Chapter 69
Climate Model Response Uncertainty
in Projections of Climate Change
Impacts on Air Quality
Fernando Garcia-Menendez, James East, Bret D. Pienkosz
and Erwan Monier
Abstract Uncertainties in climate simulations can strongly propagate to estimates of
climate change impacts, including its effects on air pollution. Here we use a coupled
modeling framework to evaluate the role of climate model response in projections
of climate-induced impacts on air quality. Within integrated economic, climate, and
air pollution projections, climate model response is altered by modifying the climate
sensitivity of the framework’s Earth system component. We find that variations in
climate sensitivity ranging from 2.0 to 4.5 °C per doubling of CO 2 can change
projections of the climate penalty on O 3 and PM 2.5 pollution in the U.S. by more
than 2 ppb and 0.5 µg m
−3 . The impact of uncertainty due to climate model response
can be as important as that related to greenhouse gas emissions scenario or natural
variability.
69.1 Introduction
Projections of climate change under different greenhouse emissions scenarios have
been widely used to investigate the potential impacts of a warmer climate on air quality. These analyses suggest that many locations may experience a “climate penalty”
on air quality as climate change leads to meteorological conditions that enhance
air pollution. Over the U.S., prior modeling studies consistently suggest a climateinduced increase in ozone (O 3 ) pollution over polluted regions, while there is less
agreement on fine particulate matter (PM 2.5 ) impacts, which also vary across different
PM 2.5 components [1].
F. Garcia-Menendez (B) · J. East · B. D. Pienkosz
Department of Civil, Construction and Environmental Engineering, North Carolina State
University, 27695 Raleigh, NC, USA
e-mail: f_garcia@ncsu.edu
E. Monier
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of
Technology, 02139 Cambridge, MA, USA
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_69
433
Climate Model Response Uncertainty
in Projections of Climate Change
Impacts on Air Quality
Fernando Garcia-Menendez, James East, Bret D. Pienkosz
and Erwan Monier
Abstract Uncertainties in climate simulations can strongly propagate to estimates of
climate change impacts, including its effects on air pollution. Here we use a coupled
modeling framework to evaluate the role of climate model response in projections
of climate-induced impacts on air quality. Within integrated economic, climate, and
air pollution projections, climate model response is altered by modifying the climate
sensitivity of the framework’s Earth system component. We find that variations in
climate sensitivity ranging from 2.0 to 4.5 °C per doubling of CO 2 can change
projections of the climate penalty on O 3 and PM 2.5 pollution in the U.S. by more
than 2 ppb and 0.5 µg m
−3 . The impact of uncertainty due to climate model response
can be as important as that related to greenhouse gas emissions scenario or natural
variability.
69.1 Introduction
Projections of climate change under different greenhouse emissions scenarios have
been widely used to investigate the potential impacts of a warmer climate on air quality. These analyses suggest that many locations may experience a “climate penalty”
on air quality as climate change leads to meteorological conditions that enhance
air pollution. Over the U.S., prior modeling studies consistently suggest a climateinduced increase in ozone (O 3 ) pollution over polluted regions, while there is less
agreement on fine particulate matter (PM 2.5 ) impacts, which also vary across different
PM 2.5 components [1].
F. Garcia-Menendez (B) · J. East · B. D. Pienkosz
Department of Civil, Construction and Environmental Engineering, North Carolina State
University, 27695 Raleigh, NC, USA
e-mail: f_garcia@ncsu.edu
E. Monier
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of
Technology, 02139 Cambridge, MA, USA
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_69
433