Diagram showing the set-up for coupling TM5 and IFS.
Diagram showing the set-up for coupling TM5 and IFS.
With the advance of satellite remote sensing of the Earth’s atmospheric composition, unprecedented information is becoming available about how natural and anthropogenic emissions are influencing the greenhouse gases and aerosols, as well as near-surface air quality, which affects human health. Examples are the observation of desert dust plumes, of the Antarctic ozone hole and of nitrogen dioxide air pollution with satellite sensors like the Ozone Monitoring Instrument (OMI). The increasing availability of satellite as well as surface in-situ observations calls for global data assimilation systems, similar to numerical weather prediction, that are capable of combining the measurements of atmospheric composition and meteorology into a comprehensive global picture (1).
Figure 1. Diagram showing the set-up for coupling TM5 and IFS. “Met” denotes the transfer of the meteorological fields from IFS to TM5. “P&L” denotes the transfer of the chemical production and loss terms from the CTM to IFS. Five chemical species are included and transported into the IFS assimilation system.
Figure 1. Diagram showing the set-up for coupling TM5 and IFS. “Met” denotes the transfer of the meteorological fields from IFS to TM5. “P&L” denotes the transfer of the chemical production and loss terms from the CTM to IFS. Five chemical species are included and transported into the IFS assimilation system.

The GEMS (2) (Global and Regional Earth-System Monitoring Using Satellite and In situ Data project; 2005-2009;(zie externe links) has built such a global assimilation/forecasting system for greenhouse gases, reactive gases and aerosols, as well as a regional air quality prediction system based on an ensemble of European air pollution forecasting models. GEMS was funded by the European Commission within the Global Monitoring for Environment and Security (GMES zie externe links) framework. GEMS is co-ordinated by ECMWF and the global assimilation and forecast system is based on the ECMWF weather model.

The KNMI divisions Chemistry and Climate and Climate Observations have made several major contributions to GEMS:

  1. The delivery of (near-real time) satellite data sets from OMI and the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) which were used both in the assimilation and for validation. These data sets have been extensively used to produce near-real time analyses and reanalyses for the period 2003-2008.
  2. The TM5 chemistry-transport model is contributed as one of the building blocks of the global atmospheric chemistry analyses.
  3. OMI NO2 data have been used for a near-real time evaluation of the European air quality forecasts.
  4. The validation sub-project of GEMS was co-ordinated by KNMI.

Two example results for the second and third contribution to the GEMS project are presented below, followed by a discussion of future developments and the relation with the EC-Earth model development.

Coupling the TM5 chemistry-transport model with the ECMWF Integrated Forecast System
In the reactive gases subproject three state-of-the-art atmospheric chemistry-transport models (CTMs) MOZART, MOCAGE and TM5 have been coupled to the ECMWF Integrated Forecast System (IFS) (3). The approach used in GEMS is shown in Figure 1. Five key chemical species have been included in the IFS model, namely ozone, carbon monoxide, nitrogen oxides, sulphur dioxide and formaldehyde. Satellite observations are available for these five gases, and have been included in the analysis. This extended IFS system is coupled to the CTMs by the coupling software OASIS version 4. This coupler passes hourly meteorological fields from IFS to the CTM. In turn, the CTM provides chemical tendencies for the five tracers to IFS. Furthermore, the assimilated spatial distribution of the five gases is passed back to the CTM to ensure consistency.

One result of the coupled IFS-CTM system is shown in Figure 2 for both IFS-MOZART and IFS-TM5 in runs with and without assimilation of ozone observed by the satellite instruments SBUV (Solar Backscatter Ultraviolet Experiment), OMI and MLS (Microwave Limb Sounder). The forecast with IFS-MOZART without assimilation predicted the start of the ozone hole at the right time. Its extent was, however, underestimated by about 40%. Stratospheric ozone in TM5 is constrained by a climatology. This climatological approach in IFS-TM5 did not capture the long duration of the 2008 ozone hole which lasted until mid-December. Hence both of these model simulations have deficiencies. In contrast to the free runs, the forecasts including the assimilation of satellite observations are very similar and also agreed very well with independent ozone observations. This means that, despite the differences in the underlying chemistry schemes of MOZART-3 and TM5, the observations were well able to constrain the GEMS modelling system and significantly improve the simulated ozone distribution during ozone-hole conditions.
Figure 2. Time series of the extent of the Antarctic ozone hole 2008, defined as the area fraction where the ozone column is < 220 Dobson Units (DU). Shown are the free run with the coupled system IFS-MOZART without assimilation (blue), a free run with IFS-TM5 (red) and two runs with assimilation of SBUV, OMI and MLS ozone retrievals with the IFS-MOZART (green) and the IFS-TM5 (magenta) set-up.
Figure 2. Time series of the extent of the Antarctic ozone hole 2008, defined as the area fraction where the ozone column is < 220 Dobson Units (DU). Shown are the free run with the coupled system IFS-MOZART without assimilation (blue), a free run with IFS-TM5 (red) and two runs with assimilation of SBUV, OMI and MLS ozone retrievals with the IFS-MOZART (green) and the IFS-TM5 (magenta) set-up.

Figure 3 shows a validation of the resulting ozone vertical profile in the TM5 forecasts with and without the analysis of satellite observations. It shows that the modelled profile resulting from the assimilation is well in line with independent ozone sonde observations. This can be attributed to the multi-sensor approach, where the microwave limb sounder (MLS) has been essential to get a good profile shape and the UV-visible sounders added important information on the total column amount of ozone. This example demonstrates the benefits of applying advanced assimilation techniques, developed in the weather community, to atmospheric chemistry.
Figure 3. Monthly averaged ozone profiles (partial pressure in mPa) forecast by IFS-TM5 with assimilation (blue), without assimilation (orange) and ozone sonde in-situ observations (red) from Neumayer Station in Antarctica for August and October 2008. The depletion of ozone inside the ozone hole is clearly visible in October. This ozone loss is not well described by the free running model, but the assimilation of multiple satellite ozone sensors with this GEMS system leads to an accurate description.
Figure 3. Monthly averaged ozone profiles (partial pressure in mPa) forecast by IFS-TM5 with assimilation (blue), without assimilation (orange) and ozone sonde in-situ observations (red) from Neumayer Station in Antarctica for August and October 2008. The depletion of ozone inside the ozone hole is clearly visible in October. This ozone loss is not well described by the free running model, but the assimilation of multiple satellite ozone sensors with this GEMS system leads to an accurate description.

Comparing OMI NO2 measurements with the GEMS regional and global atmospheric chemistry models
An evaluation of the model-simulated NO2 spatial distributions was carried out using OMI observations (4). Figure 4 shows the comparison between OMI NO2 measurements, three European-scale air quality models (EURAD, CHIMERE, SILAM), and two global atmospheric chemistry models (TM5 and MOZART). The results show the importance of the high model resolution of the regional models (20-40 km) as compared to the global models (100-300 km) to resolve the spatial variability of air pollution concentrations. The differences between the air quality forecast results give an indication of the degree of uncertainty associated with these models. The study has helped to identify and repair shortcomings of some individual models (for instance errors in the implementation and application of emissions) as well as to quantify uncertainties in the OMI retrieval (such as an overestimate of NO2 in summer) 4). Hence such an evaluation benefits both the models and the observations.
Figure 4. Mean tropospheric NO2 columns in August 2008 measured with the OMI satellite instrument, compared with simulations from three selected regional air quality models (EURAD, CHIMERE, SILAM), from the global MOZART model, and from the global TM5 model with a 1 degree resolution spatial zoom over Europe.
Figure 4. Mean tropospheric NO2 columns in August 2008 measured with the OMI satellite instrument, compared with simulations from three selected regional air quality models (EURAD, CHIMERE, SILAM), from the global MOZART model, and from the global TM5 model with a 1 degree resolution spatial zoom over Europe.



GEMS and EC-Earth
The development of the coupled TM5-IFS system for GEMS has also been useful for the parallel development of the EC-Earth climate model which is led by KNMI. In EC-Earth, TM5 is used for simulating greenhouse gases (currently ozone and methane) and various types of aerosols for simulations of climate change. The coupled systems set up for GEMS and EC-Earth necessarily differ in several respects: the version of the coupling software (OASIS4 versus OASIS3), the feedback of chemical fields (chemical tendencies in one case versus concentrations and radiative properties in the other), and the assimilation and transport of TM5 tracers in IFS which is absent in EC-Earth. However, apart from these differences, the use of online meteorological fields for driving TM5 could be done similarly in both systems. In GEMS the TM5 model has been extensively validated and model improvements have been implemented. The EC-Earth project directly benefits from these activities. The EC-Earth and GEMS activities thus resulted in a high degree of synergy.

Outlook
The European 7th framework project “Monitoring Atmospheric Composition and Climate” (MACC), co-ordinated by the ECMWF, has started at 1 June 2009 and is a direct continuation of the GEMS activity. MACC will expand the GEMS work in several respects. For instance, GEMS has focussed on building the global atmospheric composition assimilation-forecast system, while in MACC there will be more focus on the development of products for specific user communities such as environmental agencies, policy makers and the general public.
The KNMI involvement in GEMS will be continued in MACC. Two important new contributions are planned:

  • Together with TNO we will contribute to the European regional air quality model ensemble forecasts with the Netherlands national air quality model LOTOS-EUROS. Because MACC includes the leading European air quality models, and by applying an ensemble approach, MACC will produce the hitherto most advanced analysis of air pollution on the European scale.
  • Within MACC chemistry modules will be implemented directly in IFS for which we will supply and implement the TM5 model code. This is expected to lead to significant model performance improvements, and will ultimately replace the present version of the GEMS coupled system (Figure 1).

It is intended that MACC will evolve into the operational GMES atmosphere (core) service in the timeframe 2012-2014. The planned future TROPOMI satellite mission, for which KNMI has the scientific lead, will deliver the kind of observations that are crucial input for this GMES atmosphere service.

Conclusion
The GEMS consortium, led by ECMWF, has built an unprecedented modelling and assimilation system to analyse, monitor and forecast global atmospheric composition, in particular greenhouse gases, reactive gases and aerosols. This is achieved by applying advanced meteorological assimilation techniques to the field of atmospheric chemistry. The global GEMS system is coupled to an ensemble of forecasts from about 10 European regional air quality models, providing a detailed picture of air quality over Europe. In the GEMS system both the meteorology and the composition (gases, aerosols) are treated in a consistent way. The reanalyses provided by GEMS are a valuable source of information for air quality and climate monitoring. GEMS is the start of a long-term commitment, part of the European GMES programme, to provide up-to-date and accurate information on the atmospheric composition to environmental agencies, policy makers, scientists and the public.

KNMI has contributed substantially to the GEMS development, with the TM5 model, satellite observational data sets, contributions to the regional air quality subproject, and co-ordination activities. A major new activity in MACC will be the inclusion of the Dutch LOTOS-EUROS model in the ensemble of air quality models, in collaboration with TNO and RIVM.

References

  1. IGACO, 2004. An Integrated Global Atmospheric Chemistry Observation theme for the IGOS partnership. GAW Report 159, 54pp. (zie verder lezen).
  2. Hollingsworth, A., R.J. Engelen, C. Textor, A. Benedetti, O. Boucher, F. Chevallier, A. Dethof, H. Elbern, H. Eskes, J. Flemming, C. Granier, J.W. Kaiser, J.J. Morcrette, P. Rayner, V.H. Peuch, L. Rouil, M.G. Schultz, A.J. Simmons and the GEMS consortium, 2008. Toward a Monitoring and Forecasting System for Atmospheric Composition: The GEMS Project. Bull. Amer. Meteor. Soc., 89, 1147-1164.(zie externe links)
  3. Flemming, J., A. Inness, H. Flentje, V. Huijnen, P. Moinat, M.G. Schultz and O. Stein, 2009. Coupling global chemistry transport models to ECMWF's integrated forecast system. Geosci. Model Dev., 2, 253-265.
  4. Huijnen, V., H.J. Eskes, B. Amstrup, R. Bergstrom, K.F. Boersma, H. Elbern, J. Flemming, G. Foret, E. Friese, A. Gross, M. D'Isidoro, I. Kioutsioukis, A. Maurizi, D. Melas, V.-H. Peuch, A. Poupkou, L. Robertson, M. Sofiev, O. Stein, A. Strunk, A. Valdebenito, C. Zerefos and D. Zyryanov, 2009. Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models. Atmos. Chem. Phys. Discuss., 9, 22271-22330. (zie verder lezen)