2.6.3 Radiative Forcing Estimates for Aviation- Induced Cloudiness

Individual persistent contrails are routinely observed to shear and spread, covering large additional areas with cirrus cloud (Minnis et al., 1998). Aviation aerosol could also lead to changes in cirrus cloud (see Section 2.6.4). Aviation-induced cloudiness (AIC) is defined to be the sumx of all changes in cloudiness associated with aviation operations. Thus, an AIC estimate includes persistent contrail cover. Because spreading contrails lose their characteristic linear shape, a component of AIC is indistinguishable from background cirrus. This basic ambiguity, which prevented the formulation of a best estimate of AIC amounts and the associated RF in IPCC-1999, still exists for this assessment. Estimates of the ratio of induced cloudiness cover to that of persistent linear contrails range from 1.8 to 10 (Minnis et al., 2004; Mannstein and Schumann, 2005^[10]), indicating the uncertainty in estimating AIC amounts. Initial attempts to quantify AIC used trend differences in cirrus cloudiness between regions of high and low aviation fuel consumption (Boucher, 1999). Since IPCC-1999, two studies have also found significant positive trends in cirrus cloudiness in some regions of high air traffic and found lower to negative trends outside air traffic regions (Zerefos et al., 2003; Stordal et al., 2005). Using the International Satellite Cloud Climatology Project (ISCCP) database, these studies derived cirrus cover trends for Europe of 1 to 2% per decade over the last one to two decades. A study with the Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) provides further support for these trends (Stubenrauch and Schumann, 2005). However, cirrus trends that occurred due to natural variability, climate change or other anthropogenic effects could not be accounted for in these studies. Cirrus trends over the USA (but not over Europe) were found to be consistent with changes in contrail cover and frequency (Minnis et al., 2004). Thus, significant uncertainty remains in attributing observed cirrus trends to aviation.

Regional cirrus trends were used as a basis to compute a global mean RF value for AIC in 2000 of +0.030 W m^–2 with a range of +0.01 to +0.08 W m^–2 (Stordal et al., 2005). This value is not considered a best estimate because of the uncertainty in the optical properties of AIC and in the assumptions used to derive AIC cover. However, this value is in good agreement with the upper limit estimate for AIC RF in 1992 of +0.026 W m^–2 derived from surface and satellite cloudiness observations (Minnis et al., 2004). A value of +0.03 W m^–2 is close to the upper-limit estimate of +0.04 W m^–2 derived for non-contrail cloudiness in IPCC-1999. Without an AIC best estimate, the best estimate of the total RF value for aviation-induced cloudiness (Section 2.9.2, Table 2.12 and Figure 2.20) includes only that due to persistent linear contrails. Radiative forcing estimates for AIC made using cirrus trend data necessarily cannot distinguish between the components of aviation cloudiness, namely persistent linear contrails, spreading contrails and other aviation aerosol effects. Some aviation effects might be more appropriately considered feedback processes rather than an RF (see Sections 2.2 and 2.4.5). However, the low understanding of the processes involved and the lack of quantitative approaches preclude reliably making the forcing/feedback distinction for all aviation effects in this assessment.

Two issues related to the climate response of aviation cloudiness are worth noting here. First, Minnis et al. (2004, 2005) used their RF estimate for total AIC over the USA in an empirical model, and concluded that the surface temperature response for the period 1973 to 1994 could be as large as the observed surface warming over the USA (around 0.3°C per decade). In response to the Minnis et al. conclusion, contrail RF was examined in two global climate modelling studies (Hansen et al., 2005; Ponater et al., 2005). Both studies concluded that the surface temperature response calculated by Minnis et al. (2004) is too large by one to two orders of magnitude. For the Minnis et al. result to be correct, the climate efficacy or climate sensitivity of contrail RF would need to be much greater than that of other larger RF terms, (e.g., CO₂). Instead, contrail RF is found to have a smaller efficacy than an equivalent CO₂ RF (Hansen et al., 2005; Ponater et al., 2005) (see Section 2.8.5.7), which is consistent with the general ineffectiveness of high clouds in influencing diurnal surface temperatures (Hansen et al., 1995, 2005). Several substantive explanations for the incorrectness of the enhanced response found in the Minnis et al. study have been presented (Hansen et al., 2005; Ponater et al., 2005; Shine, 2005).

The second issue is that the absence of AIC has been proposed as the cause of the increased diurnal temperature range (DTR) found in surface observations made during the short period when all USA air traffic was grounded starting on 11 September 2001 (Travis et al., 2002, 2004). The Travis et al. studies show that during this period: (i) DTR was enhanced across the conterminous USA, with increases in the maximum temperatures that were not matched by increases of similar magnitude in the minimum temperatures, and (ii) the largest DTR changes corresponded to regions with the greatest contrail cover. The Travis et al. conclusions are weak because they are based on a correlation rather than a quantitative model and rely (necessarily) on very limited data (Schumann, 2005). Unusually clear weather across the USA during the shutdown period also has been proposed to account for the observed DTR changes (Kalkstein and Balling, 2004). Thus, more evidence and a quantitative physical model are needed before the validity of the proposed relationship between regional contrail cover and DTR can be considered further.