How well can we calculate the impact from subsonic aircraft in the tropopause region using the tropospheric 3-D CTMs or the stratospheric 2-D models? For these aircraft, part of the perturbation occurs in the LS and part in the UT.
As discussed in Chapter 2, based on the limited set of observational data that could be chosen for model validation, none of the 3-D CTMs could be picked as the best assessment model for impact studies of future aircraft emissions. The UiO 3-D model was selected as a representative model for the UV (Chapter 5) and climate impact studies (Chapter 6), because it gave results in the middle range of model results and it was easily available for sensitivity studies. Height profiles for 30-60°N zonal mean O3 perturbations for 1992 subsonic emissions (Scenario B-A in Table 4-4) obtained with the AER 2-D model and the UiO 3-D model are shown in Figure 4-10a. There is reasonable agreement between the two models in the 8-12 km region, near the tropopause. The UiO 3-D model, whose chemistry is most suited to the troposphere, shows a smaller O3 perturbation in the middle and lowest troposphere. The AER 2-D model, whose chemistry is most suited to the stratosphere, shows a larger O3 perturbation in the LS. An uncertainty range of a factor of 2 was adopted for O3 perturbation from future subsonic aircraft emissions. This uncertainty range was based on the range of model results obtained by participating models in basic perturbation studies and results from the limited number of sensitivity studies. A "fair" confidence is associated with this uncertainty range for 2015, and a "poor" confidence is associated with this uncertainty range for 2050.
Figure 4-10b shows zonal mean O3 changes for five calculations from the UiO 3-D model. As illustrated in the figure, predicted changes in O3 from subsonic aircraft are comparable to changes from surface sources in 2015 and 2050.
The basic assumption in market studies that determine the routing and size of the supersonic fleet is that the supersonic fleet will replace certain routes of the given subsonic fleet, corresponding to about 10% of the subsonic fuel burn. The results of these studies were used to generate the fuel burn for a combined fleet consisting of a supersonic fleet with a modified subsonic fleet. For this reason, it is more appropriate to compare the effect of the combined fleet to the subsonic fleet, rather than to look at the supersonic fleet in isolation. Taking 2015 as an example, numerical results were generated for the 2015 atmosphere without aircraft (scenario C), the 2015 atmosphere with the standard subsonic fleet (scenario D), and the 2015 atmosphere with the combined fleet (scenario S1k). Recognizing the uncertainties concerning the tropospheric response generated by the stratospheric models, the strategy is to use the change in O3 computed between S1k and D and the results from the tropospheric models from scenario D minus scenario C for the effect of the subsonic fleet, after adjusting for the 10% difference in fuel burn.
For supersonic aircraft influences on UV effects (Chapter 5) and radiative forcing and climate change (Chapter 6), we have chosen a central or most probable emission scenario (S1k-2015 and S9h-2050), along with a representative assessment model (AER). This emission scenario assumes a SO2 gas-to-particle conversion of 10%. The AER model was selected because it was the model that calculated and supplied the enhanced gas-to-particle sulfate aerosol SAD that was used in all participating assessment models. The middle plot in Figure 4-11 shows the percentage change in column O3 for the AER model using the S1k scenario. At all latitudes and months, the AER model derives a reduction in total column O3.
Because of coupling between chemistry and transport, there is no simple way to scale O3 change profiles based on a priori estimates of uncertainties. It is possible to get an idea of the uncertainties by looking at the assembly of results from various scenarios performed by different models. Concentrating on the first group of scenarios in Table 4-11 (which represent a 500-plane Mach 2.4 fleet with EI(NOx)=5 flying in a clean sulfate background), we picked S1c from UNIVAQ and S1h from GSFC as representative of the range of possible results. The differences in computed O3 changes by the AER, UNIVAQ, and GSFC models in northern mid-latitudes are illustrated in Figure 4-12a for 2015. The UNIVAQ and GSFC models were picked because these models represent different ways of treating transport and PSCs that consistently produce the smallest and largest O3 depletion in most scenarios. The sampling of scenarios also covers the possible range of effects from different assumptions of gas-to-particle conversion in plume processing of SO2 emission. For 2050, we focus on a 1,000-plane Mach 2.4 fleet with EI(NOx)=5. The AER S9h scenario is taken as the central case, and the lower and upper extremes were taken to be S9d for UNIVAQ and S9f for GSFC. The differences in O3 changes computed by the AER, UNIVAQ, and GSFC models in northern mid-latitudes are illustrated in Figure 4-12b for 2050.
It is also important to note which uncertainties, among those discussed in Chapters 2 and 3, are not included in this range. All of the models used rate data from DeMore et al. (1997). A previous study (described in Stolarski et al., 1995) showed that uncertainties in rate data could lead to an uncertainty in NH O3 column change of ±1%. Current studies indicate that most models underestimate the mean age of air as defined by inert tracers that enter the stratosphere via the tropical tropopause. Given that there appears to be a positive correlation between calculated increases in NOy and H2O from HSCT and calculated mean age, it has been suggested that models that underestimate age will also underestimate NOy and H2O increases from HSCT. It is difficult to quantify the uncertainty given current information. We did not consider the effect of plume processing and possible changes (in temperature and transport circulation) in the future background atmosphere. Finally, the range cited does not include different technology options for different EI(NOx), different cruise altitudes, and different fleet sizes.
It is possible to arrive at a subjective estimate for uncertainty estimates for changes in column O3. This value can be used to calculate changes in UV because that depends mostly on the changes in column in the lower stratosphere. It is less obvious whether this value can be used to estimate uncertainties in radiative forcing. Restricting ourselves to the 1,000-plane Mach 2.4 fleet with EI(NOx)=5, the range of model results for annual averaged Northern Hemisphere column O3 depletion is -0.1% to -1.4% (see Table 4-12). Other model studies lead us to believe that reasonable changes in the background atmosphere (including background sulfate surface area) would not change this range in a significant way. Uncertainties in rate data would expand the range to about +1 to -2.5%. One may also argue that the inability of the models to simulate the correct mean age may add to uncertainties on the negative side by another 1%. Thus, a subjective estimate is that actual atmospheric response would likely lie between +1 and -3.5%.
A 500-plane Mach 2.4 fleet with EI(NOx)=5 would have a similar range of uncertainty. Here, the range of model results for annual averaged Northern Hemisphere column O3 depletion is 0 to -1.3% (see Table 4.11)-very close to the -0.1 to -1.4% model range for 1,000 planes. Applying the same arguments for this 500-plane fleet, a subjective estimate for the actual atmospheric response would likely be in the range of +1 to -3.5%. We have "fair" confidence in this range for the 500-plane and 1,000-plane fleets.
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