The role of natural variability in projections of climate change impacts on U.S. ozone pollution

Abstract

Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground‐level ozone and aggravate associated health effects. However, modeling studies exploring air quality‐climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate‐induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic‐forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios.

Maximizing ozone signals among chemical, meteorological, and climatological variability

“…For present-day analysis, we simulate surface ozone using CAM-chem, a component of the Community Earth System Model (CESM) and available observations within the US from the EPA CASTNET database. For future analysis, and in order to examine the potential for patterns of variability to change in the future, we utilize two existing simulations of CAM-chem conducted by Garcia-Menendez et al (2017). Much of this analysis is conducted using the R language (R Project, https://www.r-project.org/, last access: 7 June 2018).…”

“…We also include two reference simulations of the future climate, MOZ_2050 and MOZ_2100 (simulating the meteorological years 2035-2065 and 2085-2115, respectively), using the CESM CAM-chem simulations described in detail by Garcia-Menendez et al (2017) with one set of initial condition data and a climate sensitivity of 3.0 • C. These simulations do not include projections of any changes in future emissions. Compared to the present-day simulation (MOZ_2000), these future simulations (MOZ_2050 and MOZ_2100) have several parametric differences: the model version is 1.1.2 (see Tilmes et al, 2015, and references for information on model development), the atmospheric component is CAM3, the emissions (which are held constant at year-2000 levels) are from the Precursors of Ozone and their Effects in the Troposphere database (see Garcia-Menendez et al, 2017), and the meteorology is derived from a linkage between the Massachusetts Institute of Technology Integrated Global System Model (MIT IGSM) and the CESM CAM model (Monier et al, 2013), and as such has 26 vertical levels.…”

“…Compared to the present-day simulation (MOZ_2000), these future simulations (MOZ_2050 and MOZ_2100) have several parametric differences: the model version is 1.1.2 (see Tilmes et al, 2015, and references for information on model development), the atmospheric component is CAM3, the emissions (which are held constant at year-2000 levels) are from the Precursors of Ozone and their Effects in the Troposphere database (see Garcia-Menendez et al, 2017), and the meteorology is derived from a linkage between the Massachusetts Institute of Technology Integrated Global System Model (MIT IGSM) and the CESM CAM model (Monier et al, 2013), and as such has 26 vertical levels. For a full description of these simulations, see Garcia-Menendez et al (2017).…”

“…The calculated ozone variability can be further reduced by utilizing even longer averaging periods, such as monthly (e.g., Rasmussen et al, 2012), seasonal (e.g., Fiore et al, 2014;Barnes et al, 2016), annual, or decadal mean values (e.g., GarciaMenendez et al, 2017). This strategy is analogous to the averaging of meteorological data to derive a climate signal, and, just as Lewandowsky et al (2015) recommend averaging 17 or more years in order to achieve climatological estimates of temperature trends, there is a growing body of literature recommending averaging short-timescale chemical variability (what could be called chemical weather, see Lawrence et al, 2005) for 15 or more years (e.g., Garcia-Menendez et al, 2017) in order to achieve an estimate of what could be called the chemical climate (see Möller, 2010).…”

“…The debate over the existence or length of a global warming hiatus (Lewandowsky et al, 2015;Roberts et al, 2015;Medhaug et al, 2017) and research examining the time of emergence of climatological (Weatherhead et al, 2002;Deser et al, 2012;Hawkins and Sutton, 2012;de Elía et al, 2013;Schurer et al, 2013), meteorological (Giorgi and Bi, 2009;King et al, 2015), chemical (Camalier et al, 2007;Strode and Pawson, 2013;Barnes et al, 2016;Garcia-Menendez et al, 2017), and other sectoral signals (e.g., Monier et al, 2016) embody an accumulation of techniques and strategies for filtering noise (due to natural variability) and maximizing the capability to detect statistically significant signals and trends in noisy data. It is well established that temporal averaging (e.g., Lewandowsky et al, 2015) and spatial averaging (e.g., Frost et al, 2006;Hawkins and Sutton, 2012;Barnes et al, 2016) can enhance signal detection capabilities in atmospheric data.…”