69 Climate Model Response Uncertainty in Projections of Climate …
437
the century at the medium climate sensitivity. However, across the range of climate
sensitivities considered, the O 3 penalty varies from 1.9 ± 0.3 to 5.7 ± 0.4 ppb. For
PM 2.5 , the range of climate sensitivities leads to population-weighted penalties spanning from 0.8 ± 0.1 to 2.1 ± 0.1 µg m
−3 . Policies leading to a stabilization of total
radiative forcing by 2100 may significantly reduce these climate-induced increases
in U.S. air pollution. Under the P45 and P37 scenarios, the range of O 3 penalties projected across the 3 climate sensitivities lowers to 0.3 ± 0.3 to 0.6 ± 0.3 ppb. The range
of PM 2.5 penalties projected is similarly constrained to 0.1 ± 0.1 to 0.7 ± 0.1 µg m
−3
under the stabilization scenarios.
69.4 Conclusions
Air quality and associated health effects are among the largest impacts reported in
climate policy benefits assessments. Adequately characterizing the uncertainties that
underlie climate projections and how these propagate to air quality simulations can
add great value to these analyses. By perturbing climate sensitivity, our ensemble
simulation of climate-induced changes to air quality shows that model response
uncertainty can be as large as that caused by natural variability or emissions scenario.
Our results also suggest that beyond reducing the climate penalty on air quality,
climate policies could reduce the risks associated with higher-than-expected levels
of response to climate forcing by the real climate system. Given the IPCC’s latest
conclusion that a best estimate for climate sensitivity can no longer be given due
to lack of agreement across studies, it is critical that this source of uncertainty is
carefully considered.
References
1. M. A. Fiore, V. Naik, E. M. Leibensperger, Air quality and climate connections. J. Air Waste
Manage. Assoc. 65(6), 645–685 (2015)
2. F. Garcia-Menendez et al., U.S. air quality and health benefits from avoided climate change
under greenhouse gas mitigation. Environ. Sci. Technol. 49(13), 7580–7588 (2013)
3. E. Hawkins, R. Sutton, The potential to narrow uncertainty in regional climate predictions. Bull.
Am. Meteorol. Soc. 90(8), 1095–1108 (2009)
4. E. Monier et al., An integrated assessment modeling framework for uncertainty studies in global
and regional climate change: the MIT IGSM-CAM (version 1.0). Geosci. Model Dev. 6, 2063–
2085 (2013)
5. E. Monier et al., A framework for modeling uncertainty in regional climate change. Clim. Change
131(1), 51–66 (2015)
6. S. Paltsev et al., Integrated economic and climate projections for impact assessment. Clim.
Change 131(1), 21–33 (2015)
7. A.P. Sokolov, E. Monier, Changing the climate sensitivity of an atmospheric general circulation
model through cloud radiative adjustment. J. Climate 25, 6567–6584 (2012)
437
the century at the medium climate sensitivity. However, across the range of climate
sensitivities considered, the O 3 penalty varies from 1.9 ± 0.3 to 5.7 ± 0.4 ppb. For
PM 2.5 , the range of climate sensitivities leads to population-weighted penalties spanning from 0.8 ± 0.1 to 2.1 ± 0.1 µg m
−3 . Policies leading to a stabilization of total
radiative forcing by 2100 may significantly reduce these climate-induced increases
in U.S. air pollution. Under the P45 and P37 scenarios, the range of O 3 penalties projected across the 3 climate sensitivities lowers to 0.3 ± 0.3 to 0.6 ± 0.3 ppb. The range
of PM 2.5 penalties projected is similarly constrained to 0.1 ± 0.1 to 0.7 ± 0.1 µg m
−3
under the stabilization scenarios.
69.4 Conclusions
Air quality and associated health effects are among the largest impacts reported in
climate policy benefits assessments. Adequately characterizing the uncertainties that
underlie climate projections and how these propagate to air quality simulations can
add great value to these analyses. By perturbing climate sensitivity, our ensemble
simulation of climate-induced changes to air quality shows that model response
uncertainty can be as large as that caused by natural variability or emissions scenario.
Our results also suggest that beyond reducing the climate penalty on air quality,
climate policies could reduce the risks associated with higher-than-expected levels
of response to climate forcing by the real climate system. Given the IPCC’s latest
conclusion that a best estimate for climate sensitivity can no longer be given due
to lack of agreement across studies, it is critical that this source of uncertainty is
carefully considered.
References
1. M. A. Fiore, V. Naik, E. M. Leibensperger, Air quality and climate connections. J. Air Waste
Manage. Assoc. 65(6), 645–685 (2015)
2. F. Garcia-Menendez et al., U.S. air quality and health benefits from avoided climate change
under greenhouse gas mitigation. Environ. Sci. Technol. 49(13), 7580–7588 (2013)
3. E. Hawkins, R. Sutton, The potential to narrow uncertainty in regional climate predictions. Bull.
Am. Meteorol. Soc. 90(8), 1095–1108 (2009)
4. E. Monier et al., An integrated assessment modeling framework for uncertainty studies in global
and regional climate change: the MIT IGSM-CAM (version 1.0). Geosci. Model Dev. 6, 2063–
2085 (2013)
5. E. Monier et al., A framework for modeling uncertainty in regional climate change. Clim. Change
131(1), 51–66 (2015)
6. S. Paltsev et al., Integrated economic and climate projections for impact assessment. Clim.
Change 131(1), 21–33 (2015)
7. A.P. Sokolov, E. Monier, Changing the climate sensitivity of an atmospheric general circulation
model through cloud radiative adjustment. J. Climate 25, 6567–6584 (2012)