replicas of satellite instruments, thus offering the valuable opportunity to sense the environment using the same technology from different platforms. We use these opportunities for synergism to contribute to Task Group 2 of ACCENT-TROPOSAT 2. An example of the kind of work envisaged has already been published [Santacesaria et al., 2003]. Scientific activities: TroCCiNOx During February and March 2005, the Geophysica was deployed in Araçatuba, Sao Paulo State, Brazil, along with the DLR Falcon and a Brazilian Bandeirante research aircraft. The three aircraft were equipped with aerosol, cloud, and chemical sensors. The campaign exploited the unique capabilities of the Geophysica. The Bandeirante surveyed the boundary layer near convective storms, the Falcon probed mid-level inflow and outflow from the storms, while the Geophysica made measurements from inside the uppermost parts of the most energetic thundercloud systems to the stratosphere. In a series of coordinated sorties, the aircraft sampled the continental-scale atmospheric composition, the local-scale composition of the outflow from thunderstorms, and the vertical profile of atmospheric components from the surface to the lower stratosphere (20 km altitude). At the same time, ground-based instrumentation gave information on lightning flash locations and times, and on the radar properties of storm clouds. The real-time ground-based measurements were used to guide the aircraft to the most intense storms. Three of the aircraft sorties were timed to coincide with ENVISAT overpasses. The general objectives of TroCCiNOx were to improve the knowledge about lightningproduced NOx (LNOx), and to improve knowledge of the occurrence of other trace gases (including water vapour and halogens) and particles (ice crystals and aerosols) in the tropical upper troposphere and lower stratosphere. Scientific results and highlights During the campaign, the Geophysica and Falcon made successful measurements in very fresh lightning plumes, thundercloud outflow, and accumulations of aged lightning-NOx. The observations of regional-scale lightning-NOx accumulations are particularly well suited for comparisons with global models, and this comparison is ongoing. The lightning signature was not confined to the low and middle troposphere; the tropical tropopause transition layer (TTL) was also strongly impacted by lightning-NOx and by compounds emitted at the surface. Water

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vapour mixing ratios at the base of the TTL were observed to be 100 jmol/mol or more, and ice supersaturation was observed inside Cirrus clouds. This is in stark contrast to the TTL observed over the Seychelles in APE-THESEO; the tropopause layer there was much drier and much less impacted by local convection. On average, the TTL observed in TroCCiNOx was dry, but not extremely dry (minimum 2.7 jmol/mol). This is partly due to the injection of moist air from below by convection, which is also the source of the TTL lightning-NOx discussed above. However, slantwise mixing-in of moist air from the troposphere and extra-tropical lower stratosphere was also observed. This may be a persistently recurring process, associated with the Bolivian high, and so constitute a 'short circuit' of the stratospheric circulation. Prior to TroCCiNOx, the Brazilian tropics were not expected to be a strong source of air entering the stratosphere — at least according to meteorological analyses such as those by the European Centre for Medium-Range Weather Forecasting (ECMWF). TroCCiNOx did observe isolated events of dehydration (evidenced by low temperatures, saturation, and the presence of Cirrus clouds) at the tropopause, but also observed clouds above the tropopause, which we consider to be indicative of local hydration of the stratosphere by overshooting convective towers. Future outlook Analysis of the data from TroCCiNOx has only just begun. Considered as part of the long-term aims of the Geophysica programme, TroCCiNOx is the second in a coordinated series of missions targeting the tropical tropopause transition layer (TTL): the source of air to the stratospheric 'overworld'. These missions target the maritime TTL, the continental TTL, and the 'maritime-continent' TTL, respectively. The last of these targets — the TTL in the special conditions of the maritime continent between the Indian and Pacific oceans — will be the focus of the SCOUT-O3 mission to Darwin, in November 2005. References Santacesaria et al., Clouds at the tropical tropopause: a case study during the APE-THESEO campaign over the western Indian Ocean, J. Geophys. Res., 108(D2) (2003) 4044, doi: 10.129/2002JD002166


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Global Measurements of Water Vapour in the Tropopause Region and Upper Troposphere with MIPAS/ENVISAT A contribution to ACCENT-TROPOSAT-2, Task Group 2 Mathias Milz Institut für Meteorologie und Klimaforschung, Universität Karlsruhe/Forschungszentrum Ka ,Eggenstein-Leopoldshafen, Germany

Summary At IMK atmospheric parameters are retrieved from MIPAS/ENVISAT measurements using a non-operational science-oriented retrieval processor. Besides other parameters water vapour profiles are retrieved from measured infrared spectra. For the period 18th September 2002 to 30th November 2003, a subset of available measurements have been used for the retrieval of water vapour. Zonal and global averaged water vapour distributions on different timescales originating from these date are presented. Introduction The scientific retrieval processor for MIPAS/ENVISAT data, which has been developed at IMK to retrieve atmospheric parameters is used to obtain, besides other parameters, vertical profiles of water vapour. Between 18th September 2002 and 30th November 2003 water vapour distributions have been determined using a subset of all available measurements. Aim of this work is to obtain average distributions of water vapour during the above mentioned period. MIPAS The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT is operating in the mid-infrared spectral region with limb geometry. The infrared spectra cover the spectral range from 685 to 2410 cm-1 with a spectral resolution of 0.025 cm-1 (unapodised) (until March 2004). ENVISAT operates in a sun-synchronous orbit with the descending node crossing the equator at 10:00 am. The standard measurement mode provides limb emission spectra for 17 tangent heights covering the altitude range from approx. 6 to 68 km. With one limb scan every 75 s, MIPAS provides profiles with a sampling of ca. 500 km along the flight track. Available data From September 2002 until February 2004 spectra of continuous measurements are available at IMK. Level 1b data (calibrated spectra) from the ESA near-real-time processor have been processed for 74 days during this period. During September/October 2002, July 2003, and October/November 2003 atmospheric profiles for all available data covering a period of 1 to 3 weeks each have been retrieved. For other months approximately 1 day every 10 days has been processed. These data have been analysed with respect to different scientific aspects such as, for example, averaged monthly or seasonal distributions, and time series.

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Scientific results and highlights Averaged water vapour distributions in summer 2003 The retrieved water vapour profiles of the processed days (17 days, more than 10900 Profiles) during June, July and August 2003) were averaged. Zonal mean distribution In Figure 1, the zonal mean distribution in the lower stratosphere and upper troposphere for summer 2003 is shown.

Figure 1. Zonal mean distribution of the processed data during summer 2003. White areas indicate regions where no data are available (e.g. cloud contamination or non-covered altitude range).

In general, the zonally averaged water vapour shows a distribution as expected from climatologies. In the troposphere below about 10 and 15 km in the polar regions and tropics, respectively, the volume mixing ratios(VMR) increase exponentially with decreasing altitude. At or slightly above the tropopause the water vapour distribution has a minimum, the so-called hygropause, and then increases with altitude in the stratosphere. In the tropics the hygropause is elevated and situated around 18 km. Here the cold tropopause temperatures result in comparatively low VMR values. From the tropics to the extra-tropics the distribution shows regions where humid air masses reach further up into the stratosphere than in the tropics (at ca. 20° N and S). In the mid-latitudes the altitude of the hygropause decreases towards the poles. Very small water vapour VMRs occur at 15 km altitude above the South Pole. Here the air parcels were subject to strong dehydration inside the polar vortex due to the low temperatures combined with the large scale formation of polar stratospheric clouds (PSC). Below 14 km no measurements are available inside the polar vortex during the shown period due to the occurrence of PSC.


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In the tropical lower stratosphere the so called tropical pipe is visible, where relative dry air parcels from the tropical tropopause are rising to the middle and upper stratosphere without strong mixing across the tropical barrier. Global mean distribution In Figure 2 the averaged distribution of water vapour at an altitude of 18 km is shown for the above mentioned period.

Figure 2, Global mean distribution at 18 km for the processed data during summer 2003. White areas indicate regions where no data are available (e.g. cloud contamination or non-covered altitude range). The values shown are ppmv.

In general, the climatological distribution of water vapour at 18 km with low values above the tropics and increased values above the mid latitudes is clearly visible. Reason for this distribution is the altitude of the tropopause which is lower in the extra-tropics. However, a band-like structure with enhanced values in the subtropics can be seen in the distribution. Especially over Asia Minor and the northern Arabic peninsula a region appears with enhanced water vapour VMRs. This structure is presumably related to the south Asian monsoon. Observations from HALOE on board of the UARS-satellite show enhanced water vapour VMRs over the same region [Jackson et al., 1998]. Model simulations Comparisons with model results from DLR (ECHAM) show qualitatively similar distributions for the lower stratosphere and the tropopause region. In Figure 3 the zonal mean for September is shown. Although the figure represents a different season, the general structure is similar with low water vapour VMR over the Antarctic continent. The transition from the tropics to the extra-tropics reverals a region with enhanced water vapour VMRs up to higher altitudes, as observed by MIPAS.

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Figure 3. Zonal mean distribution of the modelled water vapour distribution from the DLR-ECHAM model for September (courtesy, M. Dameris, DLR).

Tape-recorder effect For all retrieved water vapour profiles zonally averaged distributions have been calculated for each available day. For these days the averages for the tropics covering the latitude range from 12.5° S to +12.5°N have been used to obtain a time series for the tropical measurements. The evolution of water vapour in the tropics from 18th September 2002 until 30th November 2003 is shown in Figure 4, as the deviation of the profiles from the mean value at each altitude. In the figure the so called tape recorder effect is clearly visible. Relatively dry air enters the lowermost stratosphere in January, February and March, when the temperature of the tropical tropopause is low. During the year the dry air slowly rises in the stratosphere and new air with higher water vapour VMRs enters the stratosphere. The rise of the air is visible with alternating dry and wet bands in the time series. The further the air rises, the weaker the signal becomes and finally disappears due to mixing processes and increasing water vapour VMRs with altitude, produced by methane oxidation.


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Time series of daily averaged tropical water vapour profiles for the latitude range from 12.5 °S to 12.5 ° N. The values show the absolute difference from the tropical water vapour mean profile.

Outlook The existing data set with water vapour distributions will be further analysed. The next step to be taken is the validation of the measurements against results from other satellite borne or ground based instruments. Comparisons with model results shall provide further information about the quality and plausibility of the observed distributions. Collaboration is planned with DLR and other institutes which operate models suitable for these purposes. Since 2005 new, reprocessed MIPAS spectra are available. The quality of these data is expected to be improved, compared to the near-real-time results. Currently at IMK a first subset of the available data is being processed. These new results will be used for scientific analyses for water vapour in the UTLS region. References Jackson, D.R. et al., Troposphere to stratosphere transport at low latitudes as studied using HALOE observations of water vapour 1992-1997", Q. J. R. Meteorol. Soc., 124, 545, 169-192, 1998.

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Use of Satellite Data to Constrain Ozone Budgets in Global Tropospheric Chemistry Models A contribution to ACCENT-TROPOSAT-2, Task Group 2 Nicholas Henry Savage and Peter Cook University of Cambridge, Department of Chemistry Lensfield Road, Cambridge, CB2 1EW, UK

Summary Studies in this year have focussed on the effects of biomass burning on the composition of the troposphere. We have used data from the MOPITT and SCIAMACHY instruments to help interpret the results of aircraft observations made during the ITOP program in 2004. This has also helped test daily emissions inventories which were calculated based on MODIS data. Model results for both CO and NO2 have been shown to be in much better agreement with satellite data when using this new inventory. This work will now be extended to look at the impact of these fires on the ozone budget. Work has also begun on using a new emission inventory created as part of the RETRO project which will soon be used in a sensitivity study to look at the relative roles of meteorology and emissions on ozone concentrations and budgets. Introduction Biomass burning emissions release large quantities of ozone precursors [Crutzen and Andrea, 1990] but are highly variable on timescales from days to years. Biomass burning is a global phenomenon occurring in both boreal and tropical regions and plumes from biomass burning can extend for thousands of kilometres [Sprichtinger et al., 2001]. In 1997 an extreme example of interannual variability in emissions from biomass burning was observed when forest fires in Indonesia caused very large emissions of CO and NOx [Duncan et al., 2003, Levine, 1999]. For these reasons satellite data which give regular measurements of the emitted compounds and have global coverage are a very useful tool to study biomass burning. Satellite observations can also help validate global models studying these phenomena. We are studying variability of emissions on two timescales– daily variability and interannual variability. GOME is a very useful tool for studying interannual variability as several years of data are now available. Shorter timescales are best studied as part of a field campaign where other measurements are available. In the summer of 2004 forest fires in Alaska and Canada emitted large quantities of CO and other tracers which were then advected over the North Atlantic. Researchers from the UK using a BAe-146 research aircraft to measure O3 and its precursors, based at the Azores in July and August as part of the NERC ITOP project, also measured some of the emission plumes, in particular during the flight on 20th July. This day has now been studied in some detail with p-TOMCAT.


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Scientific activities The Cambridge global model of atmospheric chemistry and transport p-TOMCAT was used to support the ITOP campaign. For this study we ran p-TOMCAT using new emissions inventories for biomass burning during May to August 2004, and compared the predicted plume locations and tracer mixing ratios to aircraft measurements as well as data from the MOPITT CO and SCIAMACHY NO2 satellite instruments. As part of the EU FP5 project RETRO (http://retro.enes.org/) we are planning to investigate the sensitivity of tropospheric ozone to interannual variability due to changes in meteorology and changes in emissions. A new emissions inventory including interannual variability in all emissions has been developed as part of this project. We have performed the first model runs to test this inventory in p-TOMCAT and longer runs and sensitivity tests will begin soon. We have also participated in the ACCENT-IPCC Photocomp 2030 experiment which amongst other activities will make use of GOME tropospheric NO2 columns to validate the models taking part. Scientific results The default biomass burning emissions in p-TOMCAT have climatological monthly mean distributions [Hao and Liu, 1994]. To better represent the biomass burning emissions for this period Solène Turquety of Harvard University has calculated daily inventories for biomass burning emissions in North America, covering the period May to August 2004, calculated using MODIS hotspots, reported areas burned, and information on type of vegetation, fuel loading and emission factors. New biomass burning emissions for the rest of the World during this time [Yevich and Logan, 2003] were also added. In order to evaluate these new emissions we have compared the old and new model results to MOPITT CO and NO2 from SCIAMACHY. We see an overall improvement in the model results with the new inventory both in the global CO and NO2 fields and in the North America area where we have the daily biomass burning emissions. Flight B032: CO measurements with p-TOMCAT results from old and new inventories

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With the new emissions inventories including the Alaskan forest fires the results from p-TOMCAT show large plumes of CO over the North Atlantic (see Figure 2). These plumes were not seen with the standard p-TOMCAT emission inventory. The predicted mixing ratios interpolated to the aircraft positions are now closer to the measurements (see Figure 1), although the very large values observed by the aircraft are not modelled. This failure to reproduce the largest peaks is probably due partly to the model resolution of 2.8 × 2.8 degrees. The MOPITT CO and SCIAMACHY NO2 satellite instruments also show the plumes across Canada and the North Atlantic though the NO2 plumes are less distinct, but model CO plumes from fires in Central Africa have drifted too far and these emissions probably didn’t go so high into the Troposphere. As well as the increased emissions from North America the biomass burning emissions from the rest of the World, in the Yevich and Logan inventory, are reduced in comparison to the normally used monthly climatology.

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Model predictions compared to MOPITT CO measurements on the 20th July 2004.


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Future outlook The work on the ITOP campaign will now be extended studying the effect on the ozone budget of these fires and by the use of high resolution global model runs. To investigate the relative importance of changes in meteorology and emissions in driving interannual variability we will perform 5 year sensitivity studies and evaluate the results with GOME NO2 data. Finally we also plan to evaluate modelled formaldehyde columns in the ACCENT- IPCC experiment with GOME formaldehyde results. Acknowledgments We would like to thank the following people and groups: Solene Turqutey Atmospheric Chemistry Modeling Group, Harvard University, USA for the daily biomass burning inventory; the NCAR MOPITT team for the CO satellite data on their web site; Andreas Heckel at Bremen University, Germany, for supplying the SCIAMACHY NO2 data; EU for funding through the FP5 program RETRO (Project No. EVK2-CT-2002-00170); NERC Centres for Atmospheric Science for Supercomputing resources and NERC for funding through NCAS and the ITOP project. References Crutzen, P.J. and Andrea, M.O. Biomass Burning In The Tropics - Impact On Atmospheric Chemistry And Biogeochemical Cycles. Science, 250 (4988): 1669-1678 DEC 21 1990. Duncan, B. N.; Bey, I.; Chin, M.; Mickley, L. J.; Fairlie, T. D.; Martin, R. V.; Matsueda, H. Indonesian wildfires of 1997: Impact on tropospheric chemistry. J. Geophys. Res 108, D15, 4458 10.1029/2002JD003195 Hao, W. M. and Liu, M. H.: Spatial and Temporal Distribution of Tropical Biomass Burning, Global Biogeohem. Cycles, 8, (4), 495-503, 1994. Levine, J., The 1997 fires in Kalimantan and Sumatra, Indonesia: Gaseous and particulate emissions, Geophys. Res. Lett., 26, 815-818, 1999. Spichtinger, N, Wenig, M, James, P, Wagner, T, Platt, U and Stohl, A. Satellite detection of a continental-scale plume of nitrogen oxides from boreal forest fires. Geophys. Res. Lett.,28, 24, 4579-4582, 2001. Yevich, R. and J. A. Logan, An assessment of biofuel use and burning of agricultural waste in the developing world, Global Biogeochem. Cycles, 17 (4), 1095, doi:10.1029/2002GB001952, 2003.

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Integrated Use of Satellite and In-Service Aircraft Measurements to Study Upper Tropospheric Humidity A contribution to ACCENT-TROPOSAT-2, Task Group 2 Herman G.J. Smit Institute for Chemistry and Dynamics of the Geosphere: Troposphere (ICG-II) Research Centre Jülich, P.O. Box 1913, 52428 Jülich, Germany,

Summary To investigate upper tropospheric humidity (UTH) in the outflow of Cb-clouds humidity measurements made from MOZAIC aircraft have been linked with the location of convection by back tracing with 3d-trajectories in combination with satellite observations of the cloud top temperature along the trajectories. Near the outflow of the anvil clouds relative humidity over ice (RHI) shows a uni-modal probability distribution (PDF) with more than 60 percent ice super saturation (ISS). Downwards the outflow the PDF of RHI is developing from uni-modal into bi-modal PDF of RHI with one dry-branch and one wet branch. The dry branch shows a fast drying within one day from 70-90 % RHI down to 40-50 % RHI indicating clear sky and negligible mixing. Characteristic for the wet branch is that ISS (100-150 % RHI) remains even after more than one day downstream of the anvil such that the outflow is subject to dynamical processes like uplifting or radiative interaction with thin (sub-visible) cirrus clouds. Introduction Through the integrated use of satellite and in-service aircraft observations it is our intention to investigate the large scale distribution of upper tropospheric humidity (UTH) and the processes controlling it. In the first phase of TROPOSAT we developed a concept to study the UTH distribution in the outflow of deep convection through the synergistic use of MOZAIC (=Measurement of Ozone and Water Vapour by Airbus In Service Aircraft)-UTH observations with satellite data of sea surface and cloud top temperatures. The concept had been applied successfully to the equatorial Atlantic region [Nawrath, 2002]. In AT2 it is the aim to continue the investigations and to extend it more globally through the use an integrated data set of UTH-observations with the best global coverage. Therefore, it is aimed to use MOZAIC passenger aircraft data, providing accurate and quality assessed in-situ UTH-measurements, to validate/calibrate water vapour channels of different satellite instruments (e.g. SCIAMACHY or SAGE-III). Activities during the year Due to lack of funding we were not able to start with the validation of UTH measurements from satellites by comparison with MOZAIC observations. However, we continued our scientific analysis about the development of UTH in the outflow of deep convective clouds in the tropics. Investigations that are based on the concept and results obtained in TROPOSAT-I [Smit et al., 2003]. Thereby, MOZAIC-UTH observations obtained over the central Atlantic were used in combination with satellite observations of cloud top temperature (CTT) from METEOSAT to identify regions of deep convection and subsidence. The location of the convective origin of the water vapour concentration measured by the MOZAIC-aircraft is traced back with 3d-trajectory analysis in combination with the CTT along these trajectories. It is assumed that a CTT below a certain threshold (e.g. 240 K) indicates the presence of a deep convective cloud.


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Figure 1. Evolution of the probability distribution function (PDF) of RHI downward the outflow of Cb-convection in the tropics in the (ITCZ) between equator and 10oN derived from about 500 MOZAIC-flights made between August 1994 and June 2000

The results are based on a total of about 500 flights made between August 1994 and June 2001 for only tropospheric measurements at cruise altitude (Z=9-12 km) with their convective origin in the ITCZ (=Inter Tropical Convergence Zone) between the equator and 10oN over the Atlantic Ocean. For each UTH measurement the time lag between sampling and convection has been determined.

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Striking features thereby are: * near cloud edge (t = 0 h) uni-modal PDF of RHI with more than 60 percent ice super saturation (ISS) * downwards the outflow (t > 0 h) the development from uni-modal into bi-modal PDF of RHI (t > 0 h) with one dry-branch and one wet branch: - Dry branch: Fast drying within one day from 70-90 % RHI down to 40-50% RHI - Wet branch: ISS (100-150 % RHI ) remains, even after t=24 h At the present it is not understood what process, dynamical uplifting or radiative interaction, is responsible for remaining the ISS in the Cb-outflow over a time period of almost 2 days. The RHI-development of the dry branch has been compared with model calculations of air parcels which are subject of subsidence under clear sky conditions under quasi hydrostatic equilibrium. At a radiative cooling rate of -1K per day, a typical lapse rate of 8.6 K/km and assuming no mixing during subsidence the change of RHI caused by the temperature increase has been calculated. Starting with the same PDF of RHI as measured at time t=0 the PDF of RHI has been calculated for each bin of RHI using the calculated temperature increase during subsidence. The calculations show that within 2 days of clear sky subsidence the RHI of air parcels can get lower by a factor 4 while the width of the PDF changes correspondingly. This fits very well to the observed dry branch of the PDF of RHI as shown in Figure 1. It is obvious that for a typical clear sky radiative cooling rate of -1 K/min the subsiding air parcels are not subject to any significant mixing of other air parcels during the first two days in the outflow of Cb-convection in the ITCZ over the Atlantic. An interesting feature is the bi-modality of the probability distribution function (PDF) of relative humidity with respect to ice (=RHI) in the tropics [Smit et al., 2003]. Intriguing aspect thereby is that large parts of the tropics reveal very low relative humidities in the UT while other areas are rather wet with substantial fraction of ice super-saturation (ISS). This bimodality implies sharp gradients between dry and moist regimes in space and time. The typical bimodality of RHI-distribution in the tropics is qualitatively also found in radio-sondes [Brown and Zhang, 1997] and satellite [Soden and Bretherton, 1993] observations. The bimodality suggests that the radiative drying time after an injection of moisture by convection is short compared to a homogenizing time, whether physical (mixing) or mathematical (averaging). Climate models have difficulties to reproduce the observed bi-modality. This also means that there is a strong potential significance of the bi-modality in water vapour feedback to climate variation [Zhang et al., 2003]. However, the origin of the bi-modality of UTH in the tropics is not understood at all and at the present subject of a controversy debate about climate change and the role of convective processes. Future outlook Further investigations will focus on how far the dynamical processes determining the bimodality of the PDF of RHI in the outflow of Cb-convection are also responsible for the bimodality PFD of RHI on the large scale as [Smit et al., 2003]. Interesting feature thereby is that the ISS remains over almost 2 days, which cannot be explained exclusively by clear sky subsidence but must be caused by an additional counter acting of dynamical or radiative forcing processes. Thereby, in case of using UTH-satellite data an interesting question will be in how far satellite measurements are able to resolve the sharp gradients between dry and wet regions. A key question thereby will be in how far satellites can reproduce the extremely wet regions with a large fraction of ISS. I will attempt to get funding to start with the analysis and synthesis of UTH-satellite measurements (e.g. SAGE-III, AIRS or SCIAMACHY) with MOZAIC-UTH-data.


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References Brown, R.G., and C. Zhang, Variability of mid-tropospheric moisture and its effect on cloud top height distribution during TOGA-COARE, J. Atmos. Sci., 54, 2760-2774, 1997. Nawrath, S., Water Vapor in the Tropical Upper Troposphere: On the Influence of Deep Convection, PhD-Thesis at University of Cologne, May 2002 Smit, H.G.J., S. Nawrath, D. Kley, and M. Helten, Control mechanisms of water vapour in the upper troposphere: Large scale subsidence in regions of tropical Cb-convection, In: Sounding the troposphere from space: a new era for atmospheric chemistry, TROPOSAT, EUROTRAC-2 Subproject Final Report, P. Borrell, P.M. Borrell, J.P. Burrows, U. Platt (Eds.), Springer Verlag, Berlin (2003) ISBN 3-8236-1390-1, 255-258 Soden, B.J., and F.P. Bretherton, Upper tropospheric humidity from the GOES 6.7 jm channel: Method and climatology for July 1987, J. Geophys. Res., 98, 16,669-16,688, 1993. Zhang, C., B.E. Mapes, and B.J. Soden, Bimodality in tropical water vapour, Q. J. R. Meteorol. Soc., 129, 28472866, 2003.

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Synergistic Use of Satellite Data with the Global ChemistryTransport Model GEOS-Chem: Formaldehyde Column over Europe as a proxy for Biogenic Emissions and CTM Validation using Satellite Data A contribution to ACCENT-TROPOSAT-2, Task Group 2 Guido Visconti1, Gabriele Curci1, Gianluca Redaelli1 and Barbara Grassi1 1

CETEMPS, Università degli Studi dell’Aquila, 67010 Coppito – L’Aquila, Italy

Summary The Atmospheric Physics group of CETEMPS (Centre of Excellence for the integration of remote sensing TEchniques and Modelling for the Prediction of Severe weather) at University of L’Aquila has a research program for the integration of Earth Observation with global Chemistry-Transport Models. There are two main objectives: (1) use formaldehyde (HCHO) columns measured by GOME as a constraint on European volatile organic compound (VOC) biogenic emissions; and (2) innovative validation procedure to assess CTMs model variability with respect to observations. Introduction Objective 1: GOME HCHO and European Biogenic Emissions Europe is the only continent where anthropogenic VOC emissions are comparable in magnitude to biogenic emissions. European biogenic emissions are of little importance on a global scale, though still can play an important role in the oxidizing capacity of the atmosphere locally. However current biogenic emission inventories for Europe are highly uncertain, up to a factor of five [Simpson et al., 1999]. Palmer et al. [2003] demonstrated the potential use of formaldehyde columns measured by GOME, on board ERS-2, in constraining isoprene emissions on a continental scale over North America. Formaldehyde is an important intermediate oxidation product of most VOCs and has lifetime of a few hours in summer. Isoprene is the major biogenic emission, is extremely reactive (lifetime ~ 1h) and has an high HCHO yield. Palmer et al. [2003] used the GEOS-Chem chemistry transport model [Bey et al., 2001; Martin et al., 2002] to extract a local relation between isoprene emission and HCHO column and used the relation to invert GOME HCHO column [Chance et al., 2000; Palmer et al., 2001] for isoprene emissions over North America. We intend to extend this method to Europe in the attempt of narrowing down the uncertainty on isoprene emissions. Objective 2: EOFs and synthetic spectra from CTM and EO We intend to develop new method to validate Chemical Transport Models and to assess their capability to reproduce natural variability. Following a technique outlined for the thermal Earth radiation, we propose to compare the radiative covariance from synthetic spectra from models with covariance obtained from observed spectra. For spectra the covariance is calculated between the different spectral channels and from that EOFs are obtained. The principal EOF components will give a quantitative estimation of the variability. Chemical fields from CTM will be used to calculate synthetic spectra and then EOF. These will be compared with real spectra obtained in the same condition. In particular we have in mind in data from SCIAMACHY.


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Scientific activities Our group has joined the ACCENT community has associates at the end of 2004. We started to work on objective 1 of our project about at that time. We used GOME HCHO slant column product provided by the group at Harvard University [Chance et al., 2000]. Slant columns are converted to vertical columns using the AMF formulation illustrated by Palmer et al. [2001] with further improvements describer in Martin et al. [2002]. The AMF accounts for gas scattering, aerosols and clouds. The procedure makes use of the CTM model developed at Harvard University GEOS-Chem. Our group is in the community of users and developers of the model. We compared the GOME HCHO vertical column for July and August 1997 with GEOS-Chem simulations. We also used GOME NO2 column product from Harvard group [Martin et al., 2002] to assess the anthropogenic emissions influence on atmospheric budget over Europe using HCHO/NO2 column ratio. We then used linear regression to relate isoprene emissions and HCHO column from GEOS-Chem model results. Scientific results and highlights The GEOS-Chem currently implements the GEIA inventory to calculate biogenic emissions. We used the model version with 2° × 2.5° horizontal resolution to assess biogenic versus anthropogenic control on formaldehyde column over Europe. In Figure 1 we show the ratio of formaldehyde column from two model runs: in one run we switched off isoprene emissions, in the second one we switched off anthropogenic emissions. Doing the ratio of formaldehyde column from the first run to the second run we can establish what is supposed to control the column. The blue regions have ratio 1 and are controlled by oxidation of anthropogenic VOCs. It comes out that isoprene oxidation largely controls formaldehyde column over Eastern Europe, Russia, Turkey and Iberian Peninsula.

Figure 1.

Formaldehyde column ratio between two model runs: (1) without isoprene emissions; (2) without anthropogenic emissions. Ratio 1 (red) show regions under anthropogenic control.

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Figure 2.


Formaldehyde column and isoprene emissions in summer. Top-left: monthly mean model HCHO column. Top-right: GOME column. Bottom-left: GEIA isoprene emissions. Bottom-right: MEGAN isoprene emissions.

In Figure 2 we show the comparison of monthly mean HCHO column for July 1997 from model and GOME. Only model column in time interval 10:00-12:00 local time are used. In the two bottom panels monthly isoprene emission factors in GEIA inventory (bottom-left) and in MEGAN inventory [Guenther et al., 2005, manuscript in preparation] (bottom-right) are shown. Model HCHO column in summer (top-left panel) largely reflects isoprene emissions in the model (bottom-left panel). The features of the GOME HCHO column are quite different from model. The model generally overestimates the column over Spain, France, northern Europe and north-eastern Europe. Model and GOME show quite good agreement over centraleastern Europe. In the bottom-right panel we show monthly base emission factors of isoprene from the new MEGAN inventory [Guenther et al., 2005, manuscript in preparation] that revise low the emissions over much part of Europe with respect to GEIA. The general features of MEGAN emissions seem to be consistent with the GOME picture. We apply the inversion of HCHO column for isoprene emissions over central-eastern Europe first. As discussed above, the formaldehyde column there is controlled mainly by biogenic emissions and the discrepancy between model and GOME is smaller than other regions. The regression line between isoprene emissions and HCHO column from the model is shown in Figure 3. The black points are the scatter plot of model isoprene emissions and HCHO columns sampled along GOME tracks during July 1997 over central-eastern Europe. The red dots are the same plot but using results from a model run with zero isoprene emissions. The contribution of isoprene oxidation on HCHO column is evident. We use the regression line shown in Figure 3 to invert each GOME HCHO pixel for isoprene emissions. We than scale the emissions with diurnal factors to account for the fact that


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observations are only in the time interval from 10:00 to 12:00 local time. Monthly mean of GOME isoprene emissions over central-eastern Europe are shown in Figure 4. GOME indicates a reduction of a factor of 2 of isoprene emissions with respect to GEIA in this region.

Figure 3.

Regression line between model isoprene emissions and formaldehyde column sampled along GOME track in July 1997 over Central-East Europe. Black dots: scatter plot for standard run. Red dots: scatter plot for a run with isoprene emissions switched off. Black line: regression line. Regression parameters, correlation and number of data used are shown inset.

Figure 4.

Comparison of isoprene emissions from GEIA (GEOS-CHEM) and from GOME over central-eastern Europe. Total isoprene emissions on the region for July 1997 are reported (inset).

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Future outlook In the next future we are going to extend the inversion to the rest of Europe. When the times will be mature, we’ll implement the new GOME-constrained isoprene emissions into the model and verify the results against available independent data. We are going to look at the seasonal cycle and interannual variability of formaldehyde column over Europe. We are also References Bey et al., 2001, Global modelling of tropospheric chemistry with assimilated meteorology: model description and evaluation, J. Geophys. Res., 106 (D19), 23073-23095. Chance et al., 2000, Satellite observations of formaldehyde over North America from GOME, Geophys. Res. Lett., 27 (21), 3461-3464. Martin et al., 2002, An improved retrieval of tropospheric nitrogen dioxide from GOME, J. Geophys. Res., 107 (D20), 4437, doi: 10.1029/2001JD001027. Palmer et al., 2001, Air mass factor formulation for spectroscopic measurements from satellites: Application to formaldehyde retrievals from the Global Ozone Monitoring Experiment, J. Geophys. Res., 106, 14,539– 14,550. Palmer et al., 2003, Mapping isoprene emissions over North America using formaldehyde column observations from space, J. Geophys. Res., 108 (D6), 4180, doi: 10.1029/2002JD002153. Simpson et al., 1999, Inventorying emissions from nature in Europe, J. Geophys. Res., 104 (D7), 8113–8152.

Recent publications from AT2 work Curci, G., P. I. Palmer, T. M. Fu, G. Visconti, K. Chance, T. Kurosu, 2005, Formaldehyde column over Europe from GOME: model validation and proxy for biogenic emissions, EGU05-A-00983 in Session AS3.03: Trace gases in the atmosphere: observations and modelling, EGU 2nd General Assembly, Vienna, Austria, 24-29 April 2005.

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