REGIONAL FOR AIR QUALITY ASSESSMENT A Reasonable Proposition?

BY WILLIAM I. GUSTAFSON JR. AND L. RUBY LEUNG

Dynamical downscaling promises improved firiescale meteorological fields, but is the technique providing useful regional information for air quality assessment?

^ ince the development of the first regional climate application of regional downscaling has been the \ model (Dickinson et al. 1989), dynamical down- assessment of potential changes in air quality in a J scaling has been applied to provide regional future climate (Forkel and Knoche 2006; Hogrefe climate information for assessing climate change et al. 2004; Leung and Gustafson 2005; Steiner et al. impacts. In dynamical downscaling, regional-scale 2006). Air quality for a given region depends upon models are driven by large-scale boundary condi- pollutant emissions, chemical reactions, and meteo- tions, including winds, temperature, water vapor, rology, including transport and mixing. However, and sea surface temperature, from global models or emissions are relatively consistent while weather an analysis product to simulate atmospheric circula- conditions change from day to day, ultimately deter- tion for a region at higher spatial resolution (Giorgi mining the impact of the emissions on the air quality. and Mearns 1999; Leung et al. 2003a). While most Given that air quality depends heavily upon meteoro- studies focused on the assessment of water resources, logical conditions and these conditions may change agriculture, and ecosystems, more recently a new in the coming decades, a valid question is "How will climate change alter air quality?" As models become more sophisticated and interactive, the alternative AFFILIATIONS: GUSTAFSON AND LEUNG—Atmospheric Science question can also be asked, "How will air quality alter and Global Change Division, Pacific Northwest National climate?" Our ability to address these questions relies Laboratory, Richland, Washington critically on the ability of climate models to simulate CORRESPONDING AUTHOR: Dr. William I. Gustafson Jr., Pa- the meteorological conditions needed to realistically cific Northwest National Laboratory, Post Office Box 999, MSIN simulate air quality. Because of the nonlinear nature K9-30, Richland, WA 99352 E-mail: [email protected] of atmospheric chemistry and its dependence on difficult-to-model variables, such as precipitation The abstract for this article can be found in this issue, following the and the planetary boundary layer (PBL) height, table of contents. biases in variables considered acceptable for other DON 0.1175/BAMS-88-8-I2I5 downscaling applications may not be appropriate In final form 6 March 2007 ©2007 American Meteorological Society for this new application. An additional challenge in air quality assessment is the required knowledge of

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC the three-dimensional structures of the atmosphere, rology in the control and altered climate is essential. which are not needed for most other assessments. When using statistical models, it maybe possible to use Two conceptual frameworks exist for predicting a coarse representation generated with a global circula- air quality changes in an altered climate. The first is tion model (GCM) to characterize the weather regimes. to statistically identify weather conditions or regimes In the explicit modeling approach, high-resolution conducive to particular air quality events and then gridded meteorological data are needed to represent to infer changes in air quality based on how weather small-scale weather events and to drive air quality changes in the new climate. Examples of weather models. Thus, assessing future air quality at city and connections to high ozone include the following: a state scales requires dynamical downscaling. study of the Lake Michigan lake breeze (Lennartson Historically, regional modeling for air quality and Schwartz 2002); the study by Berman et al. assessment involves short-duration case studies aimed (1999) showing a connection between lower ventila- at advancing our understanding of meteorological tion coefficients, the product of the boundary layer and chemical processes, and their interactions. The height and the boundary layer wind speed, and high- short duration keeps the simulations in the realm of ozone days in New England; a connection between weather, as opposed to climate. In this type of study, recirculating air patterns and ozone in Atlanta observations can be used to constrain the simulation (John and Chameides 1997); and a connection to produce more realistic features, such as land-sea between anticyclones and warm advection in Athens breezes, that are important for air quality assessment (Kassomenos et al. 1995). Two difficulties exist for (Jiang and Fast 2004). Without data assimilation, extending these statistical relationships into the simulations beyond 1-2 weeks drift away from real future. First, connections between weather and air weather sequences and must be treated in a climato- quality are highly nonlinear and depend on multiple logical sense using simulations over much longer time variables in a complex manner; statistical relation- periods. To assess climate change impacts, at least two ships derived under the current climate may not hold simulations must be performed for the past/current when the climate changes. Second, incorporating and future climate conditions. Because no observa- nonweather influences, such as increased anthro- tions exist for the future climate, both simulations pogenic emissions, is not trivial with this method must be performed in a free-running mode with no because the nonlinearity of atmospheric chemistry observational constraints to allow for a meaningful makes it either difficult or impossible to separate comparison of past/current and future climate. The weather and nonweather effects. error structures of short- and long-term simulations To circumvent the nonlinearity problem, an alter- are related, but can differ significantly as a result of native methodology for predicting air quality changes complex feedback processes that play an important is to utilize air quality models using the meteoro- role in long-term simulations. logical conditions found in the control and altered This article investigates regional climate simula- climate. To date, this has been done with different tion skill relevant to air quality assessment. Our goal models, ranging from simple Lagrangian chemistry is to understand how errors in the regional climate transport models (LCTM) to sophisticated regulatory simulations may introduce uncertainty in air quality models. For example, Mayerhofer et al. (2002) used a assessment. No attempts are made in this paper to one-layer LCTM to study sulfur and nitrogen depo- assess air quality simulations driven by the regional sition in Europe for a future climate. Hogrefe et al. climate simulations. We present a comparison of two (2004) used a more thorough approach by running downscaled atmospheric simulations—one driven by the Community Multiscale Air Quality Model a GCM control simulation and the other driven by (CMAQ) coupled with weather fields generated by a global reanalysis—of present-day climate against the fifth-generation Pennsylvania State University observations to investigate how well meteorological (PSU)-National Center for Atmospheric Research conditions important for air quality assessment can (NCAR) Mesoscale Model (MM5) (Grell et al. 1995) be simulated by regional climate models (RCMs). to study changes in summer ozone episodes over Differences between these runs highlight the differ- the eastern United States. Though computationally ence between using a free-running GCM versus an expensive, this methodology can address concurrent observationally constrained model for deriving high- changes in climate and emissions with its physically resolution meteorological information. The readers based approach. are referred to Efthimios et al. 2007 for an evaluation Whether using statistics or explicitly modeling air of air quality simulations using the GCM-driven quality changes, an accurate representation of meteo- downscaled simulation described in this study.

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC MODEL AND DATASET DESCRIPTIONS. warm-season climate remains a significant challenge Mode/ description and simulations. The simulations (e.g., Gutowski et al. 2004; Liang et al. 2004b), and presented in this article were made using MM5 (Grell the use of GCM boundary conditions can introduce et al. 1995). This model, with some modifications additional errors in the regional simulations. described by Leung et al. (2003b), has been applied Two decade-long model runs of present-day to the western United States and found to realistically climate are presented for the United States. The first simulate many aspects of the hydroclimate domi- uses lateral boundary conditions and sea surface nated by the orographic effects and cold-season temperatures derived from a Goddard Institute for processes of that region. However, simulating the Space Studies (GISS) GCM simulation and is referred

NONLINEARITY OF ATMOSPHERIC OZONE PRODUCTION

nterplay between instantaneous trace gas concentrations, surface emissions, and meteorology leads to strong nonlineari- ties for atmospheric ozone chemistry. While the only significant ozone-forming reaction in the troposphere is the combi- nation of atomic oxygen 0(3P) with an 02 molecule, the chemical reactions dictating the production of tropospheric 0(3P) are complex. The net concentration of 03 is a balance between various sources and sinks of both 03 and other important species, such as NOx (defined as the sum of NO and N02) and volatile organic compounds (VOCs) (e.g., Seinfeld and Pandis 1998). Meteorology directly affects ozone production through several routes. For example, the amount of incoming solar radia- tion initiates the N02-N0-03 photochemical cycles:

N02 + hv-> NO + 0(3P),

0(3P) + 02 + M -> 03 + M,

NO + 03 -> N02 + 02,

where M represents a third molecule that stabilizes the 03 molecule by absorbing excess molecular energy. In the absence of other compounds and without additional NOx emissions, the above series of reactions will reach the so-called photostationary state where the concentrations of N02, NO, and 03 at any point in time are at equilibrium and can be pre- dicted from their initial concentrations, the amount of incoming solar radiation, and the temperature-dependent reaction- rate constants. This series results in an equilibrium 03 concentration. However, if NO is converted to N02 via an alternate pathway, such as can occur with many VOCs, there will be a net increase in 03. For example, formaldehyde (HCHO), an ubiquitous compound in urban environments, can oxidize NO to N02 without consuming ozone via the following reactions:

HCHO+ hv H + HCO,

HCHO+ hv H2 + CO,

H + 02 + M H02,

HCO + 02 -» H02 + CO,

NO + H02 N02 + HO,

where HCO is the formyl radical, H02 is a hydroperoxy radical, and HO is a hydroxyl radical. All of the above reactions are strongly affected by meteorology: cloud conditions control incoming sunlight, and thus the photolysis rates, water vapor concentration regulates other sources of HO production, and temperature alters the reaction rates. Other meteorological factors also affect observed concentrations. The boundary layer top can be pictured as a lid trapping pollutants below it. As the lid rises during the day, there is more volume within which they can react, and for a given quantity of gas, the increased volume leads to a decreased concentration. Winds are important because higher speeds more effectively vent pollutants away from source regions, diluting them further downwind. Temperatures are important, both for changing reaction rates and for altering emissions. Depending on the available water, increased temperatures generally lead to increased hydrocarbon emissions from plants, which can lead to increased 03 production. An example of how differences in horizontal and vertical mixing, which are related to wind speeds and the PBL height, can lead to changes in ozone can be seen by comparing two models that differ in their handling of the mixing. O'Neill and Lamb (2005) used a process analysis to investigate why CMAQ and the California Photochemical Grid Model (CALGRID), two different air quality models, produced different ozone concentrations. They found that differences in mixing and advection near emission sources led to altered ozone plume locations. Downwind, when the plumes encountered regions of complex terrain, the differences between the models magnified. Altered PBL conditions resulting from climate change can produce similar differences. However, if the PBL conditions are suspect within the model, then the plume locations will be suspect as well.

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC to as the "MM5(GISS)" run. The GISS model is a with topographic adjustment over the United States. global finite difference with a For this study, averages for the years from 1990 grid spacing of 4° latitude x 5° longitude. The simu- through 2000 are used. This time period matches lation used to drive MM5(GISS) was discussed by the reanalysis boundary conditions used for the Mickley et al. (2004), with the MM5 run driven by MM5(NRA) run and is assumed to be representative years 1995-2005 of that simulation. The MM5(GISS) of a long-term climatology. Additionally, the North run is identical to the "control run" used in Leung American Regional Reanalysis (NARR) (Mesinger and Gustafson (2005) to investigate potential future et al. 2006) is used as a proxy for observations of the meteorological impacts on air quality. The second 500-hPa height, boundary layer winds, and boundary MM5 run uses lateral boundary conditions and layer depths. Similar to the PRISM data, NARR is sea surface temperatures derived from the National averaged over 1990-2000, except that 1996 is excluded Center for Environmental Prediction (NCEP)-NCAR because of some corrupt files. global reanalysis (Kalnay et al. 1996) between the years 1990 and 2000, and is referred to as the COMPARISON OF SELECTED VARIABLES. "MM5(NRA)" run. Whereas a number of meteorological variables The setup of the two MM5 runs is identical, except are important for modeling air quality, this article for the longitudinal width of the coarse domain and focuses on a select few that, when combined, give the cumulus parameterization. Both runs consist of an estimate of the current capability of RCMs in the a nested configuration with coarse- and fine-domain context of air quality. grid spacings of 108 and 36 km, respectively. Because of the influence of the Rocky Mountains, which have Diurnal wind patterns. One of the more important a rather different topographic representation in the meteorological variables for accurately simulating GCM and MM5, and differences in model physics air quality is the overall flow pattern within the and spatial resolution between GISS and MM5, the boundary layer. This is of particular importance in adjustment of the MM5 flow field at the outflow, or regions of complex topography where mountains eastern, boundary to the GISS flow field simulated and valleys channel pollutants and generate other on an extremely coarse grid results in erroneous local flow phenomena. The downscaling approach flow patterns (large east-west gradients) over eastern offers a vast improvement over a GCM because of North America. Therefore, we extend the eastern the much higher resolution of the regional model. To boundary of the coarse MM5 grid farther east in the highlight this fact, Fig. 1 shows the diurnal difference Atlantic in the MM5(GISS) simulation to allow a in near-surface winds in the GISS GCM, MM5(GISS), larger region for the flow field to adjust. So, the size MM5(NRA), and NARR. Because the NARR of the coarse domain in MM5(GISS) (109 x 67 points) assimilated near-surface winds, it can be used as a is larger than that in MM5(NRA) (89 x 67 points). good proxy for the observed diurnal wind patterns The fine domains (169 x 115 points), which cover the (Mesinger et al. 2006). We used the MM5-diagnosed continental United States, are collocated as closely 10-m winds for this comparison. as possible, except for differences resulting from the At a much higher spatial resolution compared to map projection. Twenty-three vertical levels were that of the GCM, the MM5 simulations are able to used for both runs. The simulations used the same resolve the complicated flow pattern along coasts and physics parameterizations as those described in in mountains and valleys. For example, California's Leung et al. (2003b), except that the following two Central Valley struggles with poor air quality. Within different cumulus parameterizations were used: the the GCM, a single grid point represents the area from Grell scheme for MM5(GISS) and the Kain-Fritsch San Francisco to Santa Barbara and includes the Pacific scheme for MM5(NRA). This yields more realistic Ocean on the west side and the Sierra Nevada on the precipitation for each simulation based on summer- east side. Therefore, the GCM is unable to simulate time sensitivity studies. the complex patterns associated with wind speed and the diurnal shift in wind direction. With 36-km Data used for comparison. Representativeness of the grid spacing, MM5(GISS) is able to better resolve this MM5 runs is evaluated for a selection of variables. region and the diurnal pattern of the mountain-valley Precipitation comparisons are made using Parameter- and land-sea breezes experienced therein. The added Elevation Regressions on Independent Slopes Models information in MM5(GISS) is evident across the West (PRISM) data (Maurer et al. 2002) consisting of daily where the terrain is particularly rugged, although finer precipitation observations interpolated to a 1/8° grid details such as flow fields in the San Francisco Bay are

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC still missing because of the spatial resolution used. At a comparable spatial resolution, the directional shift of the diurnal wind patterns of the two MM5 simulations and NARR, as shown by the wind vectors in Fig. 1, are very similar. This indicates that diurnal direction shifts are mainly controlled by orographic forcing. Un- less there are significant errors in the large-scale circulation, downscaling is expected to be able to reproduce this feature of the diurnal wind patterns reasonably well. Larger differences, however, exist between the MM5 runs and NARR for the magnitude of the diurnal wind shifts, as _l shown by the colors of the FIG. I. Comparison of diurnal wind shifts (m s ) produced by (a) NARR and (c) MM5(NRA) (at 10 m) and the (b) GISS GCM (bottom model level, -220 m), wind vectors. Because the which is used as boundary conditions for the (d) MM5(GISS) run. MM5 grid 10-m winds in MM5 are spacing is 36 km and the GISS GCM is 4° latitude * 5° longitude. Values repre- generally higher than the sent decade-long average daytime (1200 UTC) minus nighttime (0000 UTC) NARR winds over land, a winds for summer (JJA). Each vector represents the magnitude (indicated relatively large positive bias by color) and direction of the wind change (indicated by the orientation of can result in the magni- the wind vector). tude of diurnal wind shifts, which are calculated as the differences between the heights diluting pollutant concentrations (e.g., Pielke winds at 1200 and 0000 UTC. Errors in the MM5 et al. 1991; Rao et al. 2003). This simplistic under- wind speeds are likely related mainly to model standing gives an indication of how local circulation physics, because both MM5 simulations show very conditions can influence air quality. Note, however, similar biases regardless of differences in the simu- that it does not take into account pollutants blowing lated large-scale circulation (Fig. 4). Uncertainty can into a region from upwind. We have set a threshold also be introduced because of the diagnostic relation- of 6000 m2 s_1 for the ventilation coefficient whereby ships used to infer 10-m winds from the winds at the the number of hours per day can be classified as models lowest level (near 40 m). "unvented" when below the threshold and "vented" when above (Pielke et al. 1991). While the threshold Unvented hours. The index of unvented hours quan- criteria are arbitrary and should be determined tifies the number of hours during a day with weak locally based on surrounding terrain and circulation winds and a shallow boundary layer, leading to patterns, the uniform threshold gives an overall increased odds of high pollutant concentrations. This impression that can be used to compare model runs. index is constructed from the ventilation coefficient, A greater number of unvented hours per day implies which is defined as the product of the boundary layer a greater chance of poor air quality. height and the wind speed averaged over the depth Figure 2 presents a comparison of the average of the boundary layer. The ventilation coefficient number of unvented hours for summer (June-August) represents the ability of the atmosphere to locally and winter (December-February). In general, an mix pollutants, with higher wind speeds transporting east-west gradient exists for unvented hours with pollutants out of a region and higher boundary layer more unvented hours east of the Rockies, especially

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC when the daily maximum PBL heights in MM5 are about 400-500 m lower than NARR in the west, but are compa- rable or higher in the east. Another prominent feature in the difference plots is the area east of the Rockies where MM5(GISS) has less unvented hours. While not apparent from the averages in the table, this is due to a higher PBL wind speed in MM5(GISS). Although the differences between the unvented hours of the MM5 runs and NARR are mostly within the 3-h preci- sion of the NARR data, Table 1 shows that the PBL wind speeds are higher in the MM5 runs by approximately 20%-30%. This difference in winds would lead to significant differences in pollutant transport. However, the biases in PBL heights have FIG. 2. Decade-long average of unvented h day-1 for summer (JJA) and winter a compensating effect. While (DJF) for (a), (b) NARR and the differences (c), (d) MM5(NRA)-NARR pollutants in the MM5 runs and (e), (f) MM5(GISS)-NARR. would be transported farther away from the source, pollut- during summer. The highest number of unvented ants in NARR would tend to be mixed higher verti- hours occurs over the eastern half of the United States cally. Hence, even with comparable unvented hours, and in the Central Valley of California, in the range the evolution of pollutants using meteorology from of 15-21 h day1. Note that the NARR unvented hours the MM5 runs and NARR can be rather different. must be taken as a crude estimate because of their For instance, in NARR the pollutants could reside in 3-h output frequency compared to the hourly output residual layers at night and remix toward the surface from MM5. Because of this, differences within 3 h the following day. In MM5, this is less likely with the between MM5 and NARR are not within the preci- pollutants being removed from the area. sion of the calculation. Biases may also exist in the NARR analysis because of the inability to resolve the Precipitation. The accurate simulation of clouds and quickly changing boundary layer conditions during precipitation is important for air quality because the morning growth and evening decay periods and clouds and rain droplets are efficient "scrubbers" for the lack of observation data with sufficient temporal many pollutants (Seinfeld and Pandis 1998). Liquid resolution to constrain winds and temperature within droplets also can provide additional pathways for the boundary layer. the transformation of various pollutants, such as Comparisons of the dominant features in NARR the aqueous phase oxidation of S02, by dissolved to MM5(NRA) and MM5(GISS) reveal that the pri- hydrogen peroxide and ozone. These materials are mary difference lies in the strength of the east-west then removed from the atmosphere during precipita- gradient, especially during summer. In the MM5 tion events. If a model produces too much rain, too simulations, the mean gradient of unvented hours much wet deposition will occur; likewise, too little is reversed compared to NARR, primarily due to a rain would leave the atmosphere overly polluted. weaker east-west PBL height gradient. As seen in Precipitation is also associated with increased mixing Table 1, the MM5 runs have lower PBL heights in and advection that comes with frontal passages or the west than the NARR, especially during summer, other storm environments. Analysis of ventilation

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC TABLE 1. Mean summer (j]A) and winter (DJF) daily averaged PBL height, PBL wind speed, number of unvented hours, and daily maximum PBL height over land points that lie either west or east of I00°W and within the box defined by I25°-60°W and 25°-50°N, which roughly encompasses the continental United States.

MM5(GISS) MM5(NRA) NARR Variable West East West East West East

JJA PBL height (m) 1161 992 1095 924 1647 886 JJA daily maximum PBL height (m) 2390 1937 2274 1502 2766 1405 JJA PBL wind speed (m s1) 7.0 6.8 7.2 7.5 5.3 5.6 JJA unvented hours (h day-1) 14.0 14.4 14.2 13.5 12.0 15.9

DJF PBL height (m) 1048 894 1034 885 1462 875 DJF daily maximum PBL height (m) 1366 1370 1264 1268 1444 1257

DJF PBL wind speed (m s~') 8.4 8.9 8.4 9.2 6.8 7.5 DJF unvented hours (h day-1) 12.3 11.5 12.5 11.5 10.8 13.0

and height anomalies discussed in the previous and MM5(GISS), with the entire eastern U.S. summer pre- next sections provides useful information about the cipitation underpredicted by 1-2 mm day-1. During model skill in simulating these conditions. winter, the overprediction in the Pacific Northwest Figure 3 presents the mean PRISM precipita- is also larger and more widespread. tion amount for summer and winter along with The differences between the MM5 runs are caused difference plots between PRISM and both the by using different cumulus schemes, different boundary MM5(NRA) and MM5(GISS) model runs. The observed precipitation pattern notice- ably changes from summer to winter. During summer less than 1 mm daof precipita- tion occurs west of the Rockies with larger amounts to the east, exceeding 3 mm day-1 for much of the eastern half of the United States. During winter the precipitation pattern shifts, with the western moun- tains receiving large amounts and the eastern precipitation maximum covering much of the Southeast. MM5(NRA) reproduced the summer pattern within 1 mm day-1 for most of the United States. Larger errors exist during winter with MM5(NRA) overpredicting precipita- tion in the Pacific North- west and California's Central Valley by 2-4 mm day-1 and underpredicting the south- FIG. 3. Decade-long average of precipitation amount (mm day-1) for sum- east maximum by a similar mer (JJA) and winter (DJF) for (a), (b) PRISM and the differences (c), amount. Larger errors exist in (d) MM5(NRA) - PRISM and (e), (f) MM5(GISS) - PRISM.

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC datasets, and different domain configurations. If upon the frequency more than the amount of the same cumulus scheme was used for both runs, precipitation. sensitivity tests over a single summer reveal that the The diurnal timing of rainfall could also be differences would be even greater. While this appears important for air quality considerations. For example, unintuitive at first, because one would think that rainfall in the afternoon may have more significant using identical physics schemes would lead to more impacts than at nighttime, because this suggests similar results, interactions between physics schemes a correlation between cloudiness/rainfall, solar in the RCM can interact with the driving large- radiation, and PBL mixing that collectively could scale boundary conditions in a nonlinear fashion. reduce ozone production and increase ozone removal. Comparing seasons also reveals a seasonal dependence Similar to previous studies that examine the diurnal in the differences between model runs that could be cycle of rainfall in climate models (e.g., Dai et al. important in air quality assessment because summer 1999; Liang et al. 2004a), our simulations reason- is typically much worse for air quality. ably captured the late-afternoon timing of rainfall in The bias patterns in rain frequency (not shown) the western United States, but failed to capture the generally follow that of the precipitation amount. dominant nocturnal maximum in the Great Plains However, during summer, the bias in rain frequency region during summer (not shown). is more acute in the western United States for both MM5 simulations. This suggests that the dry bias 500-hPa heights. The 500-hPa height reflects a broad shown in Fig. 3 is mainly related to rain frequency range of meteorological influences on air quality. rather than intensity. The MM5 summer rain While it does not necessarily imply polluted or clean amount and frequency bias implies decreased wet air by itself, ridges are often associated with lower removal compared to what the observations would wind speeds, fewer clouds (hence, more solar radiation produce. This would lead to more polluted air and less precipitation), and subsidence, all of which, because wet removal is very efficient and dependent in a general sense, negatively impact air quality. Many studies (e.g., Comrie and Yarnal 1992; McKendry 1994; Pryor et al. 1995) have identi- fied a small number of upper- air variables that relate to high- ozone episodes. Certain height features, such as ridges, lead to an increased probability of poor air quality. Therefore, if a RCM either incorrectly simulates the climatological mean and variability of the location of ridges and troughs or modifies their strength, the implication is that the prevalence for air quality episodes will change for a given region. Figure 4 shows the mean 0000 UTC zonally anomalous 500-hPa height and its daily standard deviation for summer and winter for NARR, MM5(GISS), and MM5(NRA). The anomalous height is cal- culated as

Z = Z -Zx, FIG. 4. Mean (shaded color contours) and daily std dev (white contour ssn ssn ssn (1) lines) of summer (JJA) and winter (DJF) zonal anomalies of 500-hPa height

(dam) based on 0000 UTC output for (a), (b) NARR, (c), (d) MM5(NRA), where Z ssn is the seasonallyi and (e), (f) MM5(GISS). averaged 500-hPa height and

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC Z* is the zonal average of the same quantity. By runs represent possible errors introduced by using looking at the anomalous height, any overall bias unconstrained GCM output for the RCM boundaries between datasets is removed and the mean location of instead of the observationally constrained global the troughs and ridges is immediately apparent. reanalysis. The NARR is used as a proxy for variables During summer there are noticeable differences that are hard to measure on a large scale and at reso- between the three model outputs, but the general lutions comparable to the MM5 runs. Even though features are similar. A ridge runs north-south the NARR is constrained by observations, PBL winds through the center of the United States that is and heights are less constrained because of the strong strongest in the north and decays approaching diurnal variations and regional features that may not Mexico. On either side of the ridge lie two troughs be well captured by the relatively sparse observational straddling the continent edges. The subtle differ- network in regions of complex terrain. ences between the datasets include a higher ridge in Even if the downscaled results are not completely MM5(GISS) and the ridge peaking farther east for accurate, the necessity of using the regional down- the MM5 runs than for the NARR. MM5(NRA) also scaling approach is clear. With the strong dependence produces a much weaker western trough that causes on localized flow patterns, air quality models need the ridge to encroach upon the Pacific Northwest. the higher-resolution wind, temperature, precipita- The pattern of daily variability is consistently tion, and boundary layer structure produced by a highest to the northwest and northeast for both RCM. With a GCM, even at the more typical spatial MM5 runs and the NARR. However, MM5(GISS) resolution of 1.5°-2° used today, the pollutant concen- has 10%-25% less summertime variability inland tration, transport, and mixing cannot be adequately away from the Gulf and Atlantic coasts, implying resolved for air quality simulation in most regions more consistent day-to-day weather patterns than even moderately affected by orography. those in reality. From the comparison of the diurnal surface wind Driven by stronger baroclinicity in the atmo- patterns, PBL processes, precipitation, and 500-hPa sphere, winter is marked by larger height anomalies height anomalies presented, several observations can and variability associated with synoptic events. The be made. Directional changes in the diurnal surface contrast between winter and summer is well rep- wind patterns are driven primarily by regional-scale resented in MM5(NRA), but the winter anomalies orographic forcing and are highly deterministic. in MM5(GISS) are weaker, although the daily vari- Both MM5 simulations produced realistic patterns ability is comparable to NARR and MM5(NRA). as resolved by the model orography. The surface During winter the anomalous seasonally averaged wind directions apparently have a weak dependence 500-hPa height is dominated by a dipole pattern with on model physics and biases in the large-scale a ridge over the Pacific Northwest and a trough over circulation. The surface wind speeds, however, New England. In the NARR, the trough extends to are more affected by PBL processes, and the MM5 the southwest into Mexico. Both MM5 model runs simulations show relatively large biases that could replicate the basic structure of the dipole pattern, negatively impact air quality simulations. but with some differences. Neither MM5(NRA) Biases in PBL processes seem to be determined nor MM5(GISS) maintain the trough strength in more by model physics, particularly the boundary western Mexico, and MM5(GISS) moves the centers layer turbulence transfer. Simulations using the same of both the ridge and trough southward. The shifts in model physics tend to look alike, although biases in MM5(GISS) are the result of an altered storm track simulating the large-scale circulation do have some that allows more storms to track across the Pacific influence as well. For example, the larger errors in Northwest. summertime PBL height in the Midwest simulated by MM5(GISS) compared to MM5(NRA) are likely SUMMARY. A comparison of selected meteoro- related to the positive bias in 500-hPa height in the logical variables related to air quality episodes has MM5(GISS) simulation. Nevertheless, errors in been presented for observations/analyses and two model physics have larger impacts on the simulated MM5 runs—one driven by a GCM and the other by PBL properties. Indeed, some of the PBL differences the NRA. Even though a large number of variables between the MM5 runs and NARR may be attrib- affect air quality, a subset has been presented to uted to the different PBL parameterizations used in give an impression of how well RCMs may perform MM5 [Medium-Range Forecasting (MRF) scheme] when used to downscale climate scenarios for air and the Eta Model (Mellor-Yamada scheme) used in quality modeling. Differences between the two MM5 NARR. Bright and Mullen (2002) compared MM5

AMERICAN METEOROLOGICAL SOCIETY AUGUST 2007BAf15* | M 85

Unauthenticated | Downloaded 10/10/21 03:15 PM UTC simulations of the Southwest monsoon region with quently than that observed over much of the United four operational radiosondes in the area and found States, the frequency of wet deposition will be less in no statistically significant difference (at 0.05) in MM5 runs than when using observations to model air average PBL height using MRF. However, all four quality. The rain frequency bias will have a stronger sites were statistically different using the Eta Model impact for pollutants heavily dependent upon aqueous parameterization. For comparison, Berg and Zhong chemistry occurring in cloud and raindrops, such as

(2005) compared MM5 runs using the MRF scheme sulfate production from S02 (Irving 1991). with observations from Salt Lake City, Utah, and the This study can provide some guidance for future Southern Great Plains. They found that the MRF improvements. For surface wind patterns, a higher scheme was statistically different (at 0.05) from the spatial resolution in the RCM than that typically observed PBL height and consistently overpredicted used for other downscaling purposes will allow the it. These studies highlight the strong dependence regional-scale forcing to be better resolved for air of PBL height biases on PBL parameterizations. In quality, which may lead to further improvements in a long-term simulation, biases in different physics simulating the diurnal evolution of winds, which is parameterizations, such as PBL mixing and convec- perhaps one of the most important meteorological tion, can collectively affect the large-scale circulation, factors controlling air quality at the local scale. In which complicates the interpretation of model errors a warmer future climate, changes in temperature in simulating PBL processes. gradients across terrain or land/sea can lead to For precipitation, the orographic forcings in the changes in surface wind patterns that are potentially western United States are strong enough to anchor predictable because of their deterministic nature. the spatial pattern of winter precipitation. Although For PBL and precipitation processes, improvement the two MM5 bias patterns tend to look similar, in model physics can likely substantially improve differences in large-scale circulation certainly affect the realism of model simulations, but owing to their the wet/dry biases at the larger scale [wetter/drier in dependence (especially precipitation) on the large- the western and southeastern United States in the scale circulation, they depend on the GCM providing MM5(GISS) run]. During summer, errors in large- the large-scale information. Therefore, to improve the scale circulation and model physics are related, and large-scale flow pattern in the RCM one needs better both have strong effects on the simulated rainfall, boundary conditions, which place the emphasis on and the bias patterns in the two MM5 runs do not choosing an appropriate GCM. This is demonstrated resemble each other. by the problematic seasonal cycle in the 500-hPa height pattern of MM5(GISSC). To demonstrate how DISCUSSIONS. The relative dependence of much the MM5(GISS) large-scale flow is constrained different simulated quantities on regional forcing, by the GISS GCM, Fig. 5 shows the GISS zonal anom- model parameterizations, and the imposed large- alous 500-hPa mean height and daily standard devia- scale circulation at the lateral boundaries provides a tion for comparison against Fig. 4. Accounting for the framework to understand similarities and differences coarser GCM resolution, the summertime patterns between model simulations. An important question in the GISS plots are in almost the same locations for air quality assessment is how air quality simula- as in MM5(GISS). For winter, the GISS GCM shifts tions driven by the MM5 runs and NARR may differ. the dipole pattern east compared to the NARR and Based on the comparisons presented above, it is likely does not extend the poles as far south. This winter- that air quality simulations produced using the MM5 time shift by MM5(GISS) is an improvement likely runs would noticeably differ from those driven by related to the more realistic representation of the NARR output. For example, the MM5 PBL heights Rocky Mountain and its influence on the large-scale are roughly 30% lower than those for the NARR west zonally asymmetric flow. However, MM5 is unable to of 100°W. This implies significantly larger differences shift the ridge far enough west, because it must satisfy in forecasted pollutant concentrations. For threshold- the large-scale constraints imposed by the GCM at based ozone statistics, such as the Environmental the lateral boundaries. This strong coupling of the Protection Agency's 8-h ozone standard, the changed RCM to the GCM has been observed in other RCM concentrations would lead to very different results. simulations as well (Duffy et al. 2006), implying that Differences between the MM5 runs and observed improvements must be made at the GCM level and precipitation also reveal potential difficulties in using the RCM cannot be treated in isolation. MM5 precipitation fields for estimating wet deposition. Air quality studies increase the demands on the Because during summer in MM5 rain occurs less fre- RCM, and concurrently require a more accurate GCM.

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC Three recommendations can be made regarding the methodology used to make future climatologi- cal air quality predictions. The first is that the host GCM must be chosen carefully to ensure that its large-scale flow fields are accurate for the region of interest. Because GCM biases are inevitable, model configurations that perform satisfactorily for downscaling global reanalyses may need to be FIG. 5. Mean (shaded color contours) and daily std dev (white contour modified for downscaling GCM lines) of (a) summer (JJA) and (b) winter (DJF) zonal anomalous 500-hPa output, because GCM biases at height (dam) based on 0000 UTC output for the GISS GCM run, which the lateral boundaries may have was used to provide boundary conditions for MM5(GISS). The contour negative upstream or downstream levels match Fig. 4 enabling direct comparison. effects in the RCM. Second, once the downscaling has been done by the RCM, a com- Because only meteorology simulations have been parison with the observations must be done based presented in this article, the impact on air quality is on quantities that are important for air quality speculative and still needs to be determined by taking assessment. For processes that are highly dependent the next step of using the downscaled MM5 output to on physics parameterizations, sensitivity tests should drive an air quality model. Only this will determine be performed to select an optimal scheme. Analysis of how significant the changes are between MM5 runs the meteorological simulations can also provide useful on the resultant air quality. Last, the dynamical information for interpreting air quality simulations. downscaling technique based on different model For example, compensating effects, such as higher formulations and physics parameterizations will wind speeds and lower PBL heights, might imply likely represent different model skill in simulating similar ventilation, but the resulting pollutant distri- meteorological conditions for air quality assessment. butions could be very different. This is particularly The results presented in this paper are not indicative important when a "present day" simulation will be of all dynamically downscaled simulations, but high- compared with a "future scenario." For example, light the need to intercompare them to estimate un- for a study of ozone changes in a future climate, the certainty in regional climate projections for assessing concentration of ozone and its precursors will be the influence of climate change on air quality. strongly dependent upon the PBL height. If the PBL height is too high in the current climate run, primary ACKNOWLEDGMENTS. Support for this project pollutants will be diluted and react under conditions has been provided by the U.S. Environmental Protection with lower concentrations. If synoptic changes in the Agency (EPA) through its Office of Research and Develop- future scenario lead to altered PBL heights, then the ment under IAG DW-89-93963401 and by the Department concentrations would change as well. However, the of Energy (DOE) through the bilateral agreement between nonlinearity of the reactions generating the ozone DOE and China Meteorological Administration on will produce a different amount of ozone leading to regional climate research. The authors wish to thank Elaine changes other than just the percentage change in the Chapman, Dick Easter, Rahul Zaveri, and two anonymous PBL height. Last, but not least, any climate change reviewers for providing valuable comments and sugges- impact assessment begins with a set of questions. It tions that improve the paper. The authors also thank appears that for air quality assessment, aspects that Loretta Mickley for providing the GISS model output. Daily are influenced by orographically forced circulation gridded meteorological data have been obtained from the and its potential changes in a warmer climate may be Surface Modeling group at the University of Washington appropriately addressed with the current models. To from their Web site (online at www.hydro.washington. investigate aspects that are highly nondeterministic, edu/Lettenmaier/gridded_data/). The NARR data were and hence can vary substantially among simulations, downloaded from the NOAA National Operational Model the use of an ensemble approach will be particularly Archive and Distribution System (NOMADS; online at important to provide uncertainty information and http://nomads.ncdc.noaa.gov). The views expressed in bracket the response. this paper are those of the authors and do not necessarily

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC reflect the views or policies of the EPA. The Pacific North- Gutowski, W. J., F. O. Otieno, R. W. Arritt, E. S. Takle, west National Laboratory is operated for the U.S. DOE by and Z. T. Pan, 2004: Diagnosis and attribution of Battelle Memorial Institute under Contract DE-AC06- a seasonal precipitation deficit in a U.S. regional 76RLO 1830. climate simulation. /. Hydrometeor., 5, 230-242. Hogrefe, C., and Coauthors, 2004: Simulating changes in regional air pollution over the eastern United REFERENCES States due to changes in global and regional climate Berg, L. K., and S. Zhong, 2005: Sensitivity of MM5- and emissions. /. Geophys. Res., 109, D22301, simulated boundary layer characteristics to tur- doi:10.1029/2004JD004690. bulence parameterizations. /. Appl. Meteor., 44, Irving, P. M., Ed., 1991: Emissions, Atmospheric 1467-1483. Processes, and Deposition. Vol. 1. Acidic Deposition: Berman, S., J.-Y. Ku, and S. T. Rao, 1999: Spatial and State of Science and Technology, U.S. National Acid temporal variation in the mixing depth over the Precipitation Assessment Program. northeastern United States during the summer of Jiang, G. F., and J. D. Fast, 2004: Modeling the effects of 1995. /. Appl. Meteor., 38, 1661-1673. VOC and NOX emission sources on ozone formation Bright, D. R., and S. L. Mullen, 2002: The sensitivity of in Houston during the TexAQS 2000 field campaign. the numerical simulation of the southwest monsoon Atmos. Environ., 38, 5071-5085. boundary layer to the choice of PBL turbulence John, J. C. S., and W. L. Chameides, 1997: Climatology parameterization in MM5. Wea. Forecasting, 17, of ozone exceedences in the Atlantic metropolitan 99-114. area: 1-hour vs 8-hour standard and the role of plume Comrie, A. C., and B. Yarnal, 1992: Relationships recirculation air pollution episodes. Environ. Sci. between synoptic-scale atmospheric circulation and Technol., 31, 2797-2804. ozone concentrations in metropolitan Pittsburgh, Kalnay, E., and Coauthors, 1996: The NCEP/NCAR Pennsylvania. Atmos. Environ. B, 26, 301-312. 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., Dai, A. G., F. Giorgi, and K. E. Trenberth, 1999: 77,437-471. Observed and model-simulated diurnal cycles of Kassomenos, P., V. Kotroni, and G. Kallos, 1995: precipitation over the contiguous United States. /. Analysis of climatological and air-quality observa- Geophys. Res., 104, 6377-6402. tions from greater Athens area. Atmos. Environ., 29, Dickinson, R. E., R. M. Errico, F. Giorgi, and G. T. 3671-3688. Bates, 1989: A regional for the western Lennartson, G. J., and M. D. Schwartz, 2002: The lake United States. Climatic Change, 15, 383-422. breeze-ground-level ozone connection in eastern Duffy, P. B., and Coauthors, 2006: Simulations of present Wisconsin: A climatological perspective. Int. J. and future in the western United States with Climatol., 22, 1347-1364. four nested regional climate models. /. Climate, 19, Leung, L. R., and W. I. Gustafson, 2005: Potential 873-895. regional climate change and implications to US air Efthimios, T. K., K. Manomaiphiboon, K. J. Liao, L. R. quality. Geophys. Res. Lett., 32, L16711, doi:10.1029/ Leung, J.-H. Woo, S. He, P. Amar, and G. Russell, 2005GL022911. 2007: Impacts of global climate change and emissions , L. O. Mearns, F. Giorgi, and R. L. Wilby, 2003a: on regional ozone and fine particulate matter con- Regional climate research—Needs and opportunities. centrations over North America. /. Geophys. Res., Bull. Amer. Meteor. Soc., 84, 89-95. in press. , Y. Qian, and X. Bian, 2003b: Hydroclimate of the Forkel, R., and R. Knoche, 2006: Regional climate change western United States based on observations and and its impact on photooxidant concentrations in regional climate simulation of 1981-2000. Part I: southern Germany: Simulations with a coupled Seasonal statistics./. Climate, 16, 1892-1911. regional climate-chemistry model. /. Geophys. Res., Liang, X. Z., L. Li, A. G. Dai, and K. E. Kunkel, 2004a: Ill, D12302, doi:10.1029/2005JD006748. Regional climate model simulation of summer Giorgi, F., and L. O. Mearns, 1999: Introduction to precipitation diurnal cycle over the United States. special section: Regional climate modeling revisited. Geophys. Res. Lett., 31, L24208, doi:10.1029/ /. Geophys. Res., 104, 6335-6352. 2004GL021054. Grell, G. A., J. Dudhia, and D. R. Stauffer, 1995: A , , E. Kunkel, M. F. Ting, and J. X. L. Wang, description of the fifth-generation Penn State/NCAR 2004b: Regional climate model simulation of U.S. mesoscale model (MM5). NCAR Tech. Note NCAR/ precipitation during 1982-2002. Part I: Annual cycle. TN-398+STR, 122 pp. /. Climate, 17, 3510-3529.

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for WHERE the conterminous United States. /. Climate, 15, METEOROLOGY 3237-3251. MEETS HISTORY! Mayerhofer, P., B. de Vries, M. den Elzen, D. van Vuuren, J. Onigkeit, M. Posch, and R. Guardans, 2002: Long- term, consistent scenarios of emissions, deposition, and climate change in Europe. Environ. Sci. Policy, 5, 273-305. McKendry, I. G., 1994: Synoptic circulation and summertime ground-level ozone concentrations at Vancouver, British-Columbia. /. Appl. Meteor., 33, 627-641. Mesinger, F., and Coauthors, 2006: North American regional reanalysis. Bull. Amer. Meteor. Soc., 87, 343-360. Mickley, L., D. Jacob, B. Field, and D. Rind, 2004: Effects of future climate change on regional air pollution episodes in the United States. Geophys. Res. Lett., 31, L24103, doi:10.1029/2004GL021216. O'Neill, S. M., and B. K. Lamb, 2005: Intercomparison of the community multiscale air quality model and CALGRID using process analysis. Environ. Sci. Technol., 39, 5742-5753. The Callendar Effect: Pielke, R. A., R. A. Stocker, R. W. Arritt, and R. T. The Life and Work of McNider, 1991: A procedure to estimate worst-case Guy Stewart Callendar (1898-1964) air-quality in complex terrain. Environ. Int., 17, BY JAMES RODGER FLEMING 559-57. This is the untold story of the remarkable scientist who established the carbon dioxide theory of climate change. Pryor, S. C., I. G. McKendry, and D. G. Steyn, 1995: G. S. Callendar discovered that global warming could be brought Synoptic-scale meteorological variability and about by increases in the concentration of atmospheric carbon dioxide due to human activities, primarily through burning fossil surface ozone concentrations in Vancouver, British- fuels. He did this in 1938! Using never-before-published original 34, scientific correspondence, notebooks, family letters, and Columbia. /. Appl. Meteor., 1824-1833. photographs, noted science historian James Rodger Fleming Rao, S. T., J. Y. Ku, S. Berman, K. S. Zhang, and H. T. gives us the life and work of this leading British engineer, through the World Wars and beyond, to Calendar's continuing Mao, 2003: Summertime characteristics of the atmo- legacy as the scientist who established the Callendar Effect. spheric boundary layer and relationships to ozone LIST $34.95 MEMBER $24.95 © 2007, HARDCOVER, 176 PCS, HM, AMS CODE: CLDR levels over the eastern United States. Pure Appl. ISBN 10:1-878220-76-4, ISBN 13:978-1-878220-76-9

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Unauthenticated | Downloaded 10/10/21 03:15 PM UTC GLOSSARY OF METEOROLOGY

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