SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE2567 Positive but variable sensitivity of August surface ozone to large-scale warming in the southeast United States Tzung-May Fu 1*, Yiqi Zheng 1,2, Fabien Paulot 3,4, Jingqiu Mao 3,4, Robert M. Yantosca 5 1 Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing , China 2 Department of Geology and Geophysics, Yale University, New Haven, CT 06511, USA 3 Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08544, USA 4 Geophysical Fluid Dynamics Laboratory / National Oceanic and Atmospheric Administration, Princeton, NJ 08540, USA 5 School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA *Correspondence to: tmfu@pku.edu.cn NATURE CLIMATE CHANGE 1

2 S1. Additional details on CASTNET ozone and temperature analysis Co-located, hourly measurements of surface ozone and temperature were from the Clean Air Status and Trends Network (CASTNET), managed by the U.S. Environmental Protection Agency 1. We selected sites in the Southeast U.S. (90ºW - 76ºW, 31ºN-40ºN) that are rural, non-mountainous (elevation below 600 m), and not affected by local land-sea breeze (at least 50 km from the ocean) 2. In addition, only sites with at least 18 years worth of valid August mean afternoon (1-5 pm local time) surface ozone measurements and August mean daily maximum temperature measurements during were selected. A total of 20 sites were selected (Fig. S1). Daily afternoon ozone concentration at each site was calculated as an average of at least four valid hourly measurements between 1 pm to 5 pm local time. August mean afternoon ozone concentration at each site was calculated as an average of at least 20 valid daily afternoon ozone concentrations. The long-term mean of August afternoon ozone concentrations at each site was calculated as an average of at least 18 valid August mean afternoon ozone concentrations between 1988 and Finally, the interannual anomaly of August mean ozone concentration ( O3) at each site for each year was calculated by removing the long-term ( ) mean at that site. 2 / 23

3 Daily maximum temperature at each site was calculated based on at least 18 valid hourly measurements per day. August mean daily maximum temperature at each site was calculated as an average of at least 20 valid daily maximum temperatures. The interannual anomaly of August mean daily maximum temperature ( Tmax) at each site for each year was calculated by removing the long-term ( ) mean at that site. S2. Additional details on the definition of ozone-temperature sensitivity We defined the sensitivity of ozone to temperature (do3/dtls) as the slope of the bestfit line of interannual ozone anomalies versus interannual temperature anomalies (m( O3, Tmax), Fig. 2a). Previous analyses defined the ozone-temperature sensitivity as the slope of ozone versus temperature (m(o3, Tmax)) 15,17 (Fig. 2b). Examination of Fig. 2b shows that the ozone-temperature relationships for individual sites (illustrated in Fig. 2b for two sites, ARE128 (grey dashed line) and CVL151 (grey dotted line)) were similar in slopes but shifted from one another. This is due to the differences in mean ozone levels and mean temperatures across sites. As a result, the true ozonetemperature relationship was smeared (r(o3, Tmax) = 0.55). By removing the longterm means of ozone concentrations and temperature for each site as we did in Fig. 2a, the data points were shifted to align near a well-defined line (r( O3, Tmax) = 0.86), whose slope (m( O3, Tmax)) was determined by the response of ozone to the interannual variation (IAV) of temperature (Fig. 2a). More fundamentally, the 3 / 23

4 do3/dtls m( O3, Tmax) definition is more physically appropriate for diagnosing CWO3, which is the ozone difference at a given geographical location. S3. Additional details on GEOS-Chem ozone simulations We used the GEOS-Chem global chemical transport model 3 (v , to simulate surface ozone over the Southeast U.S. GEOS-Chem was driven by the MERRA assimilated meteorological dataset at 2.5ºlongitude 2ºlatitude resolution. The lower 2 km of the atmosphere was resolved by 14 levels. Physical and chemical processes in GEOS-Chem are described in ref (3). Advection is calculated using the tpcore algorithm 4. Convective transport is computed from the convective mass fluxes from the MERRA data 5. A non-local parameterization scheme is used to simulate boundary layer mixing 6. Dry and wet deposition processes are as described in refs (7-9). We drove the model with year-specific ozone precursor emissions based on current best knowledge. Fig. S3 shows the interannual variation (IAV) of U.S. ozone precursor emissions from representative sources during Baseline anthropogenic emissions of NOx, CO, VOCs, and other pollutants for the U.S. were from the 2005 National Emissions Inventory (NEI05) by the U.S. Environmental Protection Agency ( Baseline anthropogenic emissions for the rest of the world were from the Emissions Database 4 / 23

5 for Global Atmospheric Research (EDGAR) 10 for non-vocs and the REanalysis of the TROpospheric chemical composition over the past 40 years (RETRO) inventory 11 for VOCs. Year-specific U.S. anthropogenic emissions of NOx, CO, VOCs, and SO2 were calculated by scaling the baseline fluxes using state-level sectorial (tier 1) statistics for by ref (12) and extended to 2011 here following the same methodology. Year-specific anthropogenic emissions for the rest of the world were calculated by scaling the baseline fluxes using country-level statistics for 1987 to and held constant at year 2006 levels for 2007 to Emissions of biogenic volatile organic compounds were calculated from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and dependent on temperature, shortwave radiation, and monthly leaf area index 13. Lightning NOx emissions were coupled to deep convection and seasonally and spatially distributed using satellite-based constraints 14. Soil NOx emissions were dependent on temperature and precipitation (via soil moisture) 15. Interannually-variable biomass burning emissions were not available for the entire duration between 1988 and We used monthly biomass burning emission fluxes from the year 2001 (an average burning year in the U.S.) from the Global Fire Emissions Database (GFED3) 16 for all years. We conducted a model experiment using year-specific GFED3 emissions for 1997 to 2010 and found that the IAV of U.S. biomass burning emissions had little impact on the ozone-temperature sensitivity over the Southeast U.S. 5 / 23

6 The standard HOx-NOx-VOC-ozone-aerosol chemical mechanism (except the isoprene oxidation cascade) used in GEOS-Chem was originally described by ref (17). Major updates to this standard chemical mechanism include application of the most recent JPL/IUPAC kinetic recommendations 18, corrections to the RO2+HO2 rate constants 19, and heteorogeneous uptake of HO2 by aerosols 20. We conducted model experiments using two different chemistry schemes for the isoprene photochemical cascade. Table S1 shows the list of model experiments. In Scheme C1 17, isoprene organic nitrates (ION) were produced as a first-generation product (YION = 15%), which quickly deposited to remove NOx ( = 0%). Scheme C2 was based on a recent experimental study 21,22. ION were produced as first-generation products at a bulk yield of YION = 11.7%. Subsequent oxidation of these ION recycled NOx ( = 55%) and produced secondary organic nitrates. We conducted additional model experiments by progressively removing the IAV of various emission sources and meteorological variables, with the goal of testing the impacts on the ozone-temperature sensitivity (Table S1). We removed the IAV of precursor emissions from a specific source by using fluxes from that source from the year 1998 for all years. In particular, natural emissions were locked to 1998 levels by driving the MEGAN algorithm and the soil NOx emission algorithm with temperature, 6 / 23

7 sunlight, and precipitation data from the year We removed the IAV of a specific meteorological field (humidity, cloud cover, shortwave radiation, horizontal wind, and temperature) by using values for that meteorological field from the year 1988 for all years. All model experiments were conducted from January 1988 to August Results for the month of August were analyzed. Model results were outputted hourly at each model grid. S4. Evaluation of GEOS-Chem ozone simulations over the U.S. using different photochemistry schemes We evaluated the two GEOS-Chem simulations of surface ozone using different isoprene photochemistry schemes against observations. Fig. S4 compares the simulated August afternoon surface ozone concentrations against available surface observations from CASTNET for the year Observed ozone concentrations over the eastern U.S. ranged from 37 ppb to 75 ppb, with highest observed concentrations over the central eastern U.S. Both simulations using different isoprene photochemistry schemes reproduced the spatial pattern of the observed ozone. However, both simulations showed general positive biases in surface ozone over the eastern U.S. The mean biases in the simulated August afternoon surface ozone concentrations were 3 ppb (C1) and 13 ppb (C2), respectively. 7 / 23

8 Fig. S5 is the same as Fig. S4 but for the year Observed ozone over Central and Eastern U.S. ranged from 29 to 64 ppb, with highest observed concentrations over Central U.S. Observed ozone concentrations over the Eastern U.S. were significantly lower than those observed in the year 1988 due to regional reductions in anthropogenic precursor emissions. Both simulations using different isoprene photochemistry schemes reproduced the spatial pattern of the observed ozone. However, again the simulations significantly overestimated the observed ozone levels. The mean biases in the simulated August afternoon surface ozone concentrations were 9 ppb (C1) and 17 ppb (C2), respectively. Overestimation of summertime surface ozone over the eastern U.S. is a known problem common to many chemistry models 2,23,24. Here, we diagnosed this bias to see if it would compromise the model s usefulness in evaluating the sensitivity of ozone to the IAV of temperature. Fig. S6 shows the timeseries of the 5 th, 50 th, and 95 th percentiles of observed daily afternoon surface ozone concentrations at the 20 CASTNET sites for August during Also shown in Fig. S6 are the simulated 5 th, 50 th, and 95 th percentiles of observed daily afternoon surface ozone concentrations from the two model experiments using different isoprene photochemistry schemes, C1 and C2. Previous analyses showed that the overestimation of summertime surface ozone over Southeast U.S. in GEOS-Chem 8 / 23

9 was mainly due to a positive bias in the 1 st to 10 th percentile of surface ozone by approximately 20 ppb 26,27. We found this to also be the case here, as the 5 th percentile of surface ozone over the Southeast U.S. was overestimated by approximately 20 ppb in both model simulations. We found that the 20 ppb bias in the simulated 5 th percentile of surface ozone did not affect the model s usefulness in evaluating the ozone-temperature sensitivity. The reason is three-fold. Firstly, this bias was not sensitive to isoprene photochemical oxidation, as evidenced by the relatively small difference between the 5 th percentile ozone simulated by experiments C1 and C2. Secondly, the 5 th percentile ozone mostly reflected background ozone levels not produced by U.S. anthropogenic emissions. The observed 50 th and 95 th percentiles of ozone concentrations both showed a significant decline over time (trend for the observed 50 th percentile of ozone = ppb y -1, p-value = 0.002; trend for the observed 95 th percentile of ozone = ppb y - 1, p-value < 0.001), reflecting the reduction in U.S. anthropogenic NOx emissions. In contrast, the 5 th percentile of observed ozone concentrations showed no significant trend (trend = ppb year -1, p-value = 0.7), indicating that it was insensitive to U.S. anthropogenic emissions. The simulated 5 th percentile surface ozone levels also showed no significant trends (trend = ppb y -1 and ppb y -1 for C1 and C2, respectively; both p-values > 0.12). Previous studies showed that the policy-relevant background of surface ozone level (surface ozone level in the absence of North 9 / 23

10 American anthropogenic emissions) over the Southeast U.S. is 25 to 30 ppb 28, consistent with the observed 5 th percentile of surface ozone here. Finally, the model biases in the simulated 5 th percentile surface ozone were not correlated with temperature over the Southeast U.S. (TSEUS, green line in Fig. 1a). The correlations of the model biases in the 5 th percentile surface ozone against the IAV of TSEUS were 0.06 and 0.07 for C1 and C2, respectively. Thus, the simulated ozone biases do not have large impacts to the simulated ozone-temperature sensitivity over the Southeast U.S. 10 / 23

11 Fig. S1 Locations of the 20 CASTNET sites used in this study. 11 / 23

12 Fig. S2 Observed and simulated ozone-temperature sensitivity (m( O3, Tmax)) over the Southeast U.S. for the twenty 5-year periods during a to c, Observed sensitivities are shown in black; the grey bands indicate the standard errors. Colored lines show the simulated sensitivities from model experiments. Vertical bars indicate the standard errors of the simulated sensitivities. C1 (green solid) and C2 (red solid): 12 / 23

13 experiments using two schemes for isoprene photochemical oxidation with full, yearspecific emissions and meteorology. a, Test for the impact of the sampling window length. Model experiments are the same as in Fig. 3a (Table S1), except here the observed and simulated sensitivities (m( O3, Tmax)) were calculated for each of the eighteen 7-year periods during The range of magnitudes and the interannual variability of the observed and simulated sensitivities (m( O3, Tmax)) are consistent with those shown in Fig. 3a (using a 5-year sampling window) and not affected by the sampling window length. b, Test for the impact of the calendar year from which emissions and meteorological data were taken. Model experiments (described in detail in Table S1) E1_2008 (blue dashed), E2_2008 (blue dotted), M1_2008 (orange dashed), M2_2008 (oranged dotted), M3_2008 (purple dashed), and M4_2008 (purple dotted) are the same as experiments E1, E2, M1, M2, M3, and M4 shown in Fig. 3c, respectively, except here emissions and meteorological data were taken from year 2008 instead of year Year 1988 and year 2008 represent a high-emission year and a low-emission year from U.S. anthropogenic sources, respectively. These results show that the findings presented in the main text regarding the sources of m( O3, Tmax) interannual variability are not affected by the choice of calendar year from which the emissions and meteorological data were taken. c, Test for the impact of O3-NOx-VOC photochemistry non-linearity. Model experiments (Table S1) shown are E1: same as C2 but without the IAV of natural precursor emissions (blue dashed); E2: same as E1, but further removing the IAV in anthropogenic precursor emissions (blue dotted); E2b: same as C2 but without the 13 / 23

14 IAV of anthropogenic emissions (blue solid). The impact of the IAV of natural precursor emissions on m( O3, Tmax) is large, which can be diagnosed two ways: either as the differences between C2 and E1, or as the differences between E2b and E2. The impact of the IAV of anthropogenic precursor emissions on m( O3, Tmax) is relatively small, which can also be diagnosed two ways: either as the differences between E1 and E2, or as the differences between C1 and E2b. This shows that these conclusions are unchanged if the order of anthropogenic and natural precursor emission IAV removal was reversed. 14 / 23

15 Fig. S3 U.S. annual emissions of ozone precursors from representative sources during a, Emissions of NOx (black) and CO (blue) from anthropogenic and biomass burning activities over the U.S. b, Emissions of isoprene from vegetation (black) and NOx from lightning (blue solid) and soil (blue dashed) over the U.S. c and d, the same as a and b, respectively, except that c and d are for the Southeast U.S. only (93.75ºW ºW, 31ºN-39ºN). 15 / 23

16 Fig. S4 Evaluation of simulated ozone concentrations over Eastern U.S. for the year a, August afternoon surface ozone concentrations observed at CASTNET sites. b and c, Simulated August afternoon surface ozone concentrations from model experiments C1 (b) and C2 (c). d and e, Differences between simulated and observed August afternoon surface ozone concentrations at CASTNET sites for model experiments C1 (d) and C2 (e). 16 / 23

17 Fig. S5 Same as Fig. S4 but for the year / 23

18 Fig. S6 Timeseries of the observed and simulated 5 th, 50 th, and 95 th percentiles of August afternoon surface ozone concentrations at 20 Southeast U.S. CASTNET sites for Black line: the 50 th percentile of observed afternoon surface ozone. Light grey band: the lower edge and the upper edge of this band represent the 5 th and 95 th percentiles of observed afternoon surface ozone, respectively. Colored lines: the 5 th (dotted), 50 th (solid), and 95 th (dashed) percentiles of simulated surface afternoon ozone in model experiments C1 (green) and C2 (red). 18 / 23

19 Table S1. List of model experiments Experiment types Chemistry schemes for isoprene photochemistry Emissions 1988 Meteorology 1988 Experiments C1 C2 E1 E2 M1 M2 M3 M4 Description Simulation with GEOS-Chem HOx-NOx-VOCozone-aerosol chemical mechanism. Isoprene photochemistry cascade was as described in ref (17): YION = 15%; = 0%. Full, year-specific emission fluxes were used. Same as C1, except that the isoprene photochemistry cascade was replaced by the scheme described in ref (21): YION =11.7%; = 55%. Full, year-specific emission fluxes were used. Same as C2, but without the IAV of natural ozone precursor emissions. Biogenic VOC emissions and soil NOx emissions were calculated in the model driven by meteorological fields from the year Same as E1, but without the IAV of anthropogenic ozone precursor emissions. Anthropogenic precursor emissions were set to year 1988 levels. Same as E2, but without the IAV of specific humidity in photochemistry calculations. The specific humidity data used in photochemistry calculations were taken from the year Same as M1, but without the IAV of short-wave radiation and cloud cover. Radiation and cloud cover data were taken from the year Same as M2, but without the IAV of horizontal wind. U-wind and V-wind data were taken from the year Same as M3, but without the IAV of temperature in photochemistry calculations. The temperature 19 / 23

20 data used in photochemistry calculations were taken from the year Emissions 2008 E1_2008 E2_2008 Same as E1, except natural ozone precursor emissions were set to year 2008 levels. Same as E1_2008, except anthropogenic ozone precursor emissions were set to year 2008 levels. Meteorology 2008 Chemical nonlinearity M1_2008 M2_2008 M3_2008 M4_2008 E2b Same as E2_2008, except specific humidity data used in photochemistry calculations were taken from the year Same as M1_2008, except shortwave radiation and cloud cover data were taken from the year Same as M2_2008, except U-wind and V-wind data were taken from the year Same as M3_2008, except the temperature data used in photochemistry calculations were taken from the year Same as C2, but without the IAV of anthropogenic ozone precursor emissions. Anthropogenic precursor emissions were set to 1988 levels. 20 / 23

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