Aviation and the Global Atmosphere

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6.5.2. Climate Signatures of Aircraft-Induced Ozone Perturbations

Figure 6-11: Equilibrium change of annual, zonal
mean temperature (K) caused by ozone perturbation
due to NOx emissions of a projected sub- and
supersonic aircraft fleet for the year 2015, as
simulated with the GISS model (Rind and Lonergan,
1995). This calculation is not for any specific
scenarios assessed here.

Figure 6-12: Equilibrium change of annual, zonal
mean temperature (K) caused by ozone perturbation
due to NOx emissions of 1991-92 air traffic (DLR-2),
as simulated with the ECHAM4/MLO model (Ponater
et al., 1998). This result is similar to, but not based on,
the scenarios analyzed here.

Figure 6-13: Equilibrium change of annual, zonal
mean temperature (K) resulting from the
anthropogenic increase of well-mixed greenhouse
gases from 1990 to 2015, as simulated with the
ECHAM4/MLO model (Ponater et al., 1998).

With uncertainty in relating RF to climate response noted above, it would be useful to compare CGCM-modeled climate responses for aircraft perturbations. Less agreement is expected between models because the magnitude of their feedback responses (i.e., climate sensitivity) can be quite different from one another. A review of different model sensitivities has been given in IPCC (1990, 1992, 1996); the values vary from a sensitivity of about 0.4 K/(Wm-2) to 1.2 K/(Wm-2) for doubled CO2. How the sensitivity would vary for heterogeneous aircraft perturbations is not known.

The most appropriate tool would be simulations in the transient mode using coupled atmosphere-ocean GCMs. These simulations are computationally very expensive, and it would be very difficult to separate signal from noise. To date, however, all known comprehensive model simulations of aircraft induced climate change were made in the quasi-stationary mode using either pure atmospheric GCMs (e.g., Sausen et al., 1997) or atmospheric GCMs coupled to mixed layer ocean models (examples below). In other words, these simulations studied quasi-equilibrium response to a stationary or seasonally repeating perturbation. Pure atmospheric models underestimate the response if, for instance, sea surface temperature is fixed to a prescribed value. Equilibrium simulations with coupled models overestimate the aircraft effect in absolute numbers relative to transient simulations (see discussion in Sausen and Schumann, 1999), and the spatial pattern of climate change can serve only as a first estimate (Kattenberg et al., 1996). These simulations must be compared with analogous simulations for well-mixed greenhouse gases (e.g., CO2 doubling). Then the particular climate sensitivity to aircraft-induced radiative forcing and aircraft signatures of climate changes may possibly be extracted.

Using the GISS 3-D climate/middle atmosphere model, Rind and Lonergan (1995) studied the impact of the combined effect of a stratospheric ozone decrease and a tropospheric ozone increase from an assumed sub- and supersonic aircraft fleet for the year 2015. An equilibrium climate simulation leads to a general stratospheric cooling of a few tenths of a Kelvin, combined with a warming of the lower stratosphere in northern polar regions as a result of altered atmospheric circulation (Figure 6-11). The globally averaged surface temperature change was found to be not statistically significant compared with climatic variability.

As another example, Figure 6-12 shows the equilibrium annual, zonal mean temperature change from ECHAM4/MLO (Ponater et al., 1998) due to ozone perturbations resulting from 1992 air traffic (Dameris et al., 1998), which differ slightly from the 1992 ozone perturbations reported in this assessment. The overall pattern of temperature change is statistically significant. The RF associated with this case is 0.04 W m-2, and the resulting equilibrium change of global mean surface temperature is 0.06 K, resulting in a climate sensitivity parameter l of about 1.5 K/(W m-2). Note that a climate sensitivity calculated for such small perturbations is associated with large uncertainty.

For comparison, Figure 6-13 shows the equilibrium climate change associated with the anthropogenic increase of well-mixed greenhouse gases CO2, CH4, etc.) from 1990 to 2015 according to IS92a as simulated with the same model. The associated global mean surface temperature change is 0.9 K, and the RF is 1.1 W m-2. The resulting climate sensitivity parameter, about 0.8 K/(W m-2), is smaller than for the aircraft-induced ozone perturbation. Temperature change patterns are quite different for well-mixed greenhouse gases than for the aircraft-induced ozone perturbation.

These and related experiments with the GISS and ECHAM4 models showed that: The aircraft-related climate change pattern and the aircraft-related climate sensitivity parameters are model-dependent, and the climate response for aircraft-induced ozone changes is different from that for conventional greenhouse gases. Internal feedback processes within and between climate models appear to work differently. The exact nature of the climate feedback differences to different pattern of RF remains to be investigated. Thus, the climate factors of human interest, especially the regional climate change for these regional perturbations, await further development of climate models. The details of the climate response to the contrail radiative imbalance, which is spatially heterogeneous on the smallest scales, is likewise unknown.

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