Climate-based sensitivity of air quality to climate change scenarios for the southwestern United States

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 29: (2009) Published online 23 May 2008 in Wiley InterScience ( Climate-based sensitivity of air quality to climate change scenarios for the southwestern United States Erika K. Wise* Department of Geography and Regional Development, The University of Arizona, Tucson, AZ 85721, USA ABSTRACT: The need to understand future trends in air quality is an issue that is frequently raised by air quality planners and managers. The potential for extreme events is of particular interest, but forecasts are difficult using traditional methods and further complicated by predictions of future climate change. This study aims to address this research need through a climate-based sensitivity study of ground-level ozone and particulate matter (PM) in the U.S. Southwest. Extreme value methods were used to examine ozone and PM in Tucson, Arizona, over the time period A Statistical DownScaling Model (SDSM) was employed to build regression-based models between gridded meteorological data and point time series of ozone and PM. Following model calibration and verification, future climate-based ozone and PM scenarios were created for and Daily output of projected ozone and PM were then subjected to extreme value methods in order to estimate climate-based changes in ozone and PM extremes over the next century. Results indicate that monthly mean and extreme ozone values are sensitive to predicted increases in temperature, particularly in the summer months. PM results are less certain but suggest that PM may be sensitive to changing moisture conditions. Copyright 2008 Royal Meteorological Society KEY WORDS climate change; statistical downscaling; extreme value methods; ozone; particulate matter; southwestern United States; air quality exceedances Received 12 May 2007; Revised 18 December 2007; Accepted 20 March Introduction Air quality decision makers are faced with the daunting task of trying to plan for and mitigate against future air pollution episodes. In the United States, exceedances of federally mandated thresholds, the National Ambient Air Quality Standards (NAAQS), can trigger a set of requirements that are costly and time consuming for cities to implement. The potential for extreme events (exceedances of the NAAQS) is of particular interest, but prediction of these events is difficult using traditional methods. Characterization and prediction of exceedances could aid in understanding the distribution of high particulate matter (PM) and ozone levels and help estimate future exceedances and return periods of concentrations above the NAAQS. This information can serve as a management tool to develop future control strategies. This type of information is particularly vital for the southwestern United States (Southwest), which has experienced high concentrations of ozone and PM in the past and has also been experiencing rapid population growth. Compounding concerns over future air quality due to fast growth in the Southwest is the threat of anthropogenically induced climate change. The scientific consensus is that climate change is occurring (IPCC, 2007), leading * Correspondence to: Erika K. Wise, University of Arizona, Harvill Building Box 2, Tucson, AZ 85721, USA. ekwise@ .arizona.edu to a growing number of regional impact studies to predict and plan for repercussions of change. The types of changes predicted for the Southwest, such as increased temperatures and aridity, are worrisome with regard to air quality. Research is needed to determine how changes in the distribution of values are likely to affect current compliance monitoring programs and to ensure responsiveness of air quality protection to climatically induced air quality changes, such as increased variability (Crane et al., 2005). There is currently a lack of this type of research focusing on the Southwest. This study addresses some of these research needs through a climate-based analysis of ground-level ozone (ozone) and PM in the Southwest. The objective of this study is to examine the sensitivity of air pollution to projected changes in climatic conditions. Further, the study aims to suggest methodological avenues for investigating this issue. The information gained is then applied towards understanding extreme ozone and PM air quality events, and the sensitivity of air quality extremes to predicted climate change. A three-step process is used to meet these objectives. First, extreme value methods are used to estimate ozone and PM return levels based on current climate conditions. Second, climate downscaling techniques are applied to assess the impacts of predicted climate change on air quality in the Southwest. Third, projected air quality scenarios are examined using the extreme value methods to estimate the potential impacts of climatic change on ozone and PM extreme events. Copyright 2008 Royal Meteorological Society

2 88 E. K. WISE This study utilizes data from Tucson, Arizona. The Southwest experiences a high degree of climate variability owing to its complex topography and its proximity to several different moisture sources. The specific air quality levels and the downscaling gridpoint used in this study are specific to Tucson. The overall study is applicable to the Southwest more generally in several ways: (1) current climate conditions and their relation to air quality, as described below, are similar throughout the Southwest; (2) air quality issues facing southwestern cities are the same, although of differing magnitudes; and (3) projected climatic changes are similar throughout the Southwest. For these reasons, the overall findings of this study are generalizeable, although not completely transferable, from Tucson to other southwestern cities Climate controls on ozone and PM in the Southwest Ozone and PM are the primary pollutants of concern for air quality planners and managers in the Southwest. Federal NAAQS for these pollutants have been strengthened in recent years, as their detrimental influences on human health, the environment, and visibility have become better understood (U.S. EPA, 1999;). Although air quality is influenced by a variety of sources, climate and weather conditions (e.g. stability, wind speed, wind direction, moisture, and pressure gradients) have the greatest impact on daily concentrations (Davis and Gay, 1993). Both ozone and PM are influenced by meteorological conditions; however, ozone is influenced more strongly by weather in the Southwest than is PM. Ozone concentrations in the Southwest are less dependent on the occurrence of particular synoptic weather patterns than other regions such as the eastern United States (Comrie, 1997). Previous studies (Wise and Comrie, 2005a,b) found that temperature and mixing height have the greatest influence on ozone conditions in the Southwest, while moisture levels (particularly relative humidity) are the strongest predictors of PM concentrations Extreme value methods Although trend analysis is a useful tool for obtaining a clearer understanding of the driving mechanisms behind air quality and the overall changes in a region s air quality, air quality planners and managers must primarily concern themselves with the dangers of air quality exceedances pollutants at levels high enough to violate the federally mandated NAAQS (Katz and Gilleland, 2004). Extreme events are based on a Poisson process (Katz and Gilleland, 2004), and extreme value statistics are needed to quantify the stochastic behaviour of a process at unusually high or low values. The usefulness of extreme value methods has been increasingly recognized in climate research, and a few related studies have been undertaken using air quality data (Sharma et al., 1999; Lu and Fang, 2003) Climate downscaling and projected changes Future climate change may impact air quality both directly (by influencing the fate, toxicity, or behaviour of pollutants) or indirectly (by altering the anthropogenic or biogenic production of pollutants). In addition to increases or decreases, it is possible that pollutant concentrations will become more highly variable. Extreme climatic events, such as storms, heat waves, and droughts, may increase in frequency and intensity under climate change scenarios and interact with air pollutants in complex ways (Crane et al., 2005). General Circulation Models (GCMs) have an output resolution of approximately km (Leung et al., 2004). Although appropriate for global-scale studies, this resolution is inadequate for areas with complex terrain, urban areas (which produce highly-localized effects), or impact studies (Wilby et al., 2002; Leung et al., 2004). The use of regional information will be necessary for this study, which examines climate change impacts on air quality in an urban area characterized by complex terrain. Downscaling predictions to the regional level is achieved through dynamical or statistical downscaling (SD). Dynamical downscaling is accomplished through the use of Regional Climate Models (RCMs). RCMs are nested within GCMs and are driven by boundary conditions that the GCM supplies (Barnett et al., 2004). RCMs are similar to GCMs but have a finer output resolution and are focussed on a specific region. Like GCMs, they are computationally demanding and are highly sensitive to input boundary conditions (Wilby et al., 2002). In SD, statistical transfer functions are derived between large-scale atmospheric patterns projected by GCMs and the local variable(s) of interest to estimate point-scale time series at the temporal and spatial scales needed for impact studies (Barnett et al., 2004; Diaz-Nieto and Wilby, 2005). A model is constructed on the basis of the relationships between predictors and predictands and then calibrated using observational data (Penlap et al., 2004). SD has the advantages of increased accessibility and flexibility, and it can provide station-scale information. However, there are a number of uncertainties with downscaled information that stem both from the original GCM and the downscaling process. As with RCMs, SD depends on the validity of the GCM output, and any GCM uncertainties are compounded by the SD process (Wilby et al., 2002). Input data uncertainty from the historical data used in downscaling is another potential source of error (Chen et al., 2006). SD methods may not accurately reflect regional feedback mechanisms (Christensen et al., 2007). The SD method must also assume stationarity between predictor and predictand relationships (i.e. that current statistical relationships will be the same under future climate conditions) (Wilby et al., 2002), a weakness that has been shown to underestimate future changes (Christensen et al., 2007). SD methods are likely to underestimate future variance, also leading to conservative predictions (Fowler et al., 2007). GCMs agree that the Earth will continue to warm. In the Southwest, the Canadian Climate Centre model

3 SENSITIVITY OF U.S. SOUTHWEST AIR QUALITY TO CLIMATE CHANGE 89 forecasts temperature increases of up to 7 C in winter and 5 C in summer by 2090, while the Hadley Centre model predicts increases of 3 C in summer temperatures and 4 C in winter temperatures over that same period (Sprigg and Hinkley, 2000). Moisture predictions are less certain than temperature in general and in the Southwest in particular due to challenges in modelling the North American monsoon. Previously, it was thought that the western United States would likely experience an increase in precipitation, but that warmer temperatures would lead to increased evaporation. Both the Canadian and Hadley models predicted increases in precipitation in the western United States, particularly in the Southwest, related to increases in atmospheric moisture and a predicted southward shift in Pacific Coast storm tracks (USGCRP, 2000). Recent results, however, have instead suggested a future decrease in Southwest precipitation (Seager et al., 2007). Most models now indicate a poleward displacement of storm tracks and a resulting decrease in Southwest annual precipitation, but there is a high degree of uncertainty due to the range of model results (Christensen et al., 2007). The few studies based on prediction of future air quality have largely focussed on ozone. Tropospheric ozone concentrations are usually predicted to rise (Brasseur et al., 1998; Prather et al., 2002; Hogrefe et al., 2004; Mickley et al., 2004), generally owing to increases in temperature and solar radiation (Bernard et al., 2001; Leung and Gustafson, 2005). However, a few studies suggest that a projected increase in water vapour content will enhance ozone destruction, leading to a mixed response (Zeng and Pyle, 2003; Tao et al., 2007), no change (Brasseur et al., 1998), or even a net decrease (Stevenson et al., 2005) in tropospheric ozone. Few climate-changerelated studies have been conducted on PM. Buchanan et al. (2002) examined the link between long-range transport of PM and meteorological patterns in Scotland, where they found no significant trends and some evidence that particle concentrations may decrease owing to decreased frequency of anticyclonic events. 2. Data and methods This study builds upon the discussed literature to enable a more complete understanding of present and future air quality in the Southwest. Using established climate air quality (predictor predictand) relationships (Wise and Comrie, 2005a), air quality exceedances are characterized, and estimations of probabilities of future exceedances are made using the peaks-over-threshold (PT) extreme value approach, which combines the Poisson process (exceedances of a high threshold) with a generalized Pareto distribution (GPD) (excess over threshold) (Coles, 2001; Gilleland and Katz, 2006). Understanding these extremes will help characterize the distribution of high PM and ozone levels in the Southwest and enable estimation of NAAQS exceedances and their return periods. This study also attempts to address concerns over the air quality impacts of climate change in the Southwest. SD is completed using the Statistical DownScaling Model (SDSM) to estimate future ozone and PM in the Southwest under Intergovernmental Panel on Climate Change (IPCC) scenarios. It is recognized that many non-climatic factors also affect pollution levels, including population growth, changing demographics, economic growth, and technology changes (Bernard et al., 2001). Although this is particularly true in a fast-growing region like the Southwest, this study endeavours to first understand climate air quality relationships Data Ozone and PM data for were obtained from local, state, and federal environmental agencies. This time period reflects the maximum amount of overlap available between Tucson air quality data (1990 to present) and the SDSM climate predictor variable data set ( ). Maximum daily 8-h average ozone and 24-h average PM 10 values from 2 ozone and 10 PM monitoring stations in the metropolitan Tucson area were used for the analyses. These measures were chosen in order to match the existing Environmental Protection Agency (EPA) federal standards for the two pollutants. The EPA standard for PM 10 is a 24-h average value of 150 µg/m 3. The ozone standard is complicated, but is based on a maximum daily 8-h average of 80 ppb. The Tucson ozone and PM data are summarized in Table I. The GCM output used in this study was from the Hadley Centre s HadCM3, a coupled atmosphere ocean GCM. Outputs from the Special Report on Emissions Scenarios A2 and B2 were obtained from the Canadian Institute for Climate Studies (CICS), each containing 139 years of daily GCM predictor data normalized over the period. The A2 scenario assumptions result in higher emissions estimates than the B2 scenario (Nakićenović and Swart, 2000). The predictor dataset used for the calibration period consisted of National Centers for Environmental Prediction (NCEP) Reanalysis meteorological variables that have been processed with data extraction, re-gridding, and normalization techniques to work with the SDSM. These data were obtained from CICS over the time period to match the available HadCM3 predictor dataset, which does not currently cover later years or Table I. Descriptive statistics for ozone and PM in Tucson, AZ, Descriptive statistics ( ) Ozone PM Mean Median Standard deviation Monthly average high (month) 56.9 (Aug) 37.1 (Nov) Monthly average low (month) 32.0 (Dec) 24.2 (Aug) Number of days over standard 12 2

4 90 E. K. WISE emission scenarios. The NCEP data were interpolated to the same grid as HadCM3 (2.5 latitude 3.75 longitude) prior to normalization. The grid cell covering Tucson, Arizona, was incorporated into the SDSM for modelling present and future conditions Extreme value methods For this study, the ExtRemes library (Gilleland and Katz, 2004), run through open-source R software, was used to apply extreme value methods and estimate return periods (the probability that a threshold is exceeded in a given time period) and return levels (the magnitude of the return period determined by setting the cumulative distribution function equal to the desired probability) (Sharma et al., 1999; Katz et al., 2002). The observed ozone and PM data for were fitted to their respective GPDs. The mean of the ozone data are fairly well fitted by a Gaussian distribution, and the PM data by a gamma distribution. However, the generalized extreme value (GEV) and the GPD are better fits for the maxima of the distributions of ozone and PM. The GPD was chosen over the GEV since the block maxima approach of the GEV leads to loss of data. The thresholds for the data sets were chosen through examination of diagnostic plots and output, including histograms, density plots, quantile plots, probability plots, and return level plots. The stability in the parameter estimates was checked by fitting data to the GPD distribution several times, each time using a different threshold. These same methods were applied to the daily output from the SDSM scenarios Statistical DownScaling Model The SDSM is a PC-based decision support system that can be used for regional- and local-scale climate change impact assessments (Diaz-Nieto and Wilby, 2005). SDSM allows SD to individual sites on daily time scales using GCM output (Wilby et al., 2002). The SDSM method combines a stochastic weather generator, in which climate change scenarios are generated stochastically using parameter sets scaled in proportion to corresponding changes in a GCM, and regressionbased analyses, which rely on empirical relationships between local-scale predictands and regional-scale predictors (Wilby et al., 2002). In addition to air quality, SDSM has been used to model precipitation, temperature, and changes to urban heat islands (Wilby et al., 2002). The SDSM was first used to screen predictor variables (NCEP and GCM data) in order to identify empirical relationships between the gridded meteorological predictors and the local site predictands (ozone and PM). A natural log transformation was applied to PM prior to analyses. Appropriate variables were chosen through the use of seasonal correlation analysis, partial correlation analysis, and scatterplots. They were examined on a monthly basis to identify temporal variations in predictor strength. By looking at the percentage of variance explained by specific predictand predictor pairs over time, the most appropriate combinations of predictors were chosen. The variables were screened for significance at the 0.95 level. A correlation matrix was created to investigate intervariable correlations for annual, seasonal, and monthly time periods. From the initial set of 26 NCEP variables, a much smaller subset was chosen for the final model (Table II). The available observational data were divided into two halves to allow for independent calibration ( ) and verification ( ) periods. The model was calibrated for the time period using the selected predictors and predictand through the computation of the parameters of multiple linear regression equations. A weather generator was then used to create ensembles of synthetic daily ozone and PM series using the NCEP predictor variables over the validation time period. The weather generator uses the calibration model to link the predictors to their regression model weights. Twenty individual ensemble members with daily predicted ozone and PM values were created for the time period. Each of the ensemble series is considered an equally plausible local scenario realized from the common set of regional-scale NCEP predictors; the difference between members depends on the relative significance of the deterministic and stochastic components of the calibration regression model (Wilby et al., 2002). Atmospheric predictor variables supplied by the HadCM3 climate model for the A2 and B2 emission scenarios were used to produce 20-member ensembles of synthetic daily ozone and PM time series for The HadCM3 predictor variables were normalized with respect to the reference period and contained the same variables as those used for model calibration. The HadCM3 data were divided into three time slices to enable comparison over time: , , and Statistical and graphical diagnostics were then used to analyse differences between the modelled and observed ozone and PM records, as well as predicted changes over time for ozone and PM scenarios. Table II. NCEP predictor variables used in ozone and PM SDSM models. NCEP predictor variables Tucson ozone 500 hpa divergence 850 hpa airflow strength Relative humidity at 500 hpa Near-surface relative humidity Surface specific humidity Mean temperature at 2 m Tucson PM Mean sea level pressure 500 hpa geopotential height Relative humidity at 850 hpa Near-surface relative humidity

5 SENSITIVITY OF U.S. SOUTHWEST AIR QUALITY TO CLIMATE CHANGE Results and discussion 3.1. Observed extremes The underlying distributions of Tucson s ozone and PM data are quite different. Ozone displays a fairly normal distribution, while PM is highly skewed with a heavy tail that is influenced by a small number of extreme values. In this case, there were two very high PM values in the observational record (150 and 225 µg/m 3 ) that heavily influenced the shape of the PM tail. These two extremes were determined to result from unusual natural conditions and as such were addressed by Pima County in a Natural Events Action Plan (NEAP). The underlying ξ for the ozone tail is 0.24, indicating a beta (bounded tail) distribution, while PM has ξ equal to +0.12, a Pareto (heavy tail) distribution (Reiss and Thomas, 2001). Under the climatic conditions observed during the time period, ozone has a return level (81 ppb) near the NAAQS level (80 ppb) at a period of 1 year (Table III). This is consistent with the recorded data, which contained 12 exceedances over the 12-year period. The ozone return level at the 100-year return period is not exceptionally high at 90 ppb. Over the same time period, the return level for PM that approximated the NAAQS level (150 µg/m 3 ) occurred at a 10-year return period (146 µg/m 3 ) (Table III). The 100-year return level for PM, 207 µg/m 3, is lower than what has been observed in the historical record, suggesting that the highest value observed in this time period was an anomalous event. Table III. Ozone and PM return levels for observed data under current climate conditions ( ). Return period (years) Ozone return level (ppb) (CI) PM return level (µg/m 3 )(CI) 1 81 (80 82) 100 (93 109) (85 89) 146 ( ) (87 93) 207 ( ) 3.2. SDSM modelling The downscaled regression models for ozone and PM were built using the predictor variables shown in Table II. The modelled ozone values were in close correspondence to the observed data over the verification period (significant at 0.99 level), particularly with regard to mean monthly and seasonal values. Mean monthly concentrations differed by less than 3 ppb (relative to a mean value of 48 ppb), while maximum monthly values differed by up to 9 ppb (relative to maximum values of about 85 ppb) (Figure 1). Even when examined on a daily basis, the modelled ozone corresponded well with the range and timing of values seen in the observational record. Figure 2 displays the daily time series for the modelled and observed ozone over the verification time period. The primary difference between the modelled and observed ozone is the under-prediction of ozone in the late summer and early fall. In particular, the modelled values during this time period cover a greater range than Figure 1. Differences between predicted (SDSM modelled) and observed monthly mean (top) and maximum (bottom) ozone concentrations over the verification period.

6 92 E. K. WISE the observed values, which tend to be consistently high during that time of year. PM modelled values verified well and had a highly significant relationship with the observed monthly mean values, but not with maximum monthly values (Figure 3). PM mean values differed by up to about 7 µg/m 3 (relative to the average of 31 µg/m 3 ). A problem arose with the maximum values, as both of the observed very high values occurred in the verification period, and there were no such values in the calibration period. Since the model uses the highest value for each month over the whole time period, those few very high values dominate and interfere with the overall calibration statistics. When examined on a daily basis, the inability to correctly model extreme PM values becomes apparent (Figure 4). The model does a fair job of matching observed PM values in both range and timing in a year without extreme values in the observational record. However, the model only predicted one value above 100 µg/m 3 over the verification period, when there were actually 10 such values in the observational record. Projected future ozone departures from the baseline are shown in Figure 5. The A2 and B2 scenarios are nearly identical in their projections; therefore only the more conservative B2 scenario is shown here. Figure 2. Daily time series of observed (black circles) and modelled (grey diamonds) ozone over the verification period. Figure 3. Differences between predicted (SDSM modelled) and observed monthly mean (top) and maximum (bottom) PM concentrations over the verification period.

7 SENSITIVITY OF U.S. SOUTHWEST AIR QUALITY TO CLIMATE CHANGE 93 Figure 4. Daily time series of observed (black) and modelled (grey) PM over the verification period. Figure 5. Projected monthly mean (top) and maximum (bottom) ozone concentrations for the (grey) and (black) time periods under the B2 emissions scenario baseline conditions are shown for comparison (dotted line). Climate-based increases in mean ozone of up to 4 ppb in the summer and fall months (relative to the 80 ppb NAAQS) are projected by Climate-based increases in maximum ozone concentrations of up to 11 ppb are projected (relative to current maximum concentrations of approximately 85 ppb). The largest increases are projected for late summer; when aggregated by season, the summer changes are prominent (Figure 6). This is particularly important since the Southwest already experiences its highest ozone concentrations during the summer months. Projected mean PM concentrations are also remarkably similar under the A2 and B2 emissions scenarios. Climate-based projections indicate decreases in winter PM by up to 5 µg/m 3 and summer increases up to 9 µg/m 3 (relative to the NAAQS of 150 µg/m 3 ). Unlike ozone, the major PM increases are projected for months with currently low PM concentrations, July and August (Figure 7). However, models are less skillful at replicating summer climate conditions owing to difficulties in modelling processes such as the North American monsoon (Christensen et al., 2007) Projected future extremes Output ensemble members for ozone and PM were analysed using the extreme value methods described

8 94 E. K. WISE Figure 6. Projected seasonal maximum ozone departures from the baseline for the time period under A2 (black) and B2 (grey) emissions scenarios. Figure 7. Projected monthly mean PM concentrations for the (grey) and (black) time periods under the B2 emissions scenario baseline conditions are shown for comparison (dotted line). earlier. The overlap time period was used to compare modelled versus observed extreme values. It is important to note that SDSM is a regressionbased method, which has the tendency to bias results towards the mean. For this reason, the projected extremes presented here should be a conservative estimate of the influence of future climate conditions on ozone and PM extremes. For ozone, the observed return levels for each return period fell into the confidence intervals for the modelled return levels (Table IV). Substantial climate-based increases in ozone return levels were projected for the and time periods (Table V). These changes occurred as the tail of the ozone distribution lengthened, moving from the beta (bounded tail) distribution seen in the observed data to an exponential (light tail) distribution by This change led to significant return level increases, up to 17%, by 2099 (Table V). Translated into number of days exceeding the 80 ppb federal threshold, this is an increase from slightly less than one exceedance per year under current climate conditions to over four exceedances per year by the end of the century (Figure 8). Table IV. Return levels for the time period for the observed and modelled ozone data. Return period (years) Observed return level (ppb) (CI) A2 return level (ppb) B2 return level (ppb) 1 81 (79 82) (84 89) (87 93) PM changes in extreme values are much less certain owing to the complications that arose when modelling extreme values using SDSM. Only the 10-year and 100- year return levels for the A2 emissions scenario baseline period fell within the confidence intervals of the observed PM return levels (Table VI). Climate-based projected changes in extremes under the A2 scenario suggest a decrease in high PM values, with the distribution moving from its current Pareto (heavy tail) distribution to an exponential (light tail) distribution by the end of the century. There was a significant decrease in modelled extreme PM days by 2050, followed by a levelling off to 2099 (Table VII).

9 SENSITIVITY OF U.S. SOUTHWEST AIR QUALITY TO CLIMATE CHANGE 95 Table V. Modelled changes in ozone return levels from the baseline ( ) to the and time periods. Return period (years) return level (ppb) (CI) return level (ppb) (CI/% increase) return level (ppb) (CI/% increase) 1 79 (78 80) 81 (81 82/2.53%) 87 (86 88/10.13%) (83 87) 89 (88 91/4.71%) 96 (94 98/12.94%) (86 92) 95 (92 97/7.95%) 103 ( /17.05%) Figure 8. Observed and modelled estimations of the number of days exceeding the 80 ppb federal NAAQS. Table VI. Return levels for the time period for the observed and modelled PM data. Return period (years) Observed return level (µg/m 3 )(CI) A2 return level (µg/m 3 ) B2 return level (µg/m 3 ) (93 109) ( ) ( ) Table VII. Modelled changes in PM return levels from the baseline ( ) to the and time periods. Return period (years) Return level (µg/m 3 )(CI) Return level (µg/m 3 )(CI) Return level (µg/m 3 )(CI) 1 85 (79 94) 81 (79 83) 83 (82 86) ( ) 105 (99 115) 107 ( ) ( ) 133 ( ) 131 ( ) 4. Conclusions Under current climate conditions, ozone was found to have a 1-year return period near the NAAQS level of 80 ppb. This is consistent with the observational record, in which there were 12 exceedances over the 12-year period. The 100-year return level of only 90 ppb reflects the bounded-tail nature of the ozone distribution. PM was found to have a return period of 10 years near the NAAQS threshold of 150 µg/m 3 and a 100-year return level of 207 µg/m 3. This 100-year return level has already been exceeded in the observational record (225 µg/m 3 in 1999). Additional years of data will be needed to determine whether this was truly an exceptional PM event. GCM projections were downscaled using SDSM and applied to ozone and PM air quality in this study. The SDSM models corresponded well with observed air quality over the validation period, replicating both mean and maximum ozone concentrations and mean PM values. Climate-based projected changes for ozone include monthly mean increases of 4 5 ppb in summer and autumn and a summer seasonal maximum increase of up to 11 ppb. This is particularly significant since even a 10 ppb increase in ozone is associated with an increase in daily mortality (Bell et al., 2004). The subtle change in the ozone mean resulted in a large shift in the tail of the distribution. Ozone increases in return levels at 1-year (10%), 10-year (13%), and 100- year (17%) return periods were projected by 2099, with a resulting quadrupling of days over the 80 ppb NAAQS threshold by High temperatures are closely associated with increases in ozone concentrations owing to their impact on the photochemical processes that result in ozone formation and as indicators of overall boundary layer conditions (Chock and Heuss, 1985). Even in the year-round warmth of the Southwest, ozone

10 96 E. K. WISE concentrations have been found to be most strongly and directly related to maximum daily temperatures (Wise and Comrie, 2005a), and the projected increase in summer temperature could result in an expansion of the overall ozone season. Changes in the North American monsoon and clouds/ultraviolet radiation, which currently have a high degree of uncertainty due to modelling limitations, will likely also impact future ozone concentrations. Suggested climate-based monthly mean changes for PM included increases of up to 9 µg/m 3 in the summer months and decreases in the winter (although these decreases were within the confidence interval of the study methodology). Decreases were projected for PM extremes from to , with no net change in the latter half of the century ( to ). These decreases would result in a 21stcentury 100-year return level below the current NAAQS. High PM values are typically associated with dry weather conditions (Wise and Comrie, 2005a); therefore, the wetter conditions predicted for the Southwest by the Hadley model scenario used in this study might drive projections of decreasing PM. Recent modelling projections, which instead suggest a drying pattern in the Southwest (Christensen et al., 2007), will be useful for further study of the sensitivity of PM to moisture changes. The projected PM results must be interpreted with caution, given the problems encountered with modelling PM over the observed period. It is important to note that this is a climate sensitivity study for ozone and PM air quality in the Southwest and is not meant as an air quality prediction exercise. The incorporation of emissions scenarios and chemical transport changes, which are likely to have equal or greater impacts on regional air quality, will be necessary to move to the projection stage. Urban emissions will almost certainly change over the next 100 years, but the overall effect, or even the direction, of that change is currently uncertain. While projected urban growth trends would suggest emissions increases, regulation and/or technology changes (e.g. the use of alternative fuels that would lower ozone precursor emissions) could decrease pollutant levels. This study suggests that regardless of the likely (but uncertain) emission changes, there is upward pressure on ozone from a climate change perspective, with a less certain directional influence on PM. In future research, it will be useful to compare the return levels and climate sensitivity found here with results from other Southwest cities. This will be particularly important for the examination of extreme events, as other cities in the region have a larger sample of exceedances in the observational record. The modification of the PM calibration period, statistical methods, or threshold for better simulation of PM extremes will also advance this knowledge. It is anticipated that SDSM predictor variables for other climate models and emissions scenarios will be available in the future. The use of predictors from an ensemble of models will be useful for confirming the results of this study. Acknowledgements This work was supported by the Climate Assessment for the Southwest project, a Regional Integrated Sciences and Assessment initiative funded by the National Oceanic and Atmospheric Administration. Air quality data were provided by the Pima County Department of Environmental Quality. Normalized meteorological and GCM data were obtained from the CICS. Dr Andrew C. Comrie provided helpful feedback on the work. The National Center for Atmospheric Research provided some of the tools and training used in these analyses. 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