Projected change in characteristics of near surface temperature inversions for southeast Australia.

Ji, F., J.P. Evans, A. Di Luca, N. Jiang, R. Olson, L. Fita, D. Argüeso, L. T-C Chang, Y. Scorgie, M. Riley
Climate Dynamics, doi: 10.1007/s00382-018-4214-3, 2018.


Air pollution has significant impacts on human health. Temperature inversions, especially near surface temperature inversions, can amplify air pollution by preventing convective movements and trapping pollutants close to the ground, thus decreasing air quality and increasing health issues. This effect of temperature inversions implies that trends in their frequency, strength and duration can have important implications for air quality. In this study, we evaluate the ability of three reanalysis-driven high-resolution regional climate model (RCM) simulations to represent near surface inversions at 9 sounding sites in south- east Australia. Then we use outputs of 12 historical and future RCM simulations (each with three time periods: 1990–2009, 2020–2039, and 2060–2079) from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Mod- elling (NARCliM) project to investigate changes in near surface temperature inversions. The results show that there is a substantial increase in the strength of near surface temperature inversions over southeast Australia which suggests that future inversions may intensify poor air quality events. Near surface inversions and their future changes have clear seasonal and diurnal variations. The largest differences between simulations are associated with the driving GCMs, suggesting that the large-scale circulation plays a dominant role in near surface inversion strengths.

Key Figure

Fig. 3 Mean near surface inversions and changes in near surface inversions for 2060–2079 relative to 1990–2009. Stippled (significant agreeing) areas indicate that half or more models show statistically significant change, with 80% or more of the significant models changed in the same direction. Grey (significant disagreeing) areas indicate that half or more models show statistically significant change, with less than 80% of significant models changed in the same direction

UNSW    This page is maintained by Jason Evans | Last updated 23 January 2018