MOZART

As pointed out before, during the GEMS project there were several versions for MOZART standalone (offline) runs. Newer versions were often the result of the validation exercises. The analysis of different simulations allowed for the interpretation of the impact of model parameterisation on the NO2 fields. A good example is the stratospheric definition and how much the results changed according to the dataset used for initialisation variables, or impact of different emission inventories in the tropospheric columns.

The selected 3 month averages of stratospheric NO2 determined by different MOZART versions, presented in Figure 5.7 and Figure 5.8, illustrate how the model has changed during the extent of the GEMS project. While, for the year 2003, the initial version of MOZART V1 did not perform well, the standalone V7 shows stratospheric NO2 fields that agree nicely with the satellite data (spatially and temporally). The problematic simulation of the stratosphere in the initial version (extremely high values in the high latitude regions in the winter periods of the Polar Regions) was in part related to wrong upper boundaries of the species and incorrect stratospheric chemistry.

Figure 5.7 Three month averages of global total NO2 columns measured by SCIAMACHY (top) and stratospheric columns determined by (from second of the top to bottom) MOZART V1, V7 and V10, for the periods of January – March (left) and October – December (right) of the year 2003.

Figure 5.8 Three month averages of global total NO2 columns measured by SCIAMACHY (top) and stratospheric columns determined by MOZART V9 (middle) and V10 (bottom), for the periods of July – September (left) and October – December (right) of the year 2004.

The lack of simulated ozone depletion in this layer for the year 2003 is also an indication of erroneous settings that influence both species. This was corrected for the following versions. The latest version V10 is a peculiar case, since in 2003 from January to August the NO2 columns in the stratosphere are often overestimated by a factor of 2 when compared to the satellite measurements.

On the other hand, for the year 2004 both model versions (V9 and V10) have very good results with only a slight overestimation in the winter months, in the tropics regions, for the version V10 (the output for the other versions were simulated only for specific case scenarios on the summer of 2004). It becomes then essential to emphasise that the results of V10 show a general decrease (up to

57% in May in the Northern Polar Region) in the stratospheric NO2 from 2003 to 2004. However, this trend is not observed by the satellite, and such differences can be better identified in the seasonality curves in Figure 5.9. Here it is quite evident the great improvement from recent versions compared to V1. Even with the overestimation of the satellite values in some months of 2003, the latest version provides an overall best agreement.

Figure 5.9 Seasonality curves for the year 2003 of total NO2 columns measured by SCIAMACHY (open symbols) and stratospheric columns determined by MOZART V1 (top), V7 (middle) and V10 (bottom).

Monthly averages determined for the selected regions as defined in Figure 5.6.

Figure 5.10 Seasonality curves for the year 2004 of total NO2 columns measured by SCIAMACHY (open symbols) and stratospheric columns determined by MOZART V9 (top) and V10 (bottom). Monthly averages determined for the selected regions as defined in Figure 5.6.

As mentioned earlier, the analysis of tropospheric data is more complex than for the stratosphere.

The high variability observed in time and space is often related to the location of sources and short lifetime of NO2. These factors are determinant for the correct model simulations. From the figures below it is possible to observe that above polluted regions, the modelled tropospheric NO2 columns are usually similar to the satellite measurements. MOZART V1 is once more the exception since it underestimates, throughout the year, by far, the NO2 over regions like the US, Europe or East-Asia.

The subsequent adjustments performed in the chemistry scheme (e.g., the reaction rates and constants) might explain the observed improvements. Nevertheless, for East-Asia, this error was partly attributed to the combination of inaccurate emission inventories that did not reflect the rapid population growth and consequent development and increase of industrial activities.

Figure 5.11 Three month averages of global tropospheric NO2 columns measured by SCIAMACHY (top) and determined by (from second of top to bottom) MOZART V1, V7 and V10, for the periods of January – March (left) and April – June (right) of the year 2003.

Figure 5.12 Three month averages of global tropospheric NO2 columns measured by SCIAMACHY (top) and determined by MOZART V9 (middle) and V10 (bottom), for the periods of July – September (left) and October – December (right) of the year 2004.

In V7, the introduction of up-to-date emission values for East-Asia, from the REAS inventory, resulted in a more reasonable model output in this region. In the following versions, the seasonality for CO and NOx was corrected in the model scheme and better agreement was found between simulations and measurements. Nevertheless, for the winter periods, the differences between model results and observations remained quite high. If the longer lifetime of NO2, in this season, is not correctly represented in the model scheme, then the NO2 concentrations will be too low in the simulations. In addition, also the emission inventory might underestimate the NO2 emissions for the winter time. On the other hand, in the period of November 2003 to February 2004 (and again in the end of 2004) all versions (with the exception of V1) overestimate the NO2 in Europe which is quite

unexpected. Once more, outdated emission inventories can be a possible explanation for this feature. Opposite to what was observed for East-Asia, in Europe the data used did not follow the decreasing trend in this region prompted by the implementation of environmental legislation that forced the reduction of emissions. In addition, the high emission values attributed ships might contribute, in part, to such overestimation. The differences pointed out above are more clearly observed in the seasonality plots presented in Figure 5.14 and Figure 5.15. To a certain degree, this difference could be linked to incorrect SCIAMACHY columns caused by errors in the retrieval process. It is known that East-Asia is an extremely polluted region and the aerosol load is frequently very high (Streets et al., 2009). As it was illustrated in the previous chapters, currently, the influence of the aerosol to the radiation that reaches the satellite is not fully described in the retrieval method.

However, since these effects are rather complex, it is difficult to predict if the measured tropospheric columns are under- or overestimated. Furthermore, during the winter periods, the satellite measurements are scarcer because of increased cloud cover. Also, the sensitivity of the measurements to the NO2 close to the surface is smaller in this period due to the low Sun.

Figure 5.13 Monthly averages of tropospheric NO2 columns measured by SCIAMACHY (left) and determined by MOZART V10 (right) for two different case studies: Siberia fires – May 2003 (top) and, Alaska fires – June 2004 (bottom).

As mentioned above, a dominant natural source of NO2 is the burning savannah in central Africa.

The figures above highlight a general tendency of overestimation of the NO2 for wildfire events. The exception is, for 2003, the MOZART V1, which was able to simulate the right order of magnitude

over the regions in Africa. However, NO2 in the following versions was overestimated. Figure 5.13 illustrates such discrepancy between the satellite data and the results of latest model version (V10) for two major events of boreal fires: in the region of Siberia in May 2003, and Alaska in June 2004.

This type of fires is typical for its smouldering combustion and low content of nitrogen which leads to low NOx/CO emission ratios. Furthermore, a rapid conversion to PAN might explain why, in the satellite observations, almost no NO2 is measured in these regions. The model simulations are much higher probably because of an incorrect parameterisation of the facts above mentioned.

Differences are also found for other types of fires in South America, in 2004, and central Africa, for both years. This is in fact quite unexpected since from V7 onwards the emission dataset was changed from a monthly based to 8 day period which should have reflected as an improvement on the simulations. However, apart from this, other modifications in the chemistry scheme and reaction rates, namely those for CO + OH, can also be a reason for an increase of NO2, when, e.g., limiting the formation of HNO3. Furthermore, NO2 from tropical fires is normally present in higher altitudes (due to pyroconvective lofting) which might not be well described in the model simulations. On the other hand, the differences found might also be related to some uncertainties in the retrieved vertical columns. In the case of fires, it is difficult to predict the vertical distribution of trace gas and particles, and how the sensitivity of the measurements would be influenced. The results presented in the previous chapters have shown that higher plumes of highly absorbing aerosol can shield the trace gas below. Thus, when this is not accounted for in the retrieval, the NO2

columns might be underestimated. Conversely, many particles mixed with the trace gas will enhance the scattering of the light.

As expected, the last two versions V9 and V10 present very similar results for the tropospheric NO2. The NO2 values in the lower atmospheric layer were not so influenced by this update because the difference between those two versions is mostly related to the stratospheric parameterisations.